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900 | a collection created externally or want to have the named vector used, you can configure it by providing its name.Qdrant.from_documents( docs, embeddings, location=":memory:", collection_name="my_documents_2", vector_name="custom_vector",)As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood.Metadata​Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.By default, your document is going to be stored in the following payload structure:{ "page_content": "Lorem ipsum dolor sit amet", "metadata": { "foo": "bar" }}You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse.Qdrant.from_documents( docs, embeddings, location=":memory:", collection_name="my_documents_2", content_payload_key="my_page_content_key", metadata_payload_key="my_meta",) <langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>PreviousPineconeNextRedisConnecting to Qdrant from LangChainLocal modeOn-premise server deploymentQdrant CloudRecreating the collectionSimilarity searchSimilarity search with scoreMetadata filteringMaximum marginal relevance search (MMR)Qdrant as a RetrieverCustomizing QdrantNamed vectorsMetadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications. | Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications. ->: a collection created externally or want to have the named vector used, you can configure it by providing its name.Qdrant.from_documents( docs, embeddings, location=":memory:", collection_name="my_documents_2", vector_name="custom_vector",)As a Langchain user, you won't see any difference whether you use named vectors or not. Qdrant integration will handle the conversion under the hood.Metadata​Qdrant stores your vector embeddings along with the optional JSON-like payload. Payloads are optional, but since LangChain assumes the embeddings are generated from the documents, we keep the context data, so you can extract the original texts as well.By default, your document is going to be stored in the following payload structure:{ "page_content": "Lorem ipsum dolor sit amet", "metadata": { "foo": "bar" }}You can, however, decide to use different keys for the page content and metadata. That's useful if you already have a collection that you'd like to reuse.Qdrant.from_documents( docs, embeddings, location=":memory:", collection_name="my_documents_2", content_payload_key="my_page_content_key", metadata_payload_key="my_meta",) <langchain.vectorstores.qdrant.Qdrant at 0x7fc4e2baa230>PreviousPineconeNextRedisConnecting to Qdrant from LangChainLocal modeOn-premise server deploymentQdrant CloudRecreating the collectionSimilarity searchSimilarity search with scoreMetadata filteringMaximum marginal relevance search (MMR)Qdrant as a RetrieverCustomizing QdrantNamed vectorsMetadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
901 | Postgres Embedding | ü¶úÔ∏èüîó Langchain | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: Postgres Embedding | ü¶úÔ∏èüîó Langchain |
902 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesPostgres EmbeddingOn this pagePostgres EmbeddingPostgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search.It supports:exact and approximate nearest neighbor search using HNSWL2 distanceThis notebook shows how to use the Postgres vector database (PGEmbedding).The PGEmbedding integration creates the pg_embedding extension for you, but you run the following Postgres query to add it:CREATE EXTENSION embedding;# Pip install necessary packagepip install openaipip install psycopg2-binarypip install tiktokenAdd the OpenAI API Key to the environment variables to use OpenAIEmbeddings.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key:¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑## Loading Environment Variablesfrom typing import List, Tuplefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesPostgres EmbeddingOn this pagePostgres EmbeddingPostgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search.It supports:exact and approximate nearest neighbor search using HNSWL2 distanceThis notebook shows how to use the Postgres vector database (PGEmbedding).The PGEmbedding integration creates the pg_embedding extension for you, but you run the following Postgres query to add it:CREATE EXTENSION embedding;# Pip install necessary packagepip install openaipip install psycopg2-binarypip install tiktokenAdd the OpenAI API Key to the environment variables to use OpenAIEmbeddings.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key:¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑## Loading Environment Variablesfrom typing import List, Tuplefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom |
903 | import CharacterTextSplitterfrom langchain.vectorstores import PGEmbeddingfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentos.environ["DATABASE_URL"] = getpass.getpass("Database Url:") Database Url:········loader = TextLoader("state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()connection_string = os.environ.get("DATABASE_URL")collection_name = "state_of_the_union"db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)Working with vectorstore in Postgres​Uploading a vectorstore in PG​db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string, pre_delete_collection=False,)Create HNSW Index​By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up similarity_search_with_score execution time. To create the HNSW index on your vector column, use a create_hnsw_index function:PGEmbedding.create_hnsw_index( max_elements=10000, dims=1536, m=8, ef_construction=16, ef_search=16)The function above is equivalent to running the below SQL query:CREATE INDEX ON vectors USING hnsw(vec) WITH (maxelements=10000, dims=1536, m=3, efconstruction=16, efsearch=16);The HNSW index options used in the statement above include:maxelements: Defines the maximum number of elements indexed. This is a | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: import CharacterTextSplitterfrom langchain.vectorstores import PGEmbeddingfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentos.environ["DATABASE_URL"] = getpass.getpass("Database Url:") Database Url:········loader = TextLoader("state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()connection_string = os.environ.get("DATABASE_URL")collection_name = "state_of_the_union"db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)Working with vectorstore in Postgres​Uploading a vectorstore in PG​db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string, pre_delete_collection=False,)Create HNSW Index​By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up similarity_search_with_score execution time. To create the HNSW index on your vector column, use a create_hnsw_index function:PGEmbedding.create_hnsw_index( max_elements=10000, dims=1536, m=8, ef_construction=16, ef_search=16)The function above is equivalent to running the below SQL query:CREATE INDEX ON vectors USING hnsw(vec) WITH (maxelements=10000, dims=1536, m=3, efconstruction=16, efsearch=16);The HNSW index options used in the statement above include:maxelements: Defines the maximum number of elements indexed. This is a |
904 | the maximum number of elements indexed. This is a required parameter. The example shown above has a value of 3. A real-world example would have a much large value, such as 1000000. An "element" refers to a data point (a vector) in the dataset, which is represented as a node in the HNSW graph. Typically, you would set this option to a value able to accommodate the number of rows in your in your dataset.dims: Defines the number of dimensions in your vector data. This is a required parameter. A small value is used in the example above. If you are storing data generated using OpenAI's text-embedding-ada-002 model, which supports 1536 dimensions, you would define a value of 1536, for example.m: Defines the maximum number of bi-directional links (also referred to as "edges") created for each node during graph construction. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: the maximum number of elements indexed. This is a required parameter. The example shown above has a value of 3. A real-world example would have a much large value, such as 1000000. An "element" refers to a data point (a vector) in the dataset, which is represented as a node in the HNSW graph. Typically, you would set this option to a value able to accommodate the number of rows in your in your dataset.dims: Defines the number of dimensions in your vector data. This is a required parameter. A small value is used in the example above. If you are storing data generated using OpenAI's text-embedding-ada-002 model, which supports 1536 dimensions, you would define a value of 1536, for example.m: Defines the maximum number of bi-directional links (also referred to as "edges") created for each node during graph construction. |
905 | The following additional index options are supported:efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.
For information about how you can configure these options to influence the HNSW algorithm, refer to Tuning the HNSW algorithm.Retrieving a vectorstore in PG​store = PGEmbedding( connection_string=connection_string, embedding_function=embeddings, collection_name=collection_name,)retriever = store.as_retriever()retriever VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})db1 = PGEmbedding.from_existing_index( embedding=embeddings, collection_name=collection_name, pre_delete_collection=False, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)PreviousOpenSearchNextPGVectorWorking with vectorstore in PostgresUploading a vectorstore in PGCreate HNSW IndexRetrieving a vectorstore in PGCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. | Postgres Embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds (HNSW) for approximate nearest neighbor search. ->: The following additional index options are supported:efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.
For information about how you can configure these options to influence the HNSW algorithm, refer to Tuning the HNSW algorithm.Retrieving a vectorstore in PG​store = PGEmbedding( connection_string=connection_string, embedding_function=embeddings, collection_name=collection_name,)retriever = store.as_retriever()retriever VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})db1 = PGEmbedding.from_existing_index( embedding=embeddings, collection_name=collection_name, pre_delete_collection=False, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)PreviousOpenSearchNextPGVectorWorking with vectorstore in PostgresUploading a vectorstore in PGCreate HNSW IndexRetrieving a vectorstore in PGCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
906 | Meilisearch | ü¶úÔ∏èüîó Langchain | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: Meilisearch | ü¶úÔ∏èüîó Langchain |
907 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesMeilisearchOn this pageMeilisearchMeilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. You can self-host Meilisearch or run on Meilisearch Cloud.Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search.Setup‚ÄãLaunching a Meilisearch instance‚ÄãYou will need a running Meilisearch instance to use as your vector store. You can run Meilisearch in local or create a Meilisearch Cloud account.As of Meilisearch v1.3, vector storage is an experimental feature. After launching your Meilisearch instance, you need to enable vector storage. For self-hosted Meilisearch, read the docs on enabling experimental features. On Meilisearch Cloud, enable Vector Store via your project Settings page.You should now have a running Meilisearch instance with vector storage enabled. üéâCredentials‚ÄãTo interact with your | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesMeilisearchOn this pageMeilisearchMeilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. You can self-host Meilisearch or run on Meilisearch Cloud.Meilisearch v1.3 supports vector search. This page guides you through integrating Meilisearch as a vector store and using it to perform vector search.Setup‚ÄãLaunching a Meilisearch instance‚ÄãYou will need a running Meilisearch instance to use as your vector store. You can run Meilisearch in local or create a Meilisearch Cloud account.As of Meilisearch v1.3, vector storage is an experimental feature. After launching your Meilisearch instance, you need to enable vector storage. For self-hosted Meilisearch, read the docs on enabling experimental features. On Meilisearch Cloud, enable Vector Store via your project Settings page.You should now have a running Meilisearch instance with vector storage enabled. üéâCredentials‚ÄãTo interact with your |
908 | enabled. üéâCredentials‚ÄãTo interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.HostIn local, the default host is localhost:7700On Meilisearch Cloud, find the host in your project Settings pageAPI keysMeilisearch instance provides you with three API keys out of the box: A MASTER KEY ‚Äî it should only be used to create your Meilisearch instanceA ADMIN KEY ‚Äî use it only server-side to update your database and its settingsA SEARCH KEY ‚Äî a key that you can safely share in front-end applicationsYou can create additional API keys as needed.Installing dependencies‚ÄãThis guide uses the Meilisearch Python SDK. You can install it by running:pip install meilisearchFor more information, refer to the Meilisearch Python SDK documentation.Examples‚ÄãThere are multiple ways to initialize the Meilisearch vector store: providing a Meilisearch client or the URL and API key as needed. In our examples, the credentials will be loaded from the environment.You can make environment variables available in your Notebook environment by using os and getpass. You can use this technique for all the following examples.import osimport getpassos.environ["MEILI_HTTP_ADDR"] = getpass.getpass("Meilisearch HTTP address and port:")os.environ["MEILI_MASTER_KEY"] = getpass.getpass("Meilisearch API Key:")We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Adding text and embeddings‚ÄãThis example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store.from langchain.vectorstores import Meilisearchfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterembeddings = OpenAIEmbeddings()with open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: enabled. üéâCredentials‚ÄãTo interact with your Meilisearch instance, the Meilisearch SDK needs a host (URL of your instance) and an API key.HostIn local, the default host is localhost:7700On Meilisearch Cloud, find the host in your project Settings pageAPI keysMeilisearch instance provides you with three API keys out of the box: A MASTER KEY ‚Äî it should only be used to create your Meilisearch instanceA ADMIN KEY ‚Äî use it only server-side to update your database and its settingsA SEARCH KEY ‚Äî a key that you can safely share in front-end applicationsYou can create additional API keys as needed.Installing dependencies‚ÄãThis guide uses the Meilisearch Python SDK. You can install it by running:pip install meilisearchFor more information, refer to the Meilisearch Python SDK documentation.Examples‚ÄãThere are multiple ways to initialize the Meilisearch vector store: providing a Meilisearch client or the URL and API key as needed. In our examples, the credentials will be loaded from the environment.You can make environment variables available in your Notebook environment by using os and getpass. You can use this technique for all the following examples.import osimport getpassos.environ["MEILI_HTTP_ADDR"] = getpass.getpass("Meilisearch HTTP address and port:")os.environ["MEILI_MASTER_KEY"] = getpass.getpass("Meilisearch API Key:")We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Adding text and embeddings‚ÄãThis example adds text to the Meilisearch vector database without having to initialize a Meilisearch vector store.from langchain.vectorstores import Meilisearchfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterembeddings = OpenAIEmbeddings()with open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = |
909 | chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)# Use Meilisearch vector store to store texts & associated embeddings as vectorvector_store = Meilisearch.from_texts(texts=texts, embedding=embeddings)Behind the scenes, Meilisearch will convert the text to multiple vectors. This will bring us to the same result as the following example.Adding documents and embeddings‚ÄãIn this example, we'll use Langchain TextSplitter to split the text in multiple documents. Then, we'll store these documents along with their embeddings.from langchain.document_loaders import TextLoader# Load textloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)# Create documentsdocs = text_splitter.split_documents(documents)# Import documents & embeddings in the vector storevector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)# Search in our vector storequery = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)print(docs[0].page_content)Add documents by creating a Meilisearch Vectorstore‚ÄãIn this approach, we create a vector store object and add documents to it.from langchain.vectorstores import Meilisearchimport meilisearchclient = meilisearch.Client(url="http://127.0.0.1:7700", api_key="***")vector_store = Meilisearch( embedding=embeddings, client=client, index_name="langchain_demo", text_key="text")vector_store.add_documents(documents)Similarity Search with score‚ÄãThis specific method allows you to return the documents and the distance score of the query to them.docs_and_scores = vector_store.similarity_search_with_score(query)docs_and_scores[0]Similarity Search by vector‚Äãembedding_vector = embeddings.embed_query(query)docs_and_scores = vector_store.similarity_search_by_vector(embedding_vector)docs_and_scores[0]Additional resources‚ÄãDocumentationMeilisearchMeilisearch Python | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)# Use Meilisearch vector store to store texts & associated embeddings as vectorvector_store = Meilisearch.from_texts(texts=texts, embedding=embeddings)Behind the scenes, Meilisearch will convert the text to multiple vectors. This will bring us to the same result as the following example.Adding documents and embeddings‚ÄãIn this example, we'll use Langchain TextSplitter to split the text in multiple documents. Then, we'll store these documents along with their embeddings.from langchain.document_loaders import TextLoader# Load textloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)# Create documentsdocs = text_splitter.split_documents(documents)# Import documents & embeddings in the vector storevector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)# Search in our vector storequery = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)print(docs[0].page_content)Add documents by creating a Meilisearch Vectorstore‚ÄãIn this approach, we create a vector store object and add documents to it.from langchain.vectorstores import Meilisearchimport meilisearchclient = meilisearch.Client(url="http://127.0.0.1:7700", api_key="***")vector_store = Meilisearch( embedding=embeddings, client=client, index_name="langchain_demo", text_key="text")vector_store.add_documents(documents)Similarity Search with score‚ÄãThis specific method allows you to return the documents and the distance score of the query to them.docs_and_scores = vector_store.similarity_search_with_score(query)docs_and_scores[0]Similarity Search by vector‚Äãembedding_vector = embeddings.embed_query(query)docs_and_scores = vector_store.similarity_search_by_vector(embedding_vector)docs_and_scores[0]Additional resources‚ÄãDocumentationMeilisearchMeilisearch Python |
910 | Python SDKOpen-source repositoriesMeilisearch repositoryMeilisearch Python SDKPreviousGoogle Vertex AI MatchingEngineNextMilvusSetupLaunching a Meilisearch instanceCredentialsInstalling dependenciesExamplesAdding text and embeddingsAdding documents and embeddingsAdd documents by creating a Meilisearch VectorstoreSimilarity Search with scoreSimilarity Search by vectorAdditional resourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. | Meilisearch is an open-source, lightning-fast, and hyper relevant search engine. It comes with great defaults to help developers build snappy search experiences. ->: Python SDKOpen-source repositoriesMeilisearch repositoryMeilisearch Python SDKPreviousGoogle Vertex AI MatchingEngineNextMilvusSetupLaunching a Meilisearch instanceCredentialsInstalling dependenciesExamplesAdding text and embeddingsAdding documents and embeddingsAdd documents by creating a Meilisearch VectorstoreSimilarity Search with scoreSimilarity Search by vectorAdditional resourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
911 | Alibaba Cloud OpenSearch | ü¶úÔ∏èüîó Langchain | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: Alibaba Cloud OpenSearch | ü¶úÔ∏èüîó Langchain |
912 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesAlibaba Cloud OpenSearchAlibaba Cloud OpenSearchAlibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.OpenSearch helps you develop high quality, maintenance-free, and high performance intelligent search services to provide your users with high search efficiency and accuracy.OpenSearch provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.This notebook shows how to use functionality related to the Alibaba | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesAlibaba Cloud OpenSearchAlibaba Cloud OpenSearchAlibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises.OpenSearch helps you develop high quality, maintenance-free, and high performance intelligent search services to provide your users with high search efficiency and accuracy.OpenSearch provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results.This notebook shows how to use functionality related to the Alibaba |
913 | how to use functionality related to the Alibaba Cloud OpenSearch Vector Search Edition. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: how to use functionality related to the Alibaba Cloud OpenSearch Vector Search Edition. |
914 | To run, you should have an OpenSearch Vector Search Edition instance up and running:Read the help document to quickly familiarize and configure OpenSearch Vector Search Edition instance.After the instance is up and running, follow these steps to split documents, get embeddings, connect to the alibaba cloud opensearch instance, index documents, and perform vector retrieval.We need to install the following Python packages first.#!pip install alibabacloud_ha3engine_vectorWe 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:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import ( AlibabaCloudOpenSearch, AlibabaCloudOpenSearchSettings,)Split documents and get embeddings.from langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()Create opensearch settings.settings = AlibabaCloudOpenSearchSettings( endpoint=" The endpoint of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.", instance_id="The identify of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.", protocol="Communication Protocol between SDK and Server, default is http.", username="The username specified when purchasing the instance.", password="The password specified when purchasing the instance.", namespace="The instance data will be partitioned based on the namespace field. If the namespace is enabled, you need to specify the namespace field name during initialization. Otherwise, the queries cannot be executed correctly.", tablename="The table name specified during instance configuration.", | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: To run, you should have an OpenSearch Vector Search Edition instance up and running:Read the help document to quickly familiarize and configure OpenSearch Vector Search Edition instance.After the instance is up and running, follow these steps to split documents, get embeddings, connect to the alibaba cloud opensearch instance, index documents, and perform vector retrieval.We need to install the following Python packages first.#!pip install alibabacloud_ha3engine_vectorWe 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:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import ( AlibabaCloudOpenSearch, AlibabaCloudOpenSearchSettings,)Split documents and get embeddings.from langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()Create opensearch settings.settings = AlibabaCloudOpenSearchSettings( endpoint=" The endpoint of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.", instance_id="The identify of opensearch instance, You can find it from the console of Alibaba Cloud OpenSearch.", protocol="Communication Protocol between SDK and Server, default is http.", username="The username specified when purchasing the instance.", password="The password specified when purchasing the instance.", namespace="The instance data will be partitioned based on the namespace field. If the namespace is enabled, you need to specify the namespace field name during initialization. Otherwise, the queries cannot be executed correctly.", tablename="The table name specified during instance configuration.", |
915 | specified during instance configuration.", embedding_field_separator="Delimiter specified for writing vector field data, default is comma.", output_fields="Specify the field list returned when invoking OpenSearch, by default it is the value list of the field mapping field.", field_name_mapping={ "id": "id", # The id field name mapping of index document. "document": "document", # The text field name mapping of index document. "embedding": "embedding", # The embedding field name mapping of index document. "name_of_the_metadata_specified_during_search": "opensearch_metadata_field_name,=", # The metadata field name mapping of index document, could specify multiple, The value field contains mapping name and operator, the operator would be used when executing metadata filter query, # Currently supported logical operators are: > (greater than), < (less than), = (equal to), <= (less than or equal to), >= (greater than or equal to), != (not equal to). # Refer to this link: https://help.aliyun.com/zh/open-search/vector-search-edition/filter-expression },)# for example# settings = AlibabaCloudOpenSearchSettings(# endpoint='ha-cn-5yd3fhdm102.public.ha.aliyuncs.com',# instance_id='ha-cn-5yd3fhdm102',# username='instance user name',# password='instance password',# table_name='test_table',# field_name_mapping={# "id": "id",# "document": "document",# "embedding": "embedding",# "string_field": "string_filed,=",# "int_field": "int_filed,=",# "float_field": "float_field,=",# "double_field": "double_field,="## },# )Create an opensearch access instance by settings.# Create an opensearch instance and index docs.opensearch = AlibabaCloudOpenSearch.from_texts( texts=docs, embedding=embeddings, config=settings)or# Create an opensearch instance.opensearch = AlibabaCloudOpenSearch(embedding=embeddings, config=settings)Add texts and build | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: specified during instance configuration.", embedding_field_separator="Delimiter specified for writing vector field data, default is comma.", output_fields="Specify the field list returned when invoking OpenSearch, by default it is the value list of the field mapping field.", field_name_mapping={ "id": "id", # The id field name mapping of index document. "document": "document", # The text field name mapping of index document. "embedding": "embedding", # The embedding field name mapping of index document. "name_of_the_metadata_specified_during_search": "opensearch_metadata_field_name,=", # The metadata field name mapping of index document, could specify multiple, The value field contains mapping name and operator, the operator would be used when executing metadata filter query, # Currently supported logical operators are: > (greater than), < (less than), = (equal to), <= (less than or equal to), >= (greater than or equal to), != (not equal to). # Refer to this link: https://help.aliyun.com/zh/open-search/vector-search-edition/filter-expression },)# for example# settings = AlibabaCloudOpenSearchSettings(# endpoint='ha-cn-5yd3fhdm102.public.ha.aliyuncs.com',# instance_id='ha-cn-5yd3fhdm102',# username='instance user name',# password='instance password',# table_name='test_table',# field_name_mapping={# "id": "id",# "document": "document",# "embedding": "embedding",# "string_field": "string_filed,=",# "int_field": "int_filed,=",# "float_field": "float_field,=",# "double_field": "double_field,="## },# )Create an opensearch access instance by settings.# Create an opensearch instance and index docs.opensearch = AlibabaCloudOpenSearch.from_texts( texts=docs, embedding=embeddings, config=settings)or# Create an opensearch instance.opensearch = AlibabaCloudOpenSearch(embedding=embeddings, config=settings)Add texts and build |
916 | config=settings)Add texts and build index.metadatas = [{'string_field': "value1", "int_field": 1, 'float_field': 1.0, 'double_field': 2.0}, {'string_field': "value2", "int_field": 2, 'float_field': 3.0, 'double_field': 4.0}, {'string_field': "value3", "int_field": 3, 'float_field': 5.0, 'double_field': 6.0}]# the key of metadatas must match field_name_mapping in settings.opensearch.add_texts(texts=docs, ids=[], metadatas=metadatas)Query and retrieve data.query = "What did the president say about Ketanji Brown Jackson"docs = opensearch.similarity_search(query)print(docs[0].page_content)Query and retrieve data with metadata.query = "What did the president say about Ketanji Brown Jackson"metadata = {'string_field': "value1", "int_field": 1, 'float_field': 1.0, 'double_field': 2.0}docs = opensearch.similarity_search(query, filter=metadata)print(docs[0].page_content)If you encounter any problems during use, please feel free to contact [email protected], and we will do our best to provide you with assistance and support.PreviousActiveloop Deep LakeNextAnalyticDBCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. | Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. ->: config=settings)Add texts and build index.metadatas = [{'string_field': "value1", "int_field": 1, 'float_field': 1.0, 'double_field': 2.0}, {'string_field': "value2", "int_field": 2, 'float_field': 3.0, 'double_field': 4.0}, {'string_field': "value3", "int_field": 3, 'float_field': 5.0, 'double_field': 6.0}]# the key of metadatas must match field_name_mapping in settings.opensearch.add_texts(texts=docs, ids=[], metadatas=metadatas)Query and retrieve data.query = "What did the president say about Ketanji Brown Jackson"docs = opensearch.similarity_search(query)print(docs[0].page_content)Query and retrieve data with metadata.query = "What did the president say about Ketanji Brown Jackson"metadata = {'string_field': "value1", "int_field": 1, 'float_field': 1.0, 'double_field': 2.0}docs = opensearch.similarity_search(query, filter=metadata)print(docs[0].page_content)If you encounter any problems during use, please feel free to contact [email protected], and we will do our best to provide you with assistance and support.PreviousActiveloop Deep LakeNextAnalyticDBCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
917 | Clarifai | ü¶úÔ∏èüîó Langchain | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: Clarifai | ü¶úÔ∏èüîó Langchain |
918 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesClarifaiOn this pageClarifaiClarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. This notebook shows how to use functionality related to the Clarifai vector database. Examples are shown to demonstrate text semantic search capabilities. Clarifai also supports semantic search with images, video frames, and localized search (see Rank) and attribute search (see Filter).To use Clarifai, you must have an account and a Personal Access Token (PAT) key. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesClarifaiOn this pageClarifaiClarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. This notebook shows how to use functionality related to the Clarifai vector database. Examples are shown to demonstrate text semantic search capabilities. Clarifai also supports semantic search with images, video frames, and localized search (see Rank) and attribute search (see Filter).To use Clarifai, you must have an account and a Personal Access Token (PAT) key. |
919 | Check here to get or create a PAT.Dependencies# Install required dependenciespip install clarifaiImportsHere we will be setting the personal access token. You can find your PAT under settings/security on the platform.# Please login and get your API key from https://clarifai.com/settings/securityfrom getpass import getpassCLARIFAI_PAT = getpass() ········# Import the required modulesfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import ClarifaiSetupSetup the user id and app id where the text data will be uploaded. Note: when creating that application please select an appropriate base workflow for indexing your text documents such as the Language-Understanding workflow.You will have to first create an account on Clarifai and then create an application.USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4From Texts​Create a Clarifai vectorstore from a list of texts. This section will upload each text with its respective metadata to a Clarifai Application. The Clarifai Application can then be used for semantic search to find relevant texts.texts = [ "I really enjoy spending time with you", "I hate spending time with my dog", "I want to go for a run", "I went to the movies yesterday", "I love playing soccer with my friends",]metadatas = [{"id": i, "text": text, "source": "book 1", "category": ["books", "modern"]} for i, text in enumerate(texts)]clarifai_vector_db = Clarifai.from_texts( user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas=metadatas,)docs = clarifai_vector_db.similarity_search("I would love to see you")docs [Document(page_content='I really enjoy spending time with you', metadata={'text': 'I really enjoy spending time with you', 'id': 0.0, 'source': 'book 1', 'category': ['books', 'modern']}), Document(page_content='I went to the movies yesterday', | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: Check here to get or create a PAT.Dependencies# Install required dependenciespip install clarifaiImportsHere we will be setting the personal access token. You can find your PAT under settings/security on the platform.# Please login and get your API key from https://clarifai.com/settings/securityfrom getpass import getpassCLARIFAI_PAT = getpass() ········# Import the required modulesfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import ClarifaiSetupSetup the user id and app id where the text data will be uploaded. Note: when creating that application please select an appropriate base workflow for indexing your text documents such as the Language-Understanding workflow.You will have to first create an account on Clarifai and then create an application.USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4From Texts​Create a Clarifai vectorstore from a list of texts. This section will upload each text with its respective metadata to a Clarifai Application. The Clarifai Application can then be used for semantic search to find relevant texts.texts = [ "I really enjoy spending time with you", "I hate spending time with my dog", "I want to go for a run", "I went to the movies yesterday", "I love playing soccer with my friends",]metadatas = [{"id": i, "text": text, "source": "book 1", "category": ["books", "modern"]} for i, text in enumerate(texts)]clarifai_vector_db = Clarifai.from_texts( user_id=USER_ID, app_id=APP_ID, texts=texts, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS, metadatas=metadatas,)docs = clarifai_vector_db.similarity_search("I would love to see you")docs [Document(page_content='I really enjoy spending time with you', metadata={'text': 'I really enjoy spending time with you', 'id': 0.0, 'source': 'book 1', 'category': ['books', 'modern']}), Document(page_content='I went to the movies yesterday', |
920 | went to the movies yesterday', metadata={'text': 'I went to the movies yesterday', 'id': 3.0, 'source': 'book 1', 'category': ['books', 'modern']})]# There is lots powerful filtering you can do within an app by leveraging metadata filters. # This one will limit the similarity query to only the texts that have key of "source" matching value of "book 1"book1_similar_docs = clarifai_vector_db.similarity_search("I would love to see you", filter={"source": "book 1"})# you can also use lists in the input's metadata and then select things that match an item in the list. This is useful for categories like below:book_category_similar_docs = clarifai_vector_db.similarity_search("I would love to see you", filter={"category": ["books"]})From Documents​Create a Clarifai vectorstore from a list of Documents. This section will upload each document with its respective metadata to a Clarifai Application. The Clarifai Application can then be used for semantic search to find relevant documents.loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)docs[:4] [Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: went to the movies yesterday', metadata={'text': 'I went to the movies yesterday', 'id': 3.0, 'source': 'book 1', 'category': ['books', 'modern']})]# There is lots powerful filtering you can do within an app by leveraging metadata filters. # This one will limit the similarity query to only the texts that have key of "source" matching value of "book 1"book1_similar_docs = clarifai_vector_db.similarity_search("I would love to see you", filter={"source": "book 1"})# you can also use lists in the input's metadata and then select things that match an item in the list. This is useful for categories like below:book_category_similar_docs = clarifai_vector_db.similarity_search("I would love to see you", filter={"category": ["books"]})From Documents​Create a Clarifai vectorstore from a list of Documents. This section will upload each document with its respective metadata to a Clarifai Application. The Clarifai Application can then be used for semantic search to find relevant documents.loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)docs[:4] [Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall |
921 | the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \n\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \n\nThey keep moving. \n\nAnd the costs and the threats to America and the world keep rising. \n\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \n\nThe United States is a member along with 29 other nations. \n\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \n\nHe rejected repeated efforts at diplomacy. \n\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. \n\nWe prepared extensively and carefully. \n\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \n\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight. \n\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \n\nThey keep moving. \n\nAnd the costs and the threats to America and the world keep rising. \n\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \n\nThe United States is a member along with 29 other nations. \n\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \n\nHe rejected repeated efforts at diplomacy. \n\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. \n\nWe prepared extensively and carefully. \n\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \n\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was |
922 | with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. \n\nWe countered Russia’s lies with truth. \n\nAnd now that he has acted the free world is holding him accountable. \n\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. \n\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless. \n\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come. \n\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n\nThe U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. \n\nWe are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.', metadata={'source': '../../../state_of_the_union.txt'})]USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4clarifai_vector_db = Clarifai.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS,)docs = clarifai_vector_db.similarity_search("Texts related to criminals and violence")docs [Document(page_content='And I will keep doing everything in my power to crack down on gun | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. \n\nWe countered Russia’s lies with truth. \n\nAnd now that he has acted the free world is holding him accountable. \n\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. \n\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund” worthless. \n\nWe are choking off Russia’s access to technology that will sap its economic strength and weaken its military for years to come. \n\nTonight I say to the Russian oligarchs and corrupt leaders who have bilked billions of dollars off this violent regime no more. \n\nThe U.S. Department of Justice is assembling a dedicated task force to go after the crimes of Russian oligarchs. \n\nWe are joining with our European allies to find and seize your yachts your luxury apartments your private jets. We are coming for your ill-begotten gains.', metadata={'source': '../../../state_of_the_union.txt'})]USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4clarifai_vector_db = Clarifai.from_documents( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS,)docs = clarifai_vector_db.similarity_search("Texts related to criminals and violence")docs [Document(page_content='And I will keep doing everything in my power to crack down on gun |
923 | doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced. \n\nAnd I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? \n\nBan assault weapons and high-capacity magazines. \n\nRepeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued. \n\nThese laws don’t infringe on the Second Amendment. They save lives. \n\nThe most fundamental right in America is the right to vote – and to have it counted. And it’s under assault. \n\nIn state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n\nI’ve worked on these issues a long time. \n\nI know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='A former top litigator | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced. \n\nAnd I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? \n\nBan assault weapons and high-capacity magazines. \n\nRepeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued. \n\nThese laws don’t infringe on the Second Amendment. They save lives. \n\nThe most fundamental right in America is the right to vote – and to have it counted. And it’s under assault. \n\nIn state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. \n\nWe cannot let this happen.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n\nI’ve worked on these issues a long time. \n\nI know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='A former top litigator |
924 | Document(page_content='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. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='So let’s not abandon our streets. Or choose between safety and equal justice. \n\nLet’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n\nThat’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. \n\nThat’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope. \n\nWe should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. \n\nI ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.', metadata={'source': '../../../state_of_the_union.txt'})]From existing | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: Document(page_content='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. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='So let’s not abandon our streets. Or choose between safety and equal justice. \n\nLet’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n\nThat’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. \n\nThat’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope. \n\nWe should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. \n\nI ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe.', metadata={'source': '../../../state_of_the_union.txt'})]From existing |
925 | '../../../state_of_the_union.txt'})]From existing App​Within Clarifai we have great tools for adding data to applications (essentially projects) via API or UI. Most users will already have done that before interacting with LangChain so this example will use the data in an existing app to perform searches. Check out our API docs and UI docs. The Clarifai Application can then be used for semantic search to find relevant documents.USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4clarifai_vector_db = Clarifai( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS,)docs = clarifai_vector_db.similarity_search("Texts related to criminals and violence")docsPreviousChromaNextClickHouseFrom TextsFrom DocumentsFrom existing AppCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. | Clarifai is an AI Platform that provides the full AI lifecycle ranging from data exploration, data labeling, model training, evaluation, and inference. A Clarifai application can be used as a vector database after uploading inputs. ->: '../../../state_of_the_union.txt'})]From existing App​Within Clarifai we have great tools for adding data to applications (essentially projects) via API or UI. Most users will already have done that before interacting with LangChain so this example will use the data in an existing app to perform searches. Check out our API docs and UI docs. The Clarifai Application can then be used for semantic search to find relevant documents.USER_ID = "USERNAME_ID"APP_ID = "APPLICATION_ID"NUMBER_OF_DOCS = 4clarifai_vector_db = Clarifai( user_id=USER_ID, app_id=APP_ID, documents=docs, pat=CLARIFAI_PAT, number_of_docs=NUMBER_OF_DOCS,)docs = clarifai_vector_db.similarity_search("Texts related to criminals and violence")docsPreviousChromaNextClickHouseFrom TextsFrom DocumentsFrom existing AppCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
926 | Weaviate | ü¶úÔ∏èüîó Langchain | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: Weaviate | ü¶úÔ∏èüîó Langchain |
927 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesWeaviateOn this pageWeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.This notebook shows how to use functionality related to the Weaviatevector database.See the Weaviate installation instructions.pip install weaviate-client Requirement already satisfied: weaviate-client in /workspaces/langchain/.venv/lib/python3.9/site-packages (3.19.1) Requirement already satisfied: requests<2.29.0,>=2.28.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (2.28.2) Requirement already satisfied: validators<=0.21.0,>=0.18.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (0.20.0) Requirement already satisfied: tqdm<5.0.0,>=4.59.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (4.65.0) Requirement already satisfied: authlib>=1.1.0 in | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesWeaviateOn this pageWeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.This notebook shows how to use functionality related to the Weaviatevector database.See the Weaviate installation instructions.pip install weaviate-client Requirement already satisfied: weaviate-client in /workspaces/langchain/.venv/lib/python3.9/site-packages (3.19.1) Requirement already satisfied: requests<2.29.0,>=2.28.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (2.28.2) Requirement already satisfied: validators<=0.21.0,>=0.18.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (0.20.0) Requirement already satisfied: tqdm<5.0.0,>=4.59.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (4.65.0) Requirement already satisfied: authlib>=1.1.0 in |
928 | Requirement already satisfied: authlib>=1.1.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (1.2.0) Requirement already satisfied: cryptography>=3.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from authlib>=1.1.0->weaviate-client) (40.0.2) Requirement already satisfied: charset-normalizer<4,>=2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.1.0) Requirement already satisfied: idna<4,>=2.5 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.4) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (1.26.15) Requirement already satisfied: certifi>=2017.4.17 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (2023.5.7) Requirement already satisfied: decorator>=3.4.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from validators<=0.21.0,>=0.18.2->weaviate-client) (5.1.1) Requirement already satisfied: cffi>=1.12 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client) (1.15.1) Requirement already satisfied: pycparser in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client) (2.21)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:")WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Weaviatefrom | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: Requirement already satisfied: authlib>=1.1.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (1.2.0) Requirement already satisfied: cryptography>=3.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from authlib>=1.1.0->weaviate-client) (40.0.2) Requirement already satisfied: charset-normalizer<4,>=2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.1.0) Requirement already satisfied: idna<4,>=2.5 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.4) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (1.26.15) Requirement already satisfied: certifi>=2017.4.17 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (2023.5.7) Requirement already satisfied: decorator>=3.4.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from validators<=0.21.0,>=0.18.2->weaviate-client) (5.1.1) Requirement already satisfied: cffi>=1.12 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client) (1.15.1) Requirement already satisfied: pycparser in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client) (2.21)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:")WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"]from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Weaviatefrom |
929 | langchain.vectorstores import Weaviatefrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Authentication​Weaviate instances have authentication enabled by default. You can use either a username/password combination or API key. import weaviateclient = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY))# client = weaviate.Client(# url=WEAVIATE_URL,# auth_client_secret=weaviate.AuthClientPassword(# username = "WCS_USERNAME", # Replace w/ your WCS username# password = "WCS_PASSWORD", # Replace w/ your WCS password# ),# )vectorstore = Weaviate.from_documents(documents, embeddings, client=client, by_text=False) | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: langchain.vectorstores import Weaviatefrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Authentication​Weaviate instances have authentication enabled by default. You can use either a username/password combination or API key. import weaviateclient = weaviate.Client(url=WEAVIATE_URL, auth_client_secret=weaviate.AuthApiKey(WEAVIATE_API_KEY))# client = weaviate.Client(# url=WEAVIATE_URL,# auth_client_secret=weaviate.AuthClientPassword(# username = "WCS_USERNAME", # Replace w/ your WCS username# password = "WCS_PASSWORD", # Replace w/ your WCS password# ),# )vectorstore = Weaviate.from_documents(documents, embeddings, client=client, by_text=False) |
930 | embeddings, client=client, by_text=False) <langchain.vectorstores.weaviate.Weaviate object at 0x107f46550>Similarity search with score‚ÄãSometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: embeddings, client=client, by_text=False) <langchain.vectorstores.weaviate.Weaviate object at 0x107f46550>Similarity search with score‚ÄãSometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result. |
931 | The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query, by_text=False)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'_additional': {'vector': [-0.015289668, -0.011418287, -0.018540842, 0.00274522, 0.008310737, 0.014179829, 0.0080104275, -0.0010217049, -0.022327352, -0.0055002323, 0.018958665, 0.0020548347, -0.0044393567, -0.021609223, -0.013709779, -0.004543812, 0.025722157, 0.01821442, 0.031728342, -0.031388864, -0.01051083, -0.029978717, 0.011555385, 0.0009751897, 0.014675993, -0.02102166, 0.0301354, -0.031754456, 0.013526983, -0.03392191, 0.002800712, -0.0027778621, -0.024259781, -0.006202043, -0.019950991, 0.0176138, -0.0001134321, 0.008343379, 0.034209162, -0.027654583, 0.03149332, -0.0008389079, 0.0053696632, -0.0024644958, -0.016582303, 0.0066720927, -0.005036711, -0.035514854, 0.002942706, 0.02958701, 0.032825127, 0.015694432, -0.019846536, -0.024520919, -0.021974817, -0.0063293483, -0.01081114, -0.0084282495, 0.003025944, -0.010210521, 0.008780787, 0.014793505, -0.006486031, 0.011966679, 0.01774437, -0.006985459, -0.015459408, 0.01625588, -0.016007798, 0.01706541, 0.035567082, 0.0029900377, | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query, by_text=False)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'_additional': {'vector': [-0.015289668, -0.011418287, -0.018540842, 0.00274522, 0.008310737, 0.014179829, 0.0080104275, -0.0010217049, -0.022327352, -0.0055002323, 0.018958665, 0.0020548347, -0.0044393567, -0.021609223, -0.013709779, -0.004543812, 0.025722157, 0.01821442, 0.031728342, -0.031388864, -0.01051083, -0.029978717, 0.011555385, 0.0009751897, 0.014675993, -0.02102166, 0.0301354, -0.031754456, 0.013526983, -0.03392191, 0.002800712, -0.0027778621, -0.024259781, -0.006202043, -0.019950991, 0.0176138, -0.0001134321, 0.008343379, 0.034209162, -0.027654583, 0.03149332, -0.0008389079, 0.0053696632, -0.0024644958, -0.016582303, 0.0066720927, -0.005036711, -0.035514854, 0.002942706, 0.02958701, 0.032825127, 0.015694432, -0.019846536, -0.024520919, -0.021974817, -0.0063293483, -0.01081114, -0.0084282495, 0.003025944, -0.010210521, 0.008780787, 0.014793505, -0.006486031, 0.011966679, 0.01774437, -0.006985459, -0.015459408, 0.01625588, -0.016007798, 0.01706541, 0.035567082, 0.0029900377, |
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941 | 0.026218321, -0.009009283, 0.019624569, 0.0020646274, -0.009081096, -0.01565526, -0.003358896, 0.048571788, -0.004857179, 0.022444865, 0.024181439, 0.00080708164, 0.024873456, 3.463147e-05, 0.0010535312, -0.017940223, 0.0012159267, -0.011065749, 0.008258509, -0.018527785, -0.022797402, 0.012377972, -0.002087477, 0.010791554, 0.022288183, 0.0048604426, -0.032590102, 0.013709779, 0.004922463, 0.020055447, -0.0150677, -0.0057222005, -0.036246043, 0.0021364405, 0.021387255, -0.013435584, 0.010732798, 0.0075534354, -0.00061612396, -0.002018928, -0.004432828, -0.032746784, 0.025513247, -0.0025852725, 0.014467081, -0.008617575, -0.019755138, 0.003966043, -0.0033915383, 0.0004088452, -0.025173767, 0.02796795, 0.0023763615, 0.0052358294, 0.017796598, 0.014806561, 0.0150024155, -0.005859298, 0.01259994, 0.021726735, -0.026466403, -0.017457118, -0.0025493659, 0.0070899143, 0.02668837, 0.015485522, -0.011588027, 0.01906312, -0.003388274, -0.010210521, 0.020956375, 0.028620796, -0.018540842, 0.0025722156, 0.0110331075, -0.003992157, 0.020930262, 0.008487006, 0.0016557822, -0.0009882465, 0.0062640635, -0.016242823, -0.0007785196, -0.0007213955, 0.018971723, 0.021687564, 0.0039464575, -0.01574666, 0.011783881, -0.0019797573, -0.013383356, -0.002706049, 0.0037734534, 0.020394927, -0.00021931567, 0.0041814824, 0.025121538, -0.036246043, -0.019428715, -0.023802789, 0.014845733, 0.015420238, 0.019650683, 0.008186696, 0.025304336, -0.03204171, 0.01774437, 0.0021233836, -0.008434778, -0.0059441687, 0.038335152, 0.022653777, -0.0066002794, 0.02149171, 0.015093814, 0.025382677, -0.007579549, 0.0030357367, -0.0014117807, -0.015341896, 0.014545423, 0.007135614, -0.0113595305, -0.04387129, 0.016308108, -0.008186696, -0.013370299, -0.014297341, 0.017431004, -0.022666834, 0.039458048, 0.0032005806, -0.02081275, 0.008526176, -0.0019307939, 0.024024757, 0.009068039, 0.00953156, 0.010608757, 0.013801178, 0.035932675, -0.015185213, -0.0038322096, -0.012462842, -0.03655941, 0.0013946436, | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: 0.026218321, -0.009009283, 0.019624569, 0.0020646274, -0.009081096, -0.01565526, -0.003358896, 0.048571788, -0.004857179, 0.022444865, 0.024181439, 0.00080708164, 0.024873456, 3.463147e-05, 0.0010535312, -0.017940223, 0.0012159267, -0.011065749, 0.008258509, -0.018527785, -0.022797402, 0.012377972, -0.002087477, 0.010791554, 0.022288183, 0.0048604426, -0.032590102, 0.013709779, 0.004922463, 0.020055447, -0.0150677, -0.0057222005, -0.036246043, 0.0021364405, 0.021387255, -0.013435584, 0.010732798, 0.0075534354, -0.00061612396, -0.002018928, -0.004432828, -0.032746784, 0.025513247, -0.0025852725, 0.014467081, -0.008617575, -0.019755138, 0.003966043, -0.0033915383, 0.0004088452, -0.025173767, 0.02796795, 0.0023763615, 0.0052358294, 0.017796598, 0.014806561, 0.0150024155, -0.005859298, 0.01259994, 0.021726735, -0.026466403, -0.017457118, -0.0025493659, 0.0070899143, 0.02668837, 0.015485522, -0.011588027, 0.01906312, -0.003388274, -0.010210521, 0.020956375, 0.028620796, -0.018540842, 0.0025722156, 0.0110331075, -0.003992157, 0.020930262, 0.008487006, 0.0016557822, -0.0009882465, 0.0062640635, -0.016242823, -0.0007785196, -0.0007213955, 0.018971723, 0.021687564, 0.0039464575, -0.01574666, 0.011783881, -0.0019797573, -0.013383356, -0.002706049, 0.0037734534, 0.020394927, -0.00021931567, 0.0041814824, 0.025121538, -0.036246043, -0.019428715, -0.023802789, 0.014845733, 0.015420238, 0.019650683, 0.008186696, 0.025304336, -0.03204171, 0.01774437, 0.0021233836, -0.008434778, -0.0059441687, 0.038335152, 0.022653777, -0.0066002794, 0.02149171, 0.015093814, 0.025382677, -0.007579549, 0.0030357367, -0.0014117807, -0.015341896, 0.014545423, 0.007135614, -0.0113595305, -0.04387129, 0.016308108, -0.008186696, -0.013370299, -0.014297341, 0.017431004, -0.022666834, 0.039458048, 0.0032005806, -0.02081275, 0.008526176, -0.0019307939, 0.024024757, 0.009068039, 0.00953156, 0.010608757, 0.013801178, 0.035932675, -0.015185213, -0.0038322096, -0.012462842, -0.03655941, 0.0013946436, |
942 | -0.012462842, -0.03655941, 0.0013946436, 0.00025726235, 0.008016956, -0.0042565595, 0.008447835, 0.0038191527, -0.014702106, 0.02196176, 0.0052097156, -0.010869896, 0.0051640165, 0.030840475, -0.041468814, 0.009250836, -0.018997835, 0.020107675, 0.008421721, -0.016373392, 0.004602568, 0.0327729, -0.00812794, 0.001581521, 0.019350372, 0.016112253, 0.02132197, 0.00043944738, -0.01472822, -0.025735214, -0.03313849, 0.0033817457, 0.028855821, -0.016033912, 0.0050791465, -0.01808385]}, 'source': '../../../state_of_the_union.txt'}), 0.8154189703772676)PersistenceAnything uploaded to weaviate is automatically persistent into the database. You do not need to call any specific method or pass any param for this to happen.Retriever options​This section goes over different options for how to use Weaviate as a retriever.MMR​In addition to using similarity search in the retriever object, you can also use mmr.retriever = db.as_retriever(search_type="mmr")retriever.get_relevant_documents(query)[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../../state_of_the_union.txt'})Question Answering with Sources​This section goes over how to do question-answering with sources over an | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: -0.012462842, -0.03655941, 0.0013946436, 0.00025726235, 0.008016956, -0.0042565595, 0.008447835, 0.0038191527, -0.014702106, 0.02196176, 0.0052097156, -0.010869896, 0.0051640165, 0.030840475, -0.041468814, 0.009250836, -0.018997835, 0.020107675, 0.008421721, -0.016373392, 0.004602568, 0.0327729, -0.00812794, 0.001581521, 0.019350372, 0.016112253, 0.02132197, 0.00043944738, -0.01472822, -0.025735214, -0.03313849, 0.0033817457, 0.028855821, -0.016033912, 0.0050791465, -0.01808385]}, 'source': '../../../state_of_the_union.txt'}), 0.8154189703772676)PersistenceAnything uploaded to weaviate is automatically persistent into the database. You do not need to call any specific method or pass any param for this to happen.Retriever options​This section goes over different options for how to use Weaviate as a retriever.MMR​In addition to using similarity search in the retriever object, you can also use mmr.retriever = db.as_retriever(search_type="mmr")retriever.get_relevant_documents(query)[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../../state_of_the_union.txt'})Question Answering with Sources​This section goes over how to do question-answering with sources over an |
943 | how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index. from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.llms import OpenAIwith open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)docsearch = Weaviate.from_texts( texts, embeddings, weaviate_url=WEAVIATE_URL, by_text=False, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],)chain = RetrievalQAWithSourcesChain.from_chain_type( OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())chain( {"question": "What did the president say about Justice Breyer"}, return_only_outputs=True,) {'answer': " The president honored Justice Breyer for his service and mentioned his legacy of excellence. He also nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy.\n", 'sources': '31-pl, 34-pl'}PreviousVespaNextXataAuthenticationSimilarity search with scoreRetriever optionsMMRQuestion Answering with SourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. | Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. ->: how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index. from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.llms import OpenAIwith open("../../modules/state_of_the_union.txt") as f: state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)docsearch = Weaviate.from_texts( texts, embeddings, weaviate_url=WEAVIATE_URL, by_text=False, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],)chain = RetrievalQAWithSourcesChain.from_chain_type( OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())chain( {"question": "What did the president say about Justice Breyer"}, return_only_outputs=True,) {'answer': " The president honored Justice Breyer for his service and mentioned his legacy of excellence. He also nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy.\n", 'sources': '31-pl, 34-pl'}PreviousVespaNextXataAuthenticationSimilarity search with scoreRetriever optionsMMRQuestion Answering with SourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
944 | Rockset | ü¶úÔ∏èüîó Langchain | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Rockset | ü¶úÔ∏èüîó Langchain |
945 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations: (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.* | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations: (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.* |
946 | = 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial​Follow along in your own Python notebook to generate and store vector embeddings in Rockset. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: = 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial​Follow along in your own Python notebook to generate and store vector embeddings in Rockset. |
947 | Start using Rockset to search for documents similar to your search queries.1. Define Key Variables​import osimport rocksetROCKSET_API_KEY = os.environ.get("ROCKSET_API_KEY") # Verify ROCKSET_API_KEY environment variableROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset regionrockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)COLLECTION_NAME='langchain_demo'TEXT_KEY='description'EMBEDDING_KEY='description_embedding'2. Prepare Documents​from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import Rocksetloader = TextLoader('../../modules/state_of_the_union.txt')documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)3. Insert Documents​embeddings = OpenAIEmbeddings() # Verify OPENAI_API_KEY environment variabledocsearch = Rockset( client=rockset_client, embeddings=embeddings, collection_name=COLLECTION_NAME, text_key=TEXT_KEY, embedding_key=EMBEDDING_KEY,)ids=docsearch.add_texts( texts=[d.page_content for d in docs], metadatas=[d.metadata for d in docs],)4. Search for Similar Documents​query = "What did the president say about Ketanji Brown Jackson"output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...5. Search for Similar Documents with | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Start using Rockset to search for documents similar to your search queries.1. Define Key Variables​import osimport rocksetROCKSET_API_KEY = os.environ.get("ROCKSET_API_KEY") # Verify ROCKSET_API_KEY environment variableROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset regionrockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)COLLECTION_NAME='langchain_demo'TEXT_KEY='description'EMBEDDING_KEY='description_embedding'2. Prepare Documents​from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import Rocksetloader = TextLoader('../../modules/state_of_the_union.txt')documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)3. Insert Documents​embeddings = OpenAIEmbeddings() # Verify OPENAI_API_KEY environment variabledocsearch = Rockset( client=rockset_client, embeddings=embeddings, collection_name=COLLECTION_NAME, text_key=TEXT_KEY, embedding_key=EMBEDDING_KEY,)ids=docsearch.add_texts( texts=[d.page_content for d in docs], metadatas=[d.metadata for d in docs],)4. Search for Similar Documents​query = "What did the president say about Ketanji Brown Jackson"output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...5. Search for Similar Documents with |
948 | citizens b...5. Search for Similar Documents with Filtering​output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM, where_str="{} NOT LIKE '%citizens%'".format(TEXT_KEY),)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo...6. [Optional] Delete Inserted Documents​You must have the unique ID associated with each document to delete them from your collection. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: citizens b...5. Search for Similar Documents with Filtering​output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM, where_str="{} NOT LIKE '%citizens%'".format(TEXT_KEY),)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo...6. [Optional] Delete Inserted Documents​You must have the unique ID associated with each document to delete them from your collection. |
949 | Define IDs when inserting documents with Rockset.add_texts(). Rockset will otherwise generate a unique ID for each document. Regardless, Rockset.add_texts() returns the IDs of inserted documents.To delete these docs, simply use the Rockset.delete_texts() function.docsearch.delete_texts(ids)Summary​In this tutorial, we successfully created a Rockset collection, inserted documents with OpenAI embeddings, and searched for similar documents with and without metadata filters.Keep an eye on https://rockset.com/ for future updates in this space.PreviousRedisNextScaNNSetting Up Your EnvironmentLangChain Tutorial1. Define Key Variables2. Prepare Documents3. Insert Documents4. Search for Similar Documents5. Search for Similar Documents with Filtering6. Optional Delete Inserted DocumentsSummaryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Define IDs when inserting documents with Rockset.add_texts(). Rockset will otherwise generate a unique ID for each document. Regardless, Rockset.add_texts() returns the IDs of inserted documents.To delete these docs, simply use the Rockset.delete_texts() function.docsearch.delete_texts(ids)Summary​In this tutorial, we successfully created a Rockset collection, inserted documents with OpenAI embeddings, and searched for similar documents with and without metadata filters.Keep an eye on https://rockset.com/ for future updates in this space.PreviousRedisNextScaNNSetting Up Your EnvironmentLangChain Tutorial1. Define Key Variables2. Prepare Documents3. Insert Documents4. Search for Similar Documents5. Search for Similar Documents with Filtering6. Optional Delete Inserted DocumentsSummaryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
950 | Rockset | ü¶úÔ∏èüîó Langchain | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Rockset | ü¶úÔ∏èüîó Langchain |
951 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations: (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.* | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations: (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.* |
952 | = 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial​Follow along in your own Python notebook to generate and store vector embeddings in Rockset. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: = 1536)SELECT _input.* EXCEPT(_meta), VECTOR_ENFORCE(_input.description_embedding, #length_of_vector_embedding, 'float') as description_embedding FROM _inputAfter creating your collection, use the console to retrieve an API key. For the purpose of this notebook, we assume you are using the Oregon(us-west-2) region.Install the rockset-python-client to enable LangChain to communicate directly with Rockset.pip install rocksetLangChain Tutorial​Follow along in your own Python notebook to generate and store vector embeddings in Rockset. |
953 | Start using Rockset to search for documents similar to your search queries.1. Define Key Variables​import osimport rocksetROCKSET_API_KEY = os.environ.get("ROCKSET_API_KEY") # Verify ROCKSET_API_KEY environment variableROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset regionrockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)COLLECTION_NAME='langchain_demo'TEXT_KEY='description'EMBEDDING_KEY='description_embedding'2. Prepare Documents​from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import Rocksetloader = TextLoader('../../modules/state_of_the_union.txt')documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)3. Insert Documents​embeddings = OpenAIEmbeddings() # Verify OPENAI_API_KEY environment variabledocsearch = Rockset( client=rockset_client, embeddings=embeddings, collection_name=COLLECTION_NAME, text_key=TEXT_KEY, embedding_key=EMBEDDING_KEY,)ids=docsearch.add_texts( texts=[d.page_content for d in docs], metadatas=[d.metadata for d in docs],)4. Search for Similar Documents​query = "What did the president say about Ketanji Brown Jackson"output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...5. Search for Similar Documents with | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Start using Rockset to search for documents similar to your search queries.1. Define Key Variables​import osimport rocksetROCKSET_API_KEY = os.environ.get("ROCKSET_API_KEY") # Verify ROCKSET_API_KEY environment variableROCKSET_API_SERVER = rockset.Regions.usw2a1 # Verify Rockset regionrockset_client = rockset.RocksetClient(ROCKSET_API_SERVER, ROCKSET_API_KEY)COLLECTION_NAME='langchain_demo'TEXT_KEY='description'EMBEDDING_KEY='description_embedding'2. Prepare Documents​from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores import Rocksetloader = TextLoader('../../modules/state_of_the_union.txt')documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)3. Insert Documents​embeddings = OpenAIEmbeddings() # Verify OPENAI_API_KEY environment variabledocsearch = Rockset( client=rockset_client, embeddings=embeddings, collection_name=COLLECTION_NAME, text_key=TEXT_KEY, embedding_key=EMBEDDING_KEY,)ids=docsearch.add_texts( texts=[d.page_content for d in docs], metadatas=[d.metadata for d in docs],)4. Search for Similar Documents​query = "What did the president say about Ketanji Brown Jackson"output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.764990692109871 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7485416901622112 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7468678973398306 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7436231261419488 {'source': '../../../state_of_the_union.txt'} Groups of citizens b...5. Search for Similar Documents with |
954 | citizens b...5. Search for Similar Documents with Filtering​output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM, where_str="{} NOT LIKE '%citizens%'".format(TEXT_KEY),)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo...6. [Optional] Delete Inserted Documents​You must have the unique ID associated with each document to delete them from your collection. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: citizens b...5. Search for Similar Documents with Filtering​output = docsearch.similarity_search_with_relevance_scores( query, 4, Rockset.DistanceFunction.COSINE_SIM, where_str="{} NOT LIKE '%citizens%'".format(TEXT_KEY),)print("output length:", len(output))for d, dist in output: print(dist, d.metadata, d.page_content[:20] + '...')### output length: 4# 0.7651359650263554 {'source': '../../../state_of_the_union.txt'} Madam Speaker, Madam...# 0.7486265516824893 {'source': '../../../state_of_the_union.txt'} And I’m taking robus...# 0.7469625542348115 {'source': '../../../state_of_the_union.txt'} And so many families...# 0.7344177777547739 {'source': '../../../state_of_the_union.txt'} We see the unity amo...6. [Optional] Delete Inserted Documents​You must have the unique ID associated with each document to delete them from your collection. |
955 | Define IDs when inserting documents with Rockset.add_texts(). Rockset will otherwise generate a unique ID for each document. Regardless, Rockset.add_texts() returns the IDs of inserted documents.To delete these docs, simply use the Rockset.delete_texts() function.docsearch.delete_texts(ids)Summary​In this tutorial, we successfully created a Rockset collection, inserted documents with OpenAI embeddings, and searched for similar documents with and without metadata filters.Keep an eye on https://rockset.com/ for future updates in this space.PreviousRedisNextScaNNSetting Up Your EnvironmentLangChain Tutorial1. Define Key Variables2. Prepare Documents3. Insert Documents4. Search for Similar Documents5. Search for Similar Documents with Filtering6. Optional Delete Inserted DocumentsSummaryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. | Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index™ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and handles real-time ingestion for constantly updating, streaming data. ->: Define IDs when inserting documents with Rockset.add_texts(). Rockset will otherwise generate a unique ID for each document. Regardless, Rockset.add_texts() returns the IDs of inserted documents.To delete these docs, simply use the Rockset.delete_texts() function.docsearch.delete_texts(ids)Summary​In this tutorial, we successfully created a Rockset collection, inserted documents with OpenAI embeddings, and searched for similar documents with and without metadata filters.Keep an eye on https://rockset.com/ for future updates in this space.PreviousRedisNextScaNNSetting Up Your EnvironmentLangChain Tutorial1. Define Key Variables2. Prepare Documents3. Insert Documents4. Search for Similar Documents5. Search for Similar Documents with Filtering6. Optional Delete Inserted DocumentsSummaryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
956 | Epsilla | ü¶úÔ∏èüîó Langchain | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. ->: Epsilla | ü¶úÔ∏èüîó Langchain |
957 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesEpsillaEpsillaEpsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.This notebook shows how to use the functionalities related to the Epsilla vector database.As a prerequisite, you need to have a running Epsilla vector database (for example, through our docker image), and install the pyepsilla package. View full docs at docs.pip/pip3 install pyepsillaWe 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.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import Epsillafrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()documents = CharacterTextSplitter(chunk_size=1000, | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesEpsillaEpsillaEpsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.This notebook shows how to use the functionalities related to the Epsilla vector database.As a prerequisite, you need to have a running Epsilla vector database (for example, through our docker image), and install the pyepsilla package. View full docs at docs.pip/pip3 install pyepsillaWe 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.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import Epsillafrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()documents = CharacterTextSplitter(chunk_size=1000, |
958 | = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(documents)embeddings = OpenAIEmbeddings()Epsilla vectordb is running with default host "localhost" and port "8888". We have a custom db path, db name and collection name instead of the default ones.from pyepsilla import vectordbclient = vectordb.Client()vector_store = Epsilla.from_documents( documents, embeddings, client, db_path="/tmp/mypath", db_name="MyDB", collection_name="MyCollection")query = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)print(docs[0].page_content)In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.We cannot let this happen.Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.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.PreviousElasticsearchNextFaissCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. | Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. ->: = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0).split_documents(documents)embeddings = OpenAIEmbeddings()Epsilla vectordb is running with default host "localhost" and port "8888". We have a custom db path, db name and collection name instead of the default ones.from pyepsilla import vectordbclient = vectordb.Client()vector_store = Epsilla.from_documents( documents, embeddings, client, db_path="/tmp/mypath", db_name="MyDB", collection_name="MyCollection")query = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)print(docs[0].page_content)In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections.We cannot let this happen.Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.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.PreviousElasticsearchNextFaissCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
959 | Atlas | ü¶úÔ∏èüîó Langchain | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. ->: Atlas | ü¶úÔ∏èüîó Langchain |
960 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesAtlasOn this pageAtlasAtlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser.This notebook shows you how to use functionality related to the AtlasDB vectorstore.pip install spacypython3 -m spacy download en_core_web_smpip install nomicLoad Packages‚Äãimport timefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import SpacyTextSplitterfrom langchain.vectorstores import AtlasDBfrom langchain.document_loaders import TextLoaderATLAS_TEST_API_KEY = "7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6"Prepare the Data‚Äãloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = SpacyTextSplitter(separator="|")texts = []for doc in text_splitter.split_documents(documents): texts.extend(doc.page_content.split("|"))texts = [e.strip() for e in texts]Map the Data using Nomic's Atlas‚Äãdb = | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesAtlasOn this pageAtlasAtlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser.This notebook shows you how to use functionality related to the AtlasDB vectorstore.pip install spacypython3 -m spacy download en_core_web_smpip install nomicLoad Packages‚Äãimport timefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import SpacyTextSplitterfrom langchain.vectorstores import AtlasDBfrom langchain.document_loaders import TextLoaderATLAS_TEST_API_KEY = "7xDPkYXSYDc1_ErdTPIcoAR9RNd8YDlkS3nVNXcVoIMZ6"Prepare the Data‚Äãloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = SpacyTextSplitter(separator="|")texts = []for doc in text_splitter.split_documents(documents): texts.extend(doc.page_content.split("|"))texts = [e.strip() for e in texts]Map the Data using Nomic's Atlas‚Äãdb = |
961 | in texts]Map the Data using Nomic's Atlas‚Äãdb = AtlasDB.from_texts( texts=texts, name="test_index_" + str(time.time()), # unique name for your vector store description="test_index", # a description for your vector store api_key=ATLAS_TEST_API_KEY, index_kwargs={"build_topic_model": True},)db.project.wait_for_project_lock()db.projectHere is a map with the result of this code. This map displays the texts of the State of the Union. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. ->: in texts]Map the Data using Nomic's Atlas‚Äãdb = AtlasDB.from_texts( texts=texts, name="test_index_" + str(time.time()), # unique name for your vector store description="test_index", # a description for your vector store api_key=ATLAS_TEST_API_KEY, index_kwargs={"build_topic_model": True},)db.project.wait_for_project_lock()db.projectHere is a map with the result of this code. This map displays the texts of the State of the Union. |
962 | https://atlas.nomic.ai/map/3e4de075-89ff-486a-845c-36c23f30bb67/d8ce2284-8edb-4050-8b9b-9bb543d7f647PreviousAnnoyNextAwaDBLoad PackagesPrepare the DataMap the Data using Nomic's AtlasCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. | Atlas is a platform by Nomic made for interacting with both small and internet scale unstructured datasets. It enables anyone to visualize, search, and share massive datasets in their browser. ->: https://atlas.nomic.ai/map/3e4de075-89ff-486a-845c-36c23f30bb67/d8ce2284-8edb-4050-8b9b-9bb543d7f647PreviousAnnoyNextAwaDBLoad PackagesPrepare the DataMap the Data using Nomic's AtlasCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
963 | SemaDB | ü¶úÔ∏èüîó Langchain | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. ->: SemaDB | ü¶úÔ∏èüîó Langchain |
964 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresSemaDBVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesvectorstoresSemaDBOn this pageSemaDBSemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started.The full documentation of the API along with examples and an interactive playground is available on RapidAPI.This notebook demonstrates how the langchain wrapper can be used with SemaDB Cloud.Load document embeddings‚ÄãTo run things locally, we are using Sentence Transformers which are commonly used for embedding sentences. You can use any embedding model LangChain offers.pip install sentence_transformersfrom langchain.embeddings import HuggingFaceEmbeddingsembeddings = HuggingFaceEmbeddings()from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)docs = | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresSemaDBVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesvectorstoresSemaDBOn this pageSemaDBSemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started.The full documentation of the API along with examples and an interactive playground is available on RapidAPI.This notebook demonstrates how the langchain wrapper can be used with SemaDB Cloud.Load document embeddings‚ÄãTo run things locally, we are using Sentence Transformers which are commonly used for embedding sentences. You can use any embedding model LangChain offers.pip install sentence_transformersfrom langchain.embeddings import HuggingFaceEmbeddingsembeddings = HuggingFaceEmbeddings()from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)docs = |
965 | chunk_overlap=0)docs = text_splitter.split_documents(documents)print(len(docs)) 114Connect to SemaDB​SemaDB Cloud uses RapidAPI keys to authenticate. You can obtain yours by creating a free RapidAPI account.import getpassimport osos.environ['SEMADB_API_KEY'] = getpass.getpass("SemaDB API Key:") SemaDB API Key: ········from langchain.vectorstores import SemaDBfrom langchain.vectorstores.utils import DistanceStrategyThe parameters to the SemaDB vector store reflect the API directly:"mycollection": is the collection name in which we will store these vectors.768: is dimensions of the vectors. In our case, the sentence transformer embeddings yield 768 dimensional vectors.API_KEY: is your RapidAPI key.embeddings: correspond to how the embeddings of documents, texts and queries will be generated.DistanceStrategy: is the distance metric used. The wrapper automatically normalises vectors if COSINE is used.db = SemaDB("mycollection", 768, embeddings, DistanceStrategy.COSINE)# Create collection if running for the first time. If the collection# already exists this will fail.db.create_collection() TrueThe SemaDB vector store wrapper adds the document text as point metadata to collect later. Storing large chunks of text is not recommended. If you are indexing a large collection, we instead recommend storing references to the documents such as external Ids.db.add_documents(docs)[:2] ['813c7ef3-9797-466b-8afa-587115592c6c', 'fc392f7f-082b-4932-bfcc-06800db5e017']Similarity Search​We use the default LangChain similarity search interface to search for the most similar sentences.query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) 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.docs = db.similarity_search_with_score(query)docs[0] | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. ->: chunk_overlap=0)docs = text_splitter.split_documents(documents)print(len(docs)) 114Connect to SemaDB​SemaDB Cloud uses RapidAPI keys to authenticate. You can obtain yours by creating a free RapidAPI account.import getpassimport osos.environ['SEMADB_API_KEY'] = getpass.getpass("SemaDB API Key:") SemaDB API Key: ········from langchain.vectorstores import SemaDBfrom langchain.vectorstores.utils import DistanceStrategyThe parameters to the SemaDB vector store reflect the API directly:"mycollection": is the collection name in which we will store these vectors.768: is dimensions of the vectors. In our case, the sentence transformer embeddings yield 768 dimensional vectors.API_KEY: is your RapidAPI key.embeddings: correspond to how the embeddings of documents, texts and queries will be generated.DistanceStrategy: is the distance metric used. The wrapper automatically normalises vectors if COSINE is used.db = SemaDB("mycollection", 768, embeddings, DistanceStrategy.COSINE)# Create collection if running for the first time. If the collection# already exists this will fail.db.create_collection() TrueThe SemaDB vector store wrapper adds the document text as point metadata to collect later. Storing large chunks of text is not recommended. If you are indexing a large collection, we instead recommend storing references to the documents such as external Ids.db.add_documents(docs)[:2] ['813c7ef3-9797-466b-8afa-587115592c6c', 'fc392f7f-082b-4932-bfcc-06800db5e017']Similarity Search​We use the default LangChain similarity search interface to search for the most similar sentences.query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) 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.docs = db.similarity_search_with_score(query)docs[0] |
966 | db.similarity_search_with_score(query)docs[0] (Document(page_content='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.', metadata={'source': '../../modules/state_of_the_union.txt', 'text': '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.'}), 0.42369342)Clean up​You can delete the collection to remove all data.db.delete_collection() TruePreviousVectaraNextVespaLoad document embeddingsConnect to SemaDBSimilarity SearchClean upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. | SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. ->: db.similarity_search_with_score(query)docs[0] (Document(page_content='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.', metadata={'source': '../../modules/state_of_the_union.txt', 'text': '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.'}), 0.42369342)Clean up​You can delete the collection to remove all data.db.delete_collection() TruePreviousVectaraNextVespaLoad document embeddingsConnect to SemaDBSimilarity SearchClean upCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
967 | Faiss | ü¶úÔ∏èüîó Langchain | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: Faiss | ü¶úÔ∏èüîó Langchain |
968 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesFaissOn this pageFaissFacebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.Faiss documentation.This notebook shows how to use functionality related to the FAISS vector database.pip install faiss-gpu # For CUDA 7.5+ Supported GPU's.# ORpip install faiss-cpu # For CPU InstallationWe 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:")# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization# os.environ['FAISS_NO_AVX2'] = '1'from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISSfrom langchain.document_loaders import TextLoaderfrom | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesFaissOn this pageFaissFacebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.Faiss documentation.This notebook shows how to use functionality related to the FAISS vector database.pip install faiss-gpu # For CUDA 7.5+ Supported GPU's.# ORpip install faiss-cpu # For CPU InstallationWe 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:")# Uncomment the following line if you need to initialize FAISS with no AVX2 optimization# os.environ['FAISS_NO_AVX2'] = '1'from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import FAISSfrom langchain.document_loaders import TextLoaderfrom |
969 | langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../extras/modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = FAISS.from_documents(docs, embeddings)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity Search with score​There are some FAISS specific methods. One of them is similarity_search_with_score, which allows you to return not only the documents but also the distance score of the query to them. The returned distance score is L2 distance. Therefore, a lower score is better.docs_and_scores = db.similarity_search_with_score(query)docs_and_scores[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../extras/modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = FAISS.from_documents(docs, embeddings)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity Search with score​There are some FAISS specific methods. One of them is similarity_search_with_score, which allows you to return not only the documents but also the distance score of the query to them. The returned distance score is L2 distance. Therefore, a lower score is better.docs_and_scores = db.similarity_search_with_score(query)docs_and_scores[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve |
970 | honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../modules/state_of_the_union.txt'}), 0.36913747)It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.embedding_vector = embeddings.embed_query(query)docs_and_scores = db.similarity_search_by_vector(embedding_vector)Saving and loading​You can also save and load a FAISS index. This is useful so you don't have to recreate it everytime you use it.db.save_local("faiss_index")new_db = FAISS.load_local("faiss_index", embeddings)docs = new_db.similarity_search(query)docs[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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 | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../modules/state_of_the_union.txt'}), 0.36913747)It is also possible to do a search for documents similar to a given embedding vector using similarity_search_by_vector which accepts an embedding vector as a parameter instead of a string.embedding_vector = embeddings.embed_query(query)docs_and_scores = db.similarity_search_by_vector(embedding_vector)Saving and loading​You can also save and load a FAISS index. This is useful so you don't have to recreate it everytime you use it.db.save_local("faiss_index")new_db = FAISS.load_local("faiss_index", embeddings)docs = new_db.similarity_search(query)docs[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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 |
971 | One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Serializing and De-Serializing to bytesyou can pickle the FAISS Index by these functions. If you use embeddings model which is of 90 mb (sentence-transformers/all-MiniLM-L6-v2 or any other model), the resultant pickle size would be more than 90 mb. the size of the model is also included in the overall size. To overcome this, use the below functions. These functions only serializes FAISS index and size would be much lesser. this can be helpful if you wish to store the index in database like sql.pkl = db.serialize_to_bytes() # serializes the faiss indexembeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")db = FAISS.deserialize_from_bytes(embeddings = embeddings, serialized = pkl) # Load the indexMerging​You can also merge two FAISS vectorstoresdb1 = FAISS.from_texts(["foo"], embeddings)db2 = FAISS.from_texts(["bar"], embeddings)db1.docstore._dict {'068c473b-d420-487a-806b-fb0ccea7f711': Document(page_content='foo', metadata={})}db2.docstore._dict {'807e0c63-13f6-4070-9774-5c6f0fbb9866': Document(page_content='bar', metadata={})}db1.merge_from(db2)db1.docstore._dict {'068c473b-d420-487a-806b-fb0ccea7f711': Document(page_content='foo', metadata={}), '807e0c63-13f6-4070-9774-5c6f0fbb9866': Document(page_content='bar', metadata={})}Similarity Search with filtering​FAISS vectorstore can also support filtering, since the FAISS does not natively support filtering we have to do it manually. This is done by first fetching more results than k and then filtering them. You can filter the documents based on metadata. You can also set the fetch_k parameter when calling any search method to set how many documents you want to fetch before filtering. Here is a small example:from langchain.schema import Documentlist_of_documents = [ Document(page_content="foo", metadata=dict(page=1)), | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Serializing and De-Serializing to bytesyou can pickle the FAISS Index by these functions. If you use embeddings model which is of 90 mb (sentence-transformers/all-MiniLM-L6-v2 or any other model), the resultant pickle size would be more than 90 mb. the size of the model is also included in the overall size. To overcome this, use the below functions. These functions only serializes FAISS index and size would be much lesser. this can be helpful if you wish to store the index in database like sql.pkl = db.serialize_to_bytes() # serializes the faiss indexembeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")db = FAISS.deserialize_from_bytes(embeddings = embeddings, serialized = pkl) # Load the indexMerging​You can also merge two FAISS vectorstoresdb1 = FAISS.from_texts(["foo"], embeddings)db2 = FAISS.from_texts(["bar"], embeddings)db1.docstore._dict {'068c473b-d420-487a-806b-fb0ccea7f711': Document(page_content='foo', metadata={})}db2.docstore._dict {'807e0c63-13f6-4070-9774-5c6f0fbb9866': Document(page_content='bar', metadata={})}db1.merge_from(db2)db1.docstore._dict {'068c473b-d420-487a-806b-fb0ccea7f711': Document(page_content='foo', metadata={}), '807e0c63-13f6-4070-9774-5c6f0fbb9866': Document(page_content='bar', metadata={})}Similarity Search with filtering​FAISS vectorstore can also support filtering, since the FAISS does not natively support filtering we have to do it manually. This is done by first fetching more results than k and then filtering them. You can filter the documents based on metadata. You can also set the fetch_k parameter when calling any search method to set how many documents you want to fetch before filtering. Here is a small example:from langchain.schema import Documentlist_of_documents = [ Document(page_content="foo", metadata=dict(page=1)), |
972 | metadata=dict(page=1)), Document(page_content="bar", metadata=dict(page=1)), Document(page_content="foo", metadata=dict(page=2)), Document(page_content="barbar", metadata=dict(page=2)), Document(page_content="foo", metadata=dict(page=3)), Document(page_content="bar burr", metadata=dict(page=3)), Document(page_content="foo", metadata=dict(page=4)), Document(page_content="bar bruh", metadata=dict(page=4)),]db = FAISS.from_documents(list_of_documents, embeddings)results_with_scores = db.similarity_search_with_score("foo")for doc, score in results_with_scores: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}") Content: foo, Metadata: {'page': 1}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 2}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 3}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 4}, Score: 5.159960813797904e-15Now we make the same query call but we filter for only page = 1 results_with_scores = db.similarity_search_with_score("foo", filter=dict(page=1))for doc, score in results_with_scores: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}") Content: foo, Metadata: {'page': 1}, Score: 5.159960813797904e-15 Content: bar, Metadata: {'page': 1}, Score: 0.3131446838378906Same thing can be done with the max_marginal_relevance_search as well.results = db.max_marginal_relevance_search("foo", filter=dict(page=1))for doc in results: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}") Content: foo, Metadata: {'page': 1} Content: bar, Metadata: {'page': 1}Here is an example of how to set fetch_k parameter when calling similarity_search. Usually you would want the fetch_k parameter >> k parameter. This is because the fetch_k parameter is the number of documents that will be fetched before filtering. If you set fetch_k to a low number, you might not get enough documents to filter from.results = | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: metadata=dict(page=1)), Document(page_content="bar", metadata=dict(page=1)), Document(page_content="foo", metadata=dict(page=2)), Document(page_content="barbar", metadata=dict(page=2)), Document(page_content="foo", metadata=dict(page=3)), Document(page_content="bar burr", metadata=dict(page=3)), Document(page_content="foo", metadata=dict(page=4)), Document(page_content="bar bruh", metadata=dict(page=4)),]db = FAISS.from_documents(list_of_documents, embeddings)results_with_scores = db.similarity_search_with_score("foo")for doc, score in results_with_scores: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}") Content: foo, Metadata: {'page': 1}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 2}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 3}, Score: 5.159960813797904e-15 Content: foo, Metadata: {'page': 4}, Score: 5.159960813797904e-15Now we make the same query call but we filter for only page = 1 results_with_scores = db.similarity_search_with_score("foo", filter=dict(page=1))for doc, score in results_with_scores: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}") Content: foo, Metadata: {'page': 1}, Score: 5.159960813797904e-15 Content: bar, Metadata: {'page': 1}, Score: 0.3131446838378906Same thing can be done with the max_marginal_relevance_search as well.results = db.max_marginal_relevance_search("foo", filter=dict(page=1))for doc in results: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}") Content: foo, Metadata: {'page': 1} Content: bar, Metadata: {'page': 1}Here is an example of how to set fetch_k parameter when calling similarity_search. Usually you would want the fetch_k parameter >> k parameter. This is because the fetch_k parameter is the number of documents that will be fetched before filtering. If you set fetch_k to a low number, you might not get enough documents to filter from.results = |
973 | not get enough documents to filter from.results = db.similarity_search("foo", filter=dict(page=1), k=1, fetch_k=4)for doc in results: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}") Content: foo, Metadata: {'page': 1}Delete​You can also delete ids. Note that the ids to delete should be the ids in the docstore.db.delete([db.index_to_docstore_id[0]]) True# Is now missing0 in db.index_to_docstore_id FalsePreviousEpsillaNextHologresSimilarity Search with scoreSaving and loadingMergingSimilarity Search with filteringDeleteCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. | Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. ->: not get enough documents to filter from.results = db.similarity_search("foo", filter=dict(page=1), k=1, fetch_k=4)for doc in results: print(f"Content: {doc.page_content}, Metadata: {doc.metadata}") Content: foo, Metadata: {'page': 1}Delete​You can also delete ids. Note that the ids to delete should be the ids in the docstore.db.delete([db.index_to_docstore_id[0]]) True# Is now missing0 in db.index_to_docstore_id FalsePreviousEpsillaNextHologresSimilarity Search with scoreSaving and loadingMergingSimilarity Search with filteringDeleteCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
974 | Tair | ü¶úÔ∏èüîó Langchain | Tair is a cloud native in-memory database service developed by Alibaba Cloud. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. ->: Tair | ü¶úÔ∏èüîó Langchain |
975 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesTairTairTair is a cloud native in-memory database service developed by Alibaba Cloud. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesTairTairTair is a cloud native in-memory database service developed by Alibaba Cloud. |
976 | It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open-source Redis. Tair also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.This notebook shows how to use functionality related to the Tair vector database.To run, you should have a Tair instance up and running.from langchain.embeddings.fake import FakeEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Tairfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = FakeEmbeddings(size=128)Connect to Tair using the TAIR_URL environment variable export TAIR_URL="redis://{username}:{password}@{tair_address}:{tair_port}"or the keyword argument tair_url.Then store documents and embeddings into Tair.tair_url = "redis://localhost:6379"# drop first if index already existsTair.drop_index(tair_url=tair_url)vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url)Query similar documents.query = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)docs[0]Tair Hybrid Search Index build# drop first if index already existsTair.drop_index(tair_url=tair_url)vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url, index_params={"lexical_algorithm":"bm25"})Tair Hybrid Searchquery = "What did the president say about Ketanji Brown Jackson"# hybrid_ratio: 0.5 hybrid search, 0.9999 vector search, 0.0001 text searchkwargs = {"TEXT" : query, "hybrid_ratio" : 0.5}docs = vector_store.similarity_search(query, **kwargs)docs[0]PreviousSupabase (Postgres)NextTencent Cloud VectorDBCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 | Tair is a cloud native in-memory database service developed by Alibaba Cloud. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. ->: It provides rich data models and enterprise-grade capabilities to support your real-time online scenarios while maintaining full compatibility with open-source Redis. Tair also introduces persistent memory-optimized instances that are based on the new non-volatile memory (NVM) storage medium.This notebook shows how to use functionality related to the Tair vector database.To run, you should have a Tair instance up and running.from langchain.embeddings.fake import FakeEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Tairfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = FakeEmbeddings(size=128)Connect to Tair using the TAIR_URL environment variable export TAIR_URL="redis://{username}:{password}@{tair_address}:{tair_port}"or the keyword argument tair_url.Then store documents and embeddings into Tair.tair_url = "redis://localhost:6379"# drop first if index already existsTair.drop_index(tair_url=tair_url)vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url)Query similar documents.query = "What did the president say about Ketanji Brown Jackson"docs = vector_store.similarity_search(query)docs[0]Tair Hybrid Search Index build# drop first if index already existsTair.drop_index(tair_url=tair_url)vector_store = Tair.from_documents(docs, embeddings, tair_url=tair_url, index_params={"lexical_algorithm":"bm25"})Tair Hybrid Searchquery = "What did the president say about Ketanji Brown Jackson"# hybrid_ratio: 0.5 hybrid search, 0.9999 vector search, 0.0001 text searchkwargs = {"TEXT" : query, "hybrid_ratio" : 0.5}docs = vector_store.similarity_search(query, **kwargs)docs[0]PreviousSupabase (Postgres)NextTencent Cloud VectorDBCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 |
977 | © 2023 LangChain, Inc. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. | Tair is a cloud native in-memory database service developed by Alibaba Cloud. ->: © 2023 LangChain, Inc. |
978 | Supabase (Postgres) | ü¶úÔ∏èüîó Langchain | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: Supabase (Postgres) | ü¶úÔ∏èüîó Langchain |
979 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesSupabase (Postgres)On this pageSupabase (Postgres)Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.PostgreSQL also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.This notebook shows how to use Supabase and pgvector as your VectorStore.To run this notebook, please ensure:the pgvector extension is enabledyou have installed the supabase-py packagethat you have created a match_documents function in your databasethat you have a documents table in your public schema similar to the one below.The following function determines cosine similarity, but you can adjust to your needs.-- Enable the pgvector extension to work with embedding vectorscreate extension if not exists vector;-- Create a table to store your documentscreate table documents ( id uuid primary key, | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesSupabase (Postgres)On this pageSupabase (Postgres)Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks.PostgreSQL also known as Postgres, is a free and open-source relational database management system (RDBMS) emphasizing extensibility and SQL compliance.This notebook shows how to use Supabase and pgvector as your VectorStore.To run this notebook, please ensure:the pgvector extension is enabledyou have installed the supabase-py packagethat you have created a match_documents function in your databasethat you have a documents table in your public schema similar to the one below.The following function determines cosine similarity, but you can adjust to your needs.-- Enable the pgvector extension to work with embedding vectorscreate extension if not exists vector;-- Create a table to store your documentscreate table documents ( id uuid primary key, |
980 | table documents ( id uuid primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector (1536) -- 1536 works for OpenAI embeddings, change if needed );-- Create a function to search for documentscreate function match_documents ( query_embedding vector (1536), filter jsonb default '{}') returns table ( id uuid, content text, metadata jsonb, similarity float) language plpgsql as $$#variable_conflict use_columnbegin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding;end;$$;# with pippip install supabase# with conda# !conda install -c conda-forge supabaseWe 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:")os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")# If you're storing your Supabase and OpenAI API keys in a .env file, you can load them with dotenvfrom dotenv import load_dotenvload_dotenv()First we'll create a Supabase client and instantiate a OpenAI embeddings class.import osfrom supabase.client import Client, create_clientfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import SupabaseVectorStoresupabase_url = os.environ.get("SUPABASE_URL")supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")supabase: Client = create_client(supabase_url, supabase_key)embeddings = OpenAIEmbeddings()Next we'll load and parse some data for our vector store (skip if you already have documents with embeddings stored in your DB).from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: table documents ( id uuid primary key, content text, -- corresponds to Document.pageContent metadata jsonb, -- corresponds to Document.metadata embedding vector (1536) -- 1536 works for OpenAI embeddings, change if needed );-- Create a function to search for documentscreate function match_documents ( query_embedding vector (1536), filter jsonb default '{}') returns table ( id uuid, content text, metadata jsonb, similarity float) language plpgsql as $$#variable_conflict use_columnbegin return query select id, content, metadata, 1 - (documents.embedding <=> query_embedding) as similarity from documents where metadata @> filter order by documents.embedding <=> query_embedding;end;$$;# with pippip install supabase# with conda# !conda install -c conda-forge supabaseWe 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:")os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")# If you're storing your Supabase and OpenAI API keys in a .env file, you can load them with dotenvfrom dotenv import load_dotenvload_dotenv()First we'll create a Supabase client and instantiate a OpenAI embeddings class.import osfrom supabase.client import Client, create_clientfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import SupabaseVectorStoresupabase_url = os.environ.get("SUPABASE_URL")supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")supabase: Client = create_client(supabase_url, supabase_key)embeddings = OpenAIEmbeddings()Next we'll load and parse some data for our vector store (skip if you already have documents with embeddings stored in your DB).from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = |
981 | = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)Insert the above documents into the database. Embeddings will automatically be generated for each document.vector_store = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase, table_name="documents", query_name="match_documents")Alternatively if you already have documents with embeddings in your database, simply instantiate a new SupabaseVectorStore directly:vector_store = SupabaseVectorStore(embedding=embeddings, client=supabase, table_name="documents", query_name="match_documents")Finally, test it out by performing a similarity search:query = "What did the president say about Ketanji Brown Jackson"matched_docs = vector_store.similarity_search(query)print(matched_docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity search with score​The returned distance score is cosine distance. Therefore, a lower score is better.matched_docs = vector_store.similarity_search_with_relevance_scores(query)matched_docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)Insert the above documents into the database. Embeddings will automatically be generated for each document.vector_store = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase, table_name="documents", query_name="match_documents")Alternatively if you already have documents with embeddings in your database, simply instantiate a new SupabaseVectorStore directly:vector_store = SupabaseVectorStore(embedding=embeddings, client=supabase, table_name="documents", query_name="match_documents")Finally, test it out by performing a similarity search:query = "What did the president say about Ketanji Brown Jackson"matched_docs = vector_store.similarity_search(query)print(matched_docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity search with score​The returned distance score is cosine distance. Therefore, a lower score is better.matched_docs = vector_store.similarity_search_with_relevance_scores(query)matched_docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And |
982 | Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../../state_of_the_union.txt'}), 0.802509746274066)Retriever options​This section goes over different options for how to use SupabaseVectorStore as a retriever.Maximal Marginal Relevance Searches​In addition to using similarity search in the retriever object, you can also use mmr.retriever = vector_store.as_retriever(search_type="mmr")matched_docs = retriever.get_relevant_documents(query)for i, d in enumerate(matched_docs): print(f"\n## Document {i}\n") print(d.page_content) ## Document 0 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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 | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={'source': '../../../state_of_the_union.txt'}), 0.802509746274066)Retriever options​This section goes over different options for how to use SupabaseVectorStore as a retriever.Maximal Marginal Relevance Searches​In addition to using similarity search in the retriever object, you can also use mmr.retriever = vector_store.as_retriever(search_type="mmr")matched_docs = retriever.get_relevant_documents(query)for i, d in enumerate(matched_docs): print(f"\n## Document {i}\n") print(d.page_content) ## Document 0 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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 |
983 | 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. ## Document 1 One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. When they came home, many of the world’s fittest and best trained warriors were never the same. Headaches. Numbness. Dizziness. A cancer that would put them in a flag-draped coffin. I know. One of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can. Committed to military families like Danielle Robinson from Ohio. The widow of Sergeant First Class Heath Robinson. He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. Stationed near Baghdad, just yards from burn pits the size of football fields. Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter. ## Document 2 And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. But I want you to know that we | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: 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. ## Document 1 One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. When they came home, many of the world’s fittest and best trained warriors were never the same. Headaches. Numbness. Dizziness. A cancer that would put them in a flag-draped coffin. I know. One of those soldiers was my son Major Beau Biden. We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. But I’m committed to finding out everything we can. Committed to military families like Danielle Robinson from Ohio. The widow of Sergeant First Class Heath Robinson. He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. Stationed near Baghdad, just yards from burn pits the size of football fields. Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter. ## Document 2 And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. But I want you to know that we |
984 | alarming. But I want you to know that we are going to be okay. When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly. ## Document 3 We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. Officer Mora was 27 years old. Officer Rivera was 22. Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. I’ve worked on these issues a long time. I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.PreviousStarRocksNextTairSimilarity search with scoreRetriever optionsMaximal Marginal Relevance SearchesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. | Supabase is an open-source Firebase alternative. Supabase is built on top of PostgreSQL, which offers strong SQL querying capabilities and enables a simple interface with already-existing tools and frameworks. ->: alarming. But I want you to know that we are going to be okay. When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly. ## Document 3 We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. Officer Mora was 27 years old. Officer Rivera was 22. Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. I’ve worked on these issues a long time. I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.PreviousStarRocksNextTairSimilarity search with scoreRetriever optionsMaximal Marginal Relevance SearchesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
985 | DashVector | ü¶úÔ∏èüîó Langchain | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. ->: DashVector | ü¶úÔ∏èüîó Langchain |
986 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesDashVectorOn this pageDashVectorDashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.This notebook shows how to use functionality related to the DashVector vector database.To use DashVector, you must have an API key. | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesDashVectorOn this pageDashVectorDashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.This notebook shows how to use functionality related to the DashVector vector database.To use DashVector, you must have an API key. |
987 | Here are the installation instructions.Install​pip install dashvector dashscopeWe want to use DashScopeEmbeddings so we also have to get the Dashscope API Key.import osimport getpassos.environ["DASHVECTOR_API_KEY"] = getpass.getpass("DashVector API Key:")os.environ["DASHSCOPE_API_KEY"] = getpass.getpass("DashScope API Key:")Example​from langchain.embeddings.dashscope import DashScopeEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DashVectorfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = DashScopeEmbeddings()We can create DashVector from documents.dashvector = DashVector.from_documents(docs, embeddings)query = "What did the president say about Ketanji Brown Jackson"docs = dashvector.similarity_search(query)print(docs)We can add texts with meta datas and ids, and search with meta filter.texts = ["foo", "bar", "baz"]metadatas = [{"key": i} for i in range(len(texts))]ids = ["0", "1", "2"]dashvector.add_texts(texts, metadatas=metadatas, ids=ids)docs = dashvector.similarity_search("foo", filter="key = 2")print(docs) [Document(page_content='baz', metadata={'key': 2})]PreviousClickHouseNextDingoInstallExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. | DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements. ->: Here are the installation instructions.Install​pip install dashvector dashscopeWe want to use DashScopeEmbeddings so we also have to get the Dashscope API Key.import osimport getpassos.environ["DASHVECTOR_API_KEY"] = getpass.getpass("DashVector API Key:")os.environ["DASHSCOPE_API_KEY"] = getpass.getpass("DashScope API Key:")Example​from langchain.embeddings.dashscope import DashScopeEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DashVectorfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = DashScopeEmbeddings()We can create DashVector from documents.dashvector = DashVector.from_documents(docs, embeddings)query = "What did the president say about Ketanji Brown Jackson"docs = dashvector.similarity_search(query)print(docs)We can add texts with meta datas and ids, and search with meta filter.texts = ["foo", "bar", "baz"]metadatas = [{"key": i} for i in range(len(texts))]ids = ["0", "1", "2"]dashvector.add_texts(texts, metadatas=metadatas, ids=ids)docs = dashvector.similarity_search("foo", filter="key = 2")print(docs) [Document(page_content='baz', metadata={'key': 2})]PreviousClickHouseNextDingoInstallExampleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
988 | DocArray HnswSearch | ü¶úÔ∏èüîó Langchain | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. ->: DocArray HnswSearch | ü¶úÔ∏èüîó Langchain |
989 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesDocArray HnswSearchOn this pageDocArray HnswSearchDocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.This notebook shows how to use functionality related to the DocArrayHnswSearch.Setup‚ÄãUncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so.# !pip install "docarray[hnswlib]"# Get an OpenAI token: https://platform.openai.com/account/api-keys# import os# from getpass import getpass# OPENAI_API_KEY = getpass()# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEYUsing DocArrayHnswSearch‚Äãfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DocArrayHnswSearchfrom langchain.document_loaders import TextLoaderdocuments = | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesDocArray HnswSearchOn this pageDocArray HnswSearchDocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.This notebook shows how to use functionality related to the DocArrayHnswSearch.Setup‚ÄãUncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so.# !pip install "docarray[hnswlib]"# Get an OpenAI token: https://platform.openai.com/account/api-keys# import os# from getpass import getpass# OPENAI_API_KEY = getpass()# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEYUsing DocArrayHnswSearch‚Äãfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DocArrayHnswSearchfrom langchain.document_loaders import TextLoaderdocuments = |
990 | import TextLoaderdocuments = TextLoader("../../modules/state_of_the_union.txt").load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = DocArrayHnswSearch.from_documents( docs, embeddings, work_dir="hnswlib_store/", n_dim=1536)Similarity search​query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity search with score​The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. ->: import TextLoaderdocuments = TextLoader("../../modules/state_of_the_union.txt").load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = DocArrayHnswSearch.from_documents( docs, embeddings, work_dir="hnswlib_store/", n_dim=1536)Similarity search​query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. 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.Similarity search with score​The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional |
991 | \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={}), 0.36962226)import shutil# delete the dirshutil.rmtree("hnswlib_store")PreviousDingoNextDocArray InMemorySearchSetupUsing DocArrayHnswSearchSimilarity searchSimilarity search with scoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. | DocArrayHnswSearch is a lightweight Document Index implementation provided by Docarray that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite. ->: \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.', metadata={}), 0.36962226)import shutil# delete the dirshutil.rmtree("hnswlib_store")PreviousDingoNextDocArray InMemorySearchSetupUsing DocArrayHnswSearchSimilarity searchSimilarity search with scoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
992 | Xata | ü¶úÔ∏èüîó Langchain | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. ->: Xata | ü¶úÔ∏èüîó Langchain |
993 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesXataOn this pageXataXata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data.
Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors directly to Xata, and queries it for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Xata.This notebook guides you how to use Xata as a VectorStore.Setup‚ÄãCreate a database to use as a vector store‚ÄãIn the Xata UI create a new database. You can name it whatever you want, in this notepad we'll use langchain. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesXataOn this pageXataXata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data.
Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors directly to Xata, and queries it for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Xata.This notebook guides you how to use Xata as a VectorStore.Setup‚ÄãCreate a database to use as a vector store‚ÄãIn the Xata UI create a new database. You can name it whatever you want, in this notepad we'll use langchain. |
994 | Create a table, again you can name it anything, but we will use vectors. Add the following columns via the UI:content of type "Text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use. In this notebook we use OpenAI embeddings, which have 1536 dimensions.search of type "Text". This is used as a metadata column by this example.any other columns you want to use as metadata. They are populated from the Document.metadata object. For example, if in the Document.metadata object you have a title property, you can create a title column in the table and it will be populated.Let's first install our dependencies:pip install xata openai tiktoken langchainLet's load the OpenAI key to the environemnt. If you don't have one you can create an OpenAI account and create a key on this page.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Similarly, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something like this: https://demo-uni3q8.eu-west-1.xata.sh/db/langchain.api_key = getpass.getpass("Xata API key: ")db_url = input("Xata database URL (copy it from your DB settings):")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores.xata import XataVectorStoreCreate the Xata vector store‚ÄãLet's import our test dataset:loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()Now create the actual vector store, backed by the Xata table.vector_store = | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. ->: Create a table, again you can name it anything, but we will use vectors. Add the following columns via the UI:content of type "Text". This is used to store the Document.pageContent values.embedding of type "Vector". Use the dimension used by the model you plan to use. In this notebook we use OpenAI embeddings, which have 1536 dimensions.search of type "Text". This is used as a metadata column by this example.any other columns you want to use as metadata. They are populated from the Document.metadata object. For example, if in the Document.metadata object you have a title property, you can create a title column in the table and it will be populated.Let's first install our dependencies:pip install xata openai tiktoken langchainLet's load the OpenAI key to the environemnt. If you don't have one you can create an OpenAI account and create a key on this page.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Similarly, we need to get the environment variables for Xata. You can create a new API key by visiting your account settings. To find the database URL, go to the Settings page of the database that you have created. The database URL should look something like this: https://demo-uni3q8.eu-west-1.xata.sh/db/langchain.api_key = getpass.getpass("Xata API key: ")db_url = input("Xata database URL (copy it from your DB settings):")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderfrom langchain.vectorstores.xata import XataVectorStoreCreate the Xata vector store‚ÄãLet's import our test dataset:loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()Now create the actual vector store, backed by the Xata table.vector_store = |
995 | store, backed by the Xata table.vector_store = XataVectorStore.from_documents(docs, embeddings, api_key=api_key, db_url=db_url, table_name="vectors")After running the above command, if you go to the Xata UI, you should see the documents loaded together with their embeddings.Similarity Search​query = "What did the president say about Ketanji Brown Jackson"found_docs = vector_store.similarity_search(query)print(found_docs)Similarity Search with score (vector distance)​query = "What did the president say about Ketanji Brown Jackson"result = vector_store.similarity_search_with_score(query)for doc, score in result: print(f"document={doc}, score={score}")PreviousWeaviateNextZepSetupCreate a database to use as a vector storeCreate the Xata vector storeSimilarity SearchSimilarity Search with score (vector distance)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. | Xata is a serverless data platform, based on PostgreSQL. It provides a Python SDK for interacting with your database, and a UI for managing your data. ->: store, backed by the Xata table.vector_store = XataVectorStore.from_documents(docs, embeddings, api_key=api_key, db_url=db_url, table_name="vectors")After running the above command, if you go to the Xata UI, you should see the documents loaded together with their embeddings.Similarity Search​query = "What did the president say about Ketanji Brown Jackson"found_docs = vector_store.similarity_search(query)print(found_docs)Similarity Search with score (vector distance)​query = "What did the president say about Ketanji Brown Jackson"result = vector_store.similarity_search_with_score(query)for doc, score in result: print(f"document={doc}, score={score}")PreviousWeaviateNextZepSetupCreate a database to use as a vector storeCreate the Xata vector storeSimilarity SearchSimilarity Search with score (vector distance)CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. |
996 | Momento Vector Index (MVI) | ü¶úÔ∏èüîó Langchain | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. ->: Momento Vector Index (MVI) | ü¶úÔ∏èüîó Langchain |
997 | Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesMomento Vector Index (MVI)On this pageMomento Vector Index (MVI)MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs.To sign up and access MVI, visit the Momento Console.SetupInstall prerequisites‚ÄãYou will need:the momento package for interacting with MVI, andthe openai package for interacting with the OpenAI API.the tiktoken package for tokenizing text.pip install momento openai tiktokenEnter API keys‚Äãimport osimport getpassMomento: for indexing data‚ÄãVisit the Momento Console to get your API key.os.environ["MOMENTO_API_KEY"] = getpass.getpass("Momento API Key:")OpenAI: for text embeddings‚Äãos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Load your dataHere we use the example dataset from Langchain, the state of the union address.First we load | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesMomento Vector Index (MVI)On this pageMomento Vector Index (MVI)MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs.To sign up and access MVI, visit the Momento Console.SetupInstall prerequisites‚ÄãYou will need:the momento package for interacting with MVI, andthe openai package for interacting with the OpenAI API.the tiktoken package for tokenizing text.pip install momento openai tiktokenEnter API keys‚Äãimport osimport getpassMomento: for indexing data‚ÄãVisit the Momento Console to get your API key.os.environ["MOMENTO_API_KEY"] = getpass.getpass("Momento API Key:")OpenAI: for text embeddings‚Äãos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Load your dataHere we use the example dataset from Langchain, the state of the union address.First we load |
998 | the state of the union address.First we load relevant modules:from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import MomentoVectorIndexfrom langchain.document_loaders import TextLoaderThen we load the data:loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()len(documents) 1Note the data is one large file, hence there is only one document:len(documents[0].page_content) 38539Because this is one large text file, we split it into chunks for question answering. That way, user questions will be answered from the most relevant chunk.text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)len(docs) 42Index your dataIndexing your data is as simple as instantiating the MomentoVectorIndex object. Here we use the from_documents helper to both instantiate and index the data:vector_db = MomentoVectorIndex.from_documents( docs, OpenAIEmbeddings(), index_name="sotu")This connects to the Momento Vector Index service using your API key and indexes the data. If the index did not exist before, this process creates it for you. The data is now searchable.Query your dataAsk a question directly against the index​The most direct way to query the data is to search against the index. We can do that as follows using the VectorStore API:query = "What did the president say about Ketanji Brown Jackson"docs = vector_db.similarity_search(query)docs[0].page_content 'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. ->: the state of the union address.First we load relevant modules:from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import MomentoVectorIndexfrom langchain.document_loaders import TextLoaderThen we load the data:loader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()len(documents) 1Note the data is one large file, hence there is only one document:len(documents[0].page_content) 38539Because this is one large text file, we split it into chunks for question answering. That way, user questions will be answered from the most relevant chunk.text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)len(docs) 42Index your dataIndexing your data is as simple as instantiating the MomentoVectorIndex object. Here we use the from_documents helper to both instantiate and index the data:vector_db = MomentoVectorIndex.from_documents( docs, OpenAIEmbeddings(), index_name="sotu")This connects to the Momento Vector Index service using your API key and indexes the data. If the index did not exist before, this process creates it for you. The data is now searchable.Query your dataAsk a question directly against the index​The most direct way to query the data is to search against the index. We can do that as follows using the VectorStore API:query = "What did the president say about Ketanji Brown Jackson"docs = vector_db.similarity_search(query)docs[0].page_content 'Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, |
999 | the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.'While this does contain relevant information about Ketanji Brown Jackson, we don't have a concise, human-readable answer. We'll tackle that in the next section.Use an LLM to generate fluent answers​With the data indexed in MVI, we can integrate with any chain that leverages vector similarity search. Here we use the RetrievalQA chain to demonstrate how to answer questions from the indexed data.First we load the relevant modules:from langchain.chat_models import ChatOpenAIfrom langchain.chains import RetrievalQAThen we instantiate the retrieval QA chain:llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_db.as_retriever())qa_chain({"query": "What did the president say about Ketanji Brown Jackson?"}) {'query': 'What did the president say about Ketanji Brown Jackson?', 'result': "The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds and mentioned that she has received broad support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans."}Next StepsThat's it! You've now indexed your data and can query it using the Momento Vector Index. You can use the same index to query your data from any chain that supports vector similarity search.With Momento you can not only index your vector data, but also cache your API calls and store your chat message history. Check out the other Momento | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. | MVI: the most productive, easiest to use, serverless vector index for your data. To get started with MVI, simply sign up for an account. There's no need to handle infrastructure, manage servers, or be concerned about scaling. MVI is a service that scales automatically to meet your needs. ->: the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd 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.'While this does contain relevant information about Ketanji Brown Jackson, we don't have a concise, human-readable answer. We'll tackle that in the next section.Use an LLM to generate fluent answers​With the data indexed in MVI, we can integrate with any chain that leverages vector similarity search. Here we use the RetrievalQA chain to demonstrate how to answer questions from the indexed data.First we load the relevant modules:from langchain.chat_models import ChatOpenAIfrom langchain.chains import RetrievalQAThen we instantiate the retrieval QA chain:llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)qa_chain = RetrievalQA.from_chain_type(llm, retriever=vector_db.as_retriever())qa_chain({"query": "What did the president say about Ketanji Brown Jackson?"}) {'query': 'What did the president say about Ketanji Brown Jackson?', 'result': "The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to serve on the United States Supreme Court. He described her as one of the nation's top legal minds and mentioned that she has received broad support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans."}Next StepsThat's it! You've now indexed your data and can query it using the Momento Vector Index. You can use the same index to query your data from any chain that supports vector similarity search.With Momento you can not only index your vector data, but also cache your API calls and store your chat message history. Check out the other Momento |
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