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2,200	Vectara \| ü¶úÔ∏èüîó Langchain	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: Vectara \| ü¶úÔ∏èüîó Langchain
2,201	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverVectaraOn this pageVectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverVectaraOn this pageVectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation
2,202	(aka Retrieval-augmented-generation or RAG) applications.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Vectara vector store. SetupYou will need a Vectara account to use Vectara with LangChain. To get started, use the following steps (see our quickstart guide):Sign up for a Vectara account if you don't already have one. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the "Create Corpus" button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.Next you'll need to create API keys to access the corpus. Click on the "Authorization" tab in the corpus view and then the "Create API Key" button. Give your key a name, and choose whether you want query only or query+index for your key. Click "Create" and you now have an active API key. Keep this key confidential. To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: (aka Retrieval-augmented-generation or RAG) applications.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Vectara vector store. SetupYou will need a Vectara account to use Vectara with LangChain. To get started, use the following steps (see our quickstart guide):Sign up for a Vectara account if you don't already have one. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the "Create Corpus" button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.Next you'll need to create API keys to access the corpus. Click on the "Authorization" tab in the corpus view and then the "Create API Key" button. Give your key a name, and choose whether you want query only or query+index for your key. Click "Create" and you now have an active API key. Keep this key confidential. To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.
2,203	You can provide those to LangChain in two ways:Include in your environment these three variables: VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY.For example, you can set these variables using os.environ and getpass as follows:import osimport getpassos.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")Provide them as arguments when creating the Vectara vectorstore object:vectorstore = Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key )Note: The self-query retriever requires you to have lark installed (pip install lark). Connecting to Vectara from LangChain‚ÄãIn this example, we assume that you've created an account and a corpus, and added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY (created with permissions for both indexing and query) as environment variables.The corpus has 4 fields defined as metadata for filtering: year, director, rating, and genrefrom langchain.embeddings import FakeEmbeddingsfrom langchain.schema import Documentfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Vectarafrom langchain.document_loaders import TextLoaderfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfodocs = [ Document( page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"}, ), Document( page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...", metadata={"year": 2010, "director": "Christopher	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: You can provide those to LangChain in two ways:Include in your environment these three variables: VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY.For example, you can set these variables using os.environ and getpass as follows:import osimport getpassos.environ["VECTARA_CUSTOMER_ID"] = getpass.getpass("Vectara Customer ID:")os.environ["VECTARA_CORPUS_ID"] = getpass.getpass("Vectara Corpus ID:")os.environ["VECTARA_API_KEY"] = getpass.getpass("Vectara API Key:")Provide them as arguments when creating the Vectara vectorstore object:vectorstore = Vectara( vectara_customer_id=vectara_customer_id, vectara_corpus_id=vectara_corpus_id, vectara_api_key=vectara_api_key )Note: The self-query retriever requires you to have lark installed (pip install lark). Connecting to Vectara from LangChain‚ÄãIn this example, we assume that you've created an account and a corpus, and added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY (created with permissions for both indexing and query) as environment variables.The corpus has 4 fields defined as metadata for filtering: year, director, rating, and genrefrom langchain.embeddings import FakeEmbeddingsfrom langchain.schema import Documentfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Vectarafrom langchain.document_loaders import TextLoaderfrom langchain.llms import OpenAIfrom langchain.chains import ConversationalRetrievalChainfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfodocs = [ Document( page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose", metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"}, ), Document( page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...", metadata={"year": 2010, "director": "Christopher
2,204	metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2}, ), Document( page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea", metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6}, ), Document( page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them", metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3}, ), Document( page_content="Toys come alive and have a blast doing so", metadata={"year": 1995, "genre": "animated"}, ), Document( page_content="Three men walk into the Zone, three men walk out of the Zone", metadata={ "year": 1979, "rating": 9.9, "director": "Andrei Tarkovsky", "genre": "science fiction", }, ),]vectara = Vectara()for doc in docs: vectara.add_texts([doc.page_content], embedding=FakeEmbeddings(size=768), doc_metadata=doc.metadata)Creating our self-querying retriever‚ÄãNow we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [ AttributeInfo( name="genre", description="The genre of the movie", type="string or list[string]", ), AttributeInfo( name="year", description="The year the movie was released", type="integer", ), AttributeInfo( name="director", description="The name of the movie director", type="string", ), AttributeInfo( name="rating", description="A 1-10 rating for the movie", type="float" ),]document_content_description = "Brief summary	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2}, ), Document( page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea", metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6}, ), Document( page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them", metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3}, ), Document( page_content="Toys come alive and have a blast doing so", metadata={"year": 1995, "genre": "animated"}, ), Document( page_content="Three men walk into the Zone, three men walk out of the Zone", metadata={ "year": 1979, "rating": 9.9, "director": "Andrei Tarkovsky", "genre": "science fiction", }, ),]vectara = Vectara()for doc in docs: vectara.add_texts([doc.page_content], embedding=FakeEmbeddings(size=768), doc_metadata=doc.metadata)Creating our self-querying retriever‚ÄãNow we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents.from langchain.llms import OpenAIfrom langchain.retrievers.self_query.base import SelfQueryRetrieverfrom langchain.chains.query_constructor.base import AttributeInfometadata_field_info = [ AttributeInfo( name="genre", description="The genre of the movie", type="string or list[string]", ), AttributeInfo( name="year", description="The year the movie was released", type="integer", ), AttributeInfo( name="director", description="The name of the movie director", type="string", ), AttributeInfo( name="rating", description="A 1-10 rating for the movie", type="float" ),]document_content_description = "Brief summary
2,205	),]document_content_description = "Brief summary of a movie"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm( llm, vectara, document_content_description, metadata_field_info, verbose=True)Testing it out‚ÄãAnd now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs") /Users/ofer/dev/langchain/libs/langchain/langchain/chains/llm.py:278: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='dinosaur' filter=None limit=None [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'lang': 'eng', 'offset': '0', 'len': '76', 'year': '2010', 'director': 'Christopher Nolan', 'rating': '8.2', 'source': 'langchain'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'})]# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.5") query=' '	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: ),]document_content_description = "Brief summary of a movie"llm = OpenAI(temperature=0)retriever = SelfQueryRetriever.from_llm( llm, vectara, document_content_description, metadata_field_info, verbose=True)Testing it out‚ÄãAnd now we can try actually using our retriever!# This example only specifies a relevant queryretriever.get_relevant_documents("What are some movies about dinosaurs") /Users/ofer/dev/langchain/libs/langchain/langchain/chains/llm.py:278: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain. warnings.warn( query='dinosaur' filter=None limit=None [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'}), Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'lang': 'eng', 'offset': '0', 'len': '76', 'year': '2010', 'director': 'Christopher Nolan', 'rating': '8.2', 'source': 'langchain'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'})]# This example only specifies a filterretriever.get_relevant_documents("I want to watch a movie rated higher than 8.5") query=' '
2,206	a movie rated higher than 8.5") query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'})]# This example specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women") query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'lang': 'eng', 'offset': '0', 'len': '82', 'year': '2019', 'director': 'Greta Gerwig', 'rating': '8.3', 'source': 'langchain'})]# This example specifies a composite filterretriever.get_relevant_documents( "What's a highly rated (above 8.5) science fiction film?") query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'})]# This example specifies a query and composite filterretriever.get_relevant_documents( "What's a movie after 1990 but before 2005 that's all about toys,	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: a movie rated higher than 8.5") query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'lang': 'eng', 'offset': '0', 'len': '116', 'year': '2006', 'director': 'Satoshi Kon', 'rating': '8.6', 'source': 'langchain'})]# This example specifies a query and a filterretriever.get_relevant_documents("Has Greta Gerwig directed any movies about women") query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None [Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'lang': 'eng', 'offset': '0', 'len': '82', 'year': '2019', 'director': 'Greta Gerwig', 'rating': '8.3', 'source': 'langchain'})]# This example specifies a composite filterretriever.get_relevant_documents( "What's a highly rated (above 8.5) science fiction film?") query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None [Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'lang': 'eng', 'offset': '0', 'len': '60', 'year': '1979', 'rating': '9.9', 'director': 'Andrei Tarkovsky', 'genre': 'science fiction', 'source': 'langchain'})]# This example specifies a query and composite filterretriever.get_relevant_documents( "What's a movie after 1990 but before 2005 that's all about toys,
2,207	after 1990 but before 2005 that's all about toys, and preferably is animated") query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None [Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]Filter k‚ÄãWe can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm( llm, vectara, document_content_description, metadata_field_info, enable_limit=True, verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents("what are two movies about dinosaurs") query='dinosaur' filter=None limit=2 [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]PreviousTimescale Vector (Postgres) self-queryingNextWeaviateConnecting to Vectara from LangChainCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation	Vectara is a GenAI platform for developers. It provides a simple API to build Grounded Generation ->: after 1990 but before 2005 that's all about toys, and preferably is animated") query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None [Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]Filter k‚ÄãWe can also use the self query retriever to specify k: the number of documents to fetch.We can do this by passing enable_limit=True to the constructor.retriever = SelfQueryRetriever.from_llm( llm, vectara, document_content_description, metadata_field_info, enable_limit=True, verbose=True,)# This example only specifies a relevant queryretriever.get_relevant_documents("what are two movies about dinosaurs") query='dinosaur' filter=None limit=2 [Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'lang': 'eng', 'offset': '0', 'len': '66', 'year': '1993', 'rating': '7.7', 'genre': 'science fiction', 'source': 'langchain'}), Document(page_content='Toys come alive and have a blast doing so', metadata={'lang': 'eng', 'offset': '0', 'len': '41', 'year': '1995', 'genre': 'animated', 'source': 'langchain'})]PreviousTimescale Vector (Postgres) self-queryingNextWeaviateConnecting to Vectara from LangChainCreating our self-querying retrieverTesting it outFilter kCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,208	DocArray \| ü¶úÔ∏èüîó Langchain	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: DocArray \| ü¶úÔ∏èüîó Langchain
2,209	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversDocArrayOn this pageDocArrayDocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!This notebook is split into two sections. The first section offers an introduction to all five supported document index backends. It provides guidance on setting up and indexing each backend and also instructs you on how to build a DocArrayRetriever for finding relevant documents.	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversDocArrayOn this pageDocArrayDocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!This notebook is split into two sections. The first section offers an introduction to all five supported document index backends. It provides guidance on setting up and indexing each backend and also instructs you on how to build a DocArrayRetriever for finding relevant documents.
2,210	In the second section, we'll select one of these backends and illustrate how to use it through a basic example.Document Index Backends‚Äãfrom langchain.retrievers import DocArrayRetrieverfrom docarray import BaseDocfrom docarray.typing import NdArrayimport numpy as npfrom langchain.embeddings import FakeEmbeddingsimport randomembeddings = FakeEmbeddings(size=32)Before you start building the index, it's important to define your document schema. This determines what fields your documents will have and what type of data each field will hold.For this demonstration, we'll create a somewhat random schema containing 'title' (str), 'title_embedding' (numpy array), 'year' (int), and 'color' (str)class MyDoc(BaseDoc): title: str title_embedding: NdArray[32] year: int color: strInMemoryExactNNIndex‚ÄãInMemoryExactNNIndex stores all Documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.Learn more here: https://docs.docarray.org/user_guide/storing/index_in_memory/from docarray.index import InMemoryExactNNIndex# initialize the indexdb = InMemoryExactNNIndex[MyDoc]()# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"year": {"$lte": 90}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 56', metadata={'id': '1f33e58b6468ab722f3786b96b20afe6', 'year': 56, 'color': 'red'})]HnswDocumentIndex‚ÄãHnswDocumentIndex is a lightweight Document Index implementation that runs fully locally and is	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: In the second section, we'll select one of these backends and illustrate how to use it through a basic example.Document Index Backends‚Äãfrom langchain.retrievers import DocArrayRetrieverfrom docarray import BaseDocfrom docarray.typing import NdArrayimport numpy as npfrom langchain.embeddings import FakeEmbeddingsimport randomembeddings = FakeEmbeddings(size=32)Before you start building the index, it's important to define your document schema. This determines what fields your documents will have and what type of data each field will hold.For this demonstration, we'll create a somewhat random schema containing 'title' (str), 'title_embedding' (numpy array), 'year' (int), and 'color' (str)class MyDoc(BaseDoc): title: str title_embedding: NdArray[32] year: int color: strInMemoryExactNNIndex‚ÄãInMemoryExactNNIndex stores all Documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.Learn more here: https://docs.docarray.org/user_guide/storing/index_in_memory/from docarray.index import InMemoryExactNNIndex# initialize the indexdb = InMemoryExactNNIndex[MyDoc]()# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"year": {"$lte": 90}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 56', metadata={'id': '1f33e58b6468ab722f3786b96b20afe6', 'year': 56, 'color': 'red'})]HnswDocumentIndex‚ÄãHnswDocumentIndex is a lightweight Document Index implementation that runs fully locally and is
2,211	implementation that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.Learn more here: https://docs.docarray.org/user_guide/storing/index_hnswlib/from docarray.index import HnswDocumentIndex# initialize the indexdb = HnswDocumentIndex[MyDoc](work_dir="hnsw_index")# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"year": {"$lte": 90}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 28', metadata={'id': 'ca9f3f4268eec7c97a7d6e77f541cb82', 'year': 28, 'color': 'red'})]WeaviateDocumentIndex‚ÄãWeaviateDocumentIndex is a document index that is built upon Weaviate vector database.Learn more here: https://docs.docarray.org/user_guide/storing/index_weaviate/# There's a small difference with the Weaviate backend compared to the others.# Here, you need to 'mark' the field used for vector search with 'is_embedding=True'.# So, let's create a new schema for Weaviate that takes care of this requirement.from pydantic import Fieldclass WeaviateDoc(BaseDoc): title: str title_embedding: NdArray[32] = Field(is_embedding=True) year: int color: strfrom docarray.index import WeaviateDocumentIndex# initialize the indexdbconfig = WeaviateDocumentIndex.DBConfig(host="http://localhost:8080")db = WeaviateDocumentIndex[WeaviateDoc](db_config=dbconfig)# index datadb.index( [ MyDoc( title=f"My document {i}",	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: implementation that runs fully locally and is best suited for small- to medium-sized datasets. It stores vectors on disk in hnswlib, and stores all other data in SQLite.Learn more here: https://docs.docarray.org/user_guide/storing/index_hnswlib/from docarray.index import HnswDocumentIndex# initialize the indexdb = HnswDocumentIndex[MyDoc](work_dir="hnsw_index")# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"year": {"$lte": 90}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 28', metadata={'id': 'ca9f3f4268eec7c97a7d6e77f541cb82', 'year': 28, 'color': 'red'})]WeaviateDocumentIndex‚ÄãWeaviateDocumentIndex is a document index that is built upon Weaviate vector database.Learn more here: https://docs.docarray.org/user_guide/storing/index_weaviate/# There's a small difference with the Weaviate backend compared to the others.# Here, you need to 'mark' the field used for vector search with 'is_embedding=True'.# So, let's create a new schema for Weaviate that takes care of this requirement.from pydantic import Fieldclass WeaviateDoc(BaseDoc): title: str title_embedding: NdArray[32] = Field(is_embedding=True) year: int color: strfrom docarray.index import WeaviateDocumentIndex# initialize the indexdbconfig = WeaviateDocumentIndex.DBConfig(host="http://localhost:8080")db = WeaviateDocumentIndex[WeaviateDoc](db_config=dbconfig)# index datadb.index( [ MyDoc( title=f"My document {i}",
2,212	title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"path": ["year"], "operator": "LessThanEqual", "valueInt": "90"}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 17', metadata={'id': '3a5b76e85f0d0a01785dc8f9d965ce40', 'year': 17, 'color': 'red'})]ElasticDocIndex‚ÄãElasticDocIndex is a document index that is built upon ElasticSearchLearn more herefrom docarray.index import ElasticDocIndex# initialize the indexdb = ElasticDocIndex[MyDoc]( hosts="http://localhost:9200", index_name="docarray_retriever")# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"range": {"year": {"lte": 90}}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 46', metadata={'id': 'edbc721bac1c2ad323414ad1301528a4', 'year': 46, 'color': 'green'})]QdrantDocumentIndex‚ÄãQdrantDocumentIndex is a document index that is built upon Qdrant vector databaseLearn more herefrom docarray.index import QdrantDocumentIndexfrom qdrant_client.http import models as rest# initialize the indexqdrant_config =	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"path": ["year"], "operator": "LessThanEqual", "valueInt": "90"}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 17', metadata={'id': '3a5b76e85f0d0a01785dc8f9d965ce40', 'year': 17, 'color': 'red'})]ElasticDocIndex‚ÄãElasticDocIndex is a document index that is built upon ElasticSearchLearn more herefrom docarray.index import ElasticDocIndex# initialize the indexdb = ElasticDocIndex[MyDoc]( hosts="http://localhost:9200", index_name="docarray_retriever")# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = {"range": {"year": {"lte": 90}}}# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 46', metadata={'id': 'edbc721bac1c2ad323414ad1301528a4', 'year': 46, 'color': 'green'})]QdrantDocumentIndex‚ÄãQdrantDocumentIndex is a document index that is built upon Qdrant vector databaseLearn more herefrom docarray.index import QdrantDocumentIndexfrom qdrant_client.http import models as rest# initialize the indexqdrant_config =
2,213	as rest# initialize the indexqdrant_config = QdrantDocumentIndex.DBConfig(path=":memory:")db = QdrantDocumentIndex[MyDoc](qdrant_config)# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = rest.Filter( must=[ rest.FieldCondition( key="year", range=rest.Range( gte=10, lt=90, ), ) ]) WARNING:root:Payload indexes have no effect in the local Qdrant. Please use server Qdrant if you need payload indexes.# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 80', metadata={'id': '97465f98d0810f1f330e4ecc29b13d20', 'year': 80, 'color': 'blue'})]Movie Retrieval using HnswDocumentIndex‚Äãmovies = [ { "title": "Inception", "description": "A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.", "director": "Christopher Nolan", "rating": 8.8, }, { "title": "The Dark Knight", "description": "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.", "director": "Christopher Nolan", "rating": 9.0, }, { "title": "Interstellar", "description": "Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: as rest# initialize the indexqdrant_config = QdrantDocumentIndex.DBConfig(path=":memory:")db = QdrantDocumentIndex[MyDoc](qdrant_config)# index datadb.index( [ MyDoc( title=f"My document {i}", title_embedding=embeddings.embed_query(f"query {i}"), year=i, color=random.choice(["red", "green", "blue"]), ) for i in range(100) ])# optionally, you can create a filter queryfilter_query = rest.Filter( must=[ rest.FieldCondition( key="year", range=rest.Range( gte=10, lt=90, ), ) ]) WARNING:root:Payload indexes have no effect in the local Qdrant. Please use server Qdrant if you need payload indexes.# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="title_embedding", content_field="title", filters=filter_query,)# find the relevant documentdoc = retriever.get_relevant_documents("some query")print(doc) [Document(page_content='My document 80', metadata={'id': '97465f98d0810f1f330e4ecc29b13d20', 'year': 80, 'color': 'blue'})]Movie Retrieval using HnswDocumentIndex‚Äãmovies = [ { "title": "Inception", "description": "A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.", "director": "Christopher Nolan", "rating": 8.8, }, { "title": "The Dark Knight", "description": "When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.", "director": "Christopher Nolan", "rating": 9.0, }, { "title": "Interstellar", "description": "Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure
2,214	a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.", "director": "Christopher Nolan", "rating": 8.6, }, { "title": "Pulp Fiction", "description": "The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", "director": "Quentin Tarantino", "rating": 8.9, }, { "title": "Reservoir Dogs", "description": "When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.", "director": "Quentin Tarantino", "rating": 8.3, }, { "title": "The Godfather", "description": "An aging patriarch of an organized crime dynasty transfers control of his empire to his reluctant son.", "director": "Francis Ford Coppola", "rating": 9.2, },]import getpassimport osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key: ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑from docarray import BaseDoc, DocListfrom docarray.typing import NdArrayfrom langchain.embeddings.openai import OpenAIEmbeddings# define schema for your movie documentsclass MyDoc(BaseDoc): title: str description: str description_embedding: NdArray[1536] rating: float director: strembeddings = OpenAIEmbeddings()# get "description" embeddings, and create documentsdocs = DocList[MyDoc]( [ MyDoc( description_embedding=embeddings.embed_query(movie["description"]), **movie ) for movie in movies ])from docarray.index import HnswDocumentIndex# initialize the indexdb = HnswDocumentIndex[MyDoc](work_dir="movie_search")# add datadb.index(docs)Normal Retriever‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings,	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.", "director": "Christopher Nolan", "rating": 8.6, }, { "title": "Pulp Fiction", "description": "The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", "director": "Quentin Tarantino", "rating": 8.9, }, { "title": "Reservoir Dogs", "description": "When a simple jewelry heist goes horribly wrong, the surviving criminals begin to suspect that one of them is a police informant.", "director": "Quentin Tarantino", "rating": 8.3, }, { "title": "The Godfather", "description": "An aging patriarch of an organized crime dynasty transfers control of his empire to his reluctant son.", "director": "Francis Ford Coppola", "rating": 9.2, },]import getpassimport osos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key: ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑from docarray import BaseDoc, DocListfrom docarray.typing import NdArrayfrom langchain.embeddings.openai import OpenAIEmbeddings# define schema for your movie documentsclass MyDoc(BaseDoc): title: str description: str description_embedding: NdArray[1536] rating: float director: strembeddings = OpenAIEmbeddings()# get "description" embeddings, and create documentsdocs = DocList[MyDoc]( [ MyDoc( description_embedding=embeddings.embed_query(movie["description"]), **movie ) for movie in movies ])from docarray.index import HnswDocumentIndex# initialize the indexdb = HnswDocumentIndex[MyDoc](work_dir="movie_search")# add datadb.index(docs)Normal Retriever‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings,
2,215	index=db, embeddings=embeddings, search_field="description_embedding", content_field="description",)# find the relevant documentdoc = retriever.get_relevant_documents("movie about dreams")print(doc) [Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]Retriever with Filters‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="description_embedding", content_field="description", filters={"director": {"$eq": "Christopher Nolan"}}, top_k=2,)# find relevant documentsdocs = retriever.get_relevant_documents("space travel")print(docs) [Document(page_content='Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.', metadata={'id': 'ab704cc7ae8573dc617f9a5e25df022a', 'title': 'Interstellar', 'rating': 8.6, 'director': 'Christopher Nolan'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]Retriever with MMR search‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="description_embedding", content_field="description", filters={"rating": {"$gte": 8.7}}, search_type="mmr", top_k=3,)# find relevant documentsdocs = retriever.get_relevant_documents("action	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: index=db, embeddings=embeddings, search_field="description_embedding", content_field="description",)# find the relevant documentdoc = retriever.get_relevant_documents("movie about dreams")print(doc) [Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]Retriever with Filters‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="description_embedding", content_field="description", filters={"director": {"$eq": "Christopher Nolan"}}, top_k=2,)# find relevant documentsdocs = retriever.get_relevant_documents("space travel")print(docs) [Document(page_content='Interstellar explores the boundaries of human exploration as a group of astronauts venture through a wormhole in space. In their quest to ensure the survival of humanity, they confront the vastness of space-time and grapple with love and sacrifice.', metadata={'id': 'ab704cc7ae8573dc617f9a5e25df022a', 'title': 'Interstellar', 'rating': 8.6, 'director': 'Christopher Nolan'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'})]Retriever with MMR search‚Äãfrom langchain.retrievers import DocArrayRetriever# create a retrieverretriever = DocArrayRetriever( index=db, embeddings=embeddings, search_field="description_embedding", content_field="description", filters={"rating": {"$gte": 8.7}}, search_type="mmr", top_k=3,)# find relevant documentsdocs = retriever.get_relevant_documents("action
2,216	= retriever.get_relevant_documents("action movies")print(docs) [Document(page_content="The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", metadata={'id': 'e6aa313bbde514e23fbc80ab34511afd', 'title': 'Pulp Fiction', 'rating': 8.9, 'director': 'Quentin Tarantino'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'}), Document(page_content='When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.', metadata={'id': '91dec17d4272041b669fd113333a65f7', 'title': 'The Dark Knight', 'rating': 9.0, 'director': 'Christopher Nolan'})]PreviousCohere RerankerNextElasticSearch BM25Document Index BackendsInMemoryExactNNIndexHnswDocumentIndexWeaviateDocumentIndexElasticDocIndexQdrantDocumentIndexMovie Retrieval using HnswDocumentIndexNormal RetrieverRetriever with FiltersRetriever with MMR searchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps!	DocArray is a versatile, open-source tool for managing your multi-modal data. It lets you shape your data however you want, and offers the flexibility to store and search it using various document index backends. Plus, it gets even better - you can utilize your DocArray document index to create a DocArrayRetriever, and build awesome Langchain apps! ->: = retriever.get_relevant_documents("action movies")print(docs) [Document(page_content="The lives of two mob hitmen, a boxer, a gangster's wife, and a pair of diner bandits intertwine in four tales of violence and redemption.", metadata={'id': 'e6aa313bbde514e23fbc80ab34511afd', 'title': 'Pulp Fiction', 'rating': 8.9, 'director': 'Quentin Tarantino'}), Document(page_content='A thief who steals corporate secrets through the use of dream-sharing technology is given the task of planting an idea into the mind of a CEO.', metadata={'id': 'f1649d5b6776db04fec9a116bbb6bbe5', 'title': 'Inception', 'rating': 8.8, 'director': 'Christopher Nolan'}), Document(page_content='When the menace known as the Joker wreaks havoc and chaos on the people of Gotham, Batman must accept one of the greatest psychological and physical tests of his ability to fight injustice.', metadata={'id': '91dec17d4272041b669fd113333a65f7', 'title': 'The Dark Knight', 'rating': 9.0, 'director': 'Christopher Nolan'})]PreviousCohere RerankerNextElasticSearch BM25Document Index BackendsInMemoryExactNNIndexHnswDocumentIndexWeaviateDocumentIndexElasticDocIndexQdrantDocumentIndexMovie Retrieval using HnswDocumentIndexNormal RetrieverRetriever with FiltersRetriever with MMR searchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,217	ElasticSearch BM25 \| ü¶úÔ∏èüîó Langchain	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. ->: ElasticSearch BM25 \| ü¶úÔ∏èüîó Langchain
2,218	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversElasticSearch BM25On this pageElasticSearch BM25Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Sp√§rck Jones, and others.The name of the actual ranking function is BM25. The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF-like retrieval functions used in document retrieval.This notebook shows how to use a retriever that uses ElasticSearch and BM25.For more information on the details of BM25 see this blog post.#!pip install elasticsearchfrom langchain.retrievers import ElasticSearchBM25RetrieverCreate New Retriever‚Äãelasticsearch_url =	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversElasticSearch BM25On this pageElasticSearch BM25Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.In information retrieval, Okapi BM25 (BM is an abbreviation of best matching) is a ranking function used by search engines to estimate the relevance of documents to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Sp√§rck Jones, and others.The name of the actual ranking function is BM25. The fuller name, Okapi BM25, includes the name of the first system to use it, which was the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s. BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent TF-IDF-like retrieval functions used in document retrieval.This notebook shows how to use a retriever that uses ElasticSearch and BM25.For more information on the details of BM25 see this blog post.#!pip install elasticsearchfrom langchain.retrievers import ElasticSearchBM25RetrieverCreate New Retriever‚Äãelasticsearch_url =
2,219	New Retriever‚Äãelasticsearch_url = "http://localhost:9200"retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4")# Alternatively, you can load an existing index# import elasticsearch# elasticsearch_url="http://localhost:9200"# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), "langchain-index")Add texts (if necessary)‚ÄãWe can optionally add texts to the retriever (if they aren't already in there)retriever.add_texts(["foo", "bar", "world", "hello", "foo bar"]) ['cbd4cb47-8d9f-4f34-b80e-ea871bc49856', 'f3bd2e24-76d1-4f9b-826b-ec4c0e8c7365', '8631bfc8-7c12-48ee-ab56-8ad5f373676e', '8be8374c-3253-4d87-928d-d73550a2ecf0', 'd79f457b-2842-4eab-ae10-77aa420b53d7']Use Retriever‚ÄãWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={})]PreviousDocArrayNextGoogle Cloud Enterprise SearchCreate New RetrieverAdd texts (if necessary)Use RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.	Elasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. ->: New Retriever‚Äãelasticsearch_url = "http://localhost:9200"retriever = ElasticSearchBM25Retriever.create(elasticsearch_url, "langchain-index-4")# Alternatively, you can load an existing index# import elasticsearch# elasticsearch_url="http://localhost:9200"# retriever = ElasticSearchBM25Retriever(elasticsearch.Elasticsearch(elasticsearch_url), "langchain-index")Add texts (if necessary)‚ÄãWe can optionally add texts to the retriever (if they aren't already in there)retriever.add_texts(["foo", "bar", "world", "hello", "foo bar"]) ['cbd4cb47-8d9f-4f34-b80e-ea871bc49856', 'f3bd2e24-76d1-4f9b-826b-ec4c0e8c7365', '8631bfc8-7c12-48ee-ab56-8ad5f373676e', '8be8374c-3253-4d87-928d-d73550a2ecf0', 'd79f457b-2842-4eab-ae10-77aa420b53d7']Use Retriever‚ÄãWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={})]PreviousDocArrayNextGoogle Cloud Enterprise SearchCreate New RetrieverAdd texts (if necessary)Use RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,220	ChatGPT Plugin \| ü¶úÔ∏èüîó Langchain	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. ->: ChatGPT Plugin \| ü¶úÔ∏èüîó Langchain
2,221	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversChatGPT PluginOn this pageChatGPT PluginOpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.Plugins can allow ChatGPT to do things like:Retrieve real-time information; e.g., sports scores, stock prices, the latest news, etc.Retrieve knowledge-base information; e.g., company docs, personal notes, etc.Perform actions on behalf of the user; e.g., booking a flight, ordering food, etc.This notebook shows how to use the ChatGPT Retriever Plugin within LangChain.# STEP 1: Load# Load documents using LangChain's DocumentLoaders# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.htmlfrom langchain.document_loaders.csv_loader import CSVLoaderloader = CSVLoader( file_path="../../document_loaders/examples/example_data/mlb_teams_2012.csv")data = loader.load()# STEP 2: Convert# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-pluginfrom typing import Listfrom langchain.docstore.document import Documentimport jsondef write_json(path: str, documents: List[Document]) -> None: results = [{"text": doc.page_content} for doc in	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversChatGPT PluginOn this pageChatGPT PluginOpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.Plugins can allow ChatGPT to do things like:Retrieve real-time information; e.g., sports scores, stock prices, the latest news, etc.Retrieve knowledge-base information; e.g., company docs, personal notes, etc.Perform actions on behalf of the user; e.g., booking a flight, ordering food, etc.This notebook shows how to use the ChatGPT Retriever Plugin within LangChain.# STEP 1: Load# Load documents using LangChain's DocumentLoaders# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.htmlfrom langchain.document_loaders.csv_loader import CSVLoaderloader = CSVLoader( file_path="../../document_loaders/examples/example_data/mlb_teams_2012.csv")data = loader.load()# STEP 2: Convert# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-pluginfrom typing import Listfrom langchain.docstore.document import Documentimport jsondef write_json(path: str, documents: List[Document]) -> None: results = [{"text": doc.page_content} for doc in
2,222	results = [{"text": doc.page_content} for doc in documents] with open(path, "w") as f: json.dump(results, f, indent=2)write_json("foo.json", data)# STEP 3: Use# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_jsonUsing the ChatGPT Retriever Plugin‚ÄãOkay, so we've created the ChatGPT Retriever Plugin, but how do we actually use it?The below code walks through how to do that.We want to use ChatGPTPluginRetriever 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.retrievers import ChatGPTPluginRetrieverretriever = ChatGPTPluginRetriever(url="http://0.0.0.0:8000", bearer_token="foo")retriever.get_relevant_documents("alice's phone number") [Document(page_content="This is Alice's phone number: 123-456-7890", lookup_str='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0), Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0), Document(page_content='Team: Angels "Payroll (millions)": 154.49 "Wins": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': None, 'score': 0.697888613}, lookup_index=0)]PreviousChaindeskNextCohere RerankerUsing the ChatGPT Retriever	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. ->: results = [{"text": doc.page_content} for doc in documents] with open(path, "w") as f: json.dump(results, f, indent=2)write_json("foo.json", data)# STEP 3: Use# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_jsonUsing the ChatGPT Retriever Plugin‚ÄãOkay, so we've created the ChatGPT Retriever Plugin, but how do we actually use it?The below code walks through how to do that.We want to use ChatGPTPluginRetriever 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.retrievers import ChatGPTPluginRetrieverretriever = ChatGPTPluginRetriever(url="http://0.0.0.0:8000", bearer_token="foo")retriever.get_relevant_documents("alice's phone number") [Document(page_content="This is Alice's phone number: 123-456-7890", lookup_str='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0), Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0), Document(page_content='Team: Angels "Payroll (millions)": 154.49 "Wins": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': None, 'score': 0.697888613}, lookup_index=0)]PreviousChaindeskNextCohere RerankerUsing the ChatGPT Retriever
2,223	RerankerUsing the ChatGPT Retriever PluginCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions.	OpenAI plugins connect ChatGPT to third-party applications. These plugins enable ChatGPT to interact with APIs defined by developers, enhancing ChatGPT's capabilities and allowing it to perform a wide range of actions. ->: RerankerUsing the ChatGPT Retriever PluginCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,224	Self-querying retriever \| ğŸ¦œï¸�ğŸ”— Langchain	Learn about how the self-querying retriever works here.	Learn about how the self-querying retriever works here. ->: Self-querying retriever \| ğŸ¦œï¸�ğŸ”— Langchain
2,225	Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.ğŸ“„ï¸� Deep LakeDeep Lake is a multimodal database for building AI applicationsğŸ“„ï¸� ChromaChroma is a database for building AI applications with embeddings.ğŸ“„ï¸� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.ğŸ“„ï¸� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.ğŸ“„ï¸� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.ğŸ“„ï¸� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.ğŸ“„ï¸� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on	Learn about how the self-querying retriever works here.	Learn about how the self-querying retriever works here. ->: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverSelf-querying retrieverLearn about how the self-querying retriever works here.ğŸ“„ï¸� Deep LakeDeep Lake is a multimodal database for building AI applicationsğŸ“„ï¸� ChromaChroma is a database for building AI applications with embeddings.ğŸ“„ï¸� DashVectorDashVector is a fully managed vector DB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.ğŸ“„ï¸� ElasticsearchElasticsearch is a distributed, RESTful search and analytics engine.ğŸ“„ï¸� MilvusMilvus is a database that stores, indexes, and manages massive embedding vectors generated by deep neural networks and other machine learning (ML) models.ğŸ“„ï¸� MyScaleMyScale is an integrated vector database. You can access your database in SQL and also from here, LangChain.ğŸ“„ï¸� OpenSearchOpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. OpenSearch is a distributed search and analytics engine based on
2,226	distributed search and analytics engine based on Apache Lucene.ğŸ“„ï¸� PineconePinecone is a vector database with broad functionality.ğŸ“„ï¸� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.ğŸ“„ï¸� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.ğŸ“„ï¸� SupabaseSupabase is an open-source Firebase alternative.ğŸ“„ï¸� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.ğŸ“„ï¸� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded GenerationğŸ“„ï¸� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.	Learn about how the self-querying retriever works here.	Learn about how the self-querying retriever works here. ->: distributed search and analytics engine based on Apache Lucene.ğŸ“„ï¸� PineconePinecone is a vector database with broad functionality.ğŸ“„ï¸� QdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.ğŸ“„ï¸� RedisRedis is an open-source key-value store that can be used as a cache, message broker, database, vector database and more.ğŸ“„ï¸� SupabaseSupabase is an open-source Firebase alternative.ğŸ“„ï¸� Timescale Vector (Postgres) self-queryingTimescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.ğŸ“„ï¸� VectaraVectara is a GenAI platform for developers. It provides a simple API to build Grounded GenerationğŸ“„ï¸� WeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings fromPreviousSEC filingNextDeep LakeCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.
2,227	Google Cloud Enterprise Search \| ü¶úÔ∏èüîó Langchain	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: Google Cloud Enterprise Search \| ü¶úÔ∏èüîó Langchain
2,228	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversGoogle Cloud Enterprise SearchOn this pageGoogle Cloud Enterprise SearchEnterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.Gen AI App Builder lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google‚Äôs foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. Enterprise Search lets organizations quickly build generative AI powered search engines for customers and employees.Enterprise Search is underpinned by a variety of Google Search technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user‚Äôs query input. Enterprise Search also benefits from Google‚Äôs expertise in understanding how users search and factors in content relevance to order displayed results. Google Cloud offers Enterprise Search via Gen App Builder in Google Cloud Console and via an API for enterprise workflow integration. This notebook demonstrates how to configure Enterprise Search and use the Enterprise Search	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversGoogle Cloud Enterprise SearchOn this pageGoogle Cloud Enterprise SearchEnterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.Gen AI App Builder lets developers, even those with limited machine learning skills, quickly and easily tap into the power of Google‚Äôs foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications. Enterprise Search lets organizations quickly build generative AI powered search engines for customers and employees.Enterprise Search is underpinned by a variety of Google Search technologies, including semantic search, which helps deliver more relevant results than traditional keyword-based search techniques by using natural language processing and machine learning techniques to infer relationships within the content and intent from the user‚Äôs query input. Enterprise Search also benefits from Google‚Äôs expertise in understanding how users search and factors in content relevance to order displayed results. Google Cloud offers Enterprise Search via Gen App Builder in Google Cloud Console and via an API for enterprise workflow integration. This notebook demonstrates how to configure Enterprise Search and use the Enterprise Search
2,229	Enterprise Search and use the Enterprise Search retriever. The Enterprise Search retriever encapsulates the Generative AI App Builder Python client library and uses it to access the Enterprise Search Search Service API.Install pre-requisites‚ÄãYou need to install the google-cloud-discoverengine package to use the Enterprise Search retriever.pip install google-cloud-discoveryengineConfigure access to Google Cloud and Google Cloud Enterprise Search‚ÄãEnterprise Search is generally available for the allowlist (which means customers need to be approved for access) as of June 6, 2023. Contact your Google Cloud sales team for access and pricing details. We are previewing additional features that are coming soon to the generally available offering as part of our Trusted Tester program. Sign up for Trusted Tester and contact your Google Cloud sales team for an expedited trial.Before you can run this notebook you need to:Set or create a Google Cloud project and turn on Gen App BuilderCreate and populate an unstructured data storeSet credentials to access Enterprise Search APISet or create a Google Cloud poject and turn on Gen App Builder‚ÄãFollow the instructions in the Enterprise Search Getting Started guide to set/create a GCP project and enable Gen App Builder.Create and populate an unstructured data store‚ÄãUse Google Cloud Console to create an unstructured data store and populate it with the example PDF documents from the gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs Cloud Storage folder. Make sure to use the Cloud Storage (without metadata) option.Set credentials to access Enterprise Search API‚ÄãThe Gen App Builder client libraries used by the Enterprise Search retriever provide high-level language support for authenticating to Gen App Builder programmatically. Client libraries support Application Default Credentials (ADC); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: Enterprise Search and use the Enterprise Search retriever. The Enterprise Search retriever encapsulates the Generative AI App Builder Python client library and uses it to access the Enterprise Search Search Service API.Install pre-requisites‚ÄãYou need to install the google-cloud-discoverengine package to use the Enterprise Search retriever.pip install google-cloud-discoveryengineConfigure access to Google Cloud and Google Cloud Enterprise Search‚ÄãEnterprise Search is generally available for the allowlist (which means customers need to be approved for access) as of June 6, 2023. Contact your Google Cloud sales team for access and pricing details. We are previewing additional features that are coming soon to the generally available offering as part of our Trusted Tester program. Sign up for Trusted Tester and contact your Google Cloud sales team for an expedited trial.Before you can run this notebook you need to:Set or create a Google Cloud project and turn on Gen App BuilderCreate and populate an unstructured data storeSet credentials to access Enterprise Search APISet or create a Google Cloud poject and turn on Gen App Builder‚ÄãFollow the instructions in the Enterprise Search Getting Started guide to set/create a GCP project and enable Gen App Builder.Create and populate an unstructured data store‚ÄãUse Google Cloud Console to create an unstructured data store and populate it with the example PDF documents from the gs://cloud-samples-data/gen-app-builder/search/alphabet-investor-pdfs Cloud Storage folder. Make sure to use the Cloud Storage (without metadata) option.Set credentials to access Enterprise Search API‚ÄãThe Gen App Builder client libraries used by the Enterprise Search retriever provide high-level language support for authenticating to Gen App Builder programmatically. Client libraries support Application Default Credentials (ADC); the libraries look for credentials in a set of defined locations and use those credentials to authenticate requests to
2,230	use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.If running in Google Colab authenticate with google.colab.google.auth otherwise follow one of the supported methods to make sure that you Application Default Credentials are properly set.import sysif "google.colab" in sys.modules: from google.colab import auth as google_auth google_auth.authenticate_user()Configure and use the Enterprise Search retriever‚ÄãThe Enterprise Search retriever is implemented in the langchain.retriever.GoogleCloudEntepriseSearchRetriever class. The get_relevant_documents method returns a list of langchain.schema.Document documents where the page_content field of each document is populated the document content.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: use those credentials to authenticate requests to the API. With ADC, you can make credentials available to your application in a variety of environments, such as local development or production, without needing to modify your application code.If running in Google Colab authenticate with google.colab.google.auth otherwise follow one of the supported methods to make sure that you Application Default Credentials are properly set.import sysif "google.colab" in sys.modules: from google.colab import auth as google_auth google_auth.authenticate_user()Configure and use the Enterprise Search retriever‚ÄãThe Enterprise Search retriever is implemented in the langchain.retriever.GoogleCloudEntepriseSearchRetriever class. The get_relevant_documents method returns a list of langchain.schema.Document documents where the page_content field of each document is populated the document content.
2,231	Depending on the data type used in Enterprise search (structured or unstructured) the page_content field is populated as follows:Structured data source: either an extractive segment or an extractive answer that matches a query. The metadata field is populated with metadata (if any) of the document from which the segments or answers were extracted.Unstructured data source: a string json containing all the fields returned from the structured data source. The metadata field is populated with metadata (if any) of the document Only for Unstructured data sources:‚ÄãAn extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for website and unstructured search.An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for unstructured search.For more information about extractive segments and extractive answers refer to product documentation.When creating an instance of the retriever you can specify a number of parameters that control which Enterprise data store to access and how a natural language query is processed, including configurations for extractive answers and segments.The mandatory parameters are:‚Äãproject_id - Your Google Cloud PROJECT_IDsearch_engine_id - The ID of the data store you want to use. The project_id and search_engine_id parameters can be provided explicitly in the retriever's constructor or through the environment variables - PROJECT_ID and SEARCH_ENGINE_ID.You can also configure a	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: Depending on the data type used in Enterprise search (structured or unstructured) the page_content field is populated as follows:Structured data source: either an extractive segment or an extractive answer that matches a query. The metadata field is populated with metadata (if any) of the document from which the segments or answers were extracted.Unstructured data source: a string json containing all the fields returned from the structured data source. The metadata field is populated with metadata (if any) of the document Only for Unstructured data sources:‚ÄãAn extractive answer is verbatim text that is returned with each search result. It is extracted directly from the original document. Extractive answers are typically displayed near the top of web pages to provide an end user with a brief answer that is contextually relevant to their query. Extractive answers are available for website and unstructured search.An extractive segment is verbatim text that is returned with each search result. An extractive segment is usually more verbose than an extractive answer. Extractive segments can be displayed as an answer to a query, and can be used to perform post-processing tasks and as input for large language models to generate answers or new text. Extractive segments are available for unstructured search.For more information about extractive segments and extractive answers refer to product documentation.When creating an instance of the retriever you can specify a number of parameters that control which Enterprise data store to access and how a natural language query is processed, including configurations for extractive answers and segments.The mandatory parameters are:‚Äãproject_id - Your Google Cloud PROJECT_IDsearch_engine_id - The ID of the data store you want to use. The project_id and search_engine_id parameters can be provided explicitly in the retriever's constructor or through the environment variables - PROJECT_ID and SEARCH_ENGINE_ID.You can also configure a
2,232	and SEARCH_ENGINE_ID.You can also configure a number of optional parameters, including:max_documents - The maximum number of documents used to provide extractive segments or extractive answersget_extractive_answers - By default, the retriever is configured to return extractive segments. Set this field to True to return extractive answers. This is used only when engine_data_type set to 0 (unstructured) max_extractive_answer_count - The maximum number of extractive answers returned in each search result.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: and SEARCH_ENGINE_ID.You can also configure a number of optional parameters, including:max_documents - The maximum number of documents used to provide extractive segments or extractive answersget_extractive_answers - By default, the retriever is configured to return extractive segments. Set this field to True to return extractive answers. This is used only when engine_data_type set to 0 (unstructured) max_extractive_answer_count - The maximum number of extractive answers returned in each search result.
2,233	At most 5 answers will be returned. This is used only when engine_data_type set to 0 (unstructured) max_extractive_segment_count - The maximum number of extractive segments returned in each search result. Currently one segment will be returned. This is used only when engine_data_type set to 0 (unstructured) filter - The filter expression that allows you filter the search results based on the metadata associated with the documents in the searched data store. query_expansion_condition - Specification to determine under which conditions query expansion should occur. 0 - Unspecified query expansion condition. In this case, server behavior defaults to disabled. 1 - Disabled query expansion. Only the exact search query is used, even if SearchResponse.total_size is zero. 2 - Automatic query expansion built by the Search API.engine_data_type - Defines the enterprise search data type 0 - Unstructured data	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: At most 5 answers will be returned. This is used only when engine_data_type set to 0 (unstructured) max_extractive_segment_count - The maximum number of extractive segments returned in each search result. Currently one segment will be returned. This is used only when engine_data_type set to 0 (unstructured) filter - The filter expression that allows you filter the search results based on the metadata associated with the documents in the searched data store. query_expansion_condition - Specification to determine under which conditions query expansion should occur. 0 - Unspecified query expansion condition. In this case, server behavior defaults to disabled. 1 - Disabled query expansion. Only the exact search query is used, even if SearchResponse.total_size is zero. 2 - Automatic query expansion built by the Search API.engine_data_type - Defines the enterprise search data type 0 - Unstructured data
2,234	1 - Structured dataConfigure and use the retriever for unstructured data with extractve segments‚Äãfrom langchain.retrievers import GoogleCloudEnterpriseSearchRetrieverPROJECT_ID = "<YOUR PROJECT ID>" # Set to your Project IDSEARCH_ENGINE_ID = "<YOUR SEARCH ENGINE ID>" # Set to your data store IDretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3,)query = "What are Alphabet's Other Bets?"result = retriever.get_relevant_documents(query)for doc in result: print(doc)Configure and use the retriever for unstructured data with extractve answers‚Äãretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3, max_extractive_answer_count=3, get_extractive_answers=True,)query = "What are Alphabet's Other Bets?"result = retriever.get_relevant_documents(query)for doc in result: print(doc)Configure and use the retriever for structured data with extractve answers‚Äãretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3, engine_data_type=1)result = retriever.get_relevant_documents(query)for doc in result: print(doc)PreviousElasticSearch BM25NextGoogle DriveInstall pre-requisitesConfigure access to Google Cloud and Google Cloud Enterprise SearchSet or create a Google Cloud poject and turn on Gen App BuilderCreate and populate an unstructured data storeSet credentials to access Enterprise Search APIConfigure and use the Enterprise Search retrieverOnly for Unstructured data sources:The mandatory parameters are:Configure and use the retriever for unstructured data with extractve segmentsConfigure and use the retriever for unstructured data with extractve answersConfigure and use the retriever for structured data with extractve answersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud.	Enterprise Search is a part of the Generative AI App Builder suite of tools offered by Google Cloud. ->: 1 - Structured dataConfigure and use the retriever for unstructured data with extractve segments‚Äãfrom langchain.retrievers import GoogleCloudEnterpriseSearchRetrieverPROJECT_ID = "<YOUR PROJECT ID>" # Set to your Project IDSEARCH_ENGINE_ID = "<YOUR SEARCH ENGINE ID>" # Set to your data store IDretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3,)query = "What are Alphabet's Other Bets?"result = retriever.get_relevant_documents(query)for doc in result: print(doc)Configure and use the retriever for unstructured data with extractve answers‚Äãretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3, max_extractive_answer_count=3, get_extractive_answers=True,)query = "What are Alphabet's Other Bets?"result = retriever.get_relevant_documents(query)for doc in result: print(doc)Configure and use the retriever for structured data with extractve answers‚Äãretriever = GoogleCloudEnterpriseSearchRetriever( project_id=PROJECT_ID, search_engine_id=SEARCH_ENGINE_ID, max_documents=3, engine_data_type=1)result = retriever.get_relevant_documents(query)for doc in result: print(doc)PreviousElasticSearch BM25NextGoogle DriveInstall pre-requisitesConfigure access to Google Cloud and Google Cloud Enterprise SearchSet or create a Google Cloud poject and turn on Gen App BuilderCreate and populate an unstructured data storeSet credentials to access Enterprise Search APIConfigure and use the Enterprise Search retrieverOnly for Unstructured data sources:The mandatory parameters are:Configure and use the retriever for unstructured data with extractve segmentsConfigure and use the retriever for unstructured data with extractve answersConfigure and use the retriever for structured data with extractve answersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,235	Amazon Kendra \| ü¶úÔ∏èüîó Langchain Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversAmazon KendraOn this pageAmazon KendraAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.Using the Amazon Kendra Index Retriever‚Äã%pip install boto3import boto3from langchain.retrievers import AmazonKendraRetrieverCreate New Retrieverretriever = AmazonKendraRetriever(index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03")Now you can use retrieved documents from Kendra indexretriever.get_relevant_documents("what is langchain")PreviousRetrieversNextArcee RetrieverUsing the Amazon Kendra Index RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.	Amazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making. ->: Amazon Kendra \| ü¶úÔ∏èüîó Langchain Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversAmazon KendraOn this pageAmazon KendraAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.Using the Amazon Kendra Index Retriever‚Äã%pip install boto3import boto3from langchain.retrievers import AmazonKendraRetrieverCreate New Retrieverretriever = AmazonKendraRetriever(index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03")Now you can use retrieved documents from Kendra indexretriever.get_relevant_documents("what is langchain")PreviousRetrieversNextArcee RetrieverUsing the Amazon Kendra Index RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,236	kNN \| ü¶úÔ∏èüîó Langchain Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieverskNNOn this pagekNNIn statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.This notebook goes over how to use a retriever that under the hood uses an kNN.Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.htmlfrom langchain.retrievers import KNNRetrieverfrom langchain.embeddings import OpenAIEmbeddingsCreate New Retriever with Texts‚Äãretriever = KNNRetriever.from_texts( ["foo", "bar", "world", "hello", "foo bar"], OpenAIEmbeddings())Use Retriever‚ÄãWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='bar', metadata={})]PreviousKay.aiNextLOTR (Merger Retriever)Create New Retriever with TextsUse RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.	In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.	In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression. ->: kNN \| ü¶úÔ∏èüîó Langchain Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieverskNNOn this pagekNNIn statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, and later expanded by Thomas Cover. It is used for classification and regression.This notebook goes over how to use a retriever that under the hood uses an kNN.Largely based on https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.htmlfrom langchain.retrievers import KNNRetrieverfrom langchain.embeddings import OpenAIEmbeddingsCreate New Retriever with Texts‚Äãretriever = KNNRetriever.from_texts( ["foo", "bar", "world", "hello", "foo bar"], OpenAIEmbeddings())Use Retriever‚ÄãWe can now use the retriever!result = retriever.get_relevant_documents("foo")result [Document(page_content='foo', metadata={}), Document(page_content='foo bar', metadata={}), Document(page_content='hello', metadata={}), Document(page_content='bar', metadata={})]PreviousKay.aiNextLOTR (Merger Retriever)Create New Retriever with TextsUse RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
2,237	Metal \| ü¶úÔ∏èüîó Langchain	Metal is a managed service for ML Embeddings.	Metal is a managed service for ML Embeddings. ->: Metal \| ü¶úÔ∏èüîó Langchain
2,238	Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversMetalOn this pageMetalMetal is a managed service for ML Embeddings.This notebook shows how to use Metal's retriever.First, you will need to sign up for Metal and get an API key. You can do so here# !pip install metal_sdkfrom metal_sdk.metal import MetalAPI_KEY = ""CLIENT_ID = ""INDEX_ID = ""metal = Metal(API_KEY, CLIENT_ID, INDEX_ID);Ingest Documents‚ÄãYou only need to do this if you haven't already set up an indexmetal.index({"text": "foo1"})metal.index({"text": "foo"}) {'data': {'id': '642739aa7559b026b4430e42', 'text': 'foo', 'createdAt': '2023-03-31T19:51:06.748Z'}}Query‚ÄãNow that our index is set up, we can set up a retriever and start querying it.from langchain.retrievers import MetalRetrieverretriever = MetalRetriever(metal, params={"limit": 2})retriever.get_relevant_documents("foo1") [Document(page_content='foo1', metadata={'dist': '1.19209289551e-07', 'id': '642739a17559b026b4430e40', 'createdAt': '2023-03-31T19:50:57.853Z'}), Document(page_content='foo1', metadata={'dist': '4.05311584473e-06', 'id': '642738f67559b026b4430e3c', 'createdAt': '2023-03-31T19:48:06.769Z'})]PreviousLOTR (Merger Retriever)NextPinecone Hybrid SearchIngest DocumentsQueryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain,	Metal is a managed service for ML Embeddings.	Metal is a managed service for ML Embeddings. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversMetalOn this pageMetalMetal is a managed service for ML Embeddings.This notebook shows how to use Metal's retriever.First, you will need to sign up for Metal and get an API key. You can do so here# !pip install metal_sdkfrom metal_sdk.metal import MetalAPI_KEY = ""CLIENT_ID = ""INDEX_ID = ""metal = Metal(API_KEY, CLIENT_ID, INDEX_ID);Ingest Documents‚ÄãYou only need to do this if you haven't already set up an indexmetal.index({"text": "foo1"})metal.index({"text": "foo"}) {'data': {'id': '642739aa7559b026b4430e42', 'text': 'foo', 'createdAt': '2023-03-31T19:51:06.748Z'}}Query‚ÄãNow that our index is set up, we can set up a retriever and start querying it.from langchain.retrievers import MetalRetrieverretriever = MetalRetriever(metal, params={"limit": 2})retriever.get_relevant_documents("foo1") [Document(page_content='foo1', metadata={'dist': '1.19209289551e-07', 'id': '642739a17559b026b4430e40', 'createdAt': '2023-03-31T19:50:57.853Z'}), Document(page_content='foo1', metadata={'dist': '4.05311584473e-06', 'id': '642738f67559b026b4430e3c', 'createdAt': '2023-03-31T19:48:06.769Z'})]PreviousLOTR (Merger Retriever)NextPinecone Hybrid SearchIngest DocumentsQueryCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain,
2,239	¬© 2023 LangChain, Inc.	Metal is a managed service for ML Embeddings.	Metal is a managed service for ML Embeddings. ->: ¬© 2023 LangChain, Inc.