id
stringlengths 14
15
| text
stringlengths 23
2.21k
| source
stringlengths 52
97
|
|---|---|---|
72c4d1fb1040-0 | ElasticSearch | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesElasticSearchOn this pageElasticSearchElasticsearch 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.This notebook shows how to use functionality related to the Elasticsearch database.Installation​Check out Elasticsearch installation instructions.To connect to an Elasticsearch instance that does not require
login credentials, pass the Elasticsearch URL and index name along with the
embedding object to the constructor.Example: from langchain import ElasticVectorSearch from langchain.embeddings import OpenAIEmbeddings embedding = OpenAIEmbeddings() elastic_vector_search = ElasticVectorSearch( elasticsearch_url="http://localhost:9200", index_name="test_index", embedding=embedding )To connect to an Elasticsearch instance that requires login credentials,
including Elastic Cloud, use the Elasticsearch URL format
https://username:password@es_host:9243. For example, to connect to Elastic | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-2 | https://username:password@es_host:9243. For example, to connect to Elastic
Cloud, create the Elasticsearch URL with the required authentication details and
pass it to the ElasticVectorSearch constructor as the named parameter
elasticsearch_url.You can obtain your Elastic Cloud URL and login credentials by logging in to the
Elastic Cloud console at https://cloud.elastic.co, selecting your deployment, and
navigating to the "Deployments" page.To obtain your Elastic Cloud password for the default "elastic" user:Log in to the Elastic Cloud console at https://cloud.elastic.coGo to "Security" > "Users"Locate the "elastic" user and click "Edit"Click "Reset password"Follow the prompts to reset the passwordFormat for Elastic Cloud URLs is | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-3 | https://username:password@cluster_id.region_id.gcp.cloud.es.io:9243.Example: from langchain import ElasticVectorSearch from langchain.embeddings import OpenAIEmbeddings embedding = OpenAIEmbeddings() elastic_host = "cluster_id.region_id.gcp.cloud.es.io" elasticsearch_url = f"https://username:password@{elastic_host}:9243" elastic_vector_search = ElasticVectorSearch( elasticsearch_url=elasticsearch_url, index_name="test_index", embedding=embedding )pip install elasticsearchimport osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key: ········Example​from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import ElasticVectorSearchfrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = ElasticVectorSearch.from_documents( docs, embeddings, elasticsearch_url="http://localhost:9200")query = "What did the president say about Ketanji Brown Jackson"docs = | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-4 | = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. We cannot let this happen. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.ElasticKnnSearch ClassThe ElasticKnnSearch implements features allowing storing vectors and documents in Elasticsearch for use with approximate kNN searchpip install langchain elasticsearchfrom langchain.vectorstores.elastic_vector_search import ElasticKnnSearchfrom langchain.embeddings import ElasticsearchEmbeddingsimport elasticsearch# Initialize ElasticsearchEmbeddingsmodel_id = "<model_id_from_es>"dims = dim_countes_cloud_id = "ESS_CLOUD_ID"es_user = "es_user"es_password = "es_pass"test_index = "<index_name>"# input_field = "your_input_field" # if different from | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-5 | = "<index_name>"# input_field = "your_input_field" # if different from 'text_field'# Generate embedding objectembeddings = ElasticsearchEmbeddings.from_credentials( model_id, # input_field=input_field, es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password,)# Initialize ElasticKnnSearchknn_search = ElasticKnnSearch( es_cloud_id=es_cloud_id, es_user=es_user, es_password=es_password, index_name=test_index, embedding=embeddings,)Test adding vectors​# Test `add_texts` methodtexts = ["Hello, world!", "Machine learning is fun.", "I love Python."]knn_search.add_texts(texts)# Test `from_texts` methodnew_texts = [ "This is a new text.", "Elasticsearch is powerful.", "Python is great for data analysis.",]knn_search.from_texts(new_texts, dims=dims)Test knn search using query vector builder​# Test `knn_search` method with model_id and query_textquery = "Hello"knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)print(f"kNN search results for query '{query}': {knn_result}")print( f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")# Test `hybrid_search` methodquery = "Hello"hybrid_result = knn_search.knn_hybrid_search(query=query, model_id=model_id, k=2)print(f"Hybrid search results for query '{query}': {hybrid_result}")print( f"The | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-6 | search results for query '{query}': {hybrid_result}")print( f"The 'text' field value from the top hit is: '{hybrid_result['hits']['hits'][0]['_source']['text']}'")Test knn search using pre generated vector​# Generate embedding for testsquery_text = "Hello"query_embedding = embeddings.embed_query(query_text)print( f"Length of embedding: {len(query_embedding)}\nFirst two items in embedding: {query_embedding[:2]}")# Test knn Searchknn_result = knn_search.knn_search(query_vector=query_embedding, k=2)print( f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")# Test hybrid search - Requires both query_text and query_vectorknn_result = knn_search.knn_hybrid_search( query_vector=query_embedding, query=query_text, k=2)print( f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")Test source option​# Test `knn_search` method with model_id and query_textquery = "Hello"knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, source=False)assert not "_source" in knn_result["hits"]["hits"][0].keys()# Test `hybrid_search` methodquery = "Hello"hybrid_result = knn_search.knn_hybrid_search( query=query, model_id=model_id, k=2, source=False)assert not "_source" in hybrid_result["hits"]["hits"][0].keys()Test fields option​# Test `knn_search` method with model_id and query_textquery = | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
72c4d1fb1040-7 | Test `knn_search` method with model_id and query_textquery = "Hello"knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2, fields=["text"])assert "text" in knn_result["hits"]["hits"][0]["fields"].keys()# Test `hybrid_search` methodquery = "Hello"hybrid_result = knn_search.knn_hybrid_search( query=query, model_id=model_id, k=2, fields=["text"])assert "text" in hybrid_result["hits"]["hits"][0]["fields"].keys()Test with es client connection rather than cloud_id​# Create Elasticsearch connectiones_connection = Elasticsearch( hosts=["https://es_cluster_url:port"], basic_auth=("user", "password"))# Instantiate ElasticsearchEmbeddings using es_connectionembeddings = ElasticsearchEmbeddings.from_es_connection( model_id, es_connection,)# Initialize ElasticKnnSearchknn_search = ElasticKnnSearch( es_connection=es_connection, index_name=test_index, embedding=embeddings)# Test `knn_search` method with model_id and query_textquery = "Hello"knn_result = knn_search.knn_search(query=query, model_id=model_id, k=2)print(f"kNN search results for query '{query}': {knn_result}")print( f"The 'text' field value from the top hit is: '{knn_result['hits']['hits'][0]['_source']['text']}'")PreviousDocArrayInMemorySearchNextFAISSInstallationExampleTest adding vectorsTest knn search using query vector builderTest knn search using pre generated vectorTest source optionTest fields optionTest with es client connection rather than cloud_idCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/elasticsearch |
453d349ca215-0 | Typesense | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/typesense |
453d349ca215-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesTypesenseOn this pageTypesenseTypesense is an open source, in-memory search engine, that you can either self-host or run on Typesense Cloud.Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents.This notebook shows you how to use Typesense as your VectorStore.Let's first install our dependencies:pip install typesense openapi-schema-pydantic openai tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Typesensefrom langchain.document_loaders import TextLoaderLet's import our test | https://python.langchain.com/docs/integrations/vectorstores/typesense |
453d349ca215-2 | import Typesensefrom langchain.document_loaders import TextLoaderLet's import our test dataset:loader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()docsearch = Typesense.from_documents( docs, embeddings, typesense_client_params={ "host": "localhost", # Use xxx.a1.typesense.net for Typesense Cloud "port": "8108", # Use 443 for Typesense Cloud "protocol": "http", # Use https for Typesense Cloud "typesense_api_key": "xyz", "typesense_collection_name": "lang-chain", },)Similarity Search​query = "What did the president say about Ketanji Brown Jackson"found_docs = docsearch.similarity_search(query)print(found_docs[0].page_content)Typesense as a Retriever​Typesense, as all the other vector stores, is a LangChain Retriever, by using cosine similarity.retriever = docsearch.as_retriever()retrieverquery = "What did the president say about Ketanji Brown Jackson"retriever.get_relevant_documents(query)[0]PreviousTigrisNextVectaraSimilarity SearchTypesense as a RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/typesense |
5e906769f3b1-0 | LanceDB | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
5e906769f3b1-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesLanceDBLanceDBLanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings. Fully open source.This notebook shows how to use functionality related to the LanceDB vector database based on the Lance data format.pip install lancedbWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key. import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key: ········from langchain.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import LanceDBfrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()documents = CharacterTextSplitter().split_documents(documents)embeddings = OpenAIEmbeddings()import lancedbdb = | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
5e906769f3b1-2 | = OpenAIEmbeddings()import lancedbdb = lancedb.connect("/tmp/lancedb")table = db.create_table( "my_table", data=[ { "vector": embeddings.embed_query("Hello World"), "text": "Hello World", "id": "1", } ], mode="overwrite",)docsearch = LanceDB.from_documents(documents, embeddings, connection=table)query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search(query)print(docs[0].page_content) They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. Officer Mora was 27 years old. Officer Rivera was 22. Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. I’ve worked on these issues a long time. I know what works: Investing in crime preventionand community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. So let’s not abandon our streets. Or choose between safety and equal justice. | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
5e906769f3b1-3 | not abandon our streets. Or choose between safety and equal justice. Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers. That’s why the American Rescue Plan provided $350 Billion that cities, states, and counties can use to hire more police and invest in proven strategies like community violence interruption—trusted messengers breaking the cycle of violence and trauma and giving young people hope. We should all agree: The answer is not to Defund the police. The answer is to FUND the police with the resources and training they need to protect our communities. I ask Democrats and Republicans alike: Pass my budget and keep our neighborhoods safe. And I will keep doing everything in my power to crack down on gun trafficking and ghost guns you can buy online and make at home—they have no serial numbers and can’t be traced. And I ask Congress to pass proven measures to reduce gun violence. Pass universal background checks. Why should anyone on a terrorist list be able to purchase a weapon? Ban assault weapons and high-capacity magazines. Repeal the liability shield that makes gun manufacturers the only industry in America that can’t be sued. These laws don’t infringe on the Second Amendment. They save lives. The most fundamental right in America is the right to vote – and to have it counted. And | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
5e906769f3b1-4 | The most fundamental right in America is the right to vote – and to have it counted. And it’s under assault. In state after state, new laws have been passed, not only to suppress the vote, but to subvert entire elections. We cannot let this happen. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. We can do | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
5e906769f3b1-5 | secure the Border and fix the immigration system. We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.PreviousHologresNextMarqoCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/lancedb |
40fac49160a9-0 | Azure Cognitive Search | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
40fac49160a9-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesAzure Cognitive SearchOn this pageAzure Cognitive SearchAzure Cognitive Search (formerly known as Azure Search) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.Install Azure Cognitive Search SDK​pip install --index-url=https://pkgs.dev.azure.com/azure-sdk/public/_packaging/azure-sdk-for-python/pypi/simple/ azure-search-documents==11.4.0a20230509004pip install azure-identityImport required libraries​import os, jsonimport openaifrom dotenv import load_dotenvfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores.azuresearch import AzureSearchConfigure OpenAI settings​Configure the OpenAI settings to use Azure OpenAI or OpenAI# Load environment variables from a .env file using load_dotenv():load_dotenv()openai.api_type = "azure"openai.api_base = | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
40fac49160a9-2 | = "azure"openai.api_base = "YOUR_OPENAI_ENDPOINT"openai.api_version = "2023-05-15"openai.api_key = "YOUR_OPENAI_API_KEY"model: str = "text-embedding-ada-002"Configure vector store settings​Set up the vector store settings using environment variables:vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT"vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY"index_name: str = "langchain-vector-demo"Create embeddings and vector store instances​Create instances of the OpenAIEmbeddings and AzureSearch classes:embeddings: OpenAIEmbeddings = OpenAIEmbeddings(model=model, chunk_size=1)vector_store: AzureSearch = AzureSearch( azure_search_endpoint=vector_store_address, azure_search_key=vector_store_password, index_name=index_name, embedding_function=embeddings.embed_query,)Insert text and embeddings into vector store​Add texts and metadata from the JSON data to the vector store:from langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../../state_of_the_union.txt", encoding="utf-8")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)vector_store.add_documents(documents=docs)Perform a vector similarity search​Execute a pure vector similarity search using the similarity_search() method:# Perform a similarity searchdocs = vector_store.similarity_search( query="What did the president say about Ketanji Brown Jackson", k=3, search_type="similarity",)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
40fac49160a9-3 | Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Perform a Hybrid Search​Execute hybrid search using the hybrid_search() method:# Perform a hybrid searchdocs = vector_store.similarity_search( query="What did the president say about Ketanji Brown Jackson", k=3)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
40fac49160a9-4 | One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.PreviousAwaDBNextCassandraInstall Azure Cognitive Search SDKImport required librariesConfigure OpenAI settingsConfigure vector store settingsCreate embeddings and vector store instancesInsert text and embeddings into vector storePerform a vector similarity searchPerform a Hybrid SearchCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/azuresearch |
a8b424a58e25-0 | MongoDB Atlas | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
a8b424a58e25-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesMongoDB AtlasMongoDB AtlasMongoDB Atlas is a fully-managed cloud database available in AWS , Azure, and GCP. It now has support for native Vector Search on your MongoDB document data.This notebook shows how to use MongoDB Atlas Vector Search to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm.It uses the knnBeta Operator available in MongoDB Atlas Search. This feature is in Public Preview and available for evaluation purposes, to validate functionality, and to gather feedback from public preview users. It is not recommended for production deployments as we may introduce breaking changes.To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available. | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
a8b424a58e25-2 | To get started head over to Atlas here: quick start.pip install pymongoimport osimport getpassMONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")We want to use OpenAIEmbeddings so we need to set up our OpenAI API Key. os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")Now, let's create a vector search index on your cluster. In the below example, embedding is the name of the field that contains the embedding vector. Please refer to the documentation to get more details on how to define an Atlas Vector Search index. | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
a8b424a58e25-3 | You can name the index langchain_demo and create the index on the namespace lanchain_db.langchain_col. Finally, write the following definition in the JSON editor on MongoDB Atlas:{ "mappings": { "dynamic": true, "fields": { "embedding": { "dimensions": 1536, "similarity": "cosine", "type": "knnVector" } } }}from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import MongoDBAtlasVectorSearchfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()from pymongo import MongoClient# initialize MongoDB python clientclient = MongoClient(MONGODB_ATLAS_CLUSTER_URI)db_name = "langchain_db"collection_name = "langchain_col"collection = client[db_name][collection_name]index_name = "langchain_demo"# insert the documents in MongoDB Atlas with their embeddingdocsearch = MongoDBAtlasVectorSearch.from_documents( docs, embeddings, collection=collection, index_name=index_name)# perform a similarity search between the embedding of the query and the embeddings of the documentsquery = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search(query)print(docs[0].page_content)You can also instantiate the vector store directly and execute a query as follows:# initialize vector storevectorstore = MongoDBAtlasVectorSearch( collection, OpenAIEmbeddings(), index_name=index_name)# perform a similarity | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
a8b424a58e25-4 | collection, OpenAIEmbeddings(), index_name=index_name)# perform a similarity search between a query and the ingested documentsquery = "What did the president say about Ketanji Brown Jackson"docs = vectorstore.similarity_search(query)print(docs[0].page_content)PreviousMilvusNextMyScaleCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas |
822522377f3b-0 | OpenSearch | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
822522377f3b-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesOpenSearchOn this pageOpenSearchOpenSearch 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 Apache Lucene.This notebook shows how to use functionality related to the OpenSearch database.To run, you should have an OpenSearch instance up and running: see here for an easy Docker installation.similarity_search by default performs the Approximate k-NN Search which uses one of the several algorithms like lucene, nmslib, faiss recommended for
large datasets. To perform brute force search we have other search methods known as Script Scoring and Painless Scripting. | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
822522377f3b-2 | Check this for more details.Installation​Install the Python client.pip install opensearch-pyWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import OpenSearchVectorSearchfrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()similarity_search using Approximate k-NN​similarity_search using Approximate k-NN Search with Custom Parametersdocsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200")# If using the default Docker installation, use this instantiation instead:# docsearch = OpenSearchVectorSearch.from_documents(# docs,# embeddings,# opensearch_url="https://localhost:9200",# http_auth=("admin", "admin"),# use_ssl = False,# verify_certs = False,# ssl_assert_hostname = False,# ssl_show_warn = False,# )query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search(query, k=10)print(docs[0].page_content)docsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
822522377f3b-3 | OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", engine="faiss", space_type="innerproduct", ef_construction=256, m=48,)query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search(query)print(docs[0].page_content)similarity_search using Script Scoring​similarity_search using Script Scoring with Custom Parametersdocsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search( "What did the president say about Ketanji Brown Jackson", k=1, search_type="script_scoring",)print(docs[0].page_content)similarity_search using Painless Scripting​similarity_search using Painless Scripting with Custom Parametersdocsearch = OpenSearchVectorSearch.from_documents( docs, embeddings, opensearch_url="http://localhost:9200", is_appx_search=False)filter = {"bool": {"filter": {"term": {"text": "smuggling"}}}}query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.similarity_search( "What did the president say about Ketanji Brown Jackson", search_type="painless_scripting", space_type="cosineSimilarity", pre_filter=filter,)print(docs[0].page_content)Maximum marginal relevance search (MMR)​If you’d like to look up for some similar documents, but | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
822522377f3b-4 | you’d like to look up for some similar documents, but you’d also like to receive diverse results, MMR is method you should consider. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents.query = "What did the president say about Ketanji Brown Jackson"docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10, lambda_param=0.5)Using a preexisting OpenSearch instance​It's also possible to use a preexisting OpenSearch instance with documents that already have vectors present.# this is just an example, you would need to change these values to point to another opensearch instancedocsearch = OpenSearchVectorSearch( index_name="index-*", embedding_function=embeddings, opensearch_url="http://localhost:9200",)# you can specify custom field names to match the fields you're using to store your embedding, document text value, and metadatadocs = docsearch.similarity_search( "Who was asking about getting lunch today?", search_type="script_scoring", space_type="cosinesimil", vector_field="message_embedding", text_field="message", metadata_field="message_metadata",)PreviousMyScaleNextpg_embeddingInstallationsimilarity_search using Approximate k-NNsimilarity_search using Script Scoringsimilarity_search using Painless ScriptingMaximum marginal relevance search (MMR)Using a preexisting OpenSearch instanceCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/opensearch |
576a3cc140f3-0 | StarRocks | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesStarRocksOn this pageStarRocksStarRocks is a High-Performance Analytical Database. | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-2 | StarRocks is a next-gen sub-second MPP database for full analytics scenarios, including multi-dimensional analytics, real-time analytics and ad-hoc query.Usually StarRocks is categorized into OLAP, and it has showed excellent performance in ClickBench — a Benchmark For Analytical DBMS. Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.Here we'll show how to use the StarRocks Vector Store.Setup​#!pip install pymysqlSet update_vectordb = False at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docsfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import StarRocksfrom langchain.vectorstores.starrocks import StarRocksSettingsfrom langchain.vectorstores import Chromafrom langchain.text_splitter import CharacterTextSplitter, TokenTextSplitterfrom langchain import OpenAI, VectorDBQAfrom langchain.document_loaders import DirectoryLoaderfrom langchain.chains import RetrievalQAfrom langchain.document_loaders import TextLoader, UnstructuredMarkdownLoaderupdate_vectordb = False /Users/dirlt/utils/py3env/lib/python3.9/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.7) or chardet (5.1.0)/charset_normalizer (2.0.9) doesn't match a supported version! warnings.warn("urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported "Load docs and split them into tokens​Load all markdown files under the docs directoryfor starrocks documents, you can clone repo from https://github.com/StarRocks/starrocks, and there is docs directory in it.loader = DirectoryLoader( | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-3 | and there is docs directory in it.loader = DirectoryLoader( "./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader)documents = loader.load()Split docs into tokens, and set update_vectordb = True because there are new docs/tokens.# load text splitter and split docs into snippets of texttext_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50)split_docs = text_splitter.split_documents(documents)# tell vectordb to update text embeddingsupdate_vectordb = Truesplit_docs[-20] Document(page_content='Compile StarRocks with Docker\n\nThis topic describes how to compile StarRocks using Docker.\n\nOverview\n\nStarRocks provides development environment images for both Ubuntu 22.04 and CentOS 7.9. With the image, you can launch a Docker container and compile StarRocks in the container.\n\nStarRocks version and DEV ENV image\n\nDifferent branches of StarRocks correspond to different development environment images provided on StarRocks Docker Hub.\n\nFor Ubuntu 22.04:\n\n| Branch name | Image name |\n | --------------- | ----------------------------------- |\n | main | starrocks/dev-env-ubuntu:latest |\n | branch-3.0 | starrocks/dev-env-ubuntu:3.0-latest |\n | branch-2.5 | starrocks/dev-env-ubuntu:2.5-latest |\n\nFor CentOS 7.9:\n\n| Branch name | Image name |\n | --------------- | ------------------------------------ |\n | main | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-4 | |\n | --------------- | ------------------------------------ |\n | main | starrocks/dev-env-centos7:latest |\n | branch-3.0 | starrocks/dev-env-centos7:3.0-latest |\n | branch-2.5 | starrocks/dev-env-centos7:2.5-latest |\n\nPrerequisites\n\nBefore compiling StarRocks, make sure the following requirements are satisfied:\n\nHardware\n\n', metadata={'source': 'docs/developers/build-starrocks/Build_in_docker.md'})print("# docs = %d, # splits = %d" % (len(documents), len(split_docs))) # docs = 657, # splits = 2802Create vectordb instance​Use StarRocks as vectordb​def gen_starrocks(update_vectordb, embeddings, settings): if update_vectordb: docsearch = StarRocks.from_documents(split_docs, embeddings, config=settings) else: docsearch = StarRocks(embeddings, settings) return docsearchConvert tokens into embeddings and put them into vectordb​Here we use StarRocks as vectordb, you can configure StarRocks instance via StarRocksSettings.Configuring StarRocks instance is pretty much like configuring mysql instance. You need to specify:host/portusername(default: 'root')password(default: '')database(default: 'default')table(default: 'langchain')embeddings = OpenAIEmbeddings()# configure starrocks settings(host/port/user/pw/db)settings = StarRocksSettings()settings.port = | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-5 | settings(host/port/user/pw/db)settings = StarRocksSettings()settings.port = 41003settings.host = "127.0.0.1"settings.username = "root"settings.password = ""settings.database = "zya"docsearch = gen_starrocks(update_vectordb, embeddings, settings)print(docsearch)update_vectordb = False Inserting data...: | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-6 | 100%|██████████████████████████████████████████████████████████████████████████████████████████████████■| https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-7 | ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆâ–ˆ| | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-8 | 2802/2802 [02:26<00:00, 19.11it/s] zya.langchain @ 127.0.0.1:41003 username: root Table Schema: ---------------------------------------------------------------------------- |name |type |key | ---------------------------------------------------------------------------- |id |varchar(65533) |true | |document |varchar(65533) |false | |embedding |array<float> |false | |metadata |varchar(65533) |false | ---------------------------------------------------------------------------- Build QA and ask question to it​llm = OpenAI()qa = RetrievalQA.from_chain_type( | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
576a3cc140f3-9 | it​llm = OpenAI()qa = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=docsearch.as_retriever())query = "is profile enabled by default? if not, how to enable profile?"resp = qa.run(query)print(resp) No, profile is not enabled by default. To enable profile, set the variable `enable_profile` to `true` using the command `set enable_profile = true;`Previousscikit-learnNextSupabase (Postgres)SetupLoad docs and split them into tokensCreate vectordb instanceUse StarRocks as vectordbConvert tokens into embeddings and put them into vectordbBuild QA and ask question to itCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/starrocks |
5510abcc3dba-0 | Chroma | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesChromaOn this pageChromaChroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.Install Chroma with:pip install chromadbChroma runs in various modes. See below for examples of each integrated with LangChain.in-memory - in a python script or jupyter notebookin-memory with persistance - in a script or notebook and save/load to diskin a docker container - as a server running your local machine or in the cloudLike any other database, you can: .add .get .update.upsert.delete.peekand .query runs the similarity search.View full docs at docs. To access these methods directly, you can do ._collection_.method()Basic Example​In this basic example, we take the most recent State of the Union Address, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.# importfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-2 | then query it.# importfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Chromafrom langchain.document_loaders import TextLoader# load the document and split it into chunksloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()# split it into chunkstext_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)# create the open-source embedding functionembedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")# load it into Chromadb = Chroma.from_documents(docs, embedding_function)# query itquery = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)# print resultsprint(docs[0].page_content) /Users/jeff/.pyenv/versions/3.10.10/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-3 | you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Basic Example (including saving to disk)​Extending the previous example, if you want to save to disk, simply initialize the Chroma client and pass the directory where you want the data to be saved to. Caution: Chroma makes a best-effort to automatically save data to disk, however multiple in-memory clients can stomp each other's work. As a best practice, only have one client per path running at any given time.# save to diskdb2 = Chroma.from_documents(docs, embedding_function, persist_directory="./chroma_db")docs = db2.similarity_search(query)# load from diskdb3 = Chroma(persist_directory="./chroma_db", embedding_function=embedding_function)docs = db3.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-4 | most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Passing a Chroma Client into Langchain​You can also create a Chroma Client and pass it to LangChain. This is particularly useful if you want easier access to the underlying database.You can also specify the collection name that you want LangChain to use.import chromadbpersistent_client = chromadb.PersistentClient()collection = persistent_client.get_or_create_collection("collection_name")collection.add(ids=["1", "2", "3"], documents=["a", "b", "c"])langchain_chroma = Chroma( client=persistent_client, collection_name="collection_name", embedding_function=embedding_function,)print("There are", langchain_chroma._collection.count(), "in the collection") Add of existing embedding ID: 1 Add of existing embedding ID: 2 Add of existing embedding ID: 3 Add of existing embedding ID: 1 Add of existing embedding ID: 2 Add of existing embedding ID: 3 Add of existing embedding ID: 1 Insert of existing embedding ID: 1 Add of existing embedding ID: 2 Insert of existing embedding ID: 2 Add of existing embedding ID: 3 Insert of existing embedding ID: 3 There are 3 in the collectionBasic Example (using the Docker Container)​You can also run the Chroma Server in a Docker container separately, create a Client to | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-5 | can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LangChain. Chroma has the ability to handle multiple Collections of documents, but the LangChain interface expects one, so we need to specify the collection name. The default collection name used by LangChain is "langchain".Here is how to clone, build, and run the Docker Image:git clone git@github.com:chroma-core/chroma.gitdocker-compose up -d --build# create the chroma clientimport chromadbimport uuidfrom chromadb.config import Settingsclient = chromadb.HttpClient(settings=Settings(allow_reset=True))client.reset() # resets the databasecollection = client.create_collection("my_collection")for doc in docs: collection.add( ids=[str(uuid.uuid1())], metadatas=doc.metadata, documents=doc.page_content )# tell LangChain to use our client and collection namedb4 = Chroma(client=client, collection_name="my_collection")docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-6 | Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Update and Delete​While building toward a real application, you want to go beyond adding data, and also update and delete data. Chroma has users provide ids to simplify the bookkeeping here. ids can be the name of the file, or a combined has like filename_paragraphNumber, etc.Chroma supports all these operations - though some of them are still being integrated all the way through the LangChain interface. Additional workflow improvements will be added soon.Here is a basic example showing how to do various operations:# create simple idsids = [str(i) for i in range(1, len(docs) + 1)]# add dataexample_db = Chroma.from_documents(docs, embedding_function, ids=ids)docs = example_db.similarity_search(query)print(docs[0].metadata)# update the metadata for a documentdocs[0].metadata = { "source": "../../../state_of_the_union.txt", "new_value": "hello world",}example_db.update_document(ids[0], docs[0])print(example_db._collection.get(ids=[ids[0]]))# delete the last documentprint("count before", example_db._collection.count())example_db._collection.delete(ids=[ids[-1]])print("count after", example_db._collection.count()) {'source': '../../../state_of_the_union.txt'} {'ids': ['1'], 'embeddings': None, 'metadatas': [{'new_value': 'hello world', 'source': '../../../state_of_the_union.txt'}], 'documents': ['Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-7 | I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.']} count before 46 count after 45Use OpenAI Embeddings​Many people like to use OpenAIEmbeddings, here is how to set that up.# get a token: https://platform.openai.com/account/api-keysfrom getpass import getpassfrom langchain.embeddings.openai import OpenAIEmbeddingsOPENAI_API_KEY = getpass()import osos.environ["OPENAI_API_KEY"] = OPENAI_API_KEYembeddings = OpenAIEmbeddings()new_client = chromadb.EphemeralClient()openai_lc_client = Chroma.from_documents( docs, embeddings, client=new_client, collection_name="openai_collection")query = "What did the president say about Ketanji Brown Jackson"docs = openai_lc_client.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-8 | Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Other Information​Similarity search with score​The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-9 | Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'}), 1.1972057819366455)Retriever options​This section goes over different options for how to use Chroma as a retriever.MMR​In addition to using similarity search in the retriever object, you can also use mmr.retriever = db.as_retriever(search_type="mmr")retriever.get_relevant_documents(query)[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Filtering on metadata​It can be helpful to narrow down the collection before working with it.For example, collections can be filtered on metadata using the get method.# filter collection for updated sourceexample_db.get(where={"source": "some_other_source"}) {'ids': [], | https://python.langchain.com/docs/integrations/vectorstores/chroma |
5510abcc3dba-10 | sourceexample_db.get(where={"source": "some_other_source"}) {'ids': [], 'embeddings': None, 'metadatas': [], 'documents': []}PreviousCassandraNextClarifaiBasic ExampleBasic Example (including saving to disk)Passing a Chroma Client into LangchainBasic Example (using the Docker Container)Update and DeleteUse OpenAI EmbeddingsOther InformationSimilarity search with scoreRetriever optionsFiltering on metadataCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/chroma |
f37ff2935787-0 | pg_embedding | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
f37ff2935787-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storespg_embeddingOn this pagepg_embeddingpg_embedding is an open-source vector similarity search for Postgres that uses Hierarchical Navigable Small Worlds for approximate nearest neighbor search.It supports:exact and approximate nearest neighbor search using HNSWL2 distanceThis notebook shows how to use the Postgres vector database (PGEmbedding).The PGEmbedding integration creates the pg_embedding extension for you, but you run the following Postgres query to add it:CREATE EXTENSION embedding;# Pip install necessary packagepip install openaipip install psycopg2-binarypip install tiktokenAdd the OpenAI API Key to the environment variables to use OpenAIEmbeddings.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:") OpenAI API Key:········## Loading Environment Variablesfrom typing import List, Tuplefrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
f37ff2935787-2 | import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import PGEmbeddingfrom langchain.document_loaders import TextLoaderfrom langchain.docstore.document import Documentos.environ["DATABASE_URL"] = getpass.getpass("Database Url:") Database Url:········loader = TextLoader("state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()connection_string = os.environ.get("DATABASE_URL")collection_name = "state_of_the_union"db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)Working with vectorstore in Postgres​Uploading a vectorstore in PG​db = PGEmbedding.from_documents( embedding=embeddings, documents=docs, collection_name=collection_name, connection_string=connection_string, pre_delete_collection=False,)Create HNSW Index​By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up similarity_search_with_score execution time. To | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
f37ff2935787-3 | index for approximate nearest neighbor (ANN) search to speed up similarity_search_with_score execution time. To create the HNSW index on your vector column, use a create_hnsw_index function:PGEmbedding.create_hnsw_index( max_elements=10000, dims=1536, m=8, ef_construction=16, ef_search=16)The function above is equivalent to running the below SQL query:CREATE INDEX ON vectors USING hnsw(vec) WITH (maxelements=10000, dims=1536, m=3, efconstruction=16, efsearch=16);The HNSW index options used in the statement above include:maxelements: Defines the maximum number of elements indexed. This is a required parameter. The example shown above has a value of 3. A real-world example would have a much large value, such as 1000000. An "element" refers to a data point (a vector) in the dataset, which is represented as a node in the HNSW graph. Typically, you would set this option to a value able to accommodate the number of rows in your in your dataset.dims: Defines the number of dimensions in your vector data. This is a required parameter. A small value is used in the example above. If you are storing data generated using OpenAI's text-embedding-ada-002 model, which supports 1536 dimensions, you would define a value of 1536, for example.m: Defines the maximum number of bi-directional links (also referred to as "edges") created for each node during graph construction. | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
f37ff2935787-4 | The following additional index options are supported:efConstruction: Defines the number of nearest neighbors considered during index construction. The default value is 32.efsearch: Defines the number of nearest neighbors considered during index search. The default value is 32.
For information about how you can configure these options to influence the HNSW algorithm, refer to Tuning the HNSW algorithm.Retrieving a vectorstore in PG​store = PGEmbedding( connection_string=connection_string, embedding_function=embeddings, collection_name=collection_name,)retriever = store.as_retriever()retriever VectorStoreRetriever(vectorstore=<langchain.vectorstores.pghnsw.HNSWVectoreStore object at 0x121d3c8b0>, search_type='similarity', search_kwargs={})db1 = PGEmbedding.from_existing_index( embedding=embeddings, collection_name=collection_name, pre_delete_collection=False, connection_string=connection_string,)query = "What did the president say about Ketanji Brown Jackson"docs_with_score: List[Tuple[Document, float]] = db1.similarity_search_with_score(query)for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80)PreviousOpenSearchNextPGVectorWorking with vectorstore in PostgresUploading a vectorstore in PGCreate HNSW IndexRetrieving a vectorstore in PGCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/pgembedding |
638b192b4582-0 | Weaviate | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesWeaviateOn this pageWeaviateWeaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.This notebook shows how to use functionality related to the Weaviatevector database.See the Weaviate installation instructions.pip install weaviate-client Requirement already satisfied: weaviate-client in /workspaces/langchain/.venv/lib/python3.9/site-packages (3.19.1) Requirement already satisfied: requests<2.29.0,>=2.28.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (2.28.2) Requirement already satisfied: validators<=0.21.0,>=0.18.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (0.20.0) | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-2 | (from weaviate-client) (0.20.0) Requirement already satisfied: tqdm<5.0.0,>=4.59.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (4.65.0) Requirement already satisfied: authlib>=1.1.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from weaviate-client) (1.2.0) Requirement already satisfied: cryptography>=3.2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from authlib>=1.1.0->weaviate-client) (40.0.2) Requirement already satisfied: charset-normalizer<4,>=2 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.1.0) Requirement already satisfied: idna<4,>=2.5 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (3.4) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) (1.26.15) Requirement already satisfied: certifi>=2017.4.17 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from requests<2.29.0,>=2.28.0->weaviate-client) | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-3 | requests<2.29.0,>=2.28.0->weaviate-client) (2023.5.7) Requirement already satisfied: decorator>=3.4.0 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from validators<=0.21.0,>=0.18.2->weaviate-client) (5.1.1) Requirement already satisfied: cffi>=1.12 in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cryptography>=3.2->authlib>=1.1.0->weaviate-client) (1.15.1) Requirement already satisfied: pycparser in /workspaces/langchain/.venv/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.2->authlib>=1.1.0->weaviate-client) (2.21)We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Weaviatefrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-4 | = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = Weaviate.from_documents(docs, embeddings, weaviate_url=WEAVIATE_URL, by_text=False)query = "What did the president say about Ketanji Brown Jackson"docs = db.similarity_search(query)print(docs[0].page_content) Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.Similarity search with score​Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result. | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-5 | The returned distance score is cosine distance. Therefore, a lower score is better.docs = db.similarity_search_with_score(query, by_text=False)docs[0] (Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'_additional': {'vector': [-0.015289668, -0.011418287, -0.018540842, 0.00274522, 0.008310737, 0.014179829, 0.0080104275, -0.0010217049, -0.022327352, -0.0055002323, 0.018958665, 0.0020548347, -0.0044393567, -0.021609223, -0.013709779, -0.004543812, 0.025722157, 0.01821442, 0.031728342, -0.031388864, -0.01051083, -0.029978717, 0.011555385, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-6 | -0.029978717, 0.011555385, 0.0009751897, 0.014675993, -0.02102166, 0.0301354, -0.031754456, 0.013526983, -0.03392191, 0.002800712, -0.0027778621, -0.024259781, -0.006202043, -0.019950991, 0.0176138, -0.0001134321, 0.008343379, 0.034209162, -0.027654583, 0.03149332, -0.0008389079, 0.0053696632, -0.0024644958, -0.016582303, 0.0066720927, -0.005036711, -0.035514854, 0.002942706, 0.02958701, 0.032825127, 0.015694432, -0.019846536, -0.024520919, -0.021974817, -0.0063293483, -0.01081114, -0.0084282495, 0.003025944, -0.010210521, 0.008780787, 0.014793505, -0.006486031, 0.011966679, 0.01774437, -0.006985459, -0.015459408, 0.01625588, -0.016007798, 0.01706541, 0.035567082, 0.0029900377, 0.021543937, -0.0068483613, 0.040868197, -0.010909067, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-7 | 0.040868197, -0.010909067, -0.03339963, 0.010954766, -0.014689049, -0.021596165, 0.0025607906, -0.01599474, -0.017757427, -0.0041651614, 0.010752384, 0.0053598704, -0.00019248774, 0.008480477, -0.010517359, -0.005017126, 0.0020434097, 0.011699011, 0.0051379027, 0.021687564, -0.010830725, 0.020734407, -0.006606808, 0.029769806, 0.02817686, -0.047318324, 0.024338122, -0.001150642, -0.026231378, -0.012325744, -0.0318328, -0.0094989175, -0.00897664, 0.004736402, 0.0046482678, 0.0023241339, -0.005826656, 0.0072531262, 0.015498579, -0.0077819317, -0.011953622, -0.028934162, -0.033974137, -0.01574666, 0.0086306315, -0.029299757, 0.030213742, -0.0033148287, 0.013448641, -0.013474754, 0.015851116, 0.0076578907, -0.037421167, -0.015185213, 0.010719741, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-8 | -0.015185213, 0.010719741, -0.014636821, 0.0001918757, 0.011783881, 0.0036330915, -0.02132197, 0.0031010215, 0.0024334856, -0.0033229894, 0.050086394, 0.0031973163, -0.01115062, 0.004837593, 0.01298512, -0.018645298, -0.02992649, 0.004837593, 0.0067634913, 0.02992649, 0.0145062525, 0.00566018, -0.0017055618, -0.0056667086, 0.012697867, 0.0150677, -0.007559964, -0.01991182, -0.005268472, -0.008650217, -0.008702445, 0.027550127, 0.0018296026, 0.0018589807, -0.033295177, 0.0036265631, -0.0060290387, 0.014349569, 0.019898765, 0.00023339267, 0.0034568228, -0.018958665, 0.012031963, 0.005186866, 0.020747464, -0.03817847, 0.028202975, -0.01340947, 0.00091643346, 0.014884903, -0.02314994, -0.024468692, 0.0004859627, 0.018828096, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-9 | 0.0004859627, 0.018828096, 0.012906778, 0.027941836, 0.027550127, -0.015028529, 0.018606128, 0.03449641, -0.017757427, -0.016020855, -0.012142947, 0.025304336, 0.00821281, -0.0025461016, -0.01902395, -0.635507, -0.030083172, 0.0177052, -0.0104912445, 0.012502013, -0.0010747487, 0.00465806, 0.020825805, -0.006887532, 0.013892576, -0.019977106, 0.029952602, 0.0012004217, -0.015211326, -0.008708973, -0.017809656, 0.008578404, -0.01612531, 0.022614606, -0.022327352, -0.032616217, 0.0050693536, -0.020629952, -0.01357921, 0.011477043, 0.0013938275, -0.0052390937, 0.0142581705, -0.013200559, 0.013252786, -0.033582427, 0.030579336, -0.011568441, 0.0038387382, 0.049564116, 0.016791213, -0.01991182, 0.010889481, -0.0028251936, 0.035932675, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-10 | -0.0028251936, 0.035932675, -0.02183119, -0.008611047, 0.025121538, 0.008349908, 0.00035641342, 0.009028868, 0.007631777, -0.01298512, -0.0015350056, 0.009982024, -0.024207553, -0.003332782, 0.006283649, 0.01868447, -0.010732798, -0.00876773, -0.0075273216, -0.016530076, 0.018175248, 0.016020855, -0.00067284, 0.013461698, -0.0065904865, -0.017809656, -0.014741276, 0.016582303, -0.0088526, 0.0046482678, 0.037473395, -0.02237958, 0.010112594, 0.022549322, 9.680491e-05, -0.0059082615, 0.020747464, -0.026923396, 0.01162067, -0.0074816225, 0.00024277734, 0.011842638, 0.016921783, -0.019285088, 0.005565517, 0.0046907025, 0.018109964, 0.0028676286, -0.015080757, -0.01536801, 0.0024726565, 0.020943318, 0.02187036, 0.0037767177, 0.018997835, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-11 | 0.0037767177, 0.018997835, -0.026766712, 0.005026919, 0.015942514, 0.0097469995, -0.0067830766, 0.023828901, -0.01523744, -0.0121494755, 0.00744898, 0.010445545, -0.011006993, -0.0032789223, 0.020394927, -0.017796598, -0.0029116957, 0.02318911, -0.031754456, -0.018188305, -0.031441092, -0.030579336, 0.0011832844, 0.0065023527, -0.027053965, 0.009198609, 0.022079272, -0.027785152, 0.005846241, 0.013500868, 0.016699815, 0.010445545, -0.025265165, -0.004396922, 0.0076774764, 0.014597651, -0.009851455, -0.03637661, 0.0004745379, -0.010112594, -0.009205136, 0.01578583, 0.015211326, -0.0011653311, -0.0015847852, 0.01489796, -0.01625588, -0.0029067993, -0.011411758, 0.0046286825, 0.0036330915, -0.0034143878, 0.011894866, -0.03658552, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-12 | 0.011894866, -0.03658552, 0.007266183, -0.015172156, -0.02038187, -0.033739112, 0.0018948873, -0.011379116, -0.0020923733, -0.014075373, 0.01970291, 0.0020352493, -0.0075273216, -0.02136114, 0.0027974476, -0.009577259, -0.023815846, 0.024847344, 0.014675993, -0.019454828, -0.013670608, 0.011059221, -0.005438212, 0.0406854, 0.0006218364, -0.024494806, -0.041259903, 0.022013986, -0.0040019494, -0.0052097156, 0.015798887, 0.016190596, 0.0003794671, -0.017444061, 0.012325744, 0.024769, 0.029482553, -0.0046547963, -0.015955571, -0.018397218, -0.0102431625, 0.020577725, 0.016190596, -0.02038187, 0.030030945, -0.01115062, 0.0032560725, -0.014819618, 0.005647123, -0.0032560725, 0.0038909658, 0.013311543, 0.024285894, -0.0045699263, -0.010112594, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-13 | -0.0045699263, -0.010112594, 0.009237779, 0.008728559, 0.0423828, 0.010909067, 0.04225223, -0.031806685, -0.013696723, -0.025787441, 0.00838255, -0.008715502, 0.006776548, 0.01825359, -0.014480138, -0.014427911, -0.017600743, -0.030004831, 0.0145845935, 0.013762007, -0.013226673, 0.004168425, 0.0047951583, -0.026923396, 0.014675993, 0.0055851024, 0.015616091, -0.012306159, 0.007670948, 0.038439605, -0.015759716, 0.00016178355, 0.01076544, -0.008232395, -0.009942854, 0.018801982, -0.0025314125, 0.030709906, -0.001442791, -0.042617824, -0.007409809, -0.013109161, 0.031101612, 0.016229765, 0.006162872, 0.017901054, -0.0063619902, -0.0054577976, 0.01872364, -0.0032430156, 0.02966535, 0.006495824, 0.0011008625, -0.00024318536, -0.007011573, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-14 | -0.00024318536, -0.007011573, -0.002746852, -0.004298995, 0.007710119, 0.03407859, -0.008898299, -0.008565348, 0.030527107, -0.0003027576, 0.025082368, 0.0405026, 0.03867463, 0.0014117807, -0.024076983, 0.003933401, -0.009812284, 0.00829768, -0.0074293944, 0.0061530797, -0.016647588, -0.008147526, -0.015629148, 0.02055161, 0.000504324, 0.03157166, 0.010112594, -0.009009283, 0.026557801, -0.013997031, -0.0071878415, 0.009414048, -0.03480978, 0.006626393, 0.013827291, -0.011444401, -0.011823053, -0.0042957305, -0.016229765, -0.014192886, 0.026531687, -0.012534656, -0.0056569157, -0.0010331298, 0.007977786, 0.0033654245, -0.017352663, 0.034626983, -0.011803466, 0.009035396, 0.0005288057, 0.020421041, 0.013115689, -0.0152504975, -0.0111114485, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-15 | -0.0152504975, -0.0111114485, 0.032355078, 0.0025542623, -0.0030226798, -0.00074261305, 0.030892702, -0.026218321, 0.0062803845, -0.018031623, -0.021504767, -0.012834964, 0.009009283, -0.0029198565, -0.014349569, -0.020434098, 0.009838398, -0.005993132, -0.013618381, -0.031597774, -0.019206747, 0.00086583785, 0.15835446, 0.033765227, 0.00893747, 0.015119928, -0.019128405, 0.0079582, -0.026270548, -0.015877228, 0.014153715, -0.011960151, 0.007853745, 0.006972402, -0.014101488, 0.02456009, 0.015119928, -0.0018850947, 0.019010892, -0.0046188897, -0.0050954674, -0.03548874, -0.01608614, -0.00324628, 0.009466276, 0.031911142, 7.033402e-05, -0.025095424, 0.020225188, 0.014832675, 0.023228282, -0.011829581, -0.011300774, -0.004073763, 0.0032544404, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-16 | -0.004073763, 0.0032544404, -0.0025983294, -0.020943318, 0.019650683, -0.0074424515, -0.0030977572, 0.0073379963, -0.00012455089, 0.010230106, -0.0007254758, -0.0025052987, -0.009681715, 0.03439196, -0.035123147, -0.0028806855, 0.012828437, 0.00018646932, 0.0066133365, 0.025539361, -0.00055736775, -0.025356563, -0.004537284, -0.007031158, 0.015825002, -0.013076518, 0.00736411, -0.00075689406, 0.0076578907, -0.019337315, -0.0024187965, -0.0110331075, -0.01187528, 0.0013048771, 0.0009711094, -0.027863493, -0.020616895, -0.0024481746, -0.0040802914, 0.014571536, -0.012306159, -0.037630077, 0.012652168, 0.009068039, -0.0018263385, 0.0371078, -0.0026831995, 0.011333417, -0.011548856, -0.0059049972, -0.025186824, 0.0069789304, -0.010993936, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-17 | 0.0069789304, -0.010993936, -0.0009066408, 0.0002619547, 0.01727432, -0.008082241, -0.018645298, 0.024507863, 0.0030895968, -0.0014656406, 0.011137563, -0.025513247, -0.022967143, -0.002033617, 0.006887532, 0.016621474, -0.019337315, -0.0030618508, 0.0014697209, -0.011679426, -0.003597185, -0.0049844836, -0.012332273, 0.009068039, 0.009407519, 0.027080078, -0.011215905, -0.0062542707, -0.0013114056, -0.031911142, 0.011209376, 0.009903682, -0.007351053, 0.021335026, -0.005510025, 0.0062053073, -0.010869896, -0.0045601334, 0.017561574, -0.024847344, 0.04115545, -0.00036457402, -0.0061400225, 0.013037347, -0.005480647, 0.005947433, 0.020799693, 0.014702106, 0.03272067, 0.026701428, -0.015550806, -0.036193814, -0.021126116, -0.005412098, -0.013076518, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-18 | -0.005412098, -0.013076518, 0.027080078, 0.012900249, -0.0073379963, -0.015119928, -0.019781252, 0.0062346854, -0.03266844, 0.025278222, -0.022797402, -0.0028415148, 0.021452539, -0.023162996, 0.005170545, -0.022314297, 0.011215905, -0.009838398, -0.00033233972, 0.0019650683, 0.0026326037, 0.009753528, -0.0029639236, 0.021126116, 0.01944177, -0.00044883206, -0.00961643, 0.008846072, -0.0035775995, 0.02352859, -0.0020956376, 0.0053468137, 0.013305014, 0.0006418298, 0.023802789, 0.013122218, -0.0031548813, -0.027471786, 0.005046504, 0.008545762, 0.011261604, -0.01357921, -0.01110492, -0.014845733, -0.035384286, -0.02550019, 0.008154054, -0.0058331843, -0.008702445, -0.007311882, -0.006525202, 0.03817847, 0.00372449, 0.022914914, -0.0018981516, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-19 | 0.022914914, -0.0018981516, 0.031545546, -0.01051083, 0.013801178, -0.006296706, -0.00025052988, -0.01795328, -0.026296662, 0.0017659501, 0.021883417, 0.0028937424, 0.00495837, -0.011888337, -0.008950527, -0.012058077, 0.020316586, 0.00804307, -0.0068483613, -0.0038387382, 0.019715967, -0.025069311, -0.000797697, -0.04507253, -0.009179023, -0.016242823, 0.013553096, -0.0019014158, 0.010223578, 0.0062934416, -5.5644974e-05, -0.038282923, -0.038544063, -0.03162389, -0.006815719, 0.009936325, 0.014192886, 0.02277129, -0.006972402, -0.029769806, 0.034862008, 0.01217559, -0.0037179615, 0.0008666539, 0.008924413, -0.026296662, -0.012678281, 0.014480138, 0.020734407, -0.012103776, -0.037499506, 0.022131499, 0.015028529, -0.033843566, 0.00020187242, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-20 | -0.033843566, 0.00020187242, 0.002650557, -0.0015113399, 0.021570051, -0.008284623, -0.003793039, -0.013422526, -0.009655601, -0.0016614947, -0.02388113, 0.00114901, 0.0034405016, 0.02796795, -0.039118566, 0.0023975791, -0.010608757, 0.00093438674, 0.0017382042, -0.02047327, 0.026283605, -0.020799693, 0.005947433, -0.014349569, 0.009890626, -0.022719061, -0.017248206, 0.0042565595, 0.022327352, -0.015681375, -0.013840348, 6.502964e-05, 0.015485522, -0.002678303, -0.0047984226, -0.012182118, -0.001512972, 0.013931747, -0.009642544, 0.012652168, -0.012932892, -0.027759038, -0.01085031, 0.0050236546, -0.009675186, -0.00893747, -0.0051770736, 0.036011018, 0.003528636, -0.001008648, -0.015811944, -0.008865656, 0.012364916, 0.016621474, -0.01340947, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-21 | 0.016621474, -0.01340947, 0.03219839, 0.032955695, -0.021517823, 0.00372449, -0.045124754, 0.015589978, -0.033582427, -0.01642562, -0.009609901, -0.031179955, 0.0012591778, -0.011176733, -0.018658355, -0.015224383, 0.014884903, 0.013083046, 0.0063587264, -0.008238924, -0.008917884, -0.003877909, 0.022836573, -0.004374072, -0.031127727, 0.02604858, -0.018136078, 0.000769951, -0.002312709, -0.025095424, -0.010621814, 0.013207087, 0.013944804, -0.0070899143, -0.022183727, -0.0028088724, -0.011424815, 0.026087752, -0.0058625625, -0.020186016, -0.010217049, 0.015315781, -0.012580355, 0.01374895, 0.004948577, -0.0021854038, 0.023215225, 0.00207442, 0.029639237, 0.01391869, -0.015811944, -0.005356606, -0.022327352, -0.021844247, -0.008310737, -0.020786636, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-22 | -0.008310737, -0.020786636, -0.022484036, 0.011411758, 0.005826656, 0.012188647, -0.020394927, -0.0013024289, -0.027315103, -0.017000126, -0.0010600596, -0.0019014158, 0.016712872, 0.0012673384, 0.02966535, 0.02911696, -0.03081436, 0.025552418, 0.0014215735, -0.02510848, 0.020277414, -0.02672754, 0.01829276, 0.03381745, -0.013957861, 0.0049094064, 0.033556316, 0.005167281, 0.0176138, 0.014140658, -0.0043708077, -0.0095446175, 0.012952477, 0.007853745, -0.01034109, 0.01804468, 0.0038322096, -0.04959023, 0.0023078127, 0.0053794556, -0.015106871, -0.03225062, -0.010073422, 0.007285768, 0.0056079524, -0.009002754, -0.014362626, 0.010909067, 0.009779641, -0.02796795, 0.013246258, 0.025474075, -0.001247753, 0.02442952, 0.012802322, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-23 | 0.02442952, 0.012802322, -0.032276735, 0.0029802448, 0.014179829, 0.010321504, 0.0053337566, -0.017156808, -0.010439017, 0.034444187, -0.010393318, -0.006042096, -0.018566957, 0.004517698, -0.011228961, -0.009015812, -0.02089109, 0.022484036, 0.0029867734, -0.029064732, -0.010236635, -0.0006761042, -0.029038617, 0.004367544, -0.012293102, 0.0017528932, -0.023358852, 0.02217067, 0.012606468, -0.008160583, -0.0104912445, -0.0034894652, 0.011078807, 0.00050922035, 0.015759716, 0.23774062, -0.0019291617, 0.006218364, 0.013762007, -0.029900376, 0.018188305, 0.0092965355, 0.0040574414, -0.014976301, -0.006228157, -0.016647588, 0.0035188433, -0.01919369, 0.0037506039, 0.029247528, -0.014532366, -0.049773026, -0.019624569, -0.034783665, -0.015028529, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-24 | -0.034783665, -0.015028529, 0.0097469995, 0.016281994, 0.0047135525, -0.011294246, 0.011477043, 0.015485522, 0.03426139, 0.014323455, 0.011052692, -0.008362965, -0.037969556, -0.00252162, -0.013709779, -0.0030292084, -0.016569246, -0.013879519, 0.0011849166, -0.0016925049, 0.009753528, 0.008349908, -0.008245452, 0.033007924, -0.0035873922, -0.025461018, 0.016791213, 0.05410793, -0.005950697, -0.011672897, -0.0072335405, 0.013814235, -0.0593307, -0.008624103, 0.021400312, 0.034235276, 0.015642203, -0.020068504, 0.03136275, 0.012567298, -0.010419431, 0.027445672, -0.031754456, 0.014219, -0.0075403787, 0.03812624, 0.0009988552, 0.038752973, -0.018005509, 0.013670608, 0.045882057, -0.018841153, -0.031650003, 0.010628343, -0.00459604, -0.011999321, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-25 | -0.00459604, -0.011999321, -0.028202975, -0.018593071, 0.029743692, 0.021857304, 0.01438874, 0.00014128008, -0.006156344, -0.006691678, 0.01672593, -0.012821908, -0.0024367499, -0.03219839, 0.0058233915, -0.0056405943, -0.009381405, 0.0064044255, 0.013905633, -0.011228961, -0.0013481282, -0.014023146, 0.00016239559, -0.0051901303, 0.0025265163, 0.023619989, -0.021517823, 0.024703717, -0.025643816, 0.040189236, 0.016295051, -0.0040411204, -0.0113595305, 0.0029981981, -0.015589978, 0.026479458, 0.0067439056, -0.035775993, -0.010550001, -0.014767391, -0.009897154, -0.013944804, -0.0147543335, 0.015798887, -0.02456009, -0.0018850947, 0.024442578, 0.0019715966, -0.02422061, -0.02945644, -0.003443766, 0.0004945313, 0.0011522742, -0.020773578, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-26 | 0.0011522742, -0.020773578, -0.011777353, 0.008173639, -0.012325744, -0.021348083, 0.0036461484, 0.0063228197, 0.00028970066, -0.0036200345, -0.021596165, -0.003949722, -0.0006034751, 0.007305354, -0.023424136, 0.004834329, -0.008833014, -0.013435584, 0.0026097542, -0.0012240873, -0.0028349862, -0.01706541, 0.027863493, -0.026414175, -0.011783881, 0.014075373, -0.005634066, -0.006313027, -0.004638475, -0.012495484, 0.022836573, -0.022719061, -0.031284407, -0.022405695, -0.017352663, 0.021113059, -0.03494035, 0.002772966, 0.025643816, -0.0064240107, -0.009897154, 0.0020711557, -0.16409951, 0.009688243, 0.010393318, 0.0033262535, 0.011059221, -0.012919835, 0.0014493194, -0.021857304, -0.0075730206, -0.0020695236, 0.017822713, 0.017417947, -0.034835894, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-27 | 0.017417947, -0.034835894, -0.009159437, -0.0018573486, -0.0024840813, -0.022444865, 0.0055687814, 0.0037767177, 0.0033915383, 0.0301354, -0.012227817, 0.0021854038, -0.042878963, 0.021517823, -0.010419431, -0.0051183174, 0.01659536, 0.0017333078, -0.00727924, -0.0020026069, -0.0012493852, 0.031441092, 0.0017431005, 0.008702445, -0.0072335405, -0.020081561, -0.012423672, -0.0042239176, 0.031049386, 0.04324456, 0.02550019, 0.014362626, -0.0107393265, -0.0037538682, -0.0061791935, -0.006737377, 0.011548856, -0.0166737, -0.012828437, -0.003375217, -0.01642562, -0.011424815, 0.007181313, 0.017600743, -0.0030226798, -0.014192886, 0.0128937205, -0.009975496, 0.0051444313, -0.0044654706, -0.008826486, 0.004158633, 0.004971427, -0.017835768, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-28 | 0.004971427, -0.017835768, 0.025017083, -0.021792019, 0.013657551, -0.01872364, 0.009100681, -0.0079582, -0.011640254, -0.01093518, -0.0147543335, -0.005000805, 0.02345025, -0.028908048, 0.0104912445, -0.00753385, 0.017561574, -0.012025435, 0.042670052, -0.0041978033, 0.0013056932, -0.009263893, -0.010941708, -0.004471999, 0.01008648, -0.002578744, -0.013931747, 0.018619185, -0.04029369, -0.00025909848, 0.0030063589, 0.003149985, 0.011091864, 0.006495824, 0.00026583098, 0.0045503406, -0.007586078, -0.0007475094, -0.016856499, -0.003528636, 0.038282923, -0.0010494508, 0.024494806, 0.012593412, 0.032433417, -0.003203845, 0.005947433, -0.019937934, -0.00017800271, 0.027706811, 0.03047488, 0.02047327, 0.0019258976, -0.0068940604, -0.0014990991, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-29 | -0.0068940604, -0.0014990991, 0.013305014, -0.007690533, 0.058808424, -0.0016859764, -0.0044622063, -0.0037734534, 0.01578583, -0.0018459238, -0.1196015, -0.0007075225, 0.0030341048, 0.012306159, -0.0068483613, 0.01851473, 0.015315781, 0.031388864, -0.015563863, 0.04776226, -0.008199753, -0.02591801, 0.00546759, -0.004915935, 0.0050824108, 0.0027011528, -0.009205136, -0.016712872, -0.0033409426, 0.0043218443, -0.018279705, 0.00876773, 0.0050138617, -0.009688243, -0.017783541, -0.018645298, -0.010380261, 0.018606128, 0.0077492893, 0.007324939, -0.012704396, -0.002692992, -0.01259994, -0.0076970616, -0.013814235, -0.0004365912, -0.023606932, -0.020186016, 0.025330449, -0.00991674, -0.0048278007, -0.019350372, 0.015433294, -0.0056144805, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-30 | 0.015433294, -0.0056144805, -0.0034927295, -0.00043455104, 0.008611047, 0.025748271, 0.022353467, -0.020747464, -0.015759716, 0.029038617, -0.000377631, -0.028725252, 0.018109964, -0.0016125311, -0.022719061, -0.009133324, -0.033060152, 0.011248547, -0.0019797573, -0.007181313, 0.0018867267, 0.0070899143, 0.004077027, 0.0055328747, -0.014245113, -0.021217514, -0.006750434, -0.038230695, 0.013233202, 0.014219, -0.017692143, 0.024742888, -0.008833014, -0.00753385, -0.026923396, -0.0021527617, 0.013135274, -0.018070793, -0.013500868, -0.0016696552, 0.011568441, -0.03230285, 0.023646105, 0.0111114485, -0.015172156, 0.0257091, 0.0045699263, -0.00919208, 0.021517823, 0.037838988, 0.00787333, -0.007755818, -0.028281316, 0.011170205, -0.005412098, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-31 | 0.011170205, -0.005412098, -0.016321165, 0.009929797, 0.004609097, -0.03047488, 0.002688096, -0.07264877, 0.024455635, -0.020930262, -0.015381066, -0.0033148287, 0.027236762, 0.0014501355, -0.014101488, -0.024076983, 0.026218321, -0.009009283, 0.019624569, 0.0020646274, -0.009081096, -0.01565526, -0.003358896, 0.048571788, -0.004857179, 0.022444865, 0.024181439, 0.00080708164, 0.024873456, 3.463147e-05, 0.0010535312, -0.017940223, 0.0012159267, -0.011065749, 0.008258509, -0.018527785, -0.022797402, 0.012377972, -0.002087477, 0.010791554, 0.022288183, 0.0048604426, -0.032590102, 0.013709779, 0.004922463, 0.020055447, -0.0150677, -0.0057222005, -0.036246043, 0.0021364405, 0.021387255, -0.013435584, 0.010732798, 0.0075534354, -0.00061612396, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-32 | 0.0075534354, -0.00061612396, -0.002018928, -0.004432828, -0.032746784, 0.025513247, -0.0025852725, 0.014467081, -0.008617575, -0.019755138, 0.003966043, -0.0033915383, 0.0004088452, -0.025173767, 0.02796795, 0.0023763615, 0.0052358294, 0.017796598, 0.014806561, 0.0150024155, -0.005859298, 0.01259994, 0.021726735, -0.026466403, -0.017457118, -0.0025493659, 0.0070899143, 0.02668837, 0.015485522, -0.011588027, 0.01906312, -0.003388274, -0.010210521, 0.020956375, 0.028620796, -0.018540842, 0.0025722156, 0.0110331075, -0.003992157, 0.020930262, 0.008487006, 0.0016557822, -0.0009882465, 0.0062640635, -0.016242823, -0.0007785196, -0.0007213955, 0.018971723, 0.021687564, 0.0039464575, -0.01574666, 0.011783881, -0.0019797573, -0.013383356, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-33 | -0.0019797573, -0.013383356, -0.002706049, 0.0037734534, 0.020394927, -0.00021931567, 0.0041814824, 0.025121538, -0.036246043, -0.019428715, -0.023802789, 0.014845733, 0.015420238, 0.019650683, 0.008186696, 0.025304336, -0.03204171, 0.01774437, 0.0021233836, -0.008434778, -0.0059441687, 0.038335152, 0.022653777, -0.0066002794, 0.02149171, 0.015093814, 0.025382677, -0.007579549, 0.0030357367, -0.0014117807, -0.015341896, 0.014545423, 0.007135614, -0.0113595305, -0.04387129, 0.016308108, -0.008186696, -0.013370299, -0.014297341, 0.017431004, -0.022666834, 0.039458048, 0.0032005806, -0.02081275, 0.008526176, -0.0019307939, 0.024024757, 0.009068039, 0.00953156, 0.010608757, 0.013801178, 0.035932675, -0.015185213, -0.0038322096, -0.012462842, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-34 | -0.0038322096, -0.012462842, -0.03655941, 0.0013946436, 0.00025726235, 0.008016956, -0.0042565595, 0.008447835, 0.0038191527, -0.014702106, 0.02196176, 0.0052097156, -0.010869896, 0.0051640165, 0.030840475, -0.041468814, 0.009250836, -0.018997835, 0.020107675, 0.008421721, -0.016373392, 0.004602568, 0.0327729, -0.00812794, 0.001581521, 0.019350372, 0.016112253, 0.02132197, 0.00043944738, -0.01472822, -0.025735214, -0.03313849, 0.0033817457, 0.028855821, -0.016033912, 0.0050791465, -0.01808385]}, 'source': '../../../state_of_the_union.txt'}), 0.8154189703772676)PersistanceAnything uploaded to weaviate is automatically persistent into the database. You do not need to call any specific method or pass any param for this to happen.Retriever optionsRetriever options​This section goes over different options for how to use Weaviate as a retriever.MMR​In addition to using similarity search in the retriever object, you can also use mmr.retriever = | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-35 | to using similarity search in the retriever object, you can also use mmr.retriever = db.as_retriever(search_type="mmr")retriever.get_relevant_documents(query)[0] Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../../state_of_the_union.txt'})Question Answering with Sources​This section goes over how to do question-answering with sources over an Index. It does this by using the RetrievalQAWithSourcesChain, which does the lookup of the documents from an Index. from langchain.chains import RetrievalQAWithSourcesChainfrom langchain import OpenAIwith open("../../../state_of_the_union.txt") as f: state_of_the_union = f.read()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_text(state_of_the_union)docsearch = Weaviate.from_texts( texts, embeddings, | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
638b192b4582-36 | = Weaviate.from_texts( texts, embeddings, weaviate_url=WEAVIATE_URL, by_text=False, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],)chain = RetrievalQAWithSourcesChain.from_chain_type( OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())chain( {"question": "What did the president say about Justice Breyer"}, return_only_outputs=True,) {'answer': " The president honored Justice Breyer for his service and mentioned his legacy of excellence. He also nominated Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy.\n", 'sources': '31-pl, 34-pl'}PreviousVectaraNextZillizSimilarity search with scoreRetriever optionsMMRQuestion Answering with SourcesCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright © 2023 LangChain, Inc. | https://python.langchain.com/docs/integrations/vectorstores/weaviate |
e4fa732f8a3d-0 | Annoy | 🦜�🔗 Langchain | https://python.langchain.com/docs/integrations/vectorstores/annoy |
e4fa732f8a3d-1 | Skip to main content🦜�🔗 LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesAnnoyOn this pageAnnoyAnnoy (Approximate Nearest Neighbors Oh Yeah) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data.This notebook shows how to use functionality related to the Annoy vector database.NOTE: Annoy is read-only - once the index is built you cannot add any more emebddings!If you want to progressively add new entries to your VectorStore then better choose an alternative!#!pip install annoyCreate VectorStore from texts​from langchain.embeddings import HuggingFaceEmbeddingsfrom langchain.vectorstores import Annoyembeddings_func = HuggingFaceEmbeddings()texts = ["pizza is great", "I love salad", "my car", "a dog"]# default metric is angularvector_store = Annoy.from_texts(texts, embeddings_func)# allows for custom | https://python.langchain.com/docs/integrations/vectorstores/annoy |
e4fa732f8a3d-2 | metric is angularvector_store = Annoy.from_texts(texts, embeddings_func)# allows for custom annoy parameters, defaults are n_trees=100, n_jobs=-1, metric="angular"vector_store_v2 = Annoy.from_texts( texts, embeddings_func, metric="dot", n_trees=100, n_jobs=1)vector_store.similarity_search("food", k=3) [Document(page_content='pizza is great', metadata={}), Document(page_content='I love salad', metadata={}), Document(page_content='my car', metadata={})]# the score is a distance metric, so lower is bettervector_store.similarity_search_with_score("food", k=3) [(Document(page_content='pizza is great', metadata={}), 1.0944390296936035), (Document(page_content='I love salad', metadata={}), 1.1273186206817627), (Document(page_content='my car', metadata={}), 1.1580758094787598)]Create VectorStore from docs​from langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../../state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)docs = text_splitter.split_documents(documents)docs[:5] [Document(page_content='Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty | https://python.langchain.com/docs/integrations/vectorstores/annoy |
e4fa732f8a3d-3 | meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n\nIn this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.� The Ukrainian Ambassador to the United States is here tonight. \n\nLet each of us here tonight in this Chamber send an unmistakable signal to Ukraine and to the world. \n\nPlease rise if you are able and show that, Yes, we the United States of America stand with the Ukrainian people. \n\nThroughout our history we’ve learned this lesson when dictators do not pay a price for their aggression they cause more chaos. \n\nThey keep moving. \n\nAnd the costs and the threats to America and the world keep rising. \n\nThat’s why the NATO Alliance was created to secure peace and stability in Europe after World War 2. \n\nThe United States is a member along with 29 other nations. \n\nIt matters. American diplomacy | https://python.langchain.com/docs/integrations/vectorstores/annoy |
e4fa732f8a3d-4 | United States is a member along with 29 other nations. \n\nIt matters. American diplomacy matters. American resolve matters.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='Putin’s latest attack on Ukraine was premeditated and unprovoked. \n\nHe rejected repeated efforts at diplomacy. \n\nHe thought the West and NATO wouldn’t respond. And he thought he could divide us at home. Putin was wrong. We were ready. Here is what we did. \n\nWe prepared extensively and carefully. \n\nWe spent months building a coalition of other freedom-loving nations from Europe and the Americas to Asia and Africa to confront Putin. \n\nI spent countless hours unifying our European allies. We shared with the world in advance what we knew Putin was planning and precisely how he would try to falsely justify his aggression. \n\nWe countered Russia’s lies with truth. \n\nAnd now that he has acted the free world is holding him accountable. \n\nAlong with twenty-seven members of the European Union including France, Germany, Italy, as well as countries like the United Kingdom, Canada, Japan, Korea, Australia, New Zealand, and many others, even Switzerland.', metadata={'source': '../../../state_of_the_union.txt'}), Document(page_content='We are inflicting pain on Russia and supporting the people of Ukraine. Putin is now isolated from the world more than ever. \n\nTogether with our allies –we are right now enforcing powerful economic sanctions. \n\nWe are cutting off Russia’s largest banks from the international financial system. \n\nPreventing Russia’s central bank from defending the Russian Ruble making Putin’s $630 Billion “war fund� worthless. \n\nWe are choking off Russia’s access | https://python.langchain.com/docs/integrations/vectorstores/annoy |
Subsets and Splits