Langchain filter by metadata. Multiple Filters using Chroma().
Langchain filter by metadata Querying the Index. documents = [Document(page_content='The Celtics are my favourite team. Document], *, allowed Enhance a Question-Answering system with metadata filtering with LangChain and CassIO, using Cassandra as the Vector Database. For more advanced usage, such as creating a vector store with custom metadata columns and filtering documents based on metadata, you can refer to the LangChain integration with pgvector. documents Filter out metadata types that are not Jan 24, 2024 · Auto-Retrieval from LlamaIndex and Self-Querying from LangChain both leverage an LLM to handle metadata tagging and filtering. Metadata filtering Qdrant has an extensive filtering system with rich type support. NOTE: this uses Cassandra's "Vector Similarity Search" capability. filter_complex_metadata (documents: ~typing. construct_metadata_filter (filter: Dict [str, Any]) → Tuple [str, Dict] [source Oct 2, 2023 · You can use a custom retriever to implement the filter. def read_index(): return MongoDBAtlasVectorSearch(client[DB_NAME][COLLECTION filter_complex_metadata# langchain_community. May 16, 2024 · I'm working with LangChain's Chroma VectorStore, and I'm trying to filter documents based on a list of document names. utils. This blogpost was inspired by this preceding work. Here is an example setup: Dec 9, 2024 · langchain_community. May 29, 2024 · 4. We want to make it as easy as possible Oct 2, 2023 · You can use a custom retriever to implement the filter. Here's how you can do it: Aug 31, 2024 · It is recommended to keep an eye on the following PR in Langchain which aimed to natively integrate a metadata filter into the Langchain. g. vectorstores. Make sure you are connecting to a vector-enabled database for this demo. Neo4j is a graph database and analytics company which helps organizations find hidden relationships and patterns May 21, 2024 · This approach should help you filter documents based on multiple lists of metadata effectively. neo4j_vector. AI. But what if there is a way to generate the refining keyword automatically? May 29, 2024 · This will ensure that only documents with the specified metadata are retrieved. construct_metadata_filter# langchain_community. base. from_documents(documents, embeddings Jul 5, 2024 · In this part, we will set up the necessary components to perform metadata-based retrieval using Langchain and Pinecone. Aug 23, 2023 · Yes, LangChain can indeed filter documents based on Metadata and then perform a vector search on these filtered documents. The self-querying retriever will allow us to filter the documents that are Aug 19, 2024 · This example demonstrates how to create a table with metadata, add texts with metadata, and filter documents using metadata in a vector database using pgvector. Multiple Filters using Chroma(). If you have any further questions or need additional assistance, feel free to ask! Details. This could be a great method to increase the relevancy of retrieved This is the object responsible for translating the generic StructuredQuery object into a metadata filter in the syntax of the vector store you're using. Sources. documents. It is also possible to use the filters in Langchain, by passing an additional param to both the similarity_search_with_score and similarity_search methods. List[~langchain_core. as_retriever; Filter out vectorstore by metadata; Filtering a corpus of text on metadata, before running RetrievalQA But what if you dont know the refining keyword but you know a keyword for the results you want to filter(e. LangChain comes with a number of built-in translators. This is the object responsible for translating the generic StructuredQuery object into a metadata filter in the syntax of the vector store you're using. We will start by reading the index that we already created so we can use it to query our data. This can be achieved by extending the VectorStoreRetriever class and overriding the get_relevant_documents method to filter the documents based on the source path. . from_documents(documents, embeddings Apr 25, 2024 · Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j Editor's Note: the following is a guest blog post from Tomaz Bratanic, who focuses on Graph ML and GenAI research at Neo4j. Additionally, if you are using LangChain with TimescaleVector, you can define metadata fields and use SelfQueryRetriever to perform metadata-based filtering. ', metadata=dict(topic="unknown"))] db = FAISS. toy). I have a list of document names as follows: Mar 23, 2023 · Users often want to specify metadata filters to filter results before doing semantic search; Other types of indexes, like graphs, have piqued user's interests; Second: we also realized that people may construct a retriever outside of LangChain - for example OpenAI released their ChatGPT Retrieval Plugin. ', metadata=dict(topic="sport")), Document(page_content='The Boston Celtics won the game by 20 points', metadata=dict(topic="sport")), Document(page_content='This is just a random text. (you cant just query "barbie not toy" as text embedding is bad at representing negation) You can do post processing to filter unwanted result. djpbshkxktjlmkggwawvzxaagqtjsiplyaeilqgacvwmomr