source, or particular date or some other metadata attribute.vectorstore.add_documents([Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})])vectorstore.add_documents([Document(page_content="Adios", metadata={"source": "www.example.com/adios"})])vectorstore.delete_by_metadata({"source": "www.example.com/adios"})vectorstore.add_documents([Document(page_content="Adios, but newer!", metadata={"source": "www.example.com/adios"})])    ['c6367004-53d7-11ee-8cc3-de1e4b2a0118']Overriding a vectorstore‚ÄãIf you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = TimescaleVector.from_documents(    documents=docs,    embedding=embeddings,    collection_name=COLLECTION_NAME,    service_url=SERVICE_URL,    pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]PreviousTigrisNextTypesenseWhat is Timescale Vector?How to access Timescale VectorSetup1. Similarity Search with Euclidean Distance (Default)Using a Timescale Vector as a Retriever2. Similarity Search with time-based filteringExtract content and metadata from git log JSONLoad documents and metadata into TimescaleVector vectorstoreQuerying vectors by time and similarity3. Using ANN Search Indexes to Speed Up Queries4. Self Querying Retriever with Timescale Vector5. Working with an existing TimescaleVector vectorstoreDeleting DataOverriding a vectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries.
This notebook shows how to use the Postgres vector database Timescale Vector. You'll learn how to use TimescaleVector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries. ->: source, or particular date or some other metadata attribute.vectorstore.add_documents([Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})])vectorstore.add_documents([Document(page_content="Adios", metadata={"source": "www.example.com/adios"})])vectorstore.delete_by_metadata({"source": "www.example.com/adios"})vectorstore.add_documents([Document(page_content="Adios, but newer!", metadata={"source": "www.example.com/adios"})])    ['c6367004-53d7-11ee-8cc3-de1e4b2a0118']Overriding a vectorstore‚ÄãIf you have an existing collection, you override it by doing from_documents and setting pre_delete_collection = Truedb = TimescaleVector.from_documents(    documents=docs,    embedding=embeddings,    collection_name=COLLECTION_NAME,    service_url=SERVICE_URL,    pre_delete_collection=True,)docs_with_score = db.similarity_search_with_score("foo")docs_with_score[0]PreviousTigrisNextTypesenseWhat is Timescale Vector?How to access Timescale VectorSetup1. Similarity Search with Euclidean Distance (Default)Using a Timescale Vector as a Retriever2. Similarity Search with time-based filteringExtract content and metadata from git log JSONLoad documents and metadata into TimescaleVector vectorstoreQuerying vectors by time and similarity3. Using ANN Search Indexes to Speed Up Queries4. Self Querying Retriever with Timescale Vector5. Working with an existing TimescaleVector vectorstoreDeleting DataOverriding a vectorstoreCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.