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1,100 | specified length of time. We'll use 7 days for simplicity, but you can pick whatever value make sense for your use case -- for example if you query recent vectors frequently you might want to use a smaller time delta like 1 day, or if you query vectors over a decade long time period then you might want to use a larger time delta like 6 months or 1 year.Finally, we'll create the TimescaleVector instance. We specify the ids argument to be the uuid field in our metadata that we created in the pre-processing step above. We do this because we want the time part of our uuids to reflect dates in the past (i.e when the commit was made). However, if we wanted the current date and time to be associated with our document, we can remove the id argument and uuid's will be automatically created with the current date and time.# Define collection nameCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()# Create a Timescale Vector instance from the collection of documentsdb = TimescaleVector.from_documents( embedding=embeddings, ids = [doc.metadata["id"] for doc in docs], documents=docs, collection_name=COLLECTION_NAME, service_url=SERVICE_URL, time_partition_interval=timedelta(days = 7),)Querying vectors by time and similarity‚ÄãNow that we have loaded our documents into TimescaleVector, we can query them by time and similarity.TimescaleVector provides multiple methods for querying vectors by doing similarity search with time-based filtering.Let's take a look at each method below:# Time filter variablesstart_dt = datetime(2023, 8, 1, 22, 10, 35) # Start date = 1 August 2023, 22:10:35end_dt = datetime(2023, 8, 30, 22, 10, 35) # End date = 30 August 2023, 22:10:35td = timedelta(days=7) # Time delta = 7 daysquery = "What's new with TimescaleDB functions?"Method 1: Filter within a provided start date and end date.# Method 1: Query for vectors between start_date and end_datedocs_with_score = db.similarity_search_with_score(query, | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: specified length of time. We'll use 7 days for simplicity, but you can pick whatever value make sense for your use case -- for example if you query recent vectors frequently you might want to use a smaller time delta like 1 day, or if you query vectors over a decade long time period then you might want to use a larger time delta like 6 months or 1 year.Finally, we'll create the TimescaleVector instance. We specify the ids argument to be the uuid field in our metadata that we created in the pre-processing step above. We do this because we want the time part of our uuids to reflect dates in the past (i.e when the commit was made). However, if we wanted the current date and time to be associated with our document, we can remove the id argument and uuid's will be automatically created with the current date and time.# Define collection nameCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()# Create a Timescale Vector instance from the collection of documentsdb = TimescaleVector.from_documents( embedding=embeddings, ids = [doc.metadata["id"] for doc in docs], documents=docs, collection_name=COLLECTION_NAME, service_url=SERVICE_URL, time_partition_interval=timedelta(days = 7),)Querying vectors by time and similarity‚ÄãNow that we have loaded our documents into TimescaleVector, we can query them by time and similarity.TimescaleVector provides multiple methods for querying vectors by doing similarity search with time-based filtering.Let's take a look at each method below:# Time filter variablesstart_dt = datetime(2023, 8, 1, 22, 10, 35) # Start date = 1 August 2023, 22:10:35end_dt = datetime(2023, 8, 30, 22, 10, 35) # End date = 30 August 2023, 22:10:35td = timedelta(days=7) # Time delta = 7 daysquery = "What's new with TimescaleDB functions?"Method 1: Filter within a provided start date and end date.# Method 1: Query for vectors between start_date and end_datedocs_with_score = db.similarity_search_with_score(query, |
1,101 | = db.similarity_search_with_score(query, start_date=start_dt, end_date=end_dt)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18102192878723145 Date: 2023-08-20 22:47:10+0320 {"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "} | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: = db.similarity_search_with_score(query, start_date=start_dt, end_date=end_dt)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18102192878723145 Date: 2023-08-20 22:47:10+0320 {"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "} |
1,102 | subtract_integer_from_now(regclass, bigint) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18150119891755445 Date: 2023-08-22 12:01:19+0320 {"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18422493887617963 Date: 2023-08-9 15:26:03+0500 {"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<[email protected]>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity. **Features** * #5923 Feature flags for TimescaleDB features **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable "} | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: subtract_integer_from_now(regclass, bigint) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18150119891755445 Date: 2023-08-22 12:01:19+0320 {"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18422493887617963 Date: 2023-08-9 15:26:03+0500 {"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<[email protected]>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity. **Features** * #5923 Feature flags for TimescaleDB features **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable "} |
1,103 | segmentby columns of compressed hypertable "} --------------------------------------------------------------------------------Note how the query only returns results within the specified date range.Method 2: Filter within a provided start date, and a time delta later.# Method 2: Query for vectors between start_dt and a time delta td later# Most relevant vectors between 1 August and 7 days laterdocs_with_score = db.similarity_search_with_score(query, start_date=start_dt, time_delta=td)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.18458807468414307 Date: 2023-08-3 14:30:23+0500 {"commit": " 7aeed663b9c0f337b530fd6cad47704a51a9b2ec", "author": "Dmitry Simonenko<[email protected]>", "date": "Thu Aug 3 14:30:23 2023 +0300", "change summary": "Feature flags for TimescaleDB features", "change details": "This PR adds several GUCs which allow to enable/disable major timescaledb features: - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.20492422580718994 Date: 2023-08-7 18:31:40+0320 {"commit": " 07762ea4cedefc88497f0d1f8712d1515cdc5b6e", "author": "Sven Klemm<[email protected]>", "date": "Mon Aug 7 18:31:40 2023 +0200", "change summary": "Test timescaledb debian 12 packages in CI", "change details": ""} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.21106326580047607 Date: 2023-08-3 14:36:39+0500 {"commit": " 2863daf3df83c63ee36c0cf7b66c522da5b4e127", "author": | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: segmentby columns of compressed hypertable "} --------------------------------------------------------------------------------Note how the query only returns results within the specified date range.Method 2: Filter within a provided start date, and a time delta later.# Method 2: Query for vectors between start_dt and a time delta td later# Most relevant vectors between 1 August and 7 days laterdocs_with_score = db.similarity_search_with_score(query, start_date=start_dt, time_delta=td)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.18458807468414307 Date: 2023-08-3 14:30:23+0500 {"commit": " 7aeed663b9c0f337b530fd6cad47704a51a9b2ec", "author": "Dmitry Simonenko<[email protected]>", "date": "Thu Aug 3 14:30:23 2023 +0300", "change summary": "Feature flags for TimescaleDB features", "change details": "This PR adds several GUCs which allow to enable/disable major timescaledb features: - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.20492422580718994 Date: 2023-08-7 18:31:40+0320 {"commit": " 07762ea4cedefc88497f0d1f8712d1515cdc5b6e", "author": "Sven Klemm<[email protected]>", "date": "Mon Aug 7 18:31:40 2023 +0200", "change summary": "Test timescaledb debian 12 packages in CI", "change details": ""} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.21106326580047607 Date: 2023-08-3 14:36:39+0500 {"commit": " 2863daf3df83c63ee36c0cf7b66c522da5b4e127", "author": |
1,104 | "author": "Dmitry Simonenko<[email protected]>", "date": "Thu Aug 3 14:36:39 2023 +0300", "change summary": "Support CREATE INDEX ONLY ON main table", "change details": "This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks. Fix #5908 "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.21698051691055298 Date: 2023-08-2 20:24:14+0140 {"commit": " 3af0d282ea71d9a8f27159a6171e9516e62ec9cb", "author": "Lakshmi Narayanan Sreethar<[email protected]>", "date": "Wed Aug 2 20:24:14 2023 +0100", "change summary": "PG16: ExecInsertIndexTuples requires additional parameter", "change details": "PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value. Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value. postgres/postgres@19d8e23 "} --------------------------------------------------------------------------------Once again, notice how we get results within the specified time filter, different from the previous query.Method 3: Filter within a provided end date and a time delta earlier.# Method 3: Query for vectors between end_dt and a time delta td earlier# Most relevant vectors between 30 August and 7 days earlierdocs_with_score = db.similarity_search_with_score(query, end_date=end_dt, time_delta=td)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: "author": "Dmitry Simonenko<[email protected]>", "date": "Thu Aug 3 14:36:39 2023 +0300", "change summary": "Support CREATE INDEX ONLY ON main table", "change details": "This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks. Fix #5908 "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.21698051691055298 Date: 2023-08-2 20:24:14+0140 {"commit": " 3af0d282ea71d9a8f27159a6171e9516e62ec9cb", "author": "Lakshmi Narayanan Sreethar<[email protected]>", "date": "Wed Aug 2 20:24:14 2023 +0100", "change summary": "PG16: ExecInsertIndexTuples requires additional parameter", "change details": "PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value. Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value. postgres/postgres@19d8e23 "} --------------------------------------------------------------------------------Once again, notice how we get results within the specified time filter, different from the previous query.Method 3: Filter within a provided end date and a time delta earlier.# Method 3: Query for vectors between end_dt and a time delta td earlier# Most relevant vectors between 30 August and 7 days earlierdocs_with_score = db.similarity_search_with_score(query, end_date=end_dt, time_delta=td)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) |
1,105 | print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18496227264404297 Date: 2023-08-29 10:49:47+0320 {"commit": " a9751ccd5eb030026d7b975d22753f5964972389", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 10:49:47 2023 +0200", "change summary": "Move partitioning functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.1871250867843628 Date: 2023-08-28 23:26:23+0320 {"commit": " b2a91494a11d8b82849b6f11f9ea6dc26ef8a8cb", "author": "Sven Klemm<[email protected]>", "date": "Mon Aug 28 23:26:23 2023 +0200", "change summary": "Move | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18496227264404297 Date: 2023-08-29 10:49:47+0320 {"commit": " a9751ccd5eb030026d7b975d22753f5964972389", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 10:49:47 2023 +0200", "change summary": "Move partitioning functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.1871250867843628 Date: 2023-08-28 23:26:23+0320 {"commit": " b2a91494a11d8b82849b6f11f9ea6dc26ef8a8cb", "author": "Sven Klemm<[email protected]>", "date": "Mon Aug 28 23:26:23 2023 +0200", "change summary": "Move |
1,106 | 28 23:26:23 2023 +0200", "change summary": "Move ddl_internal functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18867712088363497 Date: 2023-08-27 13:20:04+0320 {"commit": " e02b1f348eb4c48def00b7d5227238b4d9d41a4a", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 27 13:20:04 2023 +0200", "change summary": "Simplify schema move update script", "change details": "Use dynamic sql to create the ALTER FUNCTION statements for those functions that may not exist in previous versions. "} --------------------------------------------------------------------------------Method 4: We can also filter for all vectors after a given date by only specifying a start date in our query.Method 5: Similarly, we can filter for or all vectors before a given date by only specify an end date in our query.# Method 4: Query all vectors after start_datedocs_with_score = db.similarity_search_with_score(query,start_date=start_dt)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 28 23:26:23 2023 +0200", "change summary": "Move ddl_internal functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18867712088363497 Date: 2023-08-27 13:20:04+0320 {"commit": " e02b1f348eb4c48def00b7d5227238b4d9d41a4a", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 27 13:20:04 2023 +0200", "change summary": "Simplify schema move update script", "change details": "Use dynamic sql to create the ALTER FUNCTION statements for those functions that may not exist in previous versions. "} --------------------------------------------------------------------------------Method 4: We can also filter for all vectors after a given date by only specifying a start date in our query.Method 5: Similarly, we can filter for or all vectors before a given date by only specify an end date in our query.# Method 4: Query all vectors after start_datedocs_with_score = db.similarity_search_with_score(query,start_date=start_dt)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * |
1,107 | print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18102192878723145 Date: 2023-08-20 22:47:10+0320 {"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18150119891755445 Date: 2023-08-22 12:01:19+0320 | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18102192878723145 Date: 2023-08-20 22:47:10+0320 {"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18150119891755445 Date: 2023-08-22 12:01:19+0320 |
1,108 | Date: 2023-08-22 12:01:19+0320 {"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18422493887617963 Date: 2023-08-9 15:26:03+0500 {"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<[email protected]>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity. **Features** * #5923 Feature flags for TimescaleDB features **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable "} --------------------------------------------------------------------------------# Method 5: Query all vectors before end_datedocs_with_score = db.similarity_search_with_score(query, end_date=end_dt)for doc, score in docs_with_score: print("-" * 80) | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: Date: 2023-08-22 12:01:19+0320 {"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.18422493887617963 Date: 2023-08-9 15:26:03+0500 {"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<[email protected]>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity. **Features** * #5923 Feature flags for TimescaleDB features **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable "} --------------------------------------------------------------------------------# Method 5: Query all vectors before end_datedocs_with_score = db.similarity_search_with_score(query, end_date=end_dt)for doc, score in docs_with_score: print("-" * 80) |
1,109 | score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.16723191738128662 Date: 2023-04-11 22:01:14+0320 {"commit": " 0595ff0888f2ffb8d313acb0bda9642578a9ade3", "author": "Sven Klemm<[email protected]>", "date": "Tue Apr 11 22:01:14 2023 +0200", "change summary": "Move type support functions into _timescaledb_functions schema", "change details": ""} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.1706540584564209 Date: 2023-04-6 13:00:00+0320 {"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.17462033033370972 Date: 2023-03-31 08:22:57+0320 {"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: score in docs_with_score: print("-" * 80) print("Score: ", score) print("Date: ", doc.metadata["date"]) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.16723191738128662 Date: 2023-04-11 22:01:14+0320 {"commit": " 0595ff0888f2ffb8d313acb0bda9642578a9ade3", "author": "Sven Klemm<[email protected]>", "date": "Tue Apr 11 22:01:14 2023 +0200", "change summary": "Move type support functions into _timescaledb_functions schema", "change details": ""} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.1706540584564209 Date: 2023-04-6 13:00:00+0320 {"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.17462033033370972 Date: 2023-03-31 08:22:57+0320 {"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema |
1,110 | that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} --------------------------------------------------------------------------------The main takeaway is that in each result above, only vectors within the specified time range are returned. These queries are very efficient as they only need to search the relevant partitions.We can also use this functionality for question answering, where we want to find the most relevant vectors within a specified time range to use as context for answering a question. Let's take a look at an example below, using Timescale Vector as a retriever:# Set timescale vector as a retriever and specify start and end dates via kwargsretriever = db.as_retriever(search_kwargs={"start_date": start_dt, "end_date": end_dt})from langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature = 0.1, model = 'gpt-3.5-turbo-16k')from langchain.chains import RetrievalQAqa_stuff = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, verbose=True,)query = "What's new with the timescaledb functions? Tell me when these changes were made."response = | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "} -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.17488396167755127 Date: 2023-08-29 18:13:24+0320 {"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<[email protected]>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. "} --------------------------------------------------------------------------------The main takeaway is that in each result above, only vectors within the specified time range are returned. These queries are very efficient as they only need to search the relevant partitions.We can also use this functionality for question answering, where we want to find the most relevant vectors within a specified time range to use as context for answering a question. Let's take a look at an example below, using Timescale Vector as a retriever:# Set timescale vector as a retriever and specify start and end dates via kwargsretriever = db.as_retriever(search_kwargs={"start_date": start_dt, "end_date": end_dt})from langchain.chat_models import ChatOpenAIllm = ChatOpenAI(temperature = 0.1, model = 'gpt-3.5-turbo-16k')from langchain.chains import RetrievalQAqa_stuff = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=retriever, verbose=True,)query = "What's new with the timescaledb functions? Tell me when these changes were made."response = |
1,111 | Tell me when these changes were made."response = qa_stuff.run(query)print(response) > Entering new RetrievalQA chain... > Finished chain. The following changes were made to the timescaledb functions: 1. "Add compatibility layer for _timescaledb_internal functions" - This change was made on Tue Aug 29 18:13:24 2023 +0200. 2. "Move functions to _timescaledb_functions schema" - This change was made on Sun Aug 20 22:47:10 2023 +0200. 3. "Move utility functions to _timescaledb_functions schema" - This change was made on Tue Aug 22 12:01:19 2023 +0200. 4. "Move partitioning functions to _timescaledb_functions schema" - This change was made on Tue Aug 29 10:49:47 2023 +0200.Note that the context the LLM uses to compose an answer are from retrieved documents only within the specified date range. This shows how you can use Timescale Vector to enhance retrieval augmented generation by retrieving documents within time ranges relevant to your query.3. Using ANN Search Indexes to Speed Up Queries‚ÄãYou can speed up similarity queries by creating an index on the embedding column. You should only do this once you have ingested a large part of your data.Timescale Vector supports the following indexes:timescale_vector index (tsv): a disk-ann inspired graph index for fast similarity search (default).pgvector's HNSW index: a hierarchical navigable small world graph index for fast similarity search.pgvector's IVFFLAT index: an inverted file index for fast similarity search.Important note: In PostgreSQL, each table can only have one index on a particular column. So if you'd like to test the performance of different index types, you can do so either by (1) creating multiple tables with different indexes, (2) creating multiple vector columns in the same table and creating different indexes on each column, or (3) by dropping and recreating the index on the same column and comparing results.# Initialize an existing TimescaleVector storeCOLLECTION_NAME = | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: Tell me when these changes were made."response = qa_stuff.run(query)print(response) > Entering new RetrievalQA chain... > Finished chain. The following changes were made to the timescaledb functions: 1. "Add compatibility layer for _timescaledb_internal functions" - This change was made on Tue Aug 29 18:13:24 2023 +0200. 2. "Move functions to _timescaledb_functions schema" - This change was made on Sun Aug 20 22:47:10 2023 +0200. 3. "Move utility functions to _timescaledb_functions schema" - This change was made on Tue Aug 22 12:01:19 2023 +0200. 4. "Move partitioning functions to _timescaledb_functions schema" - This change was made on Tue Aug 29 10:49:47 2023 +0200.Note that the context the LLM uses to compose an answer are from retrieved documents only within the specified date range. This shows how you can use Timescale Vector to enhance retrieval augmented generation by retrieving documents within time ranges relevant to your query.3. Using ANN Search Indexes to Speed Up Queries‚ÄãYou can speed up similarity queries by creating an index on the embedding column. You should only do this once you have ingested a large part of your data.Timescale Vector supports the following indexes:timescale_vector index (tsv): a disk-ann inspired graph index for fast similarity search (default).pgvector's HNSW index: a hierarchical navigable small world graph index for fast similarity search.pgvector's IVFFLAT index: an inverted file index for fast similarity search.Important note: In PostgreSQL, each table can only have one index on a particular column. So if you'd like to test the performance of different index types, you can do so either by (1) creating multiple tables with different indexes, (2) creating multiple vector columns in the same table and creating different indexes on each column, or (3) by dropping and recreating the index on the same column and comparing results.# Initialize an existing TimescaleVector storeCOLLECTION_NAME = |
1,112 | existing TimescaleVector storeCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()db = TimescaleVector( collection_name=COLLECTION_NAME, service_url=SERVICE_URL, embedding_function=embeddings,)Using the create_index() function without additional arguments will create a timescale_vector_index by default, using the default parameters.# create an index# by default this will create a Timescale Vector (DiskANN) indexdb.create_index()You can also specify the parameters for the index. See the Timescale Vector documentation for a full discussion of the different parameters and their effects on performance.Note: You don't need to specify parameters as we set smart defaults. But you can always specify your own parameters if you want to experiment eek out more performance for your specific dataset.#drop the old indexdb.drop_index()# create an index# Note: You don't need to specify m and ef_construction parameters as we set smart defaults. db.create_index(index_type="tsv", max_alpha=1.0, num_neighbors=50)Timescale Vector also supports the HNSW ANN indexing algorithm, as well as the ivfflat ANN indexing algorithm. Simply specify in the index_type argument which index you'd like to create, and optionally specify the parameters for the index.#drop the old indexdb.drop_index()# Create an HNSW index# Note: You don't need to specify m and ef_construction parameters as we set smart defaults. db.create_index(index_type="hnsw", m=16, ef_construction=64)#drop the old indexdb.drop_index()# Create an IVFFLAT index# Note: You don't need to specify num_lists and num_records parameters as we set smart defaults.db.create_index(index_type="ivfflat", num_lists=20, num_records=1000)In general, we recommend using the default timescale vector index, or the HNSW index.#drop the old indexdb.drop_index()# Create a new timescale vector indexdb.create_index()4. Self Querying Retriever with Timescale Vector‚ÄãTimescale Vector also supports the self-querying retriever | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: existing TimescaleVector storeCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()db = TimescaleVector( collection_name=COLLECTION_NAME, service_url=SERVICE_URL, embedding_function=embeddings,)Using the create_index() function without additional arguments will create a timescale_vector_index by default, using the default parameters.# create an index# by default this will create a Timescale Vector (DiskANN) indexdb.create_index()You can also specify the parameters for the index. See the Timescale Vector documentation for a full discussion of the different parameters and their effects on performance.Note: You don't need to specify parameters as we set smart defaults. But you can always specify your own parameters if you want to experiment eek out more performance for your specific dataset.#drop the old indexdb.drop_index()# create an index# Note: You don't need to specify m and ef_construction parameters as we set smart defaults. db.create_index(index_type="tsv", max_alpha=1.0, num_neighbors=50)Timescale Vector also supports the HNSW ANN indexing algorithm, as well as the ivfflat ANN indexing algorithm. Simply specify in the index_type argument which index you'd like to create, and optionally specify the parameters for the index.#drop the old indexdb.drop_index()# Create an HNSW index# Note: You don't need to specify m and ef_construction parameters as we set smart defaults. db.create_index(index_type="hnsw", m=16, ef_construction=64)#drop the old indexdb.drop_index()# Create an IVFFLAT index# Note: You don't need to specify num_lists and num_records parameters as we set smart defaults.db.create_index(index_type="ivfflat", num_lists=20, num_records=1000)In general, we recommend using the default timescale vector index, or the HNSW index.#drop the old indexdb.drop_index()# Create a new timescale vector indexdb.create_index()4. Self Querying Retriever with Timescale Vector‚ÄãTimescale Vector also supports the self-querying retriever |
1,113 | Vector also supports the self-querying retriever functionality, which gives it the ability to query itself. Given a natural language query with a query statement and filters (single or composite), the retriever uses a query constructing LLM chain to write a SQL query and then applies it to the underlying PostgreSQL database in the Timescale Vector vectorstore.For more on self-querying, see the docs.To illustrate self-querying with Timescale Vector, we'll use the same gitlog dataset from Part 3.COLLECTION_NAME = "timescale_commits"vectorstore = TimescaleVector( embedding_function=OpenAIEmbeddings(), collection_name=COLLECTION_NAME, service_url=SERVICE_URL,)Next we'll create our self-querying retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfo# Give LLM info about the metadata fieldsmetadata_field_info = [ AttributeInfo( name="id", description="A UUID v1 generated from the date of the commit", type="uuid", ), AttributeInfo( name="date", description="The date of the commit in timestamptz format", type="timestamptz", ), AttributeInfo( name="author_name", description="The name of the author of the commit", type="string", ), AttributeInfo( name="author_email", description="The email address of the author of the commit", type="string", )]document_content_description = "The git log commit summary containing the commit hash, author, date of commit, change summary and change details"# Instantiate the self-query retriever from an LLMllm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: Vector also supports the self-querying retriever functionality, which gives it the ability to query itself. Given a natural language query with a query statement and filters (single or composite), the retriever uses a query constructing LLM chain to write a SQL query and then applies it to the underlying PostgreSQL database in the Timescale Vector vectorstore.For more on self-querying, see the docs.To illustrate self-querying with Timescale Vector, we'll use the same gitlog dataset from Part 3.COLLECTION_NAME = "timescale_commits"vectorstore = TimescaleVector( embedding_function=OpenAIEmbeddings(), collection_name=COLLECTION_NAME, service_url=SERVICE_URL,)Next we'll create our self-querying retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfo# Give LLM info about the metadata fieldsmetadata_field_info = [ AttributeInfo( name="id", description="A UUID v1 generated from the date of the commit", type="uuid", ), AttributeInfo( name="date", description="The date of the commit in timestamptz format", type="timestamptz", ), AttributeInfo( name="author_name", description="The name of the author of the commit", type="string", ), AttributeInfo( name="author_email", description="The email address of the author of the commit", type="string", )]document_content_description = "The git log commit summary containing the commit hash, author, date of commit, change summary and change details"# Instantiate the self-query retriever from an LLMllm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm( llm, vectorstore, document_content_description, metadata_field_info, |
1,114 | metadata_field_info, enable_limit=True, verbose=True)Now let's test out the self-querying retriever on our gitlog dataset. Run the queries below and note how you can specify a query, query with a filter, and query with a composite filter (filters with AND, OR) in natural language and the self-query retriever will translate that query into SQL and perform the search on the Timescale Vector PostgreSQL vectorstore.This illustrates the power of the self-query retriever. You can use it to perform complex searches over your vectorstore without you or your users having to write any SQL directly!# This example specifies a relevant queryretriever.get_relevant_documents("What are improvements made to continuous aggregates?") /Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='improvements to continuous aggregates' filter=None limit=None [Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: metadata_field_info, enable_limit=True, verbose=True)Now let's test out the self-querying retriever on our gitlog dataset. Run the queries below and note how you can specify a query, query with a filter, and query with a composite filter (filters with AND, OR) in natural language and the self-query retriever will translate that query into SQL and perform the search on the Timescale Vector PostgreSQL vectorstore.This illustrates the power of the self-query retriever. You can use it to perform complex searches over your vectorstore without you or your users having to write any SQL directly!# This example specifies a relevant queryretriever.get_relevant_documents("What are improvements made to continuous aggregates?") /Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='improvements to continuous aggregates' filter=None limit=None [Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', |
1,115 | of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': '[email protected]'}), Document(page_content='{"commit": " a6ff7ba6cc15b280a275e5acd315741ec9c86acc", "author": "Mats Kindahl<[email protected]>", "date": "Tue Feb 28 12:04:17 2023 +0100", "change summary": "Rename columns in old-style continuous aggregates", "change details": "For continuous aggregates with the old-style partial aggregates renaming | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': '[email protected]'}), Document(page_content='{"commit": " a6ff7ba6cc15b280a275e5acd315741ec9c86acc", "author": "Mats Kindahl<[email protected]>", "date": "Tue Feb 28 12:04:17 2023 +0100", "change summary": "Rename columns in old-style continuous aggregates", "change details": "For continuous aggregates with the old-style partial aggregates renaming |
1,116 | with the old-style partial aggregates renaming columns that are not in the group-by clause will generate an error when upgrading to a later version. The reason is that it is implicitly assumed that the name of the column is the same as for the direct view. This holds true for new-style continous aggregates, but is not always true for old-style continuous aggregates. In particular, columns that are not part of the `GROUP BY` clause can have an internally generated name. This commit fixes that by extracting the name of the column from the partial view and use that when renaming the partial view column and the materialized table column. "}', metadata={'id': 'a49ace80-b757-11ed-8138-2390fd44ffd9', 'date': '2023-02-28 12:04:17+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 294, 'author_name': 'Mats Kindahl', 'commit_hash': ' a6ff7ba6cc15b280a275e5acd315741ec9c86acc', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 5bba74a2ec083728f8e93e09d03d102568fd72b5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Mon Aug 7 19:49:47 2023 -0300", "change summary": "Relax strong table lock when refreshing a CAGG", "change details": "When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish. Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: with the old-style partial aggregates renaming columns that are not in the group-by clause will generate an error when upgrading to a later version. The reason is that it is implicitly assumed that the name of the column is the same as for the direct view. This holds true for new-style continous aggregates, but is not always true for old-style continuous aggregates. In particular, columns that are not part of the `GROUP BY` clause can have an internally generated name. This commit fixes that by extracting the name of the column from the partial view and use that when renaming the partial view column and the materialized table column. "}', metadata={'id': 'a49ace80-b757-11ed-8138-2390fd44ffd9', 'date': '2023-02-28 12:04:17+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 294, 'author_name': 'Mats Kindahl', 'commit_hash': ' a6ff7ba6cc15b280a275e5acd315741ec9c86acc', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 5bba74a2ec083728f8e93e09d03d102568fd72b5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Mon Aug 7 19:49:47 2023 -0300", "change summary": "Relax strong table lock when refreshing a CAGG", "change details": "When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish. Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in |
1,117 | Aggregate refresh procedure can be executed in multiple sessions with less locks. Fix #3554 "}', metadata={'id': 'b5583780-3574-11ee-a5ba-2e305874a58f', 'date': '2023-08-7 19:49:47+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 27, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 5bba74a2ec083728f8e93e09d03d102568fd72b5', 'author_email': '[email protected]'})]# This example specifies a filterretriever.get_relevant_documents("What commits did Sven Klemm add?") query=' ' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None [Document(page_content='{"commit": " e2e7ae304521b74ac6b3f157a207da047d44ab06", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 3 11:22:06 2023 +0100", "change summary": "Don\'t run sanitizer test on individual PRs", "change details": "Sanitizer tests take a long time to run so we don\'t want to run them on individual PRs but instead run them nightly and on commits to master. "}', metadata={'id': '3f401b00-b9ad-11ed-b5ea-a3fd40b9ac16', 'date': '2023-03-3 11:22:06+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 295, 'author_name': 'Sven Klemm', 'commit_hash': ' e2e7ae304521b74ac6b3f157a207da047d44ab06', 'author_email': '[email protected]'}), Document(page_content='{"commit": " d8f19e57a04d17593df5f2c694eae8775faddbc7", "author": "Sven Klemm<[email protected]>", "date": "Wed Feb 1 08:34:20 2023 +0100", "change summary": "Bump version of setup-wsl github action", "change details": "The currently used version pulls in Node.js 12 which is deprecated on github. https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/ "}', metadata={'id': 'd70de600-a202-11ed-85d6-30b6df240f49', 'date': '2023-02-1 08:34:20+0140', | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: Aggregate refresh procedure can be executed in multiple sessions with less locks. Fix #3554 "}', metadata={'id': 'b5583780-3574-11ee-a5ba-2e305874a58f', 'date': '2023-08-7 19:49:47+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 27, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 5bba74a2ec083728f8e93e09d03d102568fd72b5', 'author_email': '[email protected]'})]# This example specifies a filterretriever.get_relevant_documents("What commits did Sven Klemm add?") query=' ' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None [Document(page_content='{"commit": " e2e7ae304521b74ac6b3f157a207da047d44ab06", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 3 11:22:06 2023 +0100", "change summary": "Don\'t run sanitizer test on individual PRs", "change details": "Sanitizer tests take a long time to run so we don\'t want to run them on individual PRs but instead run them nightly and on commits to master. "}', metadata={'id': '3f401b00-b9ad-11ed-b5ea-a3fd40b9ac16', 'date': '2023-03-3 11:22:06+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 295, 'author_name': 'Sven Klemm', 'commit_hash': ' e2e7ae304521b74ac6b3f157a207da047d44ab06', 'author_email': '[email protected]'}), Document(page_content='{"commit": " d8f19e57a04d17593df5f2c694eae8775faddbc7", "author": "Sven Klemm<[email protected]>", "date": "Wed Feb 1 08:34:20 2023 +0100", "change summary": "Bump version of setup-wsl github action", "change details": "The currently used version pulls in Node.js 12 which is deprecated on github. https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/ "}', metadata={'id': 'd70de600-a202-11ed-85d6-30b6df240f49', 'date': '2023-02-1 08:34:20+0140', |
1,118 | 'date': '2023-02-1 08:34:20+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 350, 'author_name': 'Sven Klemm', 'commit_hash': ' d8f19e57a04d17593df5f2c694eae8775faddbc7', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c", "author": "Sven Klemm<[email protected]>", "date": "Fri Nov 25 08:27:45 2022 +0100", "change summary": "Use packaged postgres for sqlsmith and coverity CI", "change details": "The sqlsmith and coverity workflows used the cache postgres build but could not produce a build by themselves and therefore relied on other workflows to produce the cached binaries. This patch changes those workflows to use normal postgres packages instead of custom built postgres to remove that dependency. "}', metadata={'id': 'a786ae80-6c92-11ed-bd6c-a57bd3348b97', 'date': '2022-11-25 08:27:45+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 447, 'author_name': 'Sven Klemm', 'commit_hash': ' 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c', 'author_email': '[email protected]'}), Document(page_content='{"commit": " b1314e63f2ff6151ab5becfb105afa3682286a4d", "author": "Sven Klemm<[email protected]>", "date": "Thu Dec 22 12:03:35 2022 +0100", "change summary": "Fix RPM package test for PG15 on centos 7", "change details": "Installing PG15 on Centos 7 requires the EPEL repository to satisfy the dependencies. "}', metadata={'id': '477b1d80-81e8-11ed-9c8c-9b5abbd67c98', 'date': '2022-12-22 12:03:35+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 408, 'author_name': 'Sven Klemm', 'commit_hash': ' b1314e63f2ff6151ab5becfb105afa3682286a4d', 'author_email': '[email protected]'})]# This example specifies a query and | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 'date': '2023-02-1 08:34:20+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 350, 'author_name': 'Sven Klemm', 'commit_hash': ' d8f19e57a04d17593df5f2c694eae8775faddbc7', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c", "author": "Sven Klemm<[email protected]>", "date": "Fri Nov 25 08:27:45 2022 +0100", "change summary": "Use packaged postgres for sqlsmith and coverity CI", "change details": "The sqlsmith and coverity workflows used the cache postgres build but could not produce a build by themselves and therefore relied on other workflows to produce the cached binaries. This patch changes those workflows to use normal postgres packages instead of custom built postgres to remove that dependency. "}', metadata={'id': 'a786ae80-6c92-11ed-bd6c-a57bd3348b97', 'date': '2022-11-25 08:27:45+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 447, 'author_name': 'Sven Klemm', 'commit_hash': ' 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c', 'author_email': '[email protected]'}), Document(page_content='{"commit": " b1314e63f2ff6151ab5becfb105afa3682286a4d", "author": "Sven Klemm<[email protected]>", "date": "Thu Dec 22 12:03:35 2022 +0100", "change summary": "Fix RPM package test for PG15 on centos 7", "change details": "Installing PG15 on Centos 7 requires the EPEL repository to satisfy the dependencies. "}', metadata={'id': '477b1d80-81e8-11ed-9c8c-9b5abbd67c98', 'date': '2022-12-22 12:03:35+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 408, 'author_name': 'Sven Klemm', 'commit_hash': ' b1314e63f2ff6151ab5becfb105afa3682286a4d', 'author_email': '[email protected]'})]# This example specifies a query and |
1,119 | This example specifies a query and filterretriever.get_relevant_documents("What commits about timescaledb_functions did Sven Klemm add?") query='timescaledb_functions' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None [Document(page_content='{"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "}', metadata={'id': '2cb47800-d46a-11ed-8f0e-2b624245c561', 'date': '2023-04-6 13:00:00+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 233, 'author_name': 'Sven Klemm', 'commit_hash': ' 04f43335dea11e9c467ee558ad8edfc00c1a45ed', 'author_email': '[email protected]'}), Document(page_content='{"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "}', metadata={'id': '7a257680-cf8c-11ed-848c-a515e8687479', 'date': '2023-03-31 08:22:57+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 239, | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: This example specifies a query and filterretriever.get_relevant_documents("What commits about timescaledb_functions did Sven Klemm add?") query='timescaledb_functions' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None [Document(page_content='{"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "}', metadata={'id': '2cb47800-d46a-11ed-8f0e-2b624245c561', 'date': '2023-04-6 13:00:00+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 233, 'author_name': 'Sven Klemm', 'commit_hash': ' 04f43335dea11e9c467ee558ad8edfc00c1a45ed', 'author_email': '[email protected]'}), Document(page_content='{"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<[email protected]>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "}', metadata={'id': '7a257680-cf8c-11ed-848c-a515e8687479', 'date': '2023-03-31 08:22:57+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 239, |
1,120 | 'seq_num': 239, 'author_name': 'Sven Klemm', 'commit_hash': ' feef9206facc5c5f506661de4a81d96ef059b095', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "}', metadata={'id': 'bb99db00-3f9a-11ee-a8dc-0b9c1a5a37c4', 'date': '2023-08-20 22:47:10+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 41, 'author_name': 'Sven Klemm', 'commit_hash': ' 0a66bdb8d36a1879246bd652e4c28500c4b951ab', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 56ea8b4de93cefc38e002202d8ac96947dcbaa77", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 13 13:16:14 2023 +0200", "change summary": "Move trigger functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for our trigger functions. "}', metadata={'id': '9a255300-d9ec-11ed-988f-7086c8ca463a', 'date': '2023-04-13 13:16:14+0320', 'source': | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 'seq_num': 239, 'author_name': 'Sven Klemm', 'commit_hash': ' feef9206facc5c5f506661de4a81d96ef059b095', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<[email protected]>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "}', metadata={'id': 'bb99db00-3f9a-11ee-a8dc-0b9c1a5a37c4', 'date': '2023-08-20 22:47:10+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 41, 'author_name': 'Sven Klemm', 'commit_hash': ' 0a66bdb8d36a1879246bd652e4c28500c4b951ab', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 56ea8b4de93cefc38e002202d8ac96947dcbaa77", "author": "Sven Klemm<[email protected]>", "date": "Thu Apr 13 13:16:14 2023 +0200", "change summary": "Move trigger functions to _timescaledb_functions schema", "change details": "To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for our trigger functions. "}', metadata={'id': '9a255300-d9ec-11ed-988f-7086c8ca463a', 'date': '2023-04-13 13:16:14+0320', 'source': |
1,121 | 'date': '2023-04-13 13:16:14+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 44, 'author_name': 'Sven Klemm', 'commit_hash': ' 56ea8b4de93cefc38e002202d8ac96947dcbaa77', 'author_email': '[email protected]'})]# This example specifies a time-based filterretriever.get_relevant_documents("What commits were added in July 2023?") query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='date', value='2023-07-01T00:00:00Z'), Comparison(comparator=<Comparator.LTE: 'lte'>, attribute='date', value='2023-07-31T23:59:59Z')]) limit=None [Document(page_content='{"commit": " 5cf354e2469ee7e43248bed382a4b49fc7ccfecd", "author": "Markus Engel<[email protected]>", "date": "Mon Jul 31 11:28:25 2023 +0200", "change summary": "Fix quoting owners in sql scripts.", "change details": "When referring to a role from a string type, it must be properly quoted using pg_catalog.quote_ident before it can be casted to regrole. Fixed this, especially in update scripts. "}', metadata={'id': '99590280-2f84-11ee-915b-5715b2447de4', 'date': '2023-07-31 11:28:25+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 76, 'author_name': 'Markus Engel', 'commit_hash': ' 5cf354e2469ee7e43248bed382a4b49fc7ccfecd', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 88aaf23ae37fe7f47252b87325eb570aa417c607", "author": "noctarius aka Christoph Engelbert<[email protected]>", "date": "Wed Jul 12 14:53:40 2023 +0200", "change summary": "Allow Replica Identity (Alter Table) on CAGGs (#5868)", "change details": "This commit is a follow up of #5515, which added support for ALTER TABLE\\r ... REPLICA IDENTITY (FULL | INDEX) on hypertables.\\r \\r This commit allows the execution against materialized hypertables to\\r enable update | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 'date': '2023-04-13 13:16:14+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 44, 'author_name': 'Sven Klemm', 'commit_hash': ' 56ea8b4de93cefc38e002202d8ac96947dcbaa77', 'author_email': '[email protected]'})]# This example specifies a time-based filterretriever.get_relevant_documents("What commits were added in July 2023?") query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='date', value='2023-07-01T00:00:00Z'), Comparison(comparator=<Comparator.LTE: 'lte'>, attribute='date', value='2023-07-31T23:59:59Z')]) limit=None [Document(page_content='{"commit": " 5cf354e2469ee7e43248bed382a4b49fc7ccfecd", "author": "Markus Engel<[email protected]>", "date": "Mon Jul 31 11:28:25 2023 +0200", "change summary": "Fix quoting owners in sql scripts.", "change details": "When referring to a role from a string type, it must be properly quoted using pg_catalog.quote_ident before it can be casted to regrole. Fixed this, especially in update scripts. "}', metadata={'id': '99590280-2f84-11ee-915b-5715b2447de4', 'date': '2023-07-31 11:28:25+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 76, 'author_name': 'Markus Engel', 'commit_hash': ' 5cf354e2469ee7e43248bed382a4b49fc7ccfecd', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 88aaf23ae37fe7f47252b87325eb570aa417c607", "author": "noctarius aka Christoph Engelbert<[email protected]>", "date": "Wed Jul 12 14:53:40 2023 +0200", "change summary": "Allow Replica Identity (Alter Table) on CAGGs (#5868)", "change details": "This commit is a follow up of #5515, which added support for ALTER TABLE\\r ... REPLICA IDENTITY (FULL | INDEX) on hypertables.\\r \\r This commit allows the execution against materialized hypertables to\\r enable update |
1,122 | materialized hypertables to\\r enable update / delete operations on continuous aggregates when logical\\r replication in enabled for them."}', metadata={'id': '1fcfa200-20b3-11ee-9a18-370561c7cb1a', 'date': '2023-07-12 14:53:40+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 96, 'author_name': 'noctarius aka Christoph Engelbert', 'commit_hash': ' 88aaf23ae37fe7f47252b87325eb570aa417c607', 'author_email': '[email protected]'}), Document(page_content='{"commit": " d5268c36fbd23fa2a93c0371998286e8688247bb", "author": "Alexander Kuzmenkov<[email protected]>", "date": "Fri Jul 28 13:35:05 2023 +0200", "change summary": "Fix SQLSmith workflow", "change details": "The build was failing because it was picking up the wrong version of Postgres. Remove it. "}', metadata={'id': 'cc0fba80-2d3a-11ee-ae7d-36dc25cad3b8', 'date': '2023-07-28 13:35:05+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 82, 'author_name': 'Alexander Kuzmenkov', 'commit_hash': ' d5268c36fbd23fa2a93c0371998286e8688247bb', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9", "author": "Lakshmi Narayanan Sreethar<[email protected]>", "date": "Tue Jul 25 16:11:35 2023 +0530", "change summary": "Fix broken CI after PG12 removal", "change details": "The commit cdea343cc updated the gh_matrix_builder.py script but failed to import PG_LATEST variable into the script thus breaking the CI. Import that variable to fix the CI tests. "}', metadata={'id': 'd3835980-2ad7-11ee-b98d-c4e3092e076e', 'date': '2023-07-25 16:11:35+0850', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 84, 'author_name': 'Lakshmi Narayanan Sreethar', | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: materialized hypertables to\\r enable update / delete operations on continuous aggregates when logical\\r replication in enabled for them."}', metadata={'id': '1fcfa200-20b3-11ee-9a18-370561c7cb1a', 'date': '2023-07-12 14:53:40+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 96, 'author_name': 'noctarius aka Christoph Engelbert', 'commit_hash': ' 88aaf23ae37fe7f47252b87325eb570aa417c607', 'author_email': '[email protected]'}), Document(page_content='{"commit": " d5268c36fbd23fa2a93c0371998286e8688247bb", "author": "Alexander Kuzmenkov<[email protected]>", "date": "Fri Jul 28 13:35:05 2023 +0200", "change summary": "Fix SQLSmith workflow", "change details": "The build was failing because it was picking up the wrong version of Postgres. Remove it. "}', metadata={'id': 'cc0fba80-2d3a-11ee-ae7d-36dc25cad3b8', 'date': '2023-07-28 13:35:05+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 82, 'author_name': 'Alexander Kuzmenkov', 'commit_hash': ' d5268c36fbd23fa2a93c0371998286e8688247bb', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9", "author": "Lakshmi Narayanan Sreethar<[email protected]>", "date": "Tue Jul 25 16:11:35 2023 +0530", "change summary": "Fix broken CI after PG12 removal", "change details": "The commit cdea343cc updated the gh_matrix_builder.py script but failed to import PG_LATEST variable into the script thus breaking the CI. Import that variable to fix the CI tests. "}', metadata={'id': 'd3835980-2ad7-11ee-b98d-c4e3092e076e', 'date': '2023-07-25 16:11:35+0850', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 84, 'author_name': 'Lakshmi Narayanan Sreethar', |
1,123 | 84, 'author_name': 'Lakshmi Narayanan Sreethar', 'commit_hash': ' 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9', 'author_email': '[email protected]'})]# This example specifies a query and a LIMIT valueretriever.get_relevant_documents("What are two commits about hierarchical continuous aggregates?") query='hierarchical continuous aggregates' filter=None limit=2 [Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: 84, 'author_name': 'Lakshmi Narayanan Sreethar', 'commit_hash': ' 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9', 'author_email': '[email protected]'})]# This example specifies a query and a LIMIT valueretriever.get_relevant_documents("What are two commits about hierarchical continuous aggregates?") query='hierarchical continuous aggregates' filter=None limit=2 [Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': '[email protected]'}), Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous |
1,124 | "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': '[email protected]'})]5. Working with an existing TimescaleVector vectorstore​In the examples above, we created a vectorstore from a collection of documents. However, often we want to work insert data into and query data from an existing vectorstore. Let's see how to initialize, add documents to, and query an existing collection of documents in a TimescaleVector vector store.To work with an existing Timescale Vector store, we need to know the name of the table we want to query (COLLECTION_NAME) and the URL of the cloud PostgreSQL database (SERVICE_URL).# Initialize the existingCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()vectorstore = TimescaleVector( collection_name=COLLECTION_NAME, service_url=SERVICE_URL, embedding_function=embeddings,)To load new data into the table, we use the add_document() function. This function takes a list of documents and a list of metadata. The metadata must contain a unique id for each document. If you want your documents to be associated with the current date and time, | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': '[email protected]'})]5. Working with an existing TimescaleVector vectorstore​In the examples above, we created a vectorstore from a collection of documents. However, often we want to work insert data into and query data from an existing vectorstore. Let's see how to initialize, add documents to, and query an existing collection of documents in a TimescaleVector vector store.To work with an existing Timescale Vector store, we need to know the name of the table we want to query (COLLECTION_NAME) and the URL of the cloud PostgreSQL database (SERVICE_URL).# Initialize the existingCOLLECTION_NAME = "timescale_commits"embeddings = OpenAIEmbeddings()vectorstore = TimescaleVector( collection_name=COLLECTION_NAME, service_url=SERVICE_URL, embedding_function=embeddings,)To load new data into the table, we use the add_document() function. This function takes a list of documents and a list of metadata. The metadata must contain a unique id for each document. If you want your documents to be associated with the current date and time, |
1,125 | to be associated with the current date and time, you do not need to create a list of ids. A uuid will be automatically generated for each document.If you want your documents to be associated with a past date and time, you can create a list of ids using the uuid_from_time function in the timecale-vector python library, as shown in Section 2 above. This function takes a datetime object and returns a uuid with the date and time encoded in the uuid.# Add documents to a collection in TimescaleVectorids = vectorstore.add_documents([Document(page_content="foo")])ids ['a34f2b8a-53d7-11ee-8cc3-de1e4b2a0118']# Query the vectorstore for similar documentsdocs_with_score = vectorstore.similarity_search_with_score("foo")docs_with_score[0] (Document(page_content='foo', metadata={}), 5.006789860928507e-06)docs_with_score[1] (Document(page_content='{"commit": " 00b566dfe478c11134bcf1e7bcf38943e7fafe8f", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Mon Mar 6 15:51:03 2023 -0300", "change summary": "Remove unused functions", "change details": "We don\'t use `ts_catalog_delete[_only]` functions anywhere and instead we rely on `ts_catalog_delete_tid[_only]` functions so removing it from our code base. "}', metadata={'id': 'd7f5c580-bc4f-11ed-9712-ffa0126a201a', 'date': '2023-03-6 15:51:03+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 285, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 00b566dfe478c11134bcf1e7bcf38943e7fafe8f', 'author_email': '[email protected]'}), 0.23607668446580354)Deleting Data​You can delete data by uuid or by a filter on the metadata.ids = vectorstore.add_documents([Document(page_content="Bar")])vectorstore.delete(ids) TrueDeleting using metadata is especially useful if you want to periodically update information scraped from a particular source, or particular date or some other metadata | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: to be associated with the current date and time, you do not need to create a list of ids. A uuid will be automatically generated for each document.If you want your documents to be associated with a past date and time, you can create a list of ids using the uuid_from_time function in the timecale-vector python library, as shown in Section 2 above. This function takes a datetime object and returns a uuid with the date and time encoded in the uuid.# Add documents to a collection in TimescaleVectorids = vectorstore.add_documents([Document(page_content="foo")])ids ['a34f2b8a-53d7-11ee-8cc3-de1e4b2a0118']# Query the vectorstore for similar documentsdocs_with_score = vectorstore.similarity_search_with_score("foo")docs_with_score[0] (Document(page_content='foo', metadata={}), 5.006789860928507e-06)docs_with_score[1] (Document(page_content='{"commit": " 00b566dfe478c11134bcf1e7bcf38943e7fafe8f", "author": "Fabr\\u00edzio de Royes Mello<[email protected]>", "date": "Mon Mar 6 15:51:03 2023 -0300", "change summary": "Remove unused functions", "change details": "We don\'t use `ts_catalog_delete[_only]` functions anywhere and instead we rely on `ts_catalog_delete_tid[_only]` functions so removing it from our code base. "}', metadata={'id': 'd7f5c580-bc4f-11ed-9712-ffa0126a201a', 'date': '2023-03-6 15:51:03+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 285, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 00b566dfe478c11134bcf1e7bcf38943e7fafe8f', 'author_email': '[email protected]'}), 0.23607668446580354)Deleting Data​You can delete data by uuid or by a filter on the metadata.ids = vectorstore.add_documents([Document(page_content="Bar")])vectorstore.delete(ids) TrueDeleting using metadata is especially useful if you want to periodically update information scraped from a particular source, or particular date or some other metadata |
1,126 | source, or particular date or some other metadata attribute.vectorstore.add_documents([Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})])vectorstore.add_documents([Document(page_content="Adios", metadata={"source": "www.example.com/adios"})])vectorstore.delete_by_metadata({"source": "www.example.com/adios"})vectorstore.add_documents([Document(page_content="Adios, but newer!", metadata={"source": "www.example.com/adios"})]) ['c6367004-53d7-11ee-8cc3-de1e4b2a0118']Overriding a vectorstore​If you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = TimescaleVector.from_documents( documents=docs, embedding=embeddings, collection_name=COLLECTION_NAME, service_url=SERVICE_URL, pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]PreviousTigrisNextTypesenseWhat is Timescale Vector?How to access Timescale VectorSetup1. Similarity Search with Euclidean Distance (Default)Using a Timescale Vector as a Retriever2. Similarity Search with time-based filteringExtract content and metadata from git log JSONLoad documents and metadata into TimescaleVector vectorstoreQuerying vectors by time and similarity3. Using ANN Search Indexes to Speed Up Queries4. Self Querying Retriever with Timescale Vector5. Working with an existing TimescaleVector vectorstoreDeleting DataOverriding a vectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. | This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: source, or particular date or some other metadata attribute.vectorstore.add_documents([Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})])vectorstore.add_documents([Document(page_content="Adios", metadata={"source": "www.example.com/adios"})])vectorstore.delete_by_metadata({"source": "www.example.com/adios"})vectorstore.add_documents([Document(page_content="Adios, but newer!", metadata={"source": "www.example.com/adios"})]) ['c6367004-53d7-11ee-8cc3-de1e4b2a0118']Overriding a vectorstore​If you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = TimescaleVector.from_documents( documents=docs, embedding=embeddings, collection_name=COLLECTION_NAME, service_url=SERVICE_URL, pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]PreviousTigrisNextTypesenseWhat is Timescale Vector?How to access Timescale VectorSetup1. Similarity Search with Euclidean Distance (Default)Using a Timescale Vector as a Retriever2. Similarity Search with time-based filteringExtract content and metadata from git log JSONLoad documents and metadata into TimescaleVector vectorstoreQuerying vectors by time and similarity3. Using ANN Search Indexes to Speed Up Queries4. Self Querying Retriever with Timescale Vector5. Working with an existing TimescaleVector vectorstoreDeleting DataOverriding a vectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,127 | Retrievers | ü¶úÔ∏èüîó Langchain | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. ->: Retrievers | ü¶úÔ∏èüîó Langchain |
1,128 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverMultiVector RetrieverParent Document RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverWebResearchRetrieverIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrieversOn this pageRetrieversinfoHead to Integrations for documentation on built-in retriever integrations with 3rd-party tools.A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverMultiVector RetrieverParent Document RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverWebResearchRetrieverIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrieversOn this pageRetrieversinfoHead to Integrations for documentation on built-in retriever integrations with 3rd-party tools.A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store.
A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used |
1,129 | as the backbone of a retriever, but there are other types of retrievers as well.Retrievers implement the Runnable interface, the basic building block of the LangChain Expression Language (LCEL). This means they support invoke, ainvoke, stream, astream, batch, abatch, astream_log calls.Retrievers accept a string query as input and return a list of Document's as output.Get started​In this example we'll use a Chroma vector store-backed retriever. To get setup we'll need to run:pip install chromadbAnd download the state_of_the_union.txt file here.from langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromafull_text = open("state_of_the_union.txt", "r").read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)texts = text_splitter.split_text(full_text)embeddings = OpenAIEmbeddings()db = Chroma.from_texts(texts, embeddings)retriever = db.as_retriever()retrieved_docs = retriever.invoke("What did the president say about Ketanji Brown Jackson?")print(retrieved_docs[0].page_content) One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. We can do both. At our border, we’ve installed new technology like cutting-edge scanners | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. ->: as the backbone of a retriever, but there are other types of retrievers as well.Retrievers implement the Runnable interface, the basic building block of the LangChain Expression Language (LCEL). This means they support invoke, ainvoke, stream, astream, batch, abatch, astream_log calls.Retrievers accept a string query as input and return a list of Document's as output.Get started​In this example we'll use a Chroma vector store-backed retriever. To get setup we'll need to run:pip install chromadbAnd download the state_of_the_union.txt file here.from langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromafull_text = open("state_of_the_union.txt", "r").read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)texts = text_splitter.split_text(full_text)embeddings = OpenAIEmbeddings()db = Chroma.from_texts(texts, embeddings)retriever = db.as_retriever()retrieved_docs = retriever.invoke("What did the president say about Ketanji Brown Jackson?")print(retrieved_docs[0].page_content) One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. We can do both. At our border, we’ve installed new technology like cutting-edge scanners |
1,130 | new technology like cutting-edge scanners to better detect drug smuggling. We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.LCEL​Since retrievers are Runnable's, we can easily compose them with other Runnable objects:from langchain.chat_models import ChatOpenAIfrom langchain.prompts import ChatPromptTemplatefrom langchain.schema import StrOutputParserfrom langchain.schema.runnable import RunnablePassthroughtemplate = """Answer the question based only on the following context:{context}Question: {question}"""prompt = ChatPromptTemplate.from_template(template)model = ChatOpenAI()def format_docs(docs): return "\n\n".join([d.page_content for d in docs])chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | model | StrOutputParser())chain.invoke("What did the president say about technology?") 'The president said that technology plays a crucial role in the future and that passing the Bipartisan Innovation Act will make record investments in emerging technologies and American manufacturing. The president also mentioned Intel\'s plans to build a semiconductor "mega site" and increase their investment from $20 billion to $100 billion, which would be one of the biggest investments in manufacturing in American history.'PreviousVector storesNextMultiQueryRetrieverGet startedLCELCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. | Head to Integrations for documentation on built-in retriever integrations with 3rd-party tools. ->: new technology like cutting-edge scanners to better detect drug smuggling. We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.LCEL​Since retrievers are Runnable's, we can easily compose them with other Runnable objects:from langchain.chat_models import ChatOpenAIfrom langchain.prompts import ChatPromptTemplatefrom langchain.schema import StrOutputParserfrom langchain.schema.runnable import RunnablePassthroughtemplate = """Answer the question based only on the following context:{context}Question: {question}"""prompt = ChatPromptTemplate.from_template(template)model = ChatOpenAI()def format_docs(docs): return "\n\n".join([d.page_content for d in docs])chain = ( {"context": retriever | format_docs, "question": RunnablePassthrough()} | prompt | model | StrOutputParser())chain.invoke("What did the president say about technology?") 'The president said that technology plays a crucial role in the future and that passing the Bipartisan Innovation Act will make record investments in emerging technologies and American manufacturing. The president also mentioned Intel\'s plans to build a semiconductor "mega site" and increase their investment from $20 billion to $100 billion, which would be one of the biggest investments in manufacturing in American history.'PreviousVector storesNextMultiQueryRetrieverGet startedLCELCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,131 | Indexing | ü¶úÔ∏èüîó Langchain | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: Indexing | ü¶úÔ∏èüîó Langchain |
1,132 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalIndexingOn this pageIndexingHere, we will look at a basic indexing workflow using the LangChain indexing API. The indexing API lets you load and keep in sync documents from any source into a vector store. Specifically, it helps:Avoid writing duplicated content into the vector storeAvoid re-writing unchanged contentAvoid re-computing embeddings over unchanged contentAll of which should save you time and money, as well as improve your vector search results.Crucially, the indexing API will work even with documents that have gone through several | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalIndexingOn this pageIndexingHere, we will look at a basic indexing workflow using the LangChain indexing API. The indexing API lets you load and keep in sync documents from any source into a vector store. Specifically, it helps:Avoid writing duplicated content into the vector storeAvoid re-writing unchanged contentAvoid re-computing embeddings over unchanged contentAll of which should save you time and money, as well as improve your vector search results.Crucially, the indexing API will work even with documents that have gone through several |
1,133 | transformation steps (e.g., via text chunking) with respect to the original source documents.How it works‚ÄãLangChain indexing makes use of a record manager (RecordManager) that keeps track of document writes into the vector store.When indexing content, hashes are computed for each document, and the following information is stored in the record manager: the document hash (hash of both page content and metadata)write timethe source id -- each document should include information in its metadata to allow us to determine the ultimate source of this documentDeletion modes‚ÄãWhen indexing documents into a vector store, it's possible that some existing documents in the vector store should be deleted. In certain situations you may want to remove any existing documents that are derived from the same sources as the new documents being indexed. In others you may want to delete all existing documents wholesale. The indexing API deletion modes let you pick the behavior you want:Cleanup ModeDe-Duplicates ContentParallelizableCleans Up Deleted Source DocsCleans Up Mutations of Source Docs and/or Derived DocsClean Up TimingNone‚úÖ‚úÖ‚ùå‚ùå-Incremental‚úÖ‚úÖ‚ùå‚úÖContinuouslyFull‚úÖ‚ùå‚úÖ‚úÖAt end of indexingNone does not do any automatic clean up, allowing the user to manually do clean up of old content. incremental and full offer the following automated clean up:If the content of the source document or derived documents has changed, both incremental or full modes will clean up (delete) previous versions of the content.If the source document has been deleted (meaning it is not included in the documents currently being indexed), the full cleanup mode will delete it from the vector store correctly, but the incremental mode will not.When content is mutated (e.g., the source PDF file was revised) there will be a period of time during indexing when both the new and old versions may be returned to the user. This happens after the new content was written, but before the old version was | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: transformation steps (e.g., via text chunking) with respect to the original source documents.How it works‚ÄãLangChain indexing makes use of a record manager (RecordManager) that keeps track of document writes into the vector store.When indexing content, hashes are computed for each document, and the following information is stored in the record manager: the document hash (hash of both page content and metadata)write timethe source id -- each document should include information in its metadata to allow us to determine the ultimate source of this documentDeletion modes‚ÄãWhen indexing documents into a vector store, it's possible that some existing documents in the vector store should be deleted. In certain situations you may want to remove any existing documents that are derived from the same sources as the new documents being indexed. In others you may want to delete all existing documents wholesale. The indexing API deletion modes let you pick the behavior you want:Cleanup ModeDe-Duplicates ContentParallelizableCleans Up Deleted Source DocsCleans Up Mutations of Source Docs and/or Derived DocsClean Up TimingNone‚úÖ‚úÖ‚ùå‚ùå-Incremental‚úÖ‚úÖ‚ùå‚úÖContinuouslyFull‚úÖ‚ùå‚úÖ‚úÖAt end of indexingNone does not do any automatic clean up, allowing the user to manually do clean up of old content. incremental and full offer the following automated clean up:If the content of the source document or derived documents has changed, both incremental or full modes will clean up (delete) previous versions of the content.If the source document has been deleted (meaning it is not included in the documents currently being indexed), the full cleanup mode will delete it from the vector store correctly, but the incremental mode will not.When content is mutated (e.g., the source PDF file was revised) there will be a period of time during indexing when both the new and old versions may be returned to the user. This happens after the new content was written, but before the old version was |
1,134 | was written, but before the old version was deleted.incremental indexing minimizes this period of time as it is able to do clean up continuously, as it writes.full mode does the clean up after all batches have been written.Requirements‚ÄãDo not use with a store that has been pre-populated with content independently of the indexing API, as the record manager will not know that records have been inserted previously.Only works with LangChain vectorstore's that support:document addition by id (add_documents method with ids argument)delete by id (delete method with)Compatible Vectorstores: AnalyticDB, AwaDB, Bagel, Cassandra, Chroma, DashVector, DeepLake, Dingo, ElasticVectorSearch, ElasticsearchStore, FAISS, MyScale, PGVector, Pinecone, Qdrant, Redis, ScaNN, SupabaseVectorStore, TimescaleVector, Vald, Vearch, VespaStore, Weaviate, ZepVectorStore.Caution‚ÄãThe record manager relies on a time-based mechanism to determine what content can be cleaned up (when using full or incremental cleanup modes).If two tasks run back-to-back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.This is unlikely to be an issue in actual settings for the following reasons:The RecordManager uses higher resolution timestamps.The data would need to change between the first and the second tasks runs, which becomes unlikely if the time interval between the tasks is small.Indexing tasks typically take more than a few ms.Quickstart‚Äãfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.indexes import SQLRecordManager, indexfrom langchain.schema import Documentfrom langchain.vectorstores import ElasticsearchStoreInitialize a vector store and set up the embeddings:collection_name = "test_index"embedding = OpenAIEmbeddings()vectorstore = ElasticsearchStore( es_url="http://localhost:9200", index_name="test_index", embedding=embedding)Initialize a record manager with an appropriate namespace.Suggestion: Use a namespace | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: was written, but before the old version was deleted.incremental indexing minimizes this period of time as it is able to do clean up continuously, as it writes.full mode does the clean up after all batches have been written.Requirements‚ÄãDo not use with a store that has been pre-populated with content independently of the indexing API, as the record manager will not know that records have been inserted previously.Only works with LangChain vectorstore's that support:document addition by id (add_documents method with ids argument)delete by id (delete method with)Compatible Vectorstores: AnalyticDB, AwaDB, Bagel, Cassandra, Chroma, DashVector, DeepLake, Dingo, ElasticVectorSearch, ElasticsearchStore, FAISS, MyScale, PGVector, Pinecone, Qdrant, Redis, ScaNN, SupabaseVectorStore, TimescaleVector, Vald, Vearch, VespaStore, Weaviate, ZepVectorStore.Caution‚ÄãThe record manager relies on a time-based mechanism to determine what content can be cleaned up (when using full or incremental cleanup modes).If two tasks run back-to-back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.This is unlikely to be an issue in actual settings for the following reasons:The RecordManager uses higher resolution timestamps.The data would need to change between the first and the second tasks runs, which becomes unlikely if the time interval between the tasks is small.Indexing tasks typically take more than a few ms.Quickstart‚Äãfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.indexes import SQLRecordManager, indexfrom langchain.schema import Documentfrom langchain.vectorstores import ElasticsearchStoreInitialize a vector store and set up the embeddings:collection_name = "test_index"embedding = OpenAIEmbeddings()vectorstore = ElasticsearchStore( es_url="http://localhost:9200", index_name="test_index", embedding=embedding)Initialize a record manager with an appropriate namespace.Suggestion: Use a namespace |
1,135 | appropriate namespace.Suggestion: Use a namespace that takes into account both the vector store and the collection name in the vector store; e.g., 'redis/my_docs', 'chromadb/my_docs' or 'postgres/my_docs'.namespace = f"elasticsearch/{collection_name}"record_manager = SQLRecordManager( namespace, db_url="sqlite:///record_manager_cache.sql")Create a schema before using the record manager.record_manager.create_schema()Let's index some test documents:doc1 = Document(page_content="kitty", metadata={"source": "kitty.txt"})doc2 = Document(page_content="doggy", metadata={"source": "doggy.txt"})Indexing into an empty vector store:def _clear(): """Hacky helper method to clear content. See the `full` mode section to to understand why it works.""" index([], record_manager, vectorstore, cleanup="full", source_id_key="source")None deletion mode‚ÄãThis mode does not do automatic clean up of old versions of content; however, it still takes care of content de-duplication._clear()index( [doc1, doc1, doc1, doc1, doc1], record_manager, vectorstore, cleanup=None, source_id_key="source",) {'num_added': 1, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}_clear()index( [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Second time around all content will be skipped:index( [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 0}"incremental" deletion mode‚Äã_clear()index( [doc1, doc2], record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Indexing again should result in both documents getting skipped -- also skipping the embedding operation!index( [doc1, doc2], record_manager, vectorstore, cleanup="incremental", | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: appropriate namespace.Suggestion: Use a namespace that takes into account both the vector store and the collection name in the vector store; e.g., 'redis/my_docs', 'chromadb/my_docs' or 'postgres/my_docs'.namespace = f"elasticsearch/{collection_name}"record_manager = SQLRecordManager( namespace, db_url="sqlite:///record_manager_cache.sql")Create a schema before using the record manager.record_manager.create_schema()Let's index some test documents:doc1 = Document(page_content="kitty", metadata={"source": "kitty.txt"})doc2 = Document(page_content="doggy", metadata={"source": "doggy.txt"})Indexing into an empty vector store:def _clear(): """Hacky helper method to clear content. See the `full` mode section to to understand why it works.""" index([], record_manager, vectorstore, cleanup="full", source_id_key="source")None deletion mode‚ÄãThis mode does not do automatic clean up of old versions of content; however, it still takes care of content de-duplication._clear()index( [doc1, doc1, doc1, doc1, doc1], record_manager, vectorstore, cleanup=None, source_id_key="source",) {'num_added': 1, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}_clear()index( [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Second time around all content will be skipped:index( [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 0}"incremental" deletion mode‚Äã_clear()index( [doc1, doc2], record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Indexing again should result in both documents getting skipped -- also skipping the embedding operation!index( [doc1, doc2], record_manager, vectorstore, cleanup="incremental", |
1,136 | vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 0}If we provide no documents with incremental indexing mode, nothing will change.index( [], record_manager, vectorstore, cleanup="incremental", source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}If we mutate a document, the new version will be written and all old versions sharing the same source will be deleted.changed_doc_2 = Document(page_content="puppy", metadata={"source": "doggy.txt"})index( [changed_doc_2], record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 1, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 1}"full" deletion mode‚ÄãIn full mode the user should pass the full universe of content that should be indexed into the indexing function.Any documents that are not passed into the indexing function and are present in the vectorstore will be deleted!This behavior is useful to handle deletions of source documents._clear()all_docs = [doc1, doc2]index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Say someone deleted the first doc:del all_docs[0]all_docs [Document(page_content='doggy', metadata={'source': 'doggy.txt'})]Using full mode will clean up the deleted content as well.index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 1, 'num_deleted': 1}Source‚ÄãThe metadata attribute contains a field called source. This source should be pointing at the ultimate provenance associated with the given document.For example, if these documents are representing chunks of some parent document, the source for both documents should be the same and reference the parent document.In general, source should always be specified. Only use a | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 0}If we provide no documents with incremental indexing mode, nothing will change.index( [], record_manager, vectorstore, cleanup="incremental", source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}If we mutate a document, the new version will be written and all old versions sharing the same source will be deleted.changed_doc_2 = Document(page_content="puppy", metadata={"source": "doggy.txt"})index( [changed_doc_2], record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 1, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 1}"full" deletion mode‚ÄãIn full mode the user should pass the full universe of content that should be indexed into the indexing function.Any documents that are not passed into the indexing function and are present in the vectorstore will be deleted!This behavior is useful to handle deletions of source documents._clear()all_docs = [doc1, doc2]index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}Say someone deleted the first doc:del all_docs[0]all_docs [Document(page_content='doggy', metadata={'source': 'doggy.txt'})]Using full mode will clean up the deleted content as well.index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 0, 'num_updated': 0, 'num_skipped': 1, 'num_deleted': 1}Source‚ÄãThe metadata attribute contains a field called source. This source should be pointing at the ultimate provenance associated with the given document.For example, if these documents are representing chunks of some parent document, the source for both documents should be the same and reference the parent document.In general, source should always be specified. Only use a |
1,137 | source should always be specified. Only use a None, if you never intend to use incremental mode, and for some reason can't specify the source field correctly.from langchain.text_splitter import CharacterTextSplitterdoc1 = Document( page_content="kitty kitty kitty kitty kitty", metadata={"source": "kitty.txt"})doc2 = Document(page_content="doggy doggy the doggy", metadata={"source": "doggy.txt"})new_docs = CharacterTextSplitter( separator="t", keep_separator=True, chunk_size=12, chunk_overlap=2).split_documents([doc1, doc2])new_docs [Document(page_content='kitty kit', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty ki', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty', metadata={'source': 'kitty.txt'}), Document(page_content='doggy doggy', metadata={'source': 'doggy.txt'}), Document(page_content='the doggy', metadata={'source': 'doggy.txt'})]_clear()index( new_docs, record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 5, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}changed_doggy_docs = [ Document(page_content="woof woof", metadata={"source": "doggy.txt"}), Document(page_content="woof woof woof", metadata={"source": "doggy.txt"}),]This should delete the old versions of documents associated with doggy.txt source and replace them with the new versions.index( changed_doggy_docs, record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 2}vectorstore.similarity_search("dog", k=30) [Document(page_content='tty kitty', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty ki', metadata={'source': 'kitty.txt'}), Document(page_content='kitty kit', metadata={'source': 'kitty.txt'})]Using with loaders‚ÄãIndexing can accept either an iterable of documents or else any loader.Attention: The loader must set source keys | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: source should always be specified. Only use a None, if you never intend to use incremental mode, and for some reason can't specify the source field correctly.from langchain.text_splitter import CharacterTextSplitterdoc1 = Document( page_content="kitty kitty kitty kitty kitty", metadata={"source": "kitty.txt"})doc2 = Document(page_content="doggy doggy the doggy", metadata={"source": "doggy.txt"})new_docs = CharacterTextSplitter( separator="t", keep_separator=True, chunk_size=12, chunk_overlap=2).split_documents([doc1, doc2])new_docs [Document(page_content='kitty kit', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty ki', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty', metadata={'source': 'kitty.txt'}), Document(page_content='doggy doggy', metadata={'source': 'doggy.txt'}), Document(page_content='the doggy', metadata={'source': 'doggy.txt'})]_clear()index( new_docs, record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 5, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}changed_doggy_docs = [ Document(page_content="woof woof", metadata={"source": "doggy.txt"}), Document(page_content="woof woof woof", metadata={"source": "doggy.txt"}),]This should delete the old versions of documents associated with doggy.txt source and replace them with the new versions.index( changed_doggy_docs, record_manager, vectorstore, cleanup="incremental", source_id_key="source",) {'num_added': 0, 'num_updated': 0, 'num_skipped': 2, 'num_deleted': 2}vectorstore.similarity_search("dog", k=30) [Document(page_content='tty kitty', metadata={'source': 'kitty.txt'}), Document(page_content='tty kitty ki', metadata={'source': 'kitty.txt'}), Document(page_content='kitty kit', metadata={'source': 'kitty.txt'})]Using with loaders‚ÄãIndexing can accept either an iterable of documents or else any loader.Attention: The loader must set source keys |
1,138 | loader.Attention: The loader must set source keys correctly.from langchain.document_loaders.base import BaseLoaderclass MyCustomLoader(BaseLoader): def lazy_load(self): text_splitter = CharacterTextSplitter( separator="t", keep_separator=True, chunk_size=12, chunk_overlap=2 ) docs = [ Document(page_content="woof woof", metadata={"source": "doggy.txt"}), Document(page_content="woof woof woof", metadata={"source": "doggy.txt"}), ] yield from text_splitter.split_documents(docs) def load(self): return list(self.lazy_load())_clear()loader = MyCustomLoader()loader.load() [Document(page_content='woof woof', metadata={'source': 'doggy.txt'}), Document(page_content='woof woof woof', metadata={'source': 'doggy.txt'})]index(loader, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}vectorstore.similarity_search("dog", k=30) [Document(page_content='woof woof', metadata={'source': 'doggy.txt'}), Document(page_content='woof woof woof', metadata={'source': 'doggy.txt'})]PreviousWebResearchRetrieverNextChainsHow it worksDeletion modesRequirementsCautionQuickstartNone deletion mode"incremental" deletion mode"full" deletion modeSourceUsing with loadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Here, we will look at a basic indexing workflow using the LangChain indexing API. | Here, we will look at a basic indexing workflow using the LangChain indexing API. ->: loader.Attention: The loader must set source keys correctly.from langchain.document_loaders.base import BaseLoaderclass MyCustomLoader(BaseLoader): def lazy_load(self): text_splitter = CharacterTextSplitter( separator="t", keep_separator=True, chunk_size=12, chunk_overlap=2 ) docs = [ Document(page_content="woof woof", metadata={"source": "doggy.txt"}), Document(page_content="woof woof woof", metadata={"source": "doggy.txt"}), ] yield from text_splitter.split_documents(docs) def load(self): return list(self.lazy_load())_clear()loader = MyCustomLoader()loader.load() [Document(page_content='woof woof', metadata={'source': 'doggy.txt'}), Document(page_content='woof woof woof', metadata={'source': 'doggy.txt'})]index(loader, record_manager, vectorstore, cleanup="full", source_id_key="source") {'num_added': 2, 'num_updated': 0, 'num_skipped': 0, 'num_deleted': 0}vectorstore.similarity_search("dog", k=30) [Document(page_content='woof woof', metadata={'source': 'doggy.txt'}), Document(page_content='woof woof woof', metadata={'source': 'doggy.txt'})]PreviousWebResearchRetrieverNextChainsHow it worksDeletion modesRequirementsCautionQuickstartNone deletion mode"incremental" deletion mode"full" deletion modeSourceUsing with loadersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,139 | Ensemble Retriever | ü¶úÔ∏èüîó Langchain | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. ->: Ensemble Retriever | ü¶úÔ∏èüîó Langchain |
1,140 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverMultiVector RetrieverParent Document RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverWebResearchRetrieverIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrieversEnsemble RetrieverEnsemble RetrieverThe EnsembleRetriever takes a list of retrievers as input and ensemble the results of their get_relevant_documents() methods and rerank the results based on the Reciprocal Rank Fusion algorithm.By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm. The most common pattern is to combine a sparse retriever (like BM25) with a dense retriever (like embedding similarity), because their strengths are complementary. It is also known as "hybrid search". The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity.from langchain.retrievers import BM25Retriever, EnsembleRetrieverfrom langchain.vectorstores import FAISSdoc_list = [ "I like apples", "I like oranges", "Apples and oranges are fruits",]# initialize the bm25 retriever and faiss retrieverbm25_retriever = BM25Retriever.from_texts(doc_list)bm25_retriever.k = 2embedding = OpenAIEmbeddings()faiss_vectorstore = FAISS.from_texts(doc_list, embedding)faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={"k": 2})# initialize the ensemble retrieverensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, faiss_retriever], | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersDocument transformersText embedding modelsVector storesRetrieversMultiQueryRetrieverContextual compressionEnsemble RetrieverMultiVector RetrieverParent Document RetrieverSelf-queryingTime-weighted vector store retrieverVector store-backed retrieverWebResearchRetrieverIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalRetrieversEnsemble RetrieverEnsemble RetrieverThe EnsembleRetriever takes a list of retrievers as input and ensemble the results of their get_relevant_documents() methods and rerank the results based on the Reciprocal Rank Fusion algorithm.By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm. The most common pattern is to combine a sparse retriever (like BM25) with a dense retriever (like embedding similarity), because their strengths are complementary. It is also known as "hybrid search". The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity.from langchain.retrievers import BM25Retriever, EnsembleRetrieverfrom langchain.vectorstores import FAISSdoc_list = [ "I like apples", "I like oranges", "Apples and oranges are fruits",]# initialize the bm25 retriever and faiss retrieverbm25_retriever = BM25Retriever.from_texts(doc_list)bm25_retriever.k = 2embedding = OpenAIEmbeddings()faiss_vectorstore = FAISS.from_texts(doc_list, embedding)faiss_retriever = faiss_vectorstore.as_retriever(search_kwargs={"k": 2})# initialize the ensemble retrieverensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, faiss_retriever], |
1,141 | faiss_retriever], weights=[0.5, 0.5])docs = ensemble_retriever.get_relevant_documents("apples")docs [Document(page_content='I like apples', metadata={}), Document(page_content='Apples and oranges are fruits', metadata={})]PreviousContextual compressionNextMultiVector RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. | The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their getrelevantdocuments() methods and rerank the results based on the Reciprocal Rank Fusion algorithm. ->: faiss_retriever], weights=[0.5, 0.5])docs = ensemble_retriever.get_relevant_documents("apples")docs [Document(page_content='I like apples', metadata={}), Document(page_content='Apples and oranges are fruits', metadata={})]PreviousContextual compressionNextMultiVector RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,142 | Document loaders | ü¶úÔ∏èüîó Langchain | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. ->: Document loaders | ü¶úÔ∏èüîó Langchain |
1,143 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersOn this pageDocument loadersinfoHead to Integrations for documentation on built-in document loader integrations with 3rd-party tools.Use document loaders to load data from a source as Document's. A Document is a piece of text
and associated metadata. For example, there are document loaders for loading a simple .txt file, for loading the text
contents of any web page, or even for loading a transcript of a YouTube video.Document loaders provide a "load" method for loading data as documents from a configured source. They optionally | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersOn this pageDocument loadersinfoHead to Integrations for documentation on built-in document loader integrations with 3rd-party tools.Use document loaders to load data from a source as Document's. A Document is a piece of text
and associated metadata. For example, there are document loaders for loading a simple .txt file, for loading the text
contents of any web page, or even for loading a transcript of a YouTube video.Document loaders provide a "load" method for loading data as documents from a configured source. They optionally |
1,144 | implement a "lazy load" as well for lazily loading data into memory.Get started​The simplest loader reads in a file as text and places it all into one document.from langchain.document_loaders import TextLoaderloader = TextLoader("./index.md")loader.load()[ Document(page_content='---\nsidebar_position: 0\n---\n# Document loaders\n\nUse document loaders to load data from a source as `Document`\'s. A `Document` is a piece of text\nand associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text\ncontents of any web page, or even for loading a transcript of a YouTube video.\n\nEvery document loader exposes two methods:\n1. "Load": load documents from the configured source\n2. "Load and split": load documents from the configured source and split them using the passed in text splitter\n\nThey optionally implement:\n\n3. "Lazy load": load documents into memory lazily\n', metadata={'source': '../docs/docs/modules/data_connection/document_loaders/index.md'})]PreviousRetrievalNextCSVGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. | Head to Integrations for documentation on built-in document loader integrations with 3rd-party tools. ->: implement a "lazy load" as well for lazily loading data into memory.Get started​The simplest loader reads in a file as text and places it all into one document.from langchain.document_loaders import TextLoaderloader = TextLoader("./index.md")loader.load()[ Document(page_content='---\nsidebar_position: 0\n---\n# Document loaders\n\nUse document loaders to load data from a source as `Document`\'s. A `Document` is a piece of text\nand associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text\ncontents of any web page, or even for loading a transcript of a YouTube video.\n\nEvery document loader exposes two methods:\n1. "Load": load documents from the configured source\n2. "Load and split": load documents from the configured source and split them using the passed in text splitter\n\nThey optionally implement:\n\n3. "Lazy load": load documents into memory lazily\n', metadata={'source': '../docs/docs/modules/data_connection/document_loaders/index.md'})]PreviousRetrievalNextCSVGet startedCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,145 | HTML | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersHTMLOn this pageHTMLThe HyperText Markup Language or HTML is the standard markup language for documents designed to be displayed in a web browser.This covers how to load HTML documents into a document format that we can use downstream.from langchain.document_loaders import UnstructuredHTMLLoaderloader = UnstructuredHTMLLoader("example_data/fake-content.html")data = loader.load()data [Document(page_content='My First Heading\n\nMy first paragraph.', lookup_str='', metadata={'source': 'example_data/fake-content.html'}, lookup_index=0)]Loading HTML with BeautifulSoup4‚ÄãWe can also use BeautifulSoup4 to load HTML documents using the BSHTMLLoader. This will extract the text from the HTML into page_content, and the page title as title into metadata.from langchain.document_loaders import BSHTMLLoaderloader = BSHTMLLoader("example_data/fake-content.html")data = loader.load()data [Document(page_content='\n\nTest Title\n\n\nMy First Heading\nMy first paragraph.\n\n\n', metadata={'source': 'example_data/fake-content.html', 'title': 'Test Title'})]PreviousFile DirectoryNextJSONLoading HTML with BeautifulSoup4CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. | The HyperText Markup Language or HTML is the standard markup language for documents designed to be displayed in a web browser. | The HyperText Markup Language or HTML is the standard markup language for documents designed to be displayed in a web browser. ->: HTML | ü¶úÔ∏èüîó Langchain
Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersHTMLOn this pageHTMLThe HyperText Markup Language or HTML is the standard markup language for documents designed to be displayed in a web browser.This covers how to load HTML documents into a document format that we can use downstream.from langchain.document_loaders import UnstructuredHTMLLoaderloader = UnstructuredHTMLLoader("example_data/fake-content.html")data = loader.load()data [Document(page_content='My First Heading\n\nMy first paragraph.', lookup_str='', metadata={'source': 'example_data/fake-content.html'}, lookup_index=0)]Loading HTML with BeautifulSoup4‚ÄãWe can also use BeautifulSoup4 to load HTML documents using the BSHTMLLoader. This will extract the text from the HTML into page_content, and the page title as title into metadata.from langchain.document_loaders import BSHTMLLoaderloader = BSHTMLLoader("example_data/fake-content.html")data = loader.load()data [Document(page_content='\n\nTest Title\n\n\nMy First Heading\nMy first paragraph.\n\n\n', metadata={'source': 'example_data/fake-content.html', 'title': 'Test Title'})]PreviousFile DirectoryNextJSONLoading HTML with BeautifulSoup4CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc. |
1,146 | CSV | ü¶úÔ∏èüîó Langchain | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: CSV | ü¶úÔ∏èüîó Langchain |
1,147 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersCSVOn this pageCSVA comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.Load CSV data with a single row per document.from langchain.document_loaders.csv_loader import CSVLoaderloader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')data = loader.load()print(data) [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersCSVOn this pageCSVA comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas.Load CSV data with a single row per document.from langchain.document_loaders.csv_loader import CSVLoaderloader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')data = loader.load()print(data) [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, |
1,148 | './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': |
1,149 | 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', |
1,150 | (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)]Customizing the CSV parsing and loading‚ÄãSee the csv module documentation for more information of what csv args are supported.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={ 'delimiter': ',', 'quotechar': '"', 'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']})data = loader.load()print(data) [Document(page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0)]Customizing the CSV parsing and loading‚ÄãSee the csv module documentation for more information of what csv args are supported.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', csv_args={ 'delimiter': ',', 'quotechar': '"', 'fieldnames': ['MLB Team', 'Payroll in millions', 'Wins']})data = loader.load()print(data) [Document(page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}, lookup_index=0), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}, lookup_index=0), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}, lookup_index=0), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}, lookup_index=0), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}, lookup_index=0), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}, lookup_index=0), Document(page_content='MLB Team: Athletics\nPayroll in |
1,151 | Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: Team: Athletics\nPayroll in millions: 55.37\nWins: 94', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}, lookup_index=0), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}, lookup_index=0), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}, lookup_index=0), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}, lookup_index=0), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}, lookup_index=0), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}, lookup_index=0), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}, lookup_index=0), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}, lookup_index=0), Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}, lookup_index=0), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}, lookup_index=0), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}, lookup_index=0), |
1,152 | 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: 'row': 16}, lookup_index=0), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}, lookup_index=0), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}, lookup_index=0), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}, lookup_index=0), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}, lookup_index=0), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}, lookup_index=0), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}, lookup_index=0), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}, lookup_index=0), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}, lookup_index=0), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}, lookup_index=0), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}, lookup_index=0), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', lookup_str='', metadata={'source': |
1,153 | 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: Cubs\nPayroll in millions: 88.19\nWins: 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0), Document(page_content='MLB Team: Astros\nPayroll in millions: 60.65\nWins: 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30}, lookup_index=0)]Specify a column to identify the document source‚ÄãUse the source_column argument to specify a source for the document created from each row. Otherwise file_path will be used as the source for all documents created from the CSV file.This is useful when using documents loaded from CSV files for chains that answer questions using sources.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column="Team")data = loader.load()print(data) [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': 'Nationals', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': 'Reds', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': 'Yankees', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': 'Giants', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': 'Braves', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: 66', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}, lookup_index=0), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}, lookup_index=0), Document(page_content='MLB Team: Cubs\nPayroll in millions: 88.19\nWins: 61', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}, lookup_index=0), Document(page_content='MLB Team: Astros\nPayroll in millions: 60.65\nWins: 55', lookup_str='', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30}, lookup_index=0)]Specify a column to identify the document source‚ÄãUse the source_column argument to specify a source for the document created from each row. Otherwise file_path will be used as the source for all documents created from the CSV file.This is useful when using documents loaded from CSV files for chains that answer questions using sources.loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv', source_column="Team")data = loader.load()print(data) [Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', lookup_str='', metadata={'source': 'Nationals', 'row': 0}, lookup_index=0), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', lookup_str='', metadata={'source': 'Reds', 'row': 1}, lookup_index=0), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', lookup_str='', metadata={'source': 'Yankees', 'row': 2}, lookup_index=0), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', lookup_str='', metadata={'source': 'Giants', 'row': 3}, lookup_index=0), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', lookup_str='', metadata={'source': 'Braves', 'row': 4}, lookup_index=0), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', lookup_str='', metadata={'source': |
1,154 | 94', lookup_str='', metadata={'source': 'Athletics', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': 'Rangers', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': 'Orioles', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': 'Angels', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': 'Tigers', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': 'Cardinals', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': 'Dodgers', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': 'White Sox', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': 'Brewers', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': 'Phillies', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': 'Diamondbacks', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': 'Pirates', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: 94', lookup_str='', metadata={'source': 'Athletics', 'row': 5}, lookup_index=0), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', lookup_str='', metadata={'source': 'Rangers', 'row': 6}, lookup_index=0), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', lookup_str='', metadata={'source': 'Orioles', 'row': 7}, lookup_index=0), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', lookup_str='', metadata={'source': 'Rays', 'row': 8}, lookup_index=0), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', lookup_str='', metadata={'source': 'Angels', 'row': 9}, lookup_index=0), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', lookup_str='', metadata={'source': 'Tigers', 'row': 10}, lookup_index=0), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', lookup_str='', metadata={'source': 'Cardinals', 'row': 11}, lookup_index=0), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', lookup_str='', metadata={'source': 'Dodgers', 'row': 12}, lookup_index=0), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', lookup_str='', metadata={'source': 'White Sox', 'row': 13}, lookup_index=0), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', lookup_str='', metadata={'source': 'Brewers', 'row': 14}, lookup_index=0), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', lookup_str='', metadata={'source': 'Phillies', 'row': 15}, lookup_index=0), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', lookup_str='', metadata={'source': 'Diamondbacks', 'row': 16}, lookup_index=0), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', lookup_str='', metadata={'source': 'Pirates', 'row': 17}, lookup_index=0), Document(page_content='Team: Padres\n"Payroll |
1,155 | Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': 'Padres', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': 'Mariners', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': 'Mets', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': 'Blue Jays', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': 'Royals', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': 'Red Sox', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': 'Indians', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': 'Twins', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': 'Rockies', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': 'Cubs', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': 'Astros', 'row': 29}, lookup_index=0)]PreviousDocument loadersNextFile DirectoryCustomizing the CSV parsing and loadingSpecify a column to identify the document | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', lookup_str='', metadata={'source': 'Padres', 'row': 18}, lookup_index=0), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', lookup_str='', metadata={'source': 'Mariners', 'row': 19}, lookup_index=0), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', lookup_str='', metadata={'source': 'Mets', 'row': 20}, lookup_index=0), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', lookup_str='', metadata={'source': 'Blue Jays', 'row': 21}, lookup_index=0), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', lookup_str='', metadata={'source': 'Royals', 'row': 22}, lookup_index=0), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', lookup_str='', metadata={'source': 'Marlins', 'row': 23}, lookup_index=0), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', lookup_str='', metadata={'source': 'Red Sox', 'row': 24}, lookup_index=0), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', lookup_str='', metadata={'source': 'Indians', 'row': 25}, lookup_index=0), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', lookup_str='', metadata={'source': 'Twins', 'row': 26}, lookup_index=0), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', lookup_str='', metadata={'source': 'Rockies', 'row': 27}, lookup_index=0), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', lookup_str='', metadata={'source': 'Cubs', 'row': 28}, lookup_index=0), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', lookup_str='', metadata={'source': 'Astros', 'row': 29}, lookup_index=0)]PreviousDocument loadersNextFile DirectoryCustomizing the CSV parsing and loadingSpecify a column to identify the document |
1,156 | loadingSpecify a column to identify the document sourceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. | A comma-separated values (CSV) file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. ->: loadingSpecify a column to identify the document sourceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,157 | Markdown | ü¶úÔ∏èüîó Langchain | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. ->: Markdown | ü¶úÔ∏èüîó Langchain |
1,158 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersMarkdownOn this pageMarkdownMarkdown is a lightweight markup language for creating formatted text using a plain-text editor.This covers how to load Markdown documents into a document format that we can use downstream.# !pip install unstructured > /dev/nullfrom langchain.document_loaders import UnstructuredMarkdownLoadermarkdown_path = "../../../../../README.md"loader = UnstructuredMarkdownLoader(markdown_path)data = loader.load()data [Document(page_content="√∞\x9f¬¶\x9c√ج∏\x8f√∞\x9f‚Äù\x97 LangChain\n\n√¢\x9a¬° Building applications with LLMs through composability √¢\x9a¬°\n\nLooking for the JS/TS version? Check out LangChain.js.\n\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\nPlease fill out this form and we'll set up a dedicated support Slack channel.\n\nQuick Install\n\npip install langchain\nor\nconda install langchain -c conda-forge\n\n√∞\x9f¬§‚Äù What is this?\n\nLarge language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.\n\nThis library aims to assist in the development of those types of applications. Common examples of these applications include:\n\n√¢\x9d‚Äú Question Answering over specific | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersMarkdownOn this pageMarkdownMarkdown is a lightweight markup language for creating formatted text using a plain-text editor.This covers how to load Markdown documents into a document format that we can use downstream.# !pip install unstructured > /dev/nullfrom langchain.document_loaders import UnstructuredMarkdownLoadermarkdown_path = "../../../../../README.md"loader = UnstructuredMarkdownLoader(markdown_path)data = loader.load()data [Document(page_content="√∞\x9f¬¶\x9c√ج∏\x8f√∞\x9f‚Äù\x97 LangChain\n\n√¢\x9a¬° Building applications with LLMs through composability √¢\x9a¬°\n\nLooking for the JS/TS version? Check out LangChain.js.\n\nProduction Support: As you move your LangChains into production, we'd love to offer more comprehensive support.\nPlease fill out this form and we'll set up a dedicated support Slack channel.\n\nQuick Install\n\npip install langchain\nor\nconda install langchain -c conda-forge\n\n√∞\x9f¬§‚Äù What is this?\n\nLarge language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.\n\nThis library aims to assist in the development of those types of applications. Common examples of these applications include:\n\n√¢\x9d‚Äú Question Answering over specific |
1,159 | Question Answering over specific documents\n\nDocumentation\n\nEnd-to-end Example: Question Answering over Notion Database\n\nð\x9f’¬ Chatbots\n\nDocumentation\n\nEnd-to-end Example: Chat-LangChain\n\nð\x9f¤\x96 Agents\n\nDocumentation\n\nEnd-to-end Example: GPT+WolframAlpha\n\nð\x9f“\x96 Documentation\n\nPlease see here for full documentation on:\n\nGetting started (installation, setting up the environment, simple examples)\n\nHow-To examples (demos, integrations, helper functions)\n\nReference (full API docs)\n\nResources (high-level explanation of core concepts)\n\nð\x9f\x9a\x80 What can this help with?\n\nThere are six main areas that LangChain is designed to help with.\nThese are, in increasing order of complexity:\n\nð\x9f“\x83 LLMs and Prompts:\n\nThis includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.\n\nð\x9f”\x97 Chains:\n\nChains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\n\nð\x9f“\x9a Data Augmented Generation:\n\nData Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.\n\nð\x9f¤\x96 Agents:\n\nAgents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.\n\nð\x9f§\xa0 Memory:\n\nMemory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. ->: Question Answering over specific documents\n\nDocumentation\n\nEnd-to-end Example: Question Answering over Notion Database\n\nð\x9f’¬ Chatbots\n\nDocumentation\n\nEnd-to-end Example: Chat-LangChain\n\nð\x9f¤\x96 Agents\n\nDocumentation\n\nEnd-to-end Example: GPT+WolframAlpha\n\nð\x9f“\x96 Documentation\n\nPlease see here for full documentation on:\n\nGetting started (installation, setting up the environment, simple examples)\n\nHow-To examples (demos, integrations, helper functions)\n\nReference (full API docs)\n\nResources (high-level explanation of core concepts)\n\nð\x9f\x9a\x80 What can this help with?\n\nThere are six main areas that LangChain is designed to help with.\nThese are, in increasing order of complexity:\n\nð\x9f“\x83 LLMs and Prompts:\n\nThis includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.\n\nð\x9f”\x97 Chains:\n\nChains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.\n\nð\x9f“\x9a Data Augmented Generation:\n\nData Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.\n\nð\x9f¤\x96 Agents:\n\nAgents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.\n\nð\x9f§\xa0 Memory:\n\nMemory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of |
1,160 | of memory implementations, and examples of chains/agents that use memory.\n\nð\x9f§\x90 Evaluation:\n\n[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\n\nFor more information on these concepts, please see our full documentation.\n\nð\x9f’\x81 Contributing\n\nAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.\n\nFor detailed information on how to contribute, see here.", metadata={'source': '../../../../../README.md'})]Retain Elements​Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredMarkdownLoader(markdown_path, mode="elements")data = loader.load()data[0] Document(page_content='ð\x9f¦\x9cï¸\x8fð\x9f”\x97 LangChain', metadata={'source': '../../../../../README.md', 'page_number': 1, 'category': 'Title'})PreviousJSONNextPDFRetain ElementsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. | Markdown is a lightweight markup language for creating formatted text using a plain-text editor. ->: of memory implementations, and examples of chains/agents that use memory.\n\nð\x9f§\x90 Evaluation:\n\n[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.\n\nFor more information on these concepts, please see our full documentation.\n\nð\x9f’\x81 Contributing\n\nAs an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.\n\nFor detailed information on how to contribute, see here.", metadata={'source': '../../../../../README.md'})]Retain Elements​Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredMarkdownLoader(markdown_path, mode="elements")data = loader.load()data[0] Document(page_content='ð\x9f¦\x9cï¸\x8fð\x9f”\x97 LangChain', metadata={'source': '../../../../../README.md', 'page_number': 1, 'category': 'Title'})PreviousJSONNextPDFRetain ElementsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,161 | PDF | ü¶úÔ∏èüîó Langchain | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: PDF | ü¶úÔ∏èüîó Langchain |
1,162 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersPDFOn this pagePDFPortable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.This covers how to load PDF documents into the Document format that we use downstream.Using PyPDF‚ÄãLoad PDF using pypdf into array of documents, where each document contains the page content and metadata with page number.pip install pypdffrom langchain.document_loaders import PyPDFLoaderloader = PyPDFLoader("example_data/layout-parser-paper.pdf")pages = loader.load_and_split()pages[0] Document(page_content='LayoutParser : A Uni\x0ced Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1( \x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1Allen Institute for AI\[email protected]\n2Brown University\nruochen [email protected]\n3Harvard University\nfmelissadell,jacob carlson [email protected]\n4University of Washington\[email protected]\n5University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model con\x0cgurations complicate the easy reuse | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersPDFOn this pagePDFPortable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems.This covers how to load PDF documents into the Document format that we use downstream.Using PyPDF‚ÄãLoad PDF using pypdf into array of documents, where each document contains the page content and metadata with page number.pip install pypdffrom langchain.document_loaders import PyPDFLoaderloader = PyPDFLoader("example_data/layout-parser-paper.pdf")pages = loader.load_and_split()pages[0] Document(page_content='LayoutParser : A Uni\x0ced Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1( \x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1Allen Institute for AI\[email protected]\n2Brown University\nruochen [email protected]\n3Harvard University\nfmelissadell,jacob carlson [email protected]\n4University of Washington\[email protected]\n5University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model con\x0cgurations complicate the easy reuse |
1,163 | model con\x0cgurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\ne\x0borts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser , an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io .\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\n·Character Recognition ·Open Source library ·Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classi\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0})An advantage of this approach is that documents can be retrieved with page numbers.We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') OpenAI API Key: ········from langchain.vectorstores import FAISSfrom langchain.embeddings.openai import | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: model con\x0cgurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\ne\x0borts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser , an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io .\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\n·Character Recognition ·Open Source library ·Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classi\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0})An advantage of this approach is that documents can be retrieved with page numbers.We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:') OpenAI API Key: ········from langchain.vectorstores import FAISSfrom langchain.embeddings.openai import |
1,164 | FAISSfrom langchain.embeddings.openai import OpenAIEmbeddingsfaiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())docs = faiss_index.similarity_search("How will the community be engaged?", k=2)for doc in docs: print(str(doc.metadata["page"]) + ":", doc.page_content[:300]) 9: 10 Z. Shen et al. Fig. 4: Illustration of (a) the original historical Japanese document with layout detection results and (b) a recreated version of the document image that achieves much better character recognition recall. The reorganization algorithm rearranges the tokens based on the their detect 3: 4 Z. Shen et al. Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y ouExtracting images​Using the rapidocr-onnxruntime package we can extract images as text as well:pip install rapidocr-onnxruntimeloader = PyPDFLoader("https://arxiv.org/pdf/2103.15348.pdf", extract_images=True)pages = loader.load()pages[4].page_content'LayoutParser : A Unified Toolkit for DL-Based DIA 5\nTable 1: Current layout detection models in the LayoutParser model zoo\nDataset Base Model1Large Model Notes\nPubLayNet [38] F / M M Layouts of modern scientific documents\nPRImA [3] M - Layouts of scanned modern magazines and scientific reports\nNewspaper [17] F - Layouts of scanned US newspapers from the 20th century\nTableBank [18] F F Table region on modern scientific and business document\nHJDataset [31] F / M - Layouts of history Japanese documents\n1For each dataset, we train several models of different sizes for different needs (the trade-off between accuracy\nvs. computational cost). For “base model” and “large model”, we refer to using the ResNet 50 or ResNet 101\nbackbones [ 13], respectively. One can train models of different architectures, | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: FAISSfrom langchain.embeddings.openai import OpenAIEmbeddingsfaiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())docs = faiss_index.similarity_search("How will the community be engaged?", k=2)for doc in docs: print(str(doc.metadata["page"]) + ":", doc.page_content[:300]) 9: 10 Z. Shen et al. Fig. 4: Illustration of (a) the original historical Japanese document with layout detection results and (b) a recreated version of the document image that achieves much better character recognition recall. The reorganization algorithm rearranges the tokens based on the their detect 3: 4 Z. Shen et al. Efficient Data AnnotationC u s t o m i z e d M o d e l T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images T h e C o r e L a y o u t P a r s e r L i b r a r yOCR ModuleSt or age & VisualizationLa y ouExtracting images​Using the rapidocr-onnxruntime package we can extract images as text as well:pip install rapidocr-onnxruntimeloader = PyPDFLoader("https://arxiv.org/pdf/2103.15348.pdf", extract_images=True)pages = loader.load()pages[4].page_content'LayoutParser : A Unified Toolkit for DL-Based DIA 5\nTable 1: Current layout detection models in the LayoutParser model zoo\nDataset Base Model1Large Model Notes\nPubLayNet [38] F / M M Layouts of modern scientific documents\nPRImA [3] M - Layouts of scanned modern magazines and scientific reports\nNewspaper [17] F - Layouts of scanned US newspapers from the 20th century\nTableBank [18] F F Table region on modern scientific and business document\nHJDataset [31] F / M - Layouts of history Japanese documents\n1For each dataset, we train several models of different sizes for different needs (the trade-off between accuracy\nvs. computational cost). For “base model” and “large model”, we refer to using the ResNet 50 or ResNet 101\nbackbones [ 13], respectively. One can train models of different architectures, |
1,165 | One can train models of different architectures, like Faster R-CNN [ 28] (F) and Mask\nR-CNN [ 12] (M). For example, an F in the Large Model column indicates it has a Faster R-CNN model trained\nusing the ResNet 101 backbone. The platform is maintained and a number of additions will be made to the model\nzoo in coming months.\nlayout data structures , which are optimized for efficiency and versatility. 3) When\nnecessary, users can employ existing or customized OCR models via the unified\nAPI provided in the OCR module . 4)LayoutParser comes with a set of utility\nfunctions for the visualization and storage of the layout data. 5) LayoutParser\nis also highly customizable, via its integration with functions for layout data\nannotation and model training . We now provide detailed descriptions for each\ncomponent.\n3.1 Layout Detection Models\nInLayoutParser , a layout model takes a document image as an input and\ngenerates a list of rectangular boxes for the target content regions. Different\nfrom traditional methods, it relies on deep convolutional neural networks rather\nthan manually curated rules to identify content regions. It is formulated as an\nobject detection problem and state-of-the-art models like Faster R-CNN [ 28] and\nMask R-CNN [ 12] are used. This yields prediction results of high accuracy and\nmakes it possible to build a concise, generalized interface for layout detection.\nLayoutParser , built upon Detectron2 [ 35], provides a minimal API that can\nperform layout detection with only four lines of code in Python:\n1import layoutparser as lp\n2image = cv2. imread (" image_file ") # load images\n3model = lp. Detectron2LayoutModel (\n4 "lp :// PubLayNet / faster_rcnn_R_50_FPN_3x / config ")\n5layout = model . detect ( image )\nLayoutParser provides a wealth of pre-trained model weights using various\ndatasets covering different languages, time periods, and document types. Due to\ndomain shift [ 7], the prediction performance can notably drop when | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: One can train models of different architectures, like Faster R-CNN [ 28] (F) and Mask\nR-CNN [ 12] (M). For example, an F in the Large Model column indicates it has a Faster R-CNN model trained\nusing the ResNet 101 backbone. The platform is maintained and a number of additions will be made to the model\nzoo in coming months.\nlayout data structures , which are optimized for efficiency and versatility. 3) When\nnecessary, users can employ existing or customized OCR models via the unified\nAPI provided in the OCR module . 4)LayoutParser comes with a set of utility\nfunctions for the visualization and storage of the layout data. 5) LayoutParser\nis also highly customizable, via its integration with functions for layout data\nannotation and model training . We now provide detailed descriptions for each\ncomponent.\n3.1 Layout Detection Models\nInLayoutParser , a layout model takes a document image as an input and\ngenerates a list of rectangular boxes for the target content regions. Different\nfrom traditional methods, it relies on deep convolutional neural networks rather\nthan manually curated rules to identify content regions. It is formulated as an\nobject detection problem and state-of-the-art models like Faster R-CNN [ 28] and\nMask R-CNN [ 12] are used. This yields prediction results of high accuracy and\nmakes it possible to build a concise, generalized interface for layout detection.\nLayoutParser , built upon Detectron2 [ 35], provides a minimal API that can\nperform layout detection with only four lines of code in Python:\n1import layoutparser as lp\n2image = cv2. imread (" image_file ") # load images\n3model = lp. Detectron2LayoutModel (\n4 "lp :// PubLayNet / faster_rcnn_R_50_FPN_3x / config ")\n5layout = model . detect ( image )\nLayoutParser provides a wealth of pre-trained model weights using various\ndatasets covering different languages, time periods, and document types. Due to\ndomain shift [ 7], the prediction performance can notably drop when |
1,166 | the prediction performance can notably drop when models are ap-\nplied to target samples that are significantly different from the training dataset. As\ndocument structures and layouts vary greatly in different domains, it is important\nto select models trained on a dataset similar to the test samples. A semantic syntax\nis used for initializing the model weights in LayoutParser , using both the dataset\nname and model name lp://<dataset-name>/<model-architecture-name> .'Using MathPix​Inspired by Daniel Gross's https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21from langchain.document_loaders import MathpixPDFLoaderloader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using Unstructured​from langchain.document_loaders import UnstructuredPDFLoaderloader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Retain Elements​Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\[email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n5 University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: the prediction performance can notably drop when models are ap-\nplied to target samples that are significantly different from the training dataset. As\ndocument structures and layouts vary greatly in different domains, it is important\nto select models trained on a dataset similar to the test samples. A semantic syntax\nis used for initializing the model weights in LayoutParser , using both the dataset\nname and model name lp://<dataset-name>/<model-architecture-name> .'Using MathPix​Inspired by Daniel Gross's https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21from langchain.document_loaders import MathpixPDFLoaderloader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using Unstructured​from langchain.document_loaders import UnstructuredPDFLoaderloader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()Retain Elements​Under the hood, Unstructured creates different "elements" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying mode="elements".loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\[email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n5 University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized |
1,167 | However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', |
1,168 | 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Fetching remote PDFs using Unstructured​This covers how to load online PDFs into a document format that we can use downstream. This can be used for various online PDF sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/Note: all other PDF loaders can also be used to fetch remote PDFs, but OnlinePDFLoader is a legacy function, and works specifically with UnstructuredPDFLoader.from langchain.document_loaders import OnlinePDFLoaderloader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")data = loader.load()print(data) [Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\n\nWilliam D. Montoya\n\nInstituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,\n\nIn [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d Σ with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar´e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p ≠ d + 1 − s , on a Lefschetz\n\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: [email protected]\n\ntheorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 − s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Fetching remote PDFs using Unstructured​This covers how to load online PDFs into a document format that we can use downstream. This can be used for various online PDF sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/Note: all other PDF loaders can also be used to fetch remote PDFs, but OnlinePDFLoader is a legacy function, and works specifically with UnstructuredPDFLoader.from langchain.document_loaders import OnlinePDFLoaderloader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")data = loader.load()print(data) [Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\n\nWilliam D. Montoya\n\nInstituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,\n\nIn [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d Σ with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar´e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p ≠ d + 1 − s , on a Lefschetz\n\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: [email protected]\n\ntheorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 − s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate |
1,169 | paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\n\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .\n\nif there exist k linearly independent primitive elements e\n\n, . . . , e k ∈ N such that σ = { µ\n\ne\n\n+ ⋯ + µ k e k } . • The generators e i are integral if for every i and any nonnegative rational number µ the product µe i is in N only if µ is an integer. • Given two rational simplicial cones σ , σ ′ one says that σ ′ is a face of σ ( σ ′ < σ ) if the set of integral generators of σ ′ is a subset of the set of integral generators of σ . • A finite set Σ = { σ\n\n, . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\n\nall faces of cones in Σ are in Σ ;\n\nif σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;\n\nN R = σ\n\n∪ ⋅ ⋅ ⋅ ∪ σ t .\n\nA rational simplicial complete d -dimensional fan Σ defines a d -dimensional toric variety P d Σ having only orbifold singularities which we assume to be projective. Moreover, T ∶ = N ⊗ Z C ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -dimensional cones\n\nFor a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ\n\nand x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .\n\nDefinition 2.2. The irrelevant ideal of P d Σ is the monomial ideal B Σ ∶ =< x ˆ σ ∣ σ ∈ Σ > and the zero locus Z ( Σ ) ∶ = V ( B Σ ) in the affine space A d ∶ = Spec ( S ) is the irrelevant locus.\n\nProposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d Σ is a categorical quotient A d ∖ Z ( Σ ) by the group Hom ( Cl ( Σ ) , C ∗ ) and the group | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to\n\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\n\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .\n\nif there exist k linearly independent primitive elements e\n\n, . . . , e k ∈ N such that σ = { µ\n\ne\n\n+ ⋯ + µ k e k } . • The generators e i are integral if for every i and any nonnegative rational number µ the product µe i is in N only if µ is an integer. • Given two rational simplicial cones σ , σ ′ one says that σ ′ is a face of σ ( σ ′ < σ ) if the set of integral generators of σ ′ is a subset of the set of integral generators of σ . • A finite set Σ = { σ\n\n, . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\n\nall faces of cones in Σ are in Σ ;\n\nif σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;\n\nN R = σ\n\n∪ ⋅ ⋅ ⋅ ∪ σ t .\n\nA rational simplicial complete d -dimensional fan Σ defines a d -dimensional toric variety P d Σ having only orbifold singularities which we assume to be projective. Moreover, T ∶ = N ⊗ Z C ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -dimensional cones\n\nFor a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ\n\nand x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .\n\nDefinition 2.2. The irrelevant ideal of P d Σ is the monomial ideal B Σ ∶ =< x ˆ σ ∣ σ ∈ Σ > and the zero locus Z ( Σ ) ∶ = V ( B Σ ) in the affine space A d ∶ = Spec ( S ) is the irrelevant locus.\n\nProposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d Σ is a categorical quotient A d ∖ Z ( Σ ) by the group Hom ( Cl ( Σ ) , C ∗ ) and the group |
1,170 | the group Hom ( Cl ( Σ ) , C ∗ ) and the group action is induced by the Cl ( Σ ) - grading of S .\n\nNow we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.\n\nDefinition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for finite sub- groups G ⊂ Gl ( d, C ) .\n\nDefinition 2.5. A differential form on a complex orbifold Z is defined locally at z ∈ Z as a G -invariant differential form on C d where G ⊂ Gl ( d, C ) and Z is locally isomorphic to d\n\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\n\nWe have a complex of differential forms ( A ● ( Z ) , d ) and a double complex ( A ● , ● ( Z ) , ∂, ¯ ∂ ) of bigraded differential forms which define the de Rham and the Dolbeault cohomology groups (for a fixed p ∈ N ) respectively:\n\n(1,1)-Lefschetz theorem for projective toric orbifolds\n\nDefinition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\n\nRemark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d Σ in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient\n\nProof. From the exponential short exact sequence\n\nwe have a long exact sequence in cohomology\n\nH 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\n\nwhere the last isomorphisms is due to | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: the group Hom ( Cl ( Σ ) , C ∗ ) and the group action is induced by the Cl ( Σ ) - grading of S .\n\nNow we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.\n\nDefinition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for finite sub- groups G ⊂ Gl ( d, C ) .\n\nDefinition 2.5. A differential form on a complex orbifold Z is defined locally at z ∈ Z as a G -invariant differential form on C d where G ⊂ Gl ( d, C ) and Z is locally isomorphic to d\n\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\n\nWe have a complex of differential forms ( A ● ( Z ) , d ) and a double complex ( A ● , ● ( Z ) , ∂, ¯ ∂ ) of bigraded differential forms which define the de Rham and the Dolbeault cohomology groups (for a fixed p ∈ N ) respectively:\n\n(1,1)-Lefschetz theorem for projective toric orbifolds\n\nDefinition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\n\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\n\nRemark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d Σ in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient\n\nProof. From the exponential short exact sequence\n\nwe have a long exact sequence in cohomology\n\nH 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\n\nwhere the last isomorphisms is due to |
1,171 | diagram\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\n\nH 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )\n\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\n\nRemark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\n\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\n\nH 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )\n\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\n\nProof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.\n\nCayley trick and Cayley proposition\n\nThe Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d Σ and let π ∶ P ( E ) → P d Σ be the projective space bundle associated to the vector bundle E = L 1 ⊕ ⋯ ⊕ L s . It is known that P ( E ) is a ( d + s − 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan Σ. Furthermore, if the Cox ring, without considering the grading, of P d Σ is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is\n\nMoreover for X a quasi-smooth intersection subvariety cut off by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut off by F = y 1 f 1 + ⋅ ⋅ ⋅ + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].\n\nWe will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .\n\nThe following is a | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: diagram\n\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\n\nH 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )\n\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\n\nRemark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\n\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\n\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\n\nH 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )\n\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\n\nProof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.\n\nCayley trick and Cayley proposition\n\nThe Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d Σ and let π ∶ P ( E ) → P d Σ be the projective space bundle associated to the vector bundle E = L 1 ⊕ ⋯ ⊕ L s . It is known that P ( E ) is a ( d + s − 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan Σ. Furthermore, if the Cox ring, without considering the grading, of P d Σ is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is\n\nMoreover for X a quasi-smooth intersection subvariety cut off by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut off by F = y 1 f 1 + ⋅ ⋅ ⋅ + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].\n\nWe will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .\n\nThe following is a |
1,172 | with X and P d Σ .\n\nThe following is a key remark.\n\nRemark 4.1 . There is a morphism ι ∶ X → Y ⊂ P d + s − 1 Σ ,X . Moreover every point z ∶ = ( x, y ) ∈ Y with y ≠ 0 has a preimage. Hence for any subvariety W = V ( I W ) ⊂ X ⊂ P d Σ there exists W ′ ⊂ Y ⊂ P d + s − 1 Σ ,X such that π ( W ′ ) = W , i.e., W ′ = { z = ( x, y ) ∣ x ∈ W } .\n\nFor X ⊂ P d Σ a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i ∗ ∶ H d − s ( P d Σ , C ) → H d − s ( X, C ) is injective by Proposition 1.4 in [7].\n\nDefinition 4.2. The primitive cohomology of H d − s prim ( X ) is the quotient H d − s ( X, C )/ i ∗ ( H d − s ( P d Σ , C )) and H d − s prim ( X, Q ) with rational coefficients.\n\nH d − s ( P d Σ , C ) and H d − s ( X, C ) have pure Hodge structures, and the morphism i ∗ is com- patible with them, so that H d − s prim ( X ) gets a pure Hodge structure.\n\nThe next Proposition is the Cayley proposition.\n\nProposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 ∩⋅ ⋅ ⋅∩ X s be a quasi-smooth intersec- tion subvariety in P d Σ cut off by homogeneous polynomials f 1 . . . f s . Then for p ≠ d + s − 1 2 , d + s − 3 2\n\nRemark 4.5 . The above isomorphisms are also true with rational coefficients since H ● ( X, C ) = H ● ( X, Q ) ⊗ Q C . See the beginning of Section 7.1 in [10] for more details.\n\nTheorem 5.1. Let Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f k ⊂ P k + 2 Σ . Then on Y the Hodge conjecture holds.\n\nthe Hodge conjecture holds.\n\nProof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) ≠ 0. By the Cayley proposition H k,k prim ( Y, Q ) ≃ H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective\n\ntoric orbifolds there is a non-zero algebraic | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: with X and P d Σ .\n\nThe following is a key remark.\n\nRemark 4.1 . There is a morphism ι ∶ X → Y ⊂ P d + s − 1 Σ ,X . Moreover every point z ∶ = ( x, y ) ∈ Y with y ≠ 0 has a preimage. Hence for any subvariety W = V ( I W ) ⊂ X ⊂ P d Σ there exists W ′ ⊂ Y ⊂ P d + s − 1 Σ ,X such that π ( W ′ ) = W , i.e., W ′ = { z = ( x, y ) ∣ x ∈ W } .\n\nFor X ⊂ P d Σ a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i ∗ ∶ H d − s ( P d Σ , C ) → H d − s ( X, C ) is injective by Proposition 1.4 in [7].\n\nDefinition 4.2. The primitive cohomology of H d − s prim ( X ) is the quotient H d − s ( X, C )/ i ∗ ( H d − s ( P d Σ , C )) and H d − s prim ( X, Q ) with rational coefficients.\n\nH d − s ( P d Σ , C ) and H d − s ( X, C ) have pure Hodge structures, and the morphism i ∗ is com- patible with them, so that H d − s prim ( X ) gets a pure Hodge structure.\n\nThe next Proposition is the Cayley proposition.\n\nProposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 ∩⋅ ⋅ ⋅∩ X s be a quasi-smooth intersec- tion subvariety in P d Σ cut off by homogeneous polynomials f 1 . . . f s . Then for p ≠ d + s − 1 2 , d + s − 3 2\n\nRemark 4.5 . The above isomorphisms are also true with rational coefficients since H ● ( X, C ) = H ● ( X, Q ) ⊗ Q C . See the beginning of Section 7.1 in [10] for more details.\n\nTheorem 5.1. Let Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f k ⊂ P k + 2 Σ . Then on Y the Hodge conjecture holds.\n\nthe Hodge conjecture holds.\n\nProof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) ≠ 0. By the Cayley proposition H k,k prim ( Y, Q ) ≃ H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective\n\ntoric orbifolds there is a non-zero algebraic |
1,173 | orbifolds there is a non-zero algebraic basis λ C 1 , . . . , λ C n with rational coefficients of H 1 , 1 prim ( X, Q ) , that is, there are n ∶ = h 1 , 1 prim ( X, Q ) algebraic curves C 1 , . . . , C n in X such that under the Poincar´e duality the class in homology [ C i ] goes to λ C i , [ C i ] ↦ λ C i . Recall that the Cox ring of P k + 2 is contained in the Cox ring of P 2 k + 1 Σ ,X without considering the grading. Considering the grading we have that if α ∈ Cl ( P k + 2 Σ ) then ( α, 0 ) ∈ Cl ( P 2 k + 1 Σ ,X ) . So the polynomials defining C i ⊂ P k + 2 Σ can be interpreted in P 2 k + 1 X, Σ but with different degree. Moreover, by Remark 4.1 each C i is contained in Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } and\n\nfurthermore it has codimension k .\n\nClaim: { C i } ni = 1 is a basis of prim ( ) . It is enough to prove that λ C i is different from zero in H k,k prim ( Y, Q ) or equivalently that the cohomology classes { λ C i } ni = 1 do not come from the ambient space. By contradiction, let us assume that there exists a j and C ⊂ P 2 k + 1 Σ ,X such that λ C ∈ H k,k ( P 2 k + 1 Σ ,X , Q ) with i ∗ ( λ C ) = λ C j or in terms of homology there exists a ( k + 2 ) -dimensional algebraic subvariety V ⊂ P 2 k + 1 Σ ,X such that V ∩ Y = C j so they are equal as a homology class of P 2 k + 1 Σ ,X ,i.e., [ V ∩ Y ] = [ C j ] . It is easy to check that π ( V ) ∩ X = C j as a subvariety of P k + 2 Σ where π ∶ ( x, y ) ↦ x . Hence [ π ( V ) ∩ X ] = [ C j ] which is equivalent to say that λ C j comes from P k + 2 Σ which contradicts the choice of [ C j ] .\n\nRemark 5.2 . Into the proof of the previous theorem, the key fact was that on X the Hodge conjecture holds and we translate it to Y by contradiction. So, using an analogous argument we have:\n\nargument we have:\n\nProposition 5.3. Let Y = { F = y 1 f s +⋯+ y s f s = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to a quasi-smooth | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: orbifolds there is a non-zero algebraic basis λ C 1 , . . . , λ C n with rational coefficients of H 1 , 1 prim ( X, Q ) , that is, there are n ∶ = h 1 , 1 prim ( X, Q ) algebraic curves C 1 , . . . , C n in X such that under the Poincar´e duality the class in homology [ C i ] goes to λ C i , [ C i ] ↦ λ C i . Recall that the Cox ring of P k + 2 is contained in the Cox ring of P 2 k + 1 Σ ,X without considering the grading. Considering the grading we have that if α ∈ Cl ( P k + 2 Σ ) then ( α, 0 ) ∈ Cl ( P 2 k + 1 Σ ,X ) . So the polynomials defining C i ⊂ P k + 2 Σ can be interpreted in P 2 k + 1 X, Σ but with different degree. Moreover, by Remark 4.1 each C i is contained in Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } and\n\nfurthermore it has codimension k .\n\nClaim: { C i } ni = 1 is a basis of prim ( ) . It is enough to prove that λ C i is different from zero in H k,k prim ( Y, Q ) or equivalently that the cohomology classes { λ C i } ni = 1 do not come from the ambient space. By contradiction, let us assume that there exists a j and C ⊂ P 2 k + 1 Σ ,X such that λ C ∈ H k,k ( P 2 k + 1 Σ ,X , Q ) with i ∗ ( λ C ) = λ C j or in terms of homology there exists a ( k + 2 ) -dimensional algebraic subvariety V ⊂ P 2 k + 1 Σ ,X such that V ∩ Y = C j so they are equal as a homology class of P 2 k + 1 Σ ,X ,i.e., [ V ∩ Y ] = [ C j ] . It is easy to check that π ( V ) ∩ X = C j as a subvariety of P k + 2 Σ where π ∶ ( x, y ) ↦ x . Hence [ π ( V ) ∩ X ] = [ C j ] which is equivalent to say that λ C j comes from P k + 2 Σ which contradicts the choice of [ C j ] .\n\nRemark 5.2 . Into the proof of the previous theorem, the key fact was that on X the Hodge conjecture holds and we translate it to Y by contradiction. So, using an analogous argument we have:\n\nargument we have:\n\nProposition 5.3. Let Y = { F = y 1 f s +⋯+ y s f s = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to a quasi-smooth |
1,174 | hypersurface associated to a quasi-smooth intersection subvariety X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f s ⊂ P d Σ such that d + s = 2 ( k + 1 ) . If the Hodge conjecture holds on X then it holds as well on Y .\n\nCorollary 5.4. If the dimension of Y is 2 s − 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .\n\nProof. By Proposition 5.3 and Corollary 3.6.\n\n[\n\n] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics\n\n(\n\n),\n\n–\n\n[\n\n] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal\n\n,\n\n(Aug\n\n). [\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n). [\n\n] Caramello Jr, F. C. Introduction to orbifolds. a\n\niv:\n\nv\n\n(\n\n). [\n\n] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.\n\nAmerican Math- ematical Soc.,\n\n[\n\n] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,\n\n[\n\n] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Pacific J. of Math.\n\nNo.\n\n(\n\n),\n\n–\n\n[\n\n] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.\n\nof Cambridge Studies in Advanced Mathematics . Cambridge University Press,\n\n[\n\n] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K¨ahler orbifolds. Proceedings of the American Mathematical Society\n\n,\n\n(Aug\n\n).\n\n[2] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: hypersurface associated to a quasi-smooth intersection subvariety X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f s ⊂ P d Σ such that d + s = 2 ( k + 1 ) . If the Hodge conjecture holds on X then it holds as well on Y .\n\nCorollary 5.4. If the dimension of Y is 2 s − 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .\n\nProof. By Proposition 5.3 and Corollary 3.6.\n\n[\n\n] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics\n\n(\n\n),\n\n–\n\n[\n\n] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal\n\n,\n\n(Aug\n\n). [\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n). [\n\n] Caramello Jr, F. C. Introduction to orbifolds. a\n\niv:\n\nv\n\n(\n\n). [\n\n] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.\n\nAmerican Math- ematical Soc.,\n\n[\n\n] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,\n\n[\n\n] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Pacific J. of Math.\n\nNo.\n\n(\n\n),\n\n–\n\n[\n\n] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica\n\n,\n\n(\n\n),\n\n–\n\n[\n\n] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.\n\nof Cambridge Studies in Advanced Mathematics . Cambridge University Press,\n\n[\n\n] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K¨ahler orbifolds. Proceedings of the American Mathematical Society\n\n,\n\n(Aug\n\n).\n\n[2] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. |
1,175 | of projective hypersur- faces in toric varieties. Duke Mathematical Journal 75, 2 (Aug 1994).\n\n[\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. SÀúao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n).\n\n[3] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. SÀúao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (2021).\n\nA. R. Cohomology of complete intersections in toric varieties. Pub-', lookup_str='', metadata={'source': '/var/folders/ph/hhm7_zyx4l13k3v8z02dwp1w0000gn/T/tmpgq0ckaja/online_file.pdf'}, lookup_index=0)]Using PyPDFium2‚Äãfrom langchain.document_loaders import PyPDFium2Loaderloader = PyPDFium2Loader("example_data/layout-parser-paper.pdf")data = loader.load()Using PDFMiner‚Äãfrom langchain.document_loaders import PDFMinerLoaderloader = PDFMinerLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using PDFMiner to generate HTML text‚ÄãThis can be helpful for chunking texts semantically into sections as the output html content can be parsed via BeautifulSoup to get more structured and rich information about font size, page numbers, PDF headers/footers, etc.from langchain.document_loaders import PDFMinerPDFasHTMLLoaderloader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf")data = loader.load()[0] # entire PDF is loaded as a single Documentfrom bs4 import BeautifulSoupsoup = BeautifulSoup(data.page_content,'html.parser')content = soup.find_all('div')import recur_fs = Nonecur_text = ''snippets = [] # first collect all snippets that have the same font sizefor c in content: sp = c.find('span') if not sp: continue st = sp.get('style') if not st: continue fs = re.findall('font-size:(\d+)px',st) if not fs: continue fs = int(fs[0]) if not cur_fs: cur_fs = fs if fs == cur_fs: | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: of projective hypersur- faces in toric varieties. Duke Mathematical Journal 75, 2 (Aug 1994).\n\n[\n\n] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. SÀúao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (\n\n).\n\n[3] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. SÀúao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (2021).\n\nA. R. Cohomology of complete intersections in toric varieties. Pub-', lookup_str='', metadata={'source': '/var/folders/ph/hhm7_zyx4l13k3v8z02dwp1w0000gn/T/tmpgq0ckaja/online_file.pdf'}, lookup_index=0)]Using PyPDFium2‚Äãfrom langchain.document_loaders import PyPDFium2Loaderloader = PyPDFium2Loader("example_data/layout-parser-paper.pdf")data = loader.load()Using PDFMiner‚Äãfrom langchain.document_loaders import PDFMinerLoaderloader = PDFMinerLoader("example_data/layout-parser-paper.pdf")data = loader.load()Using PDFMiner to generate HTML text‚ÄãThis can be helpful for chunking texts semantically into sections as the output html content can be parsed via BeautifulSoup to get more structured and rich information about font size, page numbers, PDF headers/footers, etc.from langchain.document_loaders import PDFMinerPDFasHTMLLoaderloader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf")data = loader.load()[0] # entire PDF is loaded as a single Documentfrom bs4 import BeautifulSoupsoup = BeautifulSoup(data.page_content,'html.parser')content = soup.find_all('div')import recur_fs = Nonecur_text = ''snippets = [] # first collect all snippets that have the same font sizefor c in content: sp = c.find('span') if not sp: continue st = sp.get('style') if not st: continue fs = re.findall('font-size:(\d+)px',st) if not fs: continue fs = int(fs[0]) if not cur_fs: cur_fs = fs if fs == cur_fs: |
1,176 | cur_fs = fs if fs == cur_fs: cur_text += c.text else: snippets.append((cur_text,cur_fs)) cur_fs = fs cur_text = c.textsnippets.append((cur_text,cur_fs))# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as# headers/footers in a PDF appear on multiple pages so if we find duplicates it's safe to assume that it is redundant info)from langchain.docstore.document import Documentcur_idx = -1semantic_snippets = []# Assumption: headings have higher font size than their respective contentfor s in snippets: # if current snippet's font size > previous section's heading => it is a new heading if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']: metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]} metadata.update(data.metadata) semantic_snippets.append(Document(page_content='',metadata=metadata)) cur_idx += 1 continue # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific) if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']: semantic_snippets[cur_idx].page_content += s[0] semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font']) continue # if current snippet's font size > previous section's content but less than previous section's heading than also make a new # section (e.g. title of a PDF will have the highest font size but we don't want it to subsume all sections) metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]} metadata.update(data.metadata) semantic_snippets.append(Document(page_content='',metadata=metadata)) | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: cur_fs = fs if fs == cur_fs: cur_text += c.text else: snippets.append((cur_text,cur_fs)) cur_fs = fs cur_text = c.textsnippets.append((cur_text,cur_fs))# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as# headers/footers in a PDF appear on multiple pages so if we find duplicates it's safe to assume that it is redundant info)from langchain.docstore.document import Documentcur_idx = -1semantic_snippets = []# Assumption: headings have higher font size than their respective contentfor s in snippets: # if current snippet's font size > previous section's heading => it is a new heading if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']: metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]} metadata.update(data.metadata) semantic_snippets.append(Document(page_content='',metadata=metadata)) cur_idx += 1 continue # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific) if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']: semantic_snippets[cur_idx].page_content += s[0] semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font']) continue # if current snippet's font size > previous section's content but less than previous section's heading than also make a new # section (e.g. title of a PDF will have the highest font size but we don't want it to subsume all sections) metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]} metadata.update(data.metadata) semantic_snippets.append(Document(page_content='',metadata=metadata)) |
1,177 | cur_idx += 1semantic_snippets[4] Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\ntation tasks on historical documents. Object detection-based methods like Faster\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\nbeen used in table detection [27]. However, these models are usually implemented\nindividually and there is no unified framework to load and use such models.\nThere has been a surge of interest in creating open-source tools for document\nimage processing: a search of document image analysis in Github leads to 5M\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\nor provide limited functionalities. The closest prior research to our work is the\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\nanalyzing historical documents, and provides no supports for recent DL models.\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\nand Detectron2-PubLayNet10 are individual deep learning models trained on\nlayout analysis datasets without support for the full DIA pipeline. The Document\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\naim to improve the reproducibility of DIA methods (or DL models), yet they\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\npaddleOCR12 usually do not come with comprehensive functionalities for other\nDIA tasks like layout analysis.\nRecent years have also seen numerous efforts to create libraries for promoting\nreproducibility and reusability in the field of DL. Libraries like | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: cur_idx += 1semantic_snippets[4] Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\ntation tasks on historical documents. Object detection-based methods like Faster\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\nbeen used in table detection [27]. However, these models are usually implemented\nindividually and there is no unified framework to load and use such models.\nThere has been a surge of interest in creating open-source tools for document\nimage processing: a search of document image analysis in Github leads to 5M\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\nor provide limited functionalities. The closest prior research to our work is the\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\nanalyzing historical documents, and provides no supports for recent DL models.\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\nand Detectron2-PubLayNet10 are individual deep learning models trained on\nlayout analysis datasets without support for the full DIA pipeline. The Document\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\naim to improve the reproducibility of DIA methods (or DL models), yet they\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\npaddleOCR12 usually do not come with comprehensive functionalities for other\nDIA tasks like layout analysis.\nRecent years have also seen numerous efforts to create libraries for promoting\nreproducibility and reusability in the field of DL. Libraries like |
1,178 | reusability in the field of DL. Libraries like Dectectron2 [35],\n6 The number shown is obtained by specifying the search type as ‘code’.\n7 https://ocr-d.de/en/about\n8 https://github.com/BobLd/DocumentLayoutAnalysis\n9 https://github.com/leonlulu/DeepLayout\n10 https://github.com/hpanwar08/detectron2\n11 https://github.com/JaidedAI/EasyOCR\n12 https://github.com/PaddlePaddle/PaddleOCR\n4\nZ. Shen et al.\nFig. 1: The overall architecture of LayoutParser. For an input document image,\nthe core LayoutParser library provides a set of off-the-shelf tools for layout\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\ndata structure. LayoutParser also supports high level customization via efficient\nlayout annotation and model training functions. These improve model accuracy\non the target samples. The community platform enables the easy sharing of DIA\nmodels and whole digitization pipelines to promote reusability and reproducibility.\nA collection of detailed documentation, tutorials and exemplar projects make\nLayoutParser easy to learn and use.\nAllenNLP [8] and transformers [34] have provided the community with complete\nDL-based support for developing and deploying models for general computer\nvision and natural language processing problems. LayoutParser, on the other\nhand, specializes specifically in DIA tasks. LayoutParser is also equipped with a\ncommunity platform inspired by established model hubs such as Torch Hub [23]\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\nfull document processing pipelines that are unique to DIA tasks.\nThere have been a variety of document data collections to facilitate the\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\npapers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and\nHJDataset [31](historical Japanese document layouts). A | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: reusability in the field of DL. Libraries like Dectectron2 [35],\n6 The number shown is obtained by specifying the search type as ‘code’.\n7 https://ocr-d.de/en/about\n8 https://github.com/BobLd/DocumentLayoutAnalysis\n9 https://github.com/leonlulu/DeepLayout\n10 https://github.com/hpanwar08/detectron2\n11 https://github.com/JaidedAI/EasyOCR\n12 https://github.com/PaddlePaddle/PaddleOCR\n4\nZ. Shen et al.\nFig. 1: The overall architecture of LayoutParser. For an input document image,\nthe core LayoutParser library provides a set of off-the-shelf tools for layout\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\ndata structure. LayoutParser also supports high level customization via efficient\nlayout annotation and model training functions. These improve model accuracy\non the target samples. The community platform enables the easy sharing of DIA\nmodels and whole digitization pipelines to promote reusability and reproducibility.\nA collection of detailed documentation, tutorials and exemplar projects make\nLayoutParser easy to learn and use.\nAllenNLP [8] and transformers [34] have provided the community with complete\nDL-based support for developing and deploying models for general computer\nvision and natural language processing problems. LayoutParser, on the other\nhand, specializes specifically in DIA tasks. LayoutParser is also equipped with a\ncommunity platform inspired by established model hubs such as Torch Hub [23]\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\nfull document processing pipelines that are unique to DIA tasks.\nThere have been a variety of document data collections to facilitate the\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\npapers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and\nHJDataset [31](historical Japanese document layouts). A |
1,179 | [31](historical Japanese document layouts). A spectrum of models\ntrained on these datasets are currently available in the LayoutParser model zoo\nto support different use cases.\n', metadata={'heading': '2 Related Work\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})Using PyMuPDF​This is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.from langchain.document_loaders import PyMuPDFLoaderloader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\[email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n5 University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: [31](historical Japanese document layouts). A spectrum of models\ntrained on these datasets are currently available in the LayoutParser model zoo\nto support different use cases.\n', metadata={'heading': '2 Related Work\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})Using PyMuPDF​This is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page.from langchain.document_loaders import PyMuPDFLoaderloader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\[email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\n{melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n5 University of Waterloo\[email protected]\nAbstract. Recent advances in document image analysis (DIA) have been\nprimarily driven by the application of neural networks. Ideally, research\noutcomes could be easily deployed in production and extended for further\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\nportant innovations by a wide audience. Though there have been on-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopment in disciplines like natural language processing and computer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademic research across a wide range of disciplines in the social sciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for |
1,180 | LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Additionally, you can pass along any of the options from the PyMuPDF documentation as keyword arguments in the load call, and it will be pass along to the get_text() call.PyPDF Directory​Load PDFs from directoryfrom langchain.document_loaders import PyPDFDirectoryLoaderloader = PyPDFDirectoryLoader("example_data/")docs = loader.load()Using PDFPlumber​Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.from langchain.document_loaders import PDFPlumberLoaderloader = | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitive interfaces for applying and customizing DL models for layout de-\ntection, character recognition, and many other document processing tasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\n· Character Recognition · Open Source library · Toolkit.\n1\nIntroduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocument image analysis (DIA) tasks including document image classification [11,\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\n', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)Additionally, you can pass along any of the options from the PyMuPDF documentation as keyword arguments in the load call, and it will be pass along to the get_text() call.PyPDF Directory​Load PDFs from directoryfrom langchain.document_loaders import PyPDFDirectoryLoaderloader = PyPDFDirectoryLoader("example_data/")docs = loader.load()Using PDFPlumber​Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.from langchain.document_loaders import PDFPlumberLoaderloader = |
1,181 | import PDFPlumberLoaderloader = PDFPlumberLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\n1202 [email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\nnuJ {melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n12 5 University of Waterloo\[email protected]\n]VC.sc[\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\nprimarily driven by the application of neural networks. Ideally, research\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\n2v84351.3012:viXra portantinnovationsbyawideaudience.Thoughtherehavebeenon-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: import PDFPlumberLoaderloader = PDFPlumberLoader("example_data/layout-parser-paper.pdf")data = loader.load()data[0] Document(page_content='LayoutParser: A Unified Toolkit for Deep\nLearning Based Document Image Analysis\nZejiang Shen1 ((cid:0)), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\nLee4, Jacob Carlson3, and Weining Li5\n1 Allen Institute for AI\n1202 [email protected]\n2 Brown University\nruochen [email protected]\n3 Harvard University\nnuJ {melissadell,jacob carlson}@fas.harvard.edu\n4 University of Washington\[email protected]\n12 5 University of Waterloo\[email protected]\n]VC.sc[\nAbstract. Recentadvancesindocumentimageanalysis(DIA)havebeen\nprimarily driven by the application of neural networks. Ideally, research\noutcomescouldbeeasilydeployedinproductionandextendedforfurther\ninvestigation. However, various factors like loosely organized codebases\nand sophisticated model configurations complicate the easy reuse of im-\n2v84351.3012:viXra portantinnovationsbyawideaudience.Thoughtherehavebeenon-going\nefforts to improve reusability and simplify deep learning (DL) model\ndevelopmentindisciplineslikenaturallanguageprocessingandcomputer\nvision, none of them are optimized for challenges in the domain of DIA.\nThis represents a major gap in the existing toolkit, as DIA is central to\nacademicresearchacross awiderangeof disciplinesinthesocialsciences\nand humanities. This paper introduces LayoutParser, an open-source\nlibrary for streamlining the usage of DL in DIA research and applica-\ntions. The core LayoutParser library comes with a set of simple and\nintuitiveinterfacesforapplyingandcustomizingDLmodelsforlayoutde-\ntection,characterrecognition,andmanyotherdocumentprocessingtasks.\nTo promote extensibility, LayoutParser also incorporates a community\nplatform for sharing both pre-trained models and full document digiti-\nzation pipelines. We demonstrate that LayoutParser is helpful for both\nlightweight and large-scale |
1,182 | is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\n· Character Recognition · Open Source library · Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,', metadata={'source': 'example_data/layout-parser-paper.pdf', 'file_path': 'example_data/layout-parser-paper.pdf', 'page': 1, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'})Using AmazonTextractPDFParser​The AmazonTextractPDFLoader calls the Amazon Textract Service to convert PDFs into a Document structure. The loader does pure OCR at the moment, with more features like layout support planned, depending on demand. Single and multi-page documents are supported with up to 3000 pages and 512 MB of size.For the call to be successful an AWS account is required, similar to the AWS CLI requirements.Besides the AWS configuration, it is very similar to the other PDF loaders, while also supporting JPEG, PNG and TIFF and non-native PDF formats.from langchain.document_loaders import AmazonTextractPDFLoaderloader = AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg")documents = loader.load()PreviousMarkdownNextDocument transformersUsing PyPDFExtracting imagesUsing MathPixUsing UnstructuredRetain ElementsFetching remote PDFs using UnstructuredUsing PyPDFium2Using PDFMinerUsing PDFMiner to generate HTML textUsing PyMuPDFPyPDF DirectoryUsing PDFPlumberUsing | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: is helpful for both\nlightweight and large-scale digitization pipelines in real-word use cases.\nThe library is publicly available at https://layout-parser.github.io.\nKeywords: DocumentImageAnalysis·DeepLearning·LayoutAnalysis\n· Character Recognition · Open Source library · Toolkit.\n1 Introduction\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\ndocumentimageanalysis(DIA)tasksincludingdocumentimageclassification[11,', metadata={'source': 'example_data/layout-parser-paper.pdf', 'file_path': 'example_data/layout-parser-paper.pdf', 'page': 1, 'total_pages': 16, 'Author': '', 'CreationDate': 'D:20210622012710Z', 'Creator': 'LaTeX with hyperref', 'Keywords': '', 'ModDate': 'D:20210622012710Z', 'PTEX.Fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'Producer': 'pdfTeX-1.40.21', 'Subject': '', 'Title': '', 'Trapped': 'False'})Using AmazonTextractPDFParser​The AmazonTextractPDFLoader calls the Amazon Textract Service to convert PDFs into a Document structure. The loader does pure OCR at the moment, with more features like layout support planned, depending on demand. Single and multi-page documents are supported with up to 3000 pages and 512 MB of size.For the call to be successful an AWS account is required, similar to the AWS CLI requirements.Besides the AWS configuration, it is very similar to the other PDF loaders, while also supporting JPEG, PNG and TIFF and non-native PDF formats.from langchain.document_loaders import AmazonTextractPDFLoaderloader = AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg")documents = loader.load()PreviousMarkdownNextDocument transformersUsing PyPDFExtracting imagesUsing MathPixUsing UnstructuredRetain ElementsFetching remote PDFs using UnstructuredUsing PyPDFium2Using PDFMinerUsing PDFMiner to generate HTML textUsing PyMuPDFPyPDF DirectoryUsing PDFPlumberUsing |
1,183 | PyMuPDFPyPDF DirectoryUsing PDFPlumberUsing AmazonTextractPDFParserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. | Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. ->: PyMuPDFPyPDF DirectoryUsing PDFPlumberUsing AmazonTextractPDFParserCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
1,184 | File Directory | ü¶úÔ∏èüîó Langchain | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: File Directory | ü¶úÔ∏èüîó Langchain |
1,185 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersFile DirectoryOn this pageFile DirectoryThis covers how to load all documents in a directory.Under the hood, by default this uses the UnstructuredLoader.from langchain.document_loaders import DirectoryLoaderWe can use the glob parameter to control which files to load. Note that here it doesn't load the .rst file or the .html files.loader = DirectoryLoader('../', glob="**/*.md")docs = loader.load()len(docs) 1Show a progress bar‚ÄãBy default a progress bar will not be shown. To show a progress bar, install the tqdm library (e.g. pip install tqdm), and set the show_progress parameter to True.loader = DirectoryLoader('../', glob="**/*.md", show_progress=True)docs = loader.load() Requirement already satisfied: tqdm in /Users/jon/.pyenv/versions/3.9.16/envs/microbiome-app/lib/python3.9/site-packages (4.65.0) 0it [00:00, ?it/s]Use multithreading‚ÄãBy default the loading happens in one thread. In order to utilize several threads set the use_multithreading flag to true.loader = DirectoryLoader('../', glob="**/*.md", use_multithreading=True)docs = loader.load()Change loader class‚ÄãBy default this uses the UnstructuredLoader class. However, you can change up the type of loader pretty easily.from langchain.document_loaders import TextLoaderloader = DirectoryLoader('../', glob="**/*.md", loader_cls=TextLoader)docs = loader.load()len(docs) 1If you need to load Python source code files, use the PythonLoader.from langchain.document_loaders import PythonLoaderloader = | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKGet startedIntroductionInstallationQuickstartLangChain Expression LanguageInterfaceHow toCookbookLangChain Expression Language (LCEL)Why use LCEL?ModulesModel I/‚ÄãORetrievalDocument loadersCSVFile DirectoryHTMLJSONMarkdownPDFDocument transformersText embedding modelsVector storesRetrieversIndexingChainsMemoryAgentsCallbacksModulesSecurityGuidesMoreModulesRetrievalDocument loadersFile DirectoryOn this pageFile DirectoryThis covers how to load all documents in a directory.Under the hood, by default this uses the UnstructuredLoader.from langchain.document_loaders import DirectoryLoaderWe can use the glob parameter to control which files to load. Note that here it doesn't load the .rst file or the .html files.loader = DirectoryLoader('../', glob="**/*.md")docs = loader.load()len(docs) 1Show a progress bar‚ÄãBy default a progress bar will not be shown. To show a progress bar, install the tqdm library (e.g. pip install tqdm), and set the show_progress parameter to True.loader = DirectoryLoader('../', glob="**/*.md", show_progress=True)docs = loader.load() Requirement already satisfied: tqdm in /Users/jon/.pyenv/versions/3.9.16/envs/microbiome-app/lib/python3.9/site-packages (4.65.0) 0it [00:00, ?it/s]Use multithreading‚ÄãBy default the loading happens in one thread. In order to utilize several threads set the use_multithreading flag to true.loader = DirectoryLoader('../', glob="**/*.md", use_multithreading=True)docs = loader.load()Change loader class‚ÄãBy default this uses the UnstructuredLoader class. However, you can change up the type of loader pretty easily.from langchain.document_loaders import TextLoaderloader = DirectoryLoader('../', glob="**/*.md", loader_cls=TextLoader)docs = loader.load()len(docs) 1If you need to load Python source code files, use the PythonLoader.from langchain.document_loaders import PythonLoaderloader = |
1,186 | import PythonLoaderloader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)docs = loader.load()len(docs) 691Auto-detect file encodings with TextLoader​In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the TextLoader class.First to illustrate the problem, let's try to load multiple text with arbitrary encodings.path = '../../../../../tests/integration_tests/examples'loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader)A. Default Behavior​loader.load()<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="color: #800000; text-decoration-color: #800000">╭─────────────────────────────── </span><span style="color: #800000; text-decoration-color: #800000; font-weight: bold">Traceback </span><span style="color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold">(most recent call last)</span><span style="color: #800000; text-decoration-color: #800000"> ────────────────────────────────╮</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">text.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">29</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: import PythonLoaderloader = DirectoryLoader('../../../../../', glob="**/*.py", loader_cls=PythonLoader)docs = loader.load()len(docs) 691Auto-detect file encodings with TextLoader​In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the TextLoader class.First to illustrate the problem, let's try to load multiple text with arbitrary encodings.path = '../../../../../tests/integration_tests/examples'loader = DirectoryLoader(path, glob="**/*.txt", loader_cls=TextLoader)A. Default Behavior​loader.load()<pre style="white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace"><span style="color: #800000; text-decoration-color: #800000">╭─────────────────────────────── </span><span style="color: #800000; text-decoration-color: #800000; font-weight: bold">Traceback </span><span style="color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold">(most recent call last)</span><span style="color: #800000; text-decoration-color: #800000"> ────────────────────────────────╮</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">text.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">29</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; |
1,187 | <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">26 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>text = <span style="color: #808000; text-decoration-color: #808000">""</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">27 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">with</span> <span style="color: #00ffff; text-decoration-color: #00ffff">open</span>(<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path, encoding=<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.encoding) <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> f: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">28 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">try</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>29 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span>text = f.read() | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">26 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>text = <span style="color: #808000; text-decoration-color: #808000">""</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">27 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">with</span> <span style="color: #00ffff; text-decoration-color: #00ffff">open</span>(<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path, encoding=<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.encoding) <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> f: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">28 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">try</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>29 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span>text = f.read() |
1,188 | = f.read() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">30 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">UnicodeDecodeError</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">31 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.autodetect_encoding: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">32 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span>detected_encodings = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.detect_file_encodings() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: = f.read() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">30 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">UnicodeDecodeError</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">31 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.autodetect_encoding: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">32 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span>detected_encodings = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.detect_file_encodings() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: |
1,189 | style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/home/spike/.pyenv/versions/3.9.11/lib/python3.9/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">codecs.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">322</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">decode</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 319 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ </span><span style="color: #0000ff; text-decoration-color: #0000ff">def</span> <span style="color: #00ff00; text-decoration-color: #00ff00">decode</span>(<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>, <span style="color: #00ffff; text-decoration-color: #00ffff">input</span>, final=<span style="color: #0000ff; text-decoration-color: #0000ff">False</span>): <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 320 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f"># decode input (taking the buffer into account)</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/home/spike/.pyenv/versions/3.9.11/lib/python3.9/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">codecs.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">322</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">decode</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 319 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ </span><span style="color: #0000ff; text-decoration-color: #0000ff">def</span> <span style="color: #00ff00; text-decoration-color: #00ff00">decode</span>(<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>, <span style="color: #00ffff; text-decoration-color: #00ffff">input</span>, final=<span style="color: #0000ff; text-decoration-color: #0000ff">False</span>): <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 320 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f"># decode input (taking the buffer into account)</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: |
1,190 | style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 321 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>data = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.buffer + <span style="color: #00ffff; text-decoration-color: #00ffff">input</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span> 322 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>(result, consumed) = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>._buffer_decode(data, <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.errors, final) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 323 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f"># keep undecoded input until the next call</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 324 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.buffer = data[consumed:] <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 321 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>data = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.buffer + <span style="color: #00ffff; text-decoration-color: #00ffff">input</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span> 322 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span>(result, consumed) = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>._buffer_decode(data, <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.errors, final) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 323 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f"># keep undecoded input until the next call</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 324 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.buffer = data[consumed:] <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> |
1,191 | text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 325 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">return</span> result <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span><span style="color: #ff0000; text-decoration-color: #ff0000; font-weight: bold">UnicodeDecodeError: </span><span style="color: #008000; text-decoration-color: #008000">'utf-8'</span> codec can't decode byte <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0xca</span> in position <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0</span>: invalid continuation byte<span style="font-style: italic">The above exception was the direct cause of the following exception:</span><span style="color: #800000; text-decoration-color: #800000">╭─────────────────────────────── </span><span style="color: #800000; text-decoration-color: #800000; font-weight: bold">Traceback </span><span style="color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold">(most recent call last)</span><span style="color: #800000; text-decoration-color: #800000"> ────────────────────────────────╮</span><span style="color: #800000; text-decoration-color: #800000">│</span> in <span style="color: #00ff00; text-decoration-color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f"> 325 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">return</span> result <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span><span style="color: #ff0000; text-decoration-color: #ff0000; font-weight: bold">UnicodeDecodeError: </span><span style="color: #008000; text-decoration-color: #008000">'utf-8'</span> codec can't decode byte <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0xca</span> in position <span style="color: #008080; text-decoration-color: #008080; font-weight: bold">0</span>: invalid continuation byte<span style="font-style: italic">The above exception was the direct cause of the following exception:</span><span style="color: #800000; text-decoration-color: #800000">╭─────────────────────────────── </span><span style="color: #800000; text-decoration-color: #800000; font-weight: bold">Traceback </span><span style="color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold">(most recent call last)</span><span style="color: #800000; text-decoration-color: #800000"> ────────────────────────────────╮</span><span style="color: #800000; text-decoration-color: #800000">│</span> in <span style="color: #00ff00; text-decoration-color: |
1,192 | style="color: #00ff00; text-decoration-color: #00ff00"><module></span>:<span style="color: #0000ff; text-decoration-color: #0000ff">1</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>1 loader.load() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">2 </span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">directory.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">84</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: style="color: #00ff00; text-decoration-color: #00ff00"><module></span>:<span style="color: #0000ff; text-decoration-color: #0000ff">1</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>1 loader.load() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">2 </span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">directory.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">84</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: |
1,193 | style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">81 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.silent_errors: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">82 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span>logger.warning(e) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">83 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">else</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>84 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> e <span | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">81 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.silent_errors: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">82 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span>logger.warning(e) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">83 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">else</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>84 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> e <span |
1,194 | <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">85 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">finally</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">86 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> pbar: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">87 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span>pbar.update(<span style="color: #0000ff; text-decoration-color: #0000ff">1</span>) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">85 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">finally</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">86 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> pbar: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">87 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span>pbar.update(<span style="color: #0000ff; text-decoration-color: #0000ff">1</span>) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: |
1,195 | text-decoration-color: #808000; font-weight: bold">directory.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">78</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">75 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> i.is_file(): <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">76 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> _is_visible(i.relative_to(p)) <span style="color: #ff00ff; text-decoration-color: #ff00ff">or</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.load_hidden: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">77 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">try</span>: <span style="color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: text-decoration-color: #808000; font-weight: bold">directory.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">78</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">75 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> i.is_file(): <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">76 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> _is_visible(i.relative_to(p)) <span style="color: #ff00ff; text-decoration-color: #ff00ff">or</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.load_hidden: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">77 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">try</span>: <span style="color: |
1,196 | <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>78 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span>sub_docs = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.loader_cls(<span style="color: #00ffff; text-decoration-color: #00ffff">str</span>(i), **<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.loader_kwargs).load() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">79 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span>docs.extend(sub_docs) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">80 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">Exception</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">81 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>78 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span>sub_docs = <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.loader_cls(<span style="color: #00ffff; text-decoration-color: #00ffff">str</span>(i), **<span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.loader_kwargs).load() <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">79 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span>docs.extend(sub_docs) <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">80 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">Exception</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">81 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">if</span> <span |
1,197 | text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.silent_errors: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">text.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">44</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">41 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">UnicodeDecodeError</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">42 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: text-decoration-color: #0000ff">if</span> <span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.silent_errors: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #bfbf7f; text-decoration-color: #bfbf7f">/data/source/langchain/langchain/document_loaders/</span><span style="color: #808000; text-decoration-color: #808000; font-weight: bold">text.py</span>:<span style="color: #0000ff; text-decoration-color: #0000ff">44</span> in <span style="color: #00ff00; text-decoration-color: #00ff00">load</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">41 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: #00ffff">UnicodeDecodeError</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">42 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ |
1,198 | text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">continue</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">43 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">else</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>44 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> <span style="color: #00ffff; text-decoration-color: #00ffff">RuntimeError</span>(<span style="color: #808000; text-decoration-color: #808000">f"Error loading {</span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path<span style="color: #808000; text-decoration-color: #808000">}"</span>) <span style="color: #0000ff; text-decoration-color: #0000ff">from</span> <span style="color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline">e</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">45 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: text-decoration-color: #7f7f7f">│ │ │ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">continue</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">43 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">else</span>: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #800000; text-decoration-color: #800000">❱ </span>44 <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> <span style="color: #00ffff; text-decoration-color: #00ffff">RuntimeError</span>(<span style="color: #808000; text-decoration-color: #808000">f"Error loading {</span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path<span style="color: #808000; text-decoration-color: #808000">}"</span>) <span style="color: #0000ff; text-decoration-color: #0000ff">from</span> <span style="color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline">e</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">45 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">except</span> <span style="color: #00ffff; text-decoration-color: |
1,199 | style="color: #00ffff; text-decoration-color: #00ffff">Exception</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">46 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> <span style="color: #00ffff; text-decoration-color: #00ffff">RuntimeError</span>(<span style="color: #808000; text-decoration-color: #808000">f"Error loading {</span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path<span style="color: #808000; text-decoration-color: #808000">}"</span>) <span style="color: #0000ff; text-decoration-color: #0000ff">from</span> <span style="color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline">e</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">47 </span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span><span style="color: #ff0000; text-decoration-color: #ff0000; font-weight: bold">RuntimeError: </span>Error loading ..<span style="color: #800080; | This covers how to load all documents in a directory. | This covers how to load all documents in a directory. ->: style="color: #00ffff; text-decoration-color: #00ffff">Exception</span> <span style="color: #0000ff; text-decoration-color: #0000ff">as</span> e: <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">46 </span><span style="color: #7f7f7f; text-decoration-color: #7f7f7f">│ │ │ │ </span><span style="color: #0000ff; text-decoration-color: #0000ff">raise</span> <span style="color: #00ffff; text-decoration-color: #00ffff">RuntimeError</span>(<span style="color: #808000; text-decoration-color: #808000">f"Error loading {</span><span style="color: #00ffff; text-decoration-color: #00ffff">self</span>.file_path<span style="color: #808000; text-decoration-color: #808000">}"</span>) <span style="color: #0000ff; text-decoration-color: #0000ff">from</span> <span style="color: #00ffff; text-decoration-color: #00ffff; text-decoration: underline">e</span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">│</span> <span style="color: #7f7f7f; text-decoration-color: #7f7f7f">47 </span> <span style="color: #800000; text-decoration-color: #800000">│</span><span style="color: #800000; text-decoration-color: #800000">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span><span style="color: #ff0000; text-decoration-color: #ff0000; font-weight: bold">RuntimeError: </span>Error loading ..<span style="color: #800080; |
Subsets and Splits