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feat(docs): add PGVectorStore (#30950)
Thank you for contributing to LangChain! - **Adding documentation for PGVectorStore**: docs: Adding documentation for the new PGVectorStore as a part of langchain-postgres - **Add docs**: The notebook for PGVectorStore is now added to the directory `docs/docs/integrations`. As a part of this change, we've also updated the VectorStore features table and VectorStoreTabs --------- Co-authored-by: Chester Curme <chester.curme@gmail.com> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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docs/docs/integrations/vectorstores/pgvectorstore.ipynb
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docs/docs/integrations/vectorstores/pgvectorstore.ipynb
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@ -10,6 +10,7 @@ from langchain_couchbase import CouchbaseSearchVectorStore
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from langchain_milvus import Milvus
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from langchain_mongodb import MongoDBAtlasVectorSearch
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from langchain_pinecone import PineconeVectorStore
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from langchain_postgres import PGVectorStore
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from langchain_qdrant import QdrantVectorStore
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vectorstore_list = [
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@ -22,6 +23,7 @@ vectorstore_list = [
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]
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from_partners = [
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("PGVectorStore", PGVectorStore),
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("Chroma", Chroma),
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("AstraDBVectorStore", AstraDBVectorStore),
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("QdrantVectorStore", QdrantVectorStore),
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@ -52,6 +54,17 @@ The table below lists the features for some of our most popular vector stores.
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def get_vectorstore_table():
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vectorstore_feat_table = {
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"PGVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": True,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"FAISS": {
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"Delete by ID": True,
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"Filtering": True,
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@ -1174,6 +1174,19 @@ const FEATURE_TABLES = {
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local: true,
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idsInAddDocuments: true,
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},
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{
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name: "PGVectorStore",
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link: "pgvectorstore",
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deleteById: true,
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filtering: true,
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searchByVector: true,
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searchWithScore: true,
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async: true,
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passesStandardTests: true,
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multiTenancy: false,
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local: true,
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idsInAddDocuments: true,
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},
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{
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name: "PineconeVectorStore",
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link: "pinecone",
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@ -60,6 +60,13 @@ export default function VectorStoreTabs(props) {
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packageName: "langchain-postgres",
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default: false,
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},
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{
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value: "PGVectorStore",
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label: "PGVectorStore",
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text: `from langchain_postgres import PGEngine, PGVectorStore\n${useFakeEmbeddings ? fakeEmbeddingsString : ""}\n$engine = PGEngine.from_connection_string(\n url="postgresql+psycopg://..."\n)\n\n${vectorStoreVarName} = PGVectorStore.create_sync(\n engine=pg_engine,\n table_name='test_table',\n embedding_service=embedding\n)`,
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packageName: "langchain-postgres",
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default: false,
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},
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{
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value: "Pinecone",
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label: "Pinecone",
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