Datasets:

harsh-lodha
/

lc-scraped

Dataset card Data Studio Files Files and versions Community

Split (1)

train · 4.66k rows

Subsets and Splits

Unnamed: 0 int64 0 4.66k	page content stringlengths 23 2k	description stringlengths 8 925	output stringlengths 38 2.93k
1,900	Using a Retriever \| ü¶úÔ∏èüîó Langchain	This example showcases question answering over an index.	This example showcases question answering over an index. ->: Using a Retriever \| ü¶úÔ∏èüîó Langchain
1,901	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)Using a RetrieverOn this pageUsing a RetrieverThis example showcases question answering over an index.from langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromaloader = TextLoader("../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_documents(texts, embeddings)qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."Chain Type‚ÄãYou can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed	This example showcases question answering over an index.	This example showcases question answering over an index. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKQA over structured dataSQLRetrieval-augmented generation (RAG)RAG over codeUsing a RetrieverRemembering chat historyAnalyze a single long documentAgent with retrieval toolText splitting by headerRAG over in-memory documentsRAG using local modelsDynamically select from multiple retrieversRetrieving from multiple sourcesCiting retrieval sourcesRetrieve from vector stores directlyInteracting with APIsChatbotsExtractionSummarizationTaggingWeb scrapingSynthetic data generationGraph queryingRetrieval-augmented generation (RAG)Using a RetrieverOn this pageUsing a RetrieverThis example showcases question answering over an index.from langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoaderfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.llms import OpenAIfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromaloader = TextLoader("../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()docsearch = Chroma.from_documents(texts, embeddings)qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."Chain Type‚ÄãYou can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed
1,902	use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see this notebook.There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQA chain with the combine_documents_chain parameter. For example:from langchain.chains.question_answering import load_qa_chainqa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and	This example showcases question answering over an index.	This example showcases question answering over an index. ->: use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see this notebook.There are two ways to load different chain types. First, you can specify the chain type argument in the from_chain_type method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to map_reduce.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in this notebook) and then pass that directly to the the RetrievalQA chain with the combine_documents_chain parameter. For example:from langchain.chains.question_answering import load_qa_chainqa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and
1,903	to former judges appointed by Democrats and Republicans."Custom Prompts‚ÄãYou can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:"""PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "question"])chain_type_kwargs = {"prompt": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " Il presidente ha detto che Ketanji Brown Jackson √® una delle menti legali pi√π importanti del paese, che continuer√† l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."Vectorstore Retriever Options‚ÄãYou can adjust how documents are retrieved from your vectorstore depending on the specific task.There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the search_type parameter:docsearch.as_retriever(search_type="mmr")You can also modify the search by passing specific search arguments through the retriever to the search function, using the search_kwargs keyword argument.k defines how many documents are returned; defaults to 4.score_threshold allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.fetch_k determines the amount of documents to pass to the MMR algorithm; defaults to 20.lambda_mult controls the	This example showcases question answering over an index.	This example showcases question answering over an index. ->: to former judges appointed by Democrats and Republicans."Custom Prompts‚ÄãYou can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the base question answering chainfrom langchain.prompts import PromptTemplateprompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.{context}Question: {question}Answer in Italian:"""PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "question"])chain_type_kwargs = {"prompt": PROMPT}qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)query = "What did the president say about Ketanji Brown Jackson"qa.run(query) " Il presidente ha detto che Ketanji Brown Jackson √® una delle menti legali pi√π importanti del paese, che continuer√† l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."Vectorstore Retriever Options‚ÄãYou can adjust how documents are retrieved from your vectorstore depending on the specific task.There are two main ways to retrieve documents relevant to a query- Similarity Search and Max Marginal Relevance Search (MMR Search). Similarity Search is the default, but you can use MMR by adding the search_type parameter:docsearch.as_retriever(search_type="mmr")You can also modify the search by passing specific search arguments through the retriever to the search function, using the search_kwargs keyword argument.k defines how many documents are returned; defaults to 4.score_threshold allows you to set a minimum relevance for documents returned by the retriever, if you are using the "similarity_score_threshold" search type.fetch_k determines the amount of documents to pass to the MMR algorithm; defaults to 20.lambda_mult controls the
1,904	defaults to 20.lambda_mult controls the diversity of results returned by the MMR algorithm, with 1 being minimum diversity and 0 being maximum. Defaults to 0.5.filter allows you to define a filter on what documents should be retrieved, based on the documents' metadata. This has no effect if the Vectorstore doesn't store any metadata.Some examples for how these parameters can be used:# Retrieve more documents with higher diversity- useful if your dataset has many similar documentsdocsearch.as_retriever(search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25})# Fetch more documents for the MMR algorithm to consider, but only return the top 5docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50})# Only retrieve documents that have a relevance score above a certain thresholddocsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8})# Only get the single most similar document from the datasetdocsearch.as_retriever(search_kwargs={'k': 1})# Use a filter to only retrieve documents from a specific paperdocsearch.as_retriever(search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}})Return Source Documents‚ÄãAdditionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 30}), return_source_documents=True)query = "What did the president say about Ketanji Brown Jackson"result = qa({"query": query})result["result"] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and	This example showcases question answering over an index.	This example showcases question answering over an index. ->: defaults to 20.lambda_mult controls the diversity of results returned by the MMR algorithm, with 1 being minimum diversity and 0 being maximum. Defaults to 0.5.filter allows you to define a filter on what documents should be retrieved, based on the documents' metadata. This has no effect if the Vectorstore doesn't store any metadata.Some examples for how these parameters can be used:# Retrieve more documents with higher diversity- useful if your dataset has many similar documentsdocsearch.as_retriever(search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0.25})# Fetch more documents for the MMR algorithm to consider, but only return the top 5docsearch.as_retriever(search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50})# Only retrieve documents that have a relevance score above a certain thresholddocsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={'score_threshold': 0.8})# Only get the single most similar document from the datasetdocsearch.as_retriever(search_kwargs={'k': 1})# Use a filter to only retrieve documents from a specific paperdocsearch.as_retriever(search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}})Return Source Documents‚ÄãAdditionally, we can return the source documents used to answer the question by specifying an optional parameter when constructing the chain.qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(search_type="mmr", search_kwargs={'fetch_k': 30}), return_source_documents=True)query = "What did the president say about Ketanji Brown Jackson"result = qa({"query": query})result["result"] " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and
1,905	to former judges appointed by Democrats and Republicans."result["source_documents"] [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='',	This example showcases question answering over an index.	This example showcases question answering over an index. ->: to former judges appointed by Democrats and Republicans."result["source_documents"] [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='',
1,906	and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic	This example showcases question answering over an index.	This example showcases question answering over an index. ->: and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic
1,907	increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]Alternatively, if our document have a "source" metadata key, we can use the RetrievalQAWithSourcesChain to cite our sources:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))])from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.llms import OpenAIchain = 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.\n', 'sources': '31-pl'}PreviousRAG over codeNextRemembering chat historyChain TypeCustom PromptsVectorstore Retriever OptionsReturn Source DocumentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	This example showcases question answering over an index.	This example showcases question answering over an index. ->: increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]Alternatively, if our document have a "source" metadata key, we can use the RetrievalQAWithSourcesChain to cite our sources:docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))])from langchain.chains import RetrievalQAWithSourcesChainfrom langchain.llms import OpenAIchain = 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.\n', 'sources': '31-pl'}PreviousRAG over codeNextRemembering chat historyChain TypeCustom PromptsVectorstore Retriever OptionsReturn Source DocumentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,908	StarRocks \| ü¶úÔ∏èüîó Langchain	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: StarRocks \| ü¶úÔ∏èüîó Langchain
1,909	Skip to main contentü¶úÔ∏èüîó 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 storesStarRocksOn this pageStarRocksStarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: Skip to main contentü¶úÔ∏èüîó 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 storesStarRocksOn this pageStarRocksStarRocks is a High-Performance Analytical Database.
1,910	StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.Usually StarRocks is categorized into OLAP, and it has showed excellent performance in ClickBench ‚Äî a Benchmark For Analytical DBMS. Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.Here we'll show how to use the StarRocks Vector Store.Setup‚Äã#!pip install pymysqlSet update_vectordb = False at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import StarRocksfrom langchain.vectorstores.starrocks import StarRocksSettingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitter, TokenTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import VectorDBQAfrom langchain.document_loaders import DirectoryLoaderfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoader, UnstructuredMarkdownLoaderupdate_vectordb = False /Users/dirlt/utils/py3env/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (5.1.0)/charset_normalizer (2.0.9) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "Load docs and split them into tokens‚ÄãLoad all markdown files under the docs directoryfor starrocks documents, you can clone repo from https://github.com/StarRocks/starrocks, and there is docs directory in it.loader = DirectoryLoader( "./docs", glob="*/.md", loader_cls=UnstructuredMarkdownLoader)documents = loader.load()Split docs into tokens, and set update_vectordb = True because there are new docs/tokens.# load text splitter and split docs into snippets of texttext_splitter = TokenTextSplitter(chunk_size=400,	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.Usually StarRocks is categorized into OLAP, and it has showed excellent performance in ClickBench ‚Äî a Benchmark For Analytical DBMS. Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.Here we'll show how to use the StarRocks Vector Store.Setup‚Äã#!pip install pymysqlSet update_vectordb = False at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import StarRocksfrom langchain.vectorstores.starrocks import StarRocksSettingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitter, TokenTextSplitterfrom langchain.llms import OpenAIfrom langchain.chains import VectorDBQAfrom langchain.document_loaders import DirectoryLoaderfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoader, UnstructuredMarkdownLoaderupdate_vectordb = False /Users/dirlt/utils/py3env/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (5.1.0)/charset_normalizer (2.0.9) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "Load docs and split them into tokens‚ÄãLoad all markdown files under the docs directoryfor starrocks documents, you can clone repo from https://github.com/StarRocks/starrocks, and there is docs directory in it.loader = DirectoryLoader( "./docs", glob="*/.md", loader_cls=UnstructuredMarkdownLoader)documents = loader.load()Split docs into tokens, and set update_vectordb = True because there are new docs/tokens.# load text splitter and split docs into snippets of texttext_splitter = TokenTextSplitter(chunk_size=400,
1,911	= TokenTextSplitter(chunk_size=400, chunk_overlap=50)split_docs = text_splitter.split_documents(documents)# tell vectordb to update text embeddingsupdate_vectordb = Truesplit_docs[-20] Document(page_content='Compile StarRocks with Docker\n\nThis topic describes how to compile StarRocks using Docker.\n\nOverview\n\nStarRocks provides development environment images for both Ubuntu 22.04 and CentOS 7.9. With the image, you can launch a Docker container and compile StarRocks in the container.\n\nStarRocks version and DEV ENV image\n\nDifferent branches of StarRocks correspond to different development environment images provided on StarRocks Docker Hub.\n\nFor Ubuntu 22.04:\n\n\| Branch name \| Image name \|\n \| --------------- \| ----------------------------------- \|\n \| main \| starrocks/dev-env-ubuntu:latest \|\n \| branch-3.0 \| starrocks/dev-env-ubuntu:3.0-latest \|\n \| branch-2.5 \| starrocks/dev-env-ubuntu:2.5-latest \|\n\nFor CentOS 7.9:\n\n\| Branch name \| Image name \|\n \| --------------- \| ------------------------------------ \|\n \| main \| starrocks/dev-env-centos7:latest \|\n \| branch-3.0 \| starrocks/dev-env-centos7:3.0-latest \|\n \| branch-2.5 \| starrocks/dev-env-centos7:2.5-latest \|\n\nPrerequisites\n\nBefore compiling StarRocks, make sure the following requirements are satisfied:\n\nHardware\n\n', metadata={'source': 'docs/developers/build-starrocks/Build_in_docker.md'})print("# docs = %d, # splits = %d" % (len(documents), len(split_docs))) # docs = 657, # splits = 2802Create vectordb instance‚ÄãUse StarRocks as vectordb‚Äãdef gen_starrocks(update_vectordb, embeddings, settings): if update_vectordb: docsearch = StarRocks.from_documents(split_docs, embeddings, config=settings) else: docsearch = StarRocks(embeddings, settings) return docsearchConvert tokens into embeddings and put them into vectordb‚ÄãHere we use StarRocks as vectordb, you can	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: = TokenTextSplitter(chunk_size=400, chunk_overlap=50)split_docs = text_splitter.split_documents(documents)# tell vectordb to update text embeddingsupdate_vectordb = Truesplit_docs[-20] Document(page_content='Compile StarRocks with Docker\n\nThis topic describes how to compile StarRocks using Docker.\n\nOverview\n\nStarRocks provides development environment images for both Ubuntu 22.04 and CentOS 7.9. With the image, you can launch a Docker container and compile StarRocks in the container.\n\nStarRocks version and DEV ENV image\n\nDifferent branches of StarRocks correspond to different development environment images provided on StarRocks Docker Hub.\n\nFor Ubuntu 22.04:\n\n\| Branch name \| Image name \|\n \| --------------- \| ----------------------------------- \|\n \| main \| starrocks/dev-env-ubuntu:latest \|\n \| branch-3.0 \| starrocks/dev-env-ubuntu:3.0-latest \|\n \| branch-2.5 \| starrocks/dev-env-ubuntu:2.5-latest \|\n\nFor CentOS 7.9:\n\n\| Branch name \| Image name \|\n \| --------------- \| ------------------------------------ \|\n \| main \| starrocks/dev-env-centos7:latest \|\n \| branch-3.0 \| starrocks/dev-env-centos7:3.0-latest \|\n \| branch-2.5 \| starrocks/dev-env-centos7:2.5-latest \|\n\nPrerequisites\n\nBefore compiling StarRocks, make sure the following requirements are satisfied:\n\nHardware\n\n', metadata={'source': 'docs/developers/build-starrocks/Build_in_docker.md'})print("# docs = %d, # splits = %d" % (len(documents), len(split_docs))) # docs = 657, # splits = 2802Create vectordb instance‚ÄãUse StarRocks as vectordb‚Äãdef gen_starrocks(update_vectordb, embeddings, settings): if update_vectordb: docsearch = StarRocks.from_documents(split_docs, embeddings, config=settings) else: docsearch = StarRocks(embeddings, settings) return docsearchConvert tokens into embeddings and put them into vectordb‚ÄãHere we use StarRocks as vectordb, you can
1,912	we use StarRocks as vectordb, you can configure StarRocks instance via StarRocksSettings.Configuring StarRocks instance is pretty much like configuring mysql instance. You need to specify:host/portusername(default: 'root')password(default: '')database(default: 'default')table(default: 'langchain')embeddings = OpenAIEmbeddings()# configure starrocks settings(host/port/user/pw/db)settings = StarRocksSettings()settings.port = 41003settings.host = "127.0.0.1"settings.username = "root"settings.password = ""settings.database = "zya"docsearch = gen_starrocks(update_vectordb, embeddings, settings)print(docsearch)update_vectordb = False Inserting data...: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 2802/2802 [02:26<00:00, 19.11it/s] zya.langchain @ 127.0.0.1:41003 username: root Table Schema: ---------------------------------------------------------------------------- \|name \|type \|key \| ---------------------------------------------------------------------------- \|id \|varchar(65533) \|true \| \|document \|varchar(65533) \|false \| \|embedding \|array<float> \|false \| \|metadata \|varchar(65533) \|false \| ---------------------------------------------------------------------------- Build QA and ask question to it‚Äãllm = OpenAI()qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=docsearch.as_retriever())query = "is profile enabled by default? if not, how to enable profile?"resp =	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: we use StarRocks as vectordb, you can configure StarRocks instance via StarRocksSettings.Configuring StarRocks instance is pretty much like configuring mysql instance. You need to specify:host/portusername(default: 'root')password(default: '')database(default: 'default')table(default: 'langchain')embeddings = OpenAIEmbeddings()# configure starrocks settings(host/port/user/pw/db)settings = StarRocksSettings()settings.port = 41003settings.host = "127.0.0.1"settings.username = "root"settings.password = ""settings.database = "zya"docsearch = gen_starrocks(update_vectordb, embeddings, settings)print(docsearch)update_vectordb = False Inserting data...: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 2802/2802 [02:26<00:00, 19.11it/s] zya.langchain @ 127.0.0.1:41003 username: root Table Schema: ---------------------------------------------------------------------------- \|name \|type \|key \| ---------------------------------------------------------------------------- \|id \|varchar(65533) \|true \| \|document \|varchar(65533) \|false \| \|embedding \|array<float> \|false \| \|metadata \|varchar(65533) \|false \| ---------------------------------------------------------------------------- Build QA and ask question to it‚Äãllm = OpenAI()qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=docsearch.as_retriever())query = "is profile enabled by default? if not, how to enable profile?"resp =
1,913	by default? if not, how to enable profile?"resp = qa.run(query)print(resp) No, profile is not enabled by default. To enable profile, set the variable `enable_profile` to `true` using the command `set enable_profile = true;`Previoussqlite-vssNextSupabase (Postgres)SetupLoad docs and split them into tokensCreate vectordb instanceUse StarRocks as vectordbConvert tokens into embeddings and put them into vectordbBuild QA and ask question to itCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	StarRocks is a High-Performance Analytical Database.	StarRocks is a High-Performance Analytical Database. ->: by default? if not, how to enable profile?"resp = qa.run(query)print(resp) No, profile is not enabled by default. To enable profile, set the variable `enable_profile` to `true` using the command `set enable_profile = true;`Previoussqlite-vssNextSupabase (Postgres)SetupLoad docs and split them into tokensCreate vectordb instanceUse StarRocks as vectordbConvert tokens into embeddings and put them into vectordbBuild QA and ask question to itCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,914	AwaDB \| ü¶úÔ∏èüîó Langchain	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications. ->: AwaDB \| ü¶úÔ∏èüîó Langchain
1,915	Skip to main contentü¶úÔ∏èüîó 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 storesAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook shows how to use functionality related to the AwaDB.pip install awadbfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import AwaDBfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)docs = text_splitter.split_documents(documents)db = AwaDB.from_documents(docs)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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.Similarity search with score‚ÄãThe returned distance score is between 0-1. 0 is dissimilar, 1 is the most	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications. ->: Skip to main contentü¶úÔ∏èüîó 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 storesAwaDBOn this pageAwaDBAwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.This notebook shows how to use functionality related to the AwaDB.pip install awadbfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import AwaDBfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)docs = text_splitter.split_documents(documents)db = AwaDB.from_documents(docs)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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.Similarity search with score‚ÄãThe returned distance score is between 0-1. 0 is dissimilar, 1 is the most
1,916	is between 0-1. 0 is dissimilar, 1 is the most similardocs = db.similarity_search_with_score(query)print(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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'}), 0.561813814013747)Restore the table created and added data before‚ÄãAwaDB automatically persists added document dataIf you can restore the table you created and added before, you can just do this as below:awadb_client = awadb.Client()ret = awadb_client.Load("langchain_awadb")if ret: print("awadb load table success")else: print("awadb load table failed")awadb load table successPreviousAtlasNextAzure Cosmos DBSimilarity search with scoreRestore the table created and added data beforeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications.	AwaDB is an AI Native database for the search and storage of embedding vectors used by LLM Applications. ->: is between 0-1. 0 is dissimilar, 1 is the most similardocs = db.similarity_search_with_score(query)print(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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'}), 0.561813814013747)Restore the table created and added data before‚ÄãAwaDB automatically persists added document dataIf you can restore the table you created and added before, you can just do this as below:awadb_client = awadb.Client()ret = awadb_client.Load("langchain_awadb")if ret: print("awadb load table success")else: print("awadb load table failed")awadb load table successPreviousAtlasNextAzure Cosmos DBSimilarity search with scoreRestore the table created and added data beforeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,917	Neo4j Vector Index \| ü¶úÔ∏èüîó Langchain	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: Neo4j Vector Index \| ü¶úÔ∏èüîó Langchain
1,918	Skip to main contentü¶úÔ∏èüîó 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 storesNeo4j Vector IndexOn this pageNeo4j Vector IndexNeo4j is an open-source graph database with integrated support for vector similarity searchIt supports:approximate nearest neighbor searchEuclidean similarity and cosine similarityHybrid search combining vector and keyword searchesThis notebook shows how to use the Neo4j vector index (Neo4jVector).See the installation instruction.# Pip install necessary packagepip install neo4jpip install openaipip install tiktokenWe 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.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Neo4jVectorfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter =	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: Skip to main contentü¶úÔ∏èüîó 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 storesNeo4j Vector IndexOn this pageNeo4j Vector IndexNeo4j is an open-source graph database with integrated support for vector similarity searchIt supports:approximate nearest neighbor searchEuclidean similarity and cosine similarityHybrid search combining vector and keyword searchesThis notebook shows how to use the Neo4j vector index (Neo4jVector).See the installation instruction.# Pip install necessary packagepip install neo4jpip install openaipip install tiktokenWe 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.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Neo4jVectorfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter =
1,919	= loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()# Neo4jVector requires the Neo4j database credentialsurl = "bolt://localhost:7687"username = "neo4j"password = "pleaseletmein"# You can also use environment variables instead of directly passing named parameters#os.environ["NEO4J_URI"] = "bolt://localhost:7687"#os.environ["NEO4J_USERNAME"] = "neo4j"#os.environ["NEO4J_PASSWORD"] = "pleaseletmein"Similarity Search with Cosine Distance (Default)‚Äã# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.db = Neo4jVector.from_documents( docs, OpenAIEmbeddings(), url=url, username=username, password=password) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()query = "What did the president say about Ketanji Brown Jackson"docs_with_score = db.similarity_search_with_score(query, k=2)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.9099836349487305 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()# Neo4jVector requires the Neo4j database credentialsurl = "bolt://localhost:7687"username = "neo4j"password = "pleaseletmein"# You can also use environment variables instead of directly passing named parameters#os.environ["NEO4J_URI"] = "bolt://localhost:7687"#os.environ["NEO4J_USERNAME"] = "neo4j"#os.environ["NEO4J_PASSWORD"] = "pleaseletmein"Similarity Search with Cosine Distance (Default)‚Äã# The Neo4jVector Module will connect to Neo4j and create a vector index if needed.db = Neo4jVector.from_documents( docs, OpenAIEmbeddings(), url=url, username=username, password=password) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()query = "What did the president say about Ketanji Brown Jackson"docs_with_score = db.similarity_search_with_score(query, k=2)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80) -------------------------------------------------------------------------------- Score: 0.9099836349487305 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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
1,920	4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.9099686145782471 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence. --------------------------------------------------------------------------------Working with vectorstore‚ÄãAbove, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence. -------------------------------------------------------------------------------- -------------------------------------------------------------------------------- Score: 0.9099686145782471 Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence. --------------------------------------------------------------------------------Working with vectorstore‚ÄãAbove, we created a vectorstore from scratch. However, often times we want to work with an existing vectorstore.
1,921	In order to do that, we can initialize it directly.index_name = "vector" # default index namestore = Neo4jVector.from_existing_index( OpenAIEmbeddings(), url=url, username=username, password=password, index_name=index_name,) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()We can also initialize a vectorstore from existing graph using the from_existing_graph method. This method pulls relevant text information from the database, and calculates and stores the text embeddings back to the database.# First we create sample data in graphstore.query( "CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle'})") []# Now we initialize from existing graphexisting_graph = Neo4jVector.from_existing_graph( embedding=OpenAIEmbeddings(), url=url, username=username, password=password, index_name="person_index", node_label="Person", text_node_properties=["name", "location"], embedding_node_property="embedding", )result = existing_graph.similarity_search("Slovenia", k = 1) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()result[0] Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'hobby': 'Bicycle'})Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")]) ['187fc53a-5dde-11ee-ad78-1f6b05bf8513']docs_with_score = store.similarity_search_with_score("foo")docs_with_score[0] (Document(page_content='foo', metadata={}), 1.0)Hybrid search (vector + keyword)‚ÄãNeo4j integrates both vector and keyword indexes, which allows you to use a hybrid search approach# The Neo4jVector Module will connect to Neo4j and create a vector and	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: In order to do that, we can initialize it directly.index_name = "vector" # default index namestore = Neo4jVector.from_existing_index( OpenAIEmbeddings(), url=url, username=username, password=password, index_name=index_name,) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()We can also initialize a vectorstore from existing graph using the from_existing_graph method. This method pulls relevant text information from the database, and calculates and stores the text embeddings back to the database.# First we create sample data in graphstore.query( "CREATE (p:Person {name: 'Tomaz', location:'Slovenia', hobby:'Bicycle'})") []# Now we initialize from existing graphexisting_graph = Neo4jVector.from_existing_graph( embedding=OpenAIEmbeddings(), url=url, username=username, password=password, index_name="person_index", node_label="Person", text_node_properties=["name", "location"], embedding_node_property="embedding", )result = existing_graph.similarity_search("Slovenia", k = 1) /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()result[0] Document(page_content='\nname: Tomaz\nlocation: Slovenia', metadata={'hobby': 'Bicycle'})Add documents‚ÄãWe can add documents to the existing vectorstore.store.add_documents([Document(page_content="foo")]) ['187fc53a-5dde-11ee-ad78-1f6b05bf8513']docs_with_score = store.similarity_search_with_score("foo")docs_with_score[0] (Document(page_content='foo', metadata={}), 1.0)Hybrid search (vector + keyword)‚ÄãNeo4j integrates both vector and keyword indexes, which allows you to use a hybrid search approach# The Neo4jVector Module will connect to Neo4j and create a vector and
1,922	will connect to Neo4j and create a vector and keyword indices if needed.hybrid_db = Neo4jVector.from_documents( docs, OpenAIEmbeddings(), url=url, username=username, password=password, search_type="hybrid") /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()To load the hybrid search from existing indexes, you have to provide both the vector and keyword indicesindex_name = "vector" # default index namekeyword_index_name = "keyword" #default keyword index namestore = Neo4jVector.from_existing_index( OpenAIEmbeddings(), url=url, username=username, password=password, index_name=index_name, keyword_index_name=keyword_index_name, search_type="hybrid") /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()Retriever options‚ÄãThis section shows how to use Neo4jVector as a retriever.retriever = store.as_retriever()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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: will connect to Neo4j and create a vector and keyword indices if needed.hybrid_db = Neo4jVector.from_documents( docs, OpenAIEmbeddings(), url=url, username=username, password=password, search_type="hybrid") /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()To load the hybrid search from existing indexes, you have to provide both the vector and keyword indicesindex_name = "vector" # default index namekeyword_index_name = "keyword" #default keyword index namestore = Neo4jVector.from_existing_index( OpenAIEmbeddings(), url=url, username=username, password=password, index_name=index_name, keyword_index_name=keyword_index_name, search_type="hybrid") /home/tomaz/neo4j/langchain/libs/langchain/langchain/vectorstores/neo4j_vector.py:165: ExperimentalWarning: The configuration may change in the future. self._driver.verify_connectivity()Retriever options‚ÄãThis section shows how to use Neo4jVector as a retriever.retriever = store.as_retriever()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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs
1,923	legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'})Question Answering with Sources‚ÄãThis section goes over 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.chat_models import ChatOpenAIchain = RetrievalQAWithSourcesChain.from_chain_type( ChatOpenAI(temperature=0), chain_type="stuff", retriever=retriever)chain( {"question": "What did the president say about Justice Breyer"}, return_only_outputs=True,) {'answer': "The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court. He thanked him for his service and mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellence. \n", 'sources': '../../modules/state_of_the_union.txt'}PreviousMyScaleNextNucliaDBSimilarity Search with Cosine Distance (Default)Working with vectorstoreAdd documentsHybrid search (vector + keyword)Retriever optionsQuestion Answering with SourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Neo4j is an open-source graph database with integrated support for vector similarity search	Neo4j is an open-source graph database with integrated support for vector similarity search ->: legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt'})Question Answering with Sources‚ÄãThis section goes over 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.chat_models import ChatOpenAIchain = RetrievalQAWithSourcesChain.from_chain_type( ChatOpenAI(temperature=0), chain_type="stuff", retriever=retriever)chain( {"question": "What did the president say about Justice Breyer"}, return_only_outputs=True,) {'answer': "The president honored Justice Stephen Breyer, who is retiring from the United States Supreme Court. He thanked him for his service and mentioned that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellence. \n", 'sources': '../../modules/state_of_the_union.txt'}PreviousMyScaleNextNucliaDBSimilarity Search with Cosine Distance (Default)Working with vectorstoreAdd documentsHybrid search (vector + keyword)Retriever optionsQuestion Answering with SourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
1,924	Activeloop Deep Lake \| ü¶úÔ∏èüîó Langchain	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: Activeloop Deep Lake \| ü¶úÔ∏èüîó Langchain
1,925	Skip to main contentü¶úÔ∏èüîó 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 storesActiveloop Deep LakeOn this pageActiveloop Deep LakeActiveloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.This notebook showcases basic functionality related to Activeloop Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks. For more information, please see the Deep Lake documentation or api referenceSetting up‚Äãpip install openai 'deeplake[enterprise]' tiktokenExample provided by Activeloop‚ÄãIntegration with LangChain.Deep Lake locally‚Äãfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DeepLakeimport osimport	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: Skip to main contentü¶úÔ∏èüîó 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 storesActiveloop Deep LakeOn this pageActiveloop Deep LakeActiveloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.This notebook showcases basic functionality related to Activeloop Deep Lake. While Deep Lake can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks. For more information, please see the Deep Lake documentation or api referenceSetting up‚Äãpip install openai 'deeplake[enterprise]' tiktokenExample provided by Activeloop‚ÄãIntegration with LangChain.Deep Lake locally‚Äãfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DeepLakeimport osimport
1,926	import DeepLakeimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")activeloop_token = getpass.getpass("activeloop token:")embeddings = OpenAIEmbeddings()from 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()Create a local dataset‚ÄãCreate a dataset locally at ./deeplake/, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.db = DeepLake( dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)db.add_documents(docs)# or shorter# db = DeepLake.from_documents(docs, dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)Query dataset‚Äãquery = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query) Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initialization.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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: import DeepLakeimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")activeloop_token = getpass.getpass("activeloop token:")embeddings = OpenAIEmbeddings()from 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()Create a local dataset‚ÄãCreate a dataset locally at ./deeplake/, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so dataset and vector store are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, adjust the path accordingly.db = DeepLake( dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)db.add_documents(docs)# or shorter# db = DeepLake.from_documents(docs, dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)Query dataset‚Äãquery = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query) Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initialization.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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this
1,927	someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.Later, you can reload the dataset without recomputing embeddingsdb = DeepLake( dataset_path="./my_deeplake/", embedding=embeddings, read_only=True)docs = db.similarity_search(query) Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storageDeep Lake, for now, is single writer and multiple reader. Setting read_only=True helps to avoid acquiring the writer lock.Retrieval Question/Answering‚Äãfrom langchain.chains import RetrievalQAfrom langchain.llms import OpenAIChatqa = RetrievalQA.from_chain_type( llm=OpenAIChat(model="gpt-3.5-turbo"), chain_type="stuff", retriever=db.as_retriever(),) /home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI` warnings.warn(query = "What did the president say about Ketanji Brown Jackson"qa.run(query) 'The president said that Ketanji Brown Jackson is a former top litigator in private practice and a former federal public defender. She comes from a family of public school educators and police officers. She is a consensus builder and has received a broad range of support since being nominated.'Attribute based filtering in metadata‚ÄãLet's create another vector store containing metadata with the year the documents were created.import	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.Later, you can reload the dataset without recomputing embeddingsdb = DeepLake( dataset_path="./my_deeplake/", embedding=embeddings, read_only=True)docs = db.similarity_search(query) Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storageDeep Lake, for now, is single writer and multiple reader. Setting read_only=True helps to avoid acquiring the writer lock.Retrieval Question/Answering‚Äãfrom langchain.chains import RetrievalQAfrom langchain.llms import OpenAIChatqa = RetrievalQA.from_chain_type( llm=OpenAIChat(model="gpt-3.5-turbo"), chain_type="stuff", retriever=db.as_retriever(),) /home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI` warnings.warn(query = "What did the president say about Ketanji Brown Jackson"qa.run(query) 'The president said that Ketanji Brown Jackson is a former top litigator in private practice and a former federal public defender. She comes from a family of public school educators and police officers. She is a consensus builder and has received a broad range of support since being nominated.'Attribute based filtering in metadata‚ÄãLet's create another vector store containing metadata with the year the documents were created.import
1,928	with the year the documents were created.import randomfor d in docs: d.metadata["year"] = random.randint(2012, 2014)db = DeepLake.from_documents( docs, embeddings, dataset_path="./my_deeplake/", overwrite=True) Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (4, 1536) float32 None id text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None db.similarity_search( "What did the president say about Ketanji Brown Jackson", filter={"metadata": {"year": 2013}},) 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 4/4 [00:00<00:00, 2936.16it/s] [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: with the year the documents were created.import randomfor d in docs: d.metadata["year"] = random.randint(2012, 2014)db = DeepLake.from_documents( docs, embeddings, dataset_path="./my_deeplake/", overwrite=True) Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (4, 1536) float32 None id text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None db.similarity_search( "What did the president say about Ketanji Brown Jackson", filter={"metadata": {"year": 2013}},) 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 4/4 [00:00<00:00, 2936.16it/s] [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range
1,929	been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013})]Choosing distance function‚ÄãDistance function L2 for Euclidean, L1 for Nuclear, Max l-infinity distance, cos for	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013})]Choosing distance function‚ÄãDistance function L2 for Euclidean, L1 for Nuclear, Max l-infinity distance, cos for
1,930	L1 for Nuclear, Max l-infinity distance, cos for cosine similarity, dot for dot product db.similarity_search( "What did the president say about Ketanji Brown Jackson?", distance_metric="cos") [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: L1 for Nuclear, Max l-infinity distance, cos for cosine similarity, dot for dot product db.similarity_search( "What did the president say about Ketanji Brown Jackson?", distance_metric="cos") [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South
1,931	commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for
1,932	wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Maximal Marginal relevance‚ÄãUsing maximal marginal relevancedb.max_marginal_relevance_search( "What did the president say about Ketanji Brown Jackson?") [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Maximal Marginal relevance‚ÄãUsing maximal marginal relevancedb.max_marginal_relevance_search( "What did the president say about Ketanji Brown Jackson?") [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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='Tonight, I‚Äôm announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe‚Äôll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet‚Äôs pass
1,933	on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: on their skills not degrees. \n\nLet‚Äôs pass the Paycheck Fairness Act and paid leave. \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet‚Äôs increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill‚Äîour First Lady who teaches full-time‚Äîcalls America‚Äôs best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), 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‚Äôs been nominated, she‚Äôs received a broad range of support‚Äîfrom 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‚Äôve installed new technology like cutting-edge scanners to better detect drug smuggling. \n\nWe‚Äôve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \n\nWe‚Äôre putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe‚Äôre securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}), Document(page_content='And for our LGBTQ+ Americans, let‚Äôs finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn‚Äôt true. I signed 80
1,934	we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Delete dataset‚Äãdb.delete_dataset() and if delete fails you can also force deleteDeepLake.force_delete_by_path("./my_deeplake") Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory‚ÄãBy default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path and credentials when creating the vector store. Some paths require registration with Activeloop and creation of an API token that can be retrieved hereos.environ["ACTIVELOOP_TOKEN"] = activeloop_token# Embed and store the textsusername = "<USERNAME_OR_ORG>" # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing_python" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)ids = db.add_documents(docs) Your Deep Lake dataset has been successfully created! Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: we never agree, that isn‚Äôt true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we‚Äôll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I‚Äôm offering a Unity Agenda for the Nation. Four big things we can do together. \n\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]Delete dataset‚Äãdb.delete_dataset() and if delete fails you can also force deleteDeepLake.force_delete_by_path("./my_deeplake") Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory‚ÄãBy default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the corresponding path and credentials when creating the vector store. Some paths require registration with Activeloop and creation of an API token that can be retrieved hereos.environ["ACTIVELOOP_TOKEN"] = activeloop_token# Embed and store the textsusername = "<USERNAME_OR_ORG>" # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing_python" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)ids = db.add_documents(docs) Your Deep Lake dataset has been successfully created! Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id
1,935	(42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None 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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.tensor_db execution option‚ÄãIn order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps.# Embed and store the textsusername = "<USERNAME_OR_ORG>" # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing"docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None 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‚Äôre at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I‚Äôd like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer‚Äîan 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‚Äôs top legal minds, who will continue Justice Breyer‚Äôs legacy of excellence.tensor_db execution option‚ÄãIn order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps.# Embed and store the textsusername = "<USERNAME_OR_ORG>" # your username on app.activeloop.aidataset_path = f"hub://{username}/langchain_testing"docs = text_splitter.split_documents(documents)embedding = OpenAIEmbeddings()db = DeepLake(
1,936	= OpenAIEmbeddings()db = DeepLake( dataset_path=dataset_path, embedding=embeddings, overwrite=True, runtime={"tensor_db": True},)ids = db.add_documents(docs) Your Deep Lake dataset has been successfully created! \| Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None TQL Search‚ÄãFurthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL).search_id = db.vectorstore.dataset.id[0].numpy()search_id[0] '8a6ff326-3a85-11ee-b840-13905694aaaf'docs = db.similarity_search( query=None, tql=f"SELECT * WHERE id == '{search_id[0]}'",)db.vectorstore.summary() Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None Creating vector stores on AWS S3‚Äãdataset_path = f"s3://BUCKET/langchain_test" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.embedding = OpenAIEmbeddings()db = DeepLake.from_documents( docs, dataset_path=dataset_path, embedding=embeddings, overwrite=True, creds={ "aws_access_key_id": os.environ["AWS_ACCESS_KEY_ID"], "aws_secret_access_key":	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: = OpenAIEmbeddings()db = DeepLake( dataset_path=dataset_path, embedding=embeddings, overwrite=True, runtime={"tensor_db": True},)ids = db.add_documents(docs) Your Deep Lake dataset has been successfully created! \| Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None TQL Search‚ÄãFurthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL).search_id = db.vectorstore.dataset.id[0].numpy()search_id[0] '8a6ff326-3a85-11ee-b840-13905694aaaf'docs = db.similarity_search( query=None, tql=f"SELECT * WHERE id == '{search_id[0]}'",)db.vectorstore.summary() Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None Creating vector stores on AWS S3‚Äãdataset_path = f"s3://BUCKET/langchain_test" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.embedding = OpenAIEmbeddings()db = DeepLake.from_documents( docs, dataset_path=dataset_path, embedding=embeddings, overwrite=True, creds={ "aws_access_key_id": os.environ["AWS_ACCESS_KEY_ID"], "aws_secret_access_key":
1,937	"aws_secret_access_key": os.environ["AWS_SECRET_ACCESS_KEY"], "aws_session_token": os.environ["AWS_SESSION_TOKEN"], # Optional },) s3://hub-2.0-datasets-n/langchain_test loaded successfully. Evaluating ingest: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 1/1 [00:10<00:00 \ Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (4, 1536) float32 None ids text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None Deep Lake API‚Äãyou can access the Deep Lake dataset at db.vectorstore# get structure of the datasetdb.vectorstore.summary() Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None # get embeddings numpy arrayembeds = db.vectorstore.dataset.embedding.numpy()Transfer local dataset to cloud‚ÄãCopy already created dataset to the cloud. You can also transfer from cloud to local.import deeplakeusername = "davitbun" # your username on app.activeloop.aisource = f"hub://{username}/langchain_testing" # could be local, s3, gcs, etc.destination = f"hub://{username}/langchain_test_copy" # could be local, s3, gcs, etc.deeplake.deepcopy(src=source, dest=destination, overwrite=True) Copying dataset: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 56/56 [00:38<00:00 This dataset can be visualized in Jupyter Notebook by ds.visualize() or at	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: "aws_secret_access_key": os.environ["AWS_SECRET_ACCESS_KEY"], "aws_session_token": os.environ["AWS_SESSION_TOKEN"], # Optional },) s3://hub-2.0-datasets-n/langchain_test loaded successfully. Evaluating ingest: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 1/1 [00:10<00:00 \ Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (4, 1536) float32 None ids text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None Deep Lake API‚Äãyou can access the Deep Lake dataset at db.vectorstore# get structure of the datasetdb.vectorstore.summary() Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding embedding (42, 1536) float32 None id text (42, 1) str None metadata json (42, 1) str None text text (42, 1) str None # get embeddings numpy arrayembeds = db.vectorstore.dataset.embedding.numpy()Transfer local dataset to cloud‚ÄãCopy already created dataset to the cloud. You can also transfer from cloud to local.import deeplakeusername = "davitbun" # your username on app.activeloop.aisource = f"hub://{username}/langchain_testing" # could be local, s3, gcs, etc.destination = f"hub://{username}/langchain_test_copy" # could be local, s3, gcs, etc.deeplake.deepcopy(src=source, dest=destination, overwrite=True) Copying dataset: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 56/56 [00:38<00:00 This dataset can be visualized in Jupyter Notebook by ds.visualize() or at
1,938	in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy Your Deep Lake dataset has been successfully created! The dataset is private so make sure you are logged in! Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])db = DeepLake(dataset_path=destination, embedding=embeddings)db.add_documents(docs) This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy / hub://davitbun/langchain_test_copy loaded successfully. Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (4, 1536) float32 None ids text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None Evaluating ingest: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 1/1 [00:31<00:00 - Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (8, 1536) float32 None ids text (8, 1) str None metadata json (8, 1) str None text text (8, 1) str None ['ad42f3fe-e188-11ed-b66d-41c5f7b85421', 'ad42f3ff-e188-11ed-b66d-41c5f7b85421', 'ad42f400-e188-11ed-b66d-41c5f7b85421', 'ad42f401-e188-11ed-b66d-41c5f7b85421']PreviousVector storesNextAlibaba Cloud OpenSearchSetting upExample provided by ActiveloopDeep Lake locallyCreate a local datasetQuery datasetRetrieval	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy Your Deep Lake dataset has been successfully created! The dataset is private so make sure you are logged in! Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])db = DeepLake(dataset_path=destination, embedding=embeddings)db.add_documents(docs) This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy / hub://davitbun/langchain_test_copy loaded successfully. Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (4, 1536) float32 None ids text (4, 1) str None metadata json (4, 1) str None text text (4, 1) str None Evaluating ingest: 100%\|‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà‚ñà\| 1/1 [00:31<00:00 - Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text']) tensor htype shape dtype compression ------- ------- ------- ------- ------- embedding generic (8, 1536) float32 None ids text (8, 1) str None metadata json (8, 1) str None text text (8, 1) str None ['ad42f3fe-e188-11ed-b66d-41c5f7b85421', 'ad42f3ff-e188-11ed-b66d-41c5f7b85421', 'ad42f400-e188-11ed-b66d-41c5f7b85421', 'ad42f401-e188-11ed-b66d-41c5f7b85421']PreviousVector storesNextAlibaba Cloud OpenSearchSetting upExample provided by ActiveloopDeep Lake locallyCreate a local datasetQuery datasetRetrieval
1,939	a local datasetQuery datasetRetrieval Question/AnsweringAttribute based filtering in metadataChoosing distance functionMaximal Marginal relevanceDelete datasetDeep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memoryTQL SearchCreating vector stores on AWS S3Deep Lake APITransfer local dataset to cloudCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.	Activeloop Deep Lake as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes. ->: a local datasetQuery datasetRetrieval Question/AnsweringAttribute based filtering in metadataChoosing distance functionMaximal Marginal relevanceDelete datasetDeep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memoryTQL SearchCreating vector stores on AWS S3Deep Lake APITransfer local dataset to cloudCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.