Langchain filter by metadata. Args: documents: The documents to convert.

Langchain filter by metadata. for result in response [0]: page_content = self.

Langchain filter by metadata. List of Documents most similar to the embedding. filter (Optional[Dict[str, str]]): Filter by metadata. However, the framework does support the inclusion of metadata from the retriever in the response. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and To retrieve the metadata of the document that the agent used to provide an answer in your chatbot, you can use the similarity_search_with_score method of the Pinecone class. 知识库问答系统包含了一系列的功能，包括：文档载入、文档分隔、文档 Embedding、文档存储、文档检索 Jan 10, 2020 · Since FAISS doesn't store metadata, I guess I'd need to do a search on all vectors, then filter them by date. It provides a type-safe TypeScript/JavaScript SDK for interacting with your database, and a UI for managing your data. There have been discussions in the comments about different approaches and guidance on using metadata for filtering, with I providing a possible approach to achieve this. For example, I would like to retrieve documents with metadata having the metadata "category" : 'value1' OR 'value2' OR 'value3' this is my current implementation: Types. I use LangChain, and the MongoDBAtlasVectorSearch as a retriever. In the documentation it says I can add the filter, as explained here. My code: from langchain Creating our self-querying retriever . Based on the context provided, it seems like you're trying to use metadata filtering with Pinecone in LangChain and NodeJS v18 Firebase cloud functions. with_payload=True, limit=k, ) Then, in qdrant. """Utility functions for working with vectors and vectorstores. document_loaders import UnstructuredMarkdownLoader from langchain. This is useful when you don’t know the exact value of the metadata field. I've created a vector store using e5-large embeddings and stored it in a Chroma db. metadata_payload_key to key in your field condition so LangChain offers many different types of text splitters. embedding ( Embeddings) – Text embedding model to use. addExample (example);} Mar 8, 2023 · As of now querying weaviate is not very configurable. The technical limit of a metadata field associated with a vector is 1GB. 5. kind == "text1" OR metadata. The filter is a dictionary where the keys are the metadata keys and the values are the values to filter by. Review all integrations for many great hosted offerings. This walkthrough uses the chroma vector database, which runs on your local machine as a library. _download_from_gcs (f "documents/ {result. similarity_search(query, filter=filter_dict, k=1, fetch_k=1) Example: Filter by Partial Match This example shows how to filter by partial match. vectorstores. Flowise is an open source UI visual tool to build L Nov 9, 2023 · Based on the context provided, you can create a complex filter using the qdrant_client. Keep in mind different embeddings models may have a different number of dimensions: Oct 16, 2023 · I have created chunks using HTMLHeaderTextSplitter and I have only one key with different value in metadata {"header": "something going on"} for each chunked document and while retrieving documents from vector store based on query I also want to look in metadata if it has found word(s) to bring that document too. (Optional) Content Filter dictionary. Additional conditions on metadata filtering are eventually passed as a key-value filter = {"source": <file name>} parameter to the vector store's similarity search methods. Returns. It looks like you opened this issue to discuss passing Document metadata into prompts when using VectorDBQA. k (int) – Number of results to return. as_retriever() Imagine a chat scenario. Metadata Query Language. The filter parameter allows you to filter the collection based on metadata. Thanks for your help. There is then the issue of converting that Pydantic model into a filter that can be passed into a retriever. . From the context, it appears that the PineconeStore class in Nov 16, 2023 · I had the same issue, in order to use metadata filtering you must access the field in metadata dictionary (like metadata. Please keep in mind that this is just one way to use the filter parameter. from_documents function in LangChain v0. We’ll take the same sentences we’ve discussed in our previous lecture. Aug 14, 2023 · I'm trying to add metadata filtering of the underlying vector store (chroma). 3 days ago · metadata: Optional[Dict[str, Any]] - The metadata of the runnable. Adds Metadata: Whether or not this text splitter adds metadata about where each chunk came from. The 'metadata' parameter in the 'as_retriever' function is not intended for filtering data. Return type. Also having a similar interface for metadata filtering for different Vectorstores to make Vectorstores easily exchangeable would be interesting. Searches with metadata filters retrieve exactly the number of nearest-neighbor results that match the filters. The EnsembleRetriever takes a list of retrievers as input and ensemble the results of their get_relevant_documents () methods and rerank the results based on the Reciprocal Rank Fusion algorithm. Feb 13, 2024 · To access the page_content, you would need to modify your code to access the Document object itself, not just its metadata. vectorstore = Chroma. LangChain inserts vectors directly to Xata, and queries it for the nearest Apr 25, 2023 · “🪙Metadata Filters Being able to filter a vectorstore based on metadata is super handy. 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. . Qdrant (read: quadrant ) is a vector similarity search engine. from_documents (docs Feb 18, 2023 · I'm helping the LangChain team manage their backlog and am marking this issue as stale. similarity_search(query, filter={"source":"SOURCE_1"}) # or retriever = chroma_db. id document = Document (page Self-querying. 0. Boolean. 2 days ago · similarity_search (query: str, k: int = 4, filter: Optional [Dict [str, str]] = None, ** kwargs: Any) → List [Document] [source] ¶ Run similarity search with Chroma. The pre_filter parameter in the similarity_search method is used to pre-filter documents before identifying nearest neighbors. LangChain 101: Part 2ab. One of the most common ways to store and search over unstructured data is to embed it and store the resulting embedding vectors, and then at query time to embed the unstructured query and retrieve the embedding vectors that are 'most similar' to the embedded query. Returns: A list of documents with state May 21, 2023 · query_filter=self. However, the InMemoryDocstore object in LangChain does not directly expose the Document objects it stores. as_retriever( search_type="mmr", search_kwargs={'k': 6, 'lambda_mult': 0. As a result, allowed metadata types are drawn from JSON primitive types. Here is how you can modify your code to implement the conjunction (metadata. py file. We may want to do query analysis to extract filters to pass into retrievers. The Loader requires the following parameters: MongoDB connection string. Number. But in that case I can't precisely control the number of matches (e. LangChain is a vast library for GenAI orchestration, it supports numerous LLMs, vector stores, document loaders and agents. You can update your existing index with the filter defined and do pre-filtering with vector search. [docs] def get_stateful_documents( documents: Sequence[Document], ) -> Sequence[_DocumentWithState]: """Convert a list of documents to a list of documents with state. List[Tuple[Document, float]] Examples using Pinecone¶ Pinecone Feb 12, 2024 · 2. You can also set the fetch_k parameter when calling any search method to set how many documents you want to fetch before filtering. Basically trying to build a retriever that is scoped to a single document that is represented by the hash_code. 2 days ago · EmbeddingStore. filter_complex_metadata (documents: ~typing. namespace (Optional[str]) – Namespace to search in. Aug 22, 2023 · Hello, I created an Vector Search Index in my Atlas cluster, on the “embedding” field of a “embeddings” collection. 25} ) # Fetch more documents for the MMR algorithm to consider # But only return the top 5 db. It works well. The Document object has a metadata attribute that contains the metadata of With metadata filtering. page == 4): Oct 24, 2023 · As for your second question, yes, you can create a copy of the SelfQueryRetriever class and modify the _get_relevant_documents method to print the JSON from the _create_request(query) line for debugging purposes. An implementation of LangChain vectorstore abstraction using postgres as the backend and utilizing the pgvector extension. Nov 19, 2023 · I've built a RAG using Langchain, specifically with the goal of using SelfQueryRetriever to filter based on metadata. Below is a table listing all of them, along with a few characteristics: Name: Name of the text splitter. from_documents(texts, embeddings) It works like this: qa = ConversationalRetrievalChain. fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering. filter (Optional[Dict[str, str]]) – Filter by metadata. 352 does exclude metadata in documents when embedding and storing vectors. There are also cases when you have multiple documents in your vectorstore, or potentially other metadata you can specify. filter_complex_metadata (docs) db = Chroma. py to append self. If the "filters" argument is not provided, a new filter is created. - RedisNum: Filter by numeric range against metadata fields. 使えそうなretrieverの候補として、MultiVectorRetrieverとParentDocumentRetrieverがあるので、それらの使い分けを理解するための整理を行いました The metadata is used for enhancing document information rather than for filtering or searching based on metadata. keyword). 4. Below is an example index and query on the same data loaded above that allows you do metadata filtering on the “page” field. db = Chroma. for result in response [0]: page_content = self. food_type === "vegetable",}); for (const example of examples) {// Format and add an example to the underlying vector store await exampleSelector. Looking into the documentation the only example about filters is using just one filter. kind == "text2") AND (metadata. 3 days ago · llm (BaseLanguageModel) – The language model to use for filtering. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. By leveraging the strengths of different algorithms, the EnsembleRetriever can achieve better performance than any single algorithm. Running into this issue where I need to pre-filter before the search vectorstore = Weaviate(client, CLASS_NAME, PAGE_CONTENT_FIELD, [METADATA_FIELDS]) But there is no way to extend th python. vectorstores import Chroma from langchain. Defaults to None. Metadata is stored as binary JSON. Here is how you can do it: Oct 19, 2023 · # Retrieve more documents with higher diversity # Useful if your dataset has many similar documents db. However, for large numbers of documents, performing this labelling process manually can be tedious. Default will search in ‘’ namespace. MongoDB collection name. Nov 16, 2023 · Based on the current implementation of the DirectoryLoader class in the LangChain framework, it does not support loading file metadata such as creation and modification dates. We provide a basic translator * translator here, but you can create your own translator by extending BaseTranslator * abstract class. com Redirecting Nov 22, 2023 · HI there, I am trying to use Multiquery retiever on pinecone vectordb with multiple filters. When using the index's query method, this means supplying a retriever_kwargs argument as follows: In [16]: Jan 24, 2024 · Automatic Metadata Tagging & Filtering Frameworks like LangChain and LlamaIndex offer capabilities to automatically tag incoming queries with metadata, and to apply filtering behind the scenes. To do this we’ll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents. pip install langchain-chroma. // See the section of the docs for the specific vector store you are using. The output takes the following format: Aug 31, 2023 · You can find this in the test_from_texts_with_metadatas method in the test_pinecone. prompt (Optional[BasePromptTemplate]) – The prompt to use for the filter. FieldCondition classes. page. List[~langchain_core. Author. Dec 23, 2023 · Based on the provided context, it appears that the Chroma. that generated the event. astext == str (value) filter_clauses. as_retriever(filter={"source":"SOURCE_1"}) However, setting the filters manually isn’t very flexible. Qdrant is tailored to extended filtering support. You would need to implement this functionality yourself. All You Need to Know About (Large Language) Models The Models component is the backbone of Langchain. vectorstores import utils as chromautils loader = UnstructuredMarkdownLoader (filename, mode = "elements") docs = loader. Contextual Compression 6 days ago · Parameters. Aug 15, 2023 · To correctly filter by metadata to return specific documents, you would need to parse the metadata field back into a dictionary in your application code after retrieving the documents from the Azure Search index. The similarity_search method will return documents that match the search query and also satisfy the filter condition. For example, if you want to filter by the metadata field author and you don’t know the exact value of the author, you can use a partial match to filter by the author’s last name This filter is either a callble that takes as input a metadata dict and returns a bool, or a metadata dict where each missing key is ignored and each present k must be in a list of values. It's a simple yet powerful 4 days ago · langchain_community. 4 days ago · This # means that the match method will always return an array with only # one element. You can run the following command to spin up a a postgres container with the pgvector extension: docker run --name pgvector-container -e POSTGRES_USER 1 day ago · filter (Optional[dict]) – Dictionary of argument(s) to filter on metadata. load () docs = chromautils. */ structuredQueryTranslator: new PineconeTranslator (), Feb 12, 2024 · This is crucial for LLMs and LangChain applications. (Optional) List of field names to include in the output. However, it's important to note that the _create_request(query) method does not exist in the provided context. Oct 9, 2023 · Thank you for reaching out and providing detailed information about your issue. from_documents(documents=splits, embedding=OpenAIEmbeddings()) retriever = vectorstore. I'm able to query the Chroma db using similarity search with no issues - the results are pretty good, actually. utils. Now we can instantiate our retriever. 在之前的文章中，我们介绍了如何使用 LangChain 打造一个垂直领域的知识库问答系统。. cmetadata [key]. Dec 4, 2023 · Langchain | How to make use of metadata attribute while retrieving documents from vector store after text-chunked with HTMLHeaderTextSplitter 0 How to filter a table with 2 where clauses in breeze js Oct 19, 2023 · To exclude documents with a specific "doc_id" from the results in the LangChain framework, you can use the filter parameter in the similarity_search method. In practice you should keep metadata fields as small as possible to maximize performance. append (filter_by_metadata) return filter_clauses def _create_filter_clause (self, filters: Any)-> Any: """Convert LangChain IR filter representation to matching SQLAlchemy clauses. # 1) You can add examples into the prompt template to improve extraction quality # 2) Introduce additional parameters to take context into account (e. Jan 1, 2023 · However, LangChain does not provide a built-in way to filter documents based on their metadata and date of effectiveness. chroma_db. embedding_key ( str) – MongoDB field that will contain the embedding for each document. Supports exact, fuzzy, and wildcard matching. The function does not take any arguments that would allow for filtering by metadata fields. We want to make it as easy as possible Mar 28, 2023 · I know this is closed but for those of you who figured out how to filter, could you show me another example? I am trying to initialize a retriever with a filter based on an the hash_code in the metadata. You've noticed that the 'metadata' parameter doesn't seem to affect the retrieval process, while the 'search_kwargs' parameter does. metadata = {} if self. My goal is to pre-filter in multiple ways. filter: (doc: Document) => doc. com Redirecting pnpm add @langchain/pinecone @pinecone-database/pinecone The below examples use OpenAI embeddings, but you can swap in whichever provider you'd like. I specifically need to use an OR operator. These all live in the langchain-text-splitters package. It manages templates, composes components into chains and supports monitoring and observability. LangChain Neo4j Integration. Here is an example of how you can filter by a Dec 27, 2023 · Based on the context provided, it seems like you're trying to filter documents based on the id field in the metadata. This filter is then passed to the similarity_search method of the VectorSearchIndex object. Note that the vector store needs to support filtering on the metadata * attributes you want to query on. A vector store takes care of storing embedded data and performing vector search Dec 4, 2023 · It's important to filter out complex metadata not supported by ChromaDB using the filter_complex_metadata function from Langchain. As for passing a UNIX timestamp as metadata through the agent in the search_kwargs for a filtered query, the PineconeTranslator class in the pinecone. User: I am looking for X. OpenAI functions metadata tagger. Filtering metadata. A self-querying retriever is one that, as the name suggests, has the ability to query itself. The filter parameter accepts a MetadataFilter which is a dictionary where the keys are the metadata fields and the values are the conditions to filter by. text_key ( str) – MongoDB field that will contain the text for each document. metadata. There have been contributions from other users sharing similar use cases and suggesting potential solutions. math import cosine_similarity. Instead, it's used to store additional information about the retriever. Sep 7, 2023 · Graph schema of imported documents. If the "filters" argument is provided, the new filter is added to the existing filters. page) and then access the keyword version of the field (like metadata. Chain definitions have been included after the table. classmethod from_orm (obj: Any) → Model Jul 19, 2023 · LangChain 知识库检索问题及解决方案. Returns: List[Tuple[Document, float]]: List of documents most similar to the query text and cosine distance in float for each. May 2, 2023 · It would be nice to have the option to filter for documents from a list of document names but also for more complex metadata filtering. However, this would not allow you to filter on the metadata field within the Azure Search query itself. LangChain offers many different types of text splitters. g. This filter parameter is a JSON object, and the match_documents function will use the Postgres JSONB Containment operator @> to filter documents by the metadata field values you specify. Lance. GitHub user `Zzz233` added support for filtering ElasticSearch GitHub user `rubell` added support for `where_filter` in @weaviate_io” Pre-filtering with Similarity Search Atlas Vector Search supports pre-filtering using MQL Operators for filtering. Defaults to 20. , include metadata python. id} ") # TODO: return all metadata. For more information, you can refer to the unit tests for the FAISS vector store in the LangChain repository. It only provides a search method that allows you to retrieve a Document object by its ID. if I query for the top 100 matches, it's possible none of them would be in the desired date range). You could do this by adding a filtering step in the rank_fusion and arank_fusion methods, before the rank fusion is applied. For vector storage, Chroma is used, coupled with Qdrant FastEmbed 5 days ago · Source code for langchain_community. You can also create a function in qdrant. as_retriever( search_type="mmr", search_kwargs={'k': 5, 'fetch_k': 50 Langchain Langchain Embeddings 🦜⛓️ Langchain Retriever Llamaindex Llamaindex Chroma provides two types of filters: Metadata Xata is a serverless data platform, based on PostgreSQL. At the top level, we still don't know if we're working with a field or an operator for the keys. Metadata fields have been omitted from the table for brevity. Multiple tags can be specified like Mar 9, 2017 · from langchain. document_id_key is not None: metadata [self. py file shows that the LangChain framework supports a variety of comparison operators, including equality and inequality, less than and greater than Alibaba Cloud Opensearch is a one-stop platform to develop intelligent search services. The following are the available filter types: - RedisText: Filter by full-text search against metadata fields. However, the issue remains unresolved. document_transformers. One way we ask the LLM to represent these filters is as a Pydantic model. Sep 26, 2023 · You're correct that the current implementation of LangChain only uses the page_content from the retrieved documents. Args: documents: The documents to convert. It can often be useful to tag ingested documents with structured metadata, such as the title, tone, or length of a document, to allow for more targeted similarity search later. Defaults Nov 21, 2023 · langchainで検索拡張生成（RAG）を実装するときに、検索用の文章とLLMに渡す用の文章を分ける方法を整理しました。. // Filter type will depend on your specific vector store. 4 days ago · Source code for langchain_community. I tried filtering using metadata to answer based on a specific paragraph: filter_dict = {"paragraph_id":19, "page":5} results = db. FAISS. There's other methods like "get" that Jul 7, 2023 · In any case, I would like to gain access of the original paragraph I'm querying together with its metadata. **kwargs (Any) – Additional arguments to pass to the constructor. You can see a full list of options for the vectorize command in the Oct 24, 2023 · The similarity_search_with_score function in the OpenSearchVectorSearch class of LangChain does not directly support filtering by metadata fields. OpenSearch helps develop search services in different search from langchain_core. If a callable, it must take as input the metadata dict of Document and return a bool. For most cases, the search latency will be even lower than unfiltered searches. Jun 29, 2023 · To use metadata filtering, you’ll want to add a filter to your options object. langchain. Aug 3, 2023 · Aug 3, 2023. The MongoDB Document Loader returns a list of Langchain Documents from a MongoDB database. """ from enum import Enum from typing import List, Tuple, Type import numpy as np from langchain_core. After setting up your project , create an index by running the following Wrangler command: $ npx wrangler vectorize create <index_name> --preset @cf/baai/bge-small-en-v1. * filter format that the vector store can understand. As usual, all the code is provided on Github and Colab. A LLMChainFilter that uses the given language model. String. Cloudflare Vectorize is currently in open beta, and requires a Cloudflare account on a paid plan to use. kwargs (Any) – Returns. Here’s what it looks like in action: {filter: { assetId: { $eq: assetId } }}. There are many great vector store options, here are a few that are free, open-source, and run entirely on your local machine. List of Documents most similar to the query and score for each. Now I want to filter the results to only retrieve entries for a specific “project”. I have a VectorStore that contains multiple pdfs and associated metadata. query (str) – Query text to search for. Code implementation. The implementation allows you to customize the node label, text and Oct 12, 2023 · From what I understand, you raised a question about the role and functionality of 'metadata' and 'page_content' in Langchain, and Dosubot provided a detailed response explaining their roles. Given the above match_documents Postgres function, you can also pass a filter parameter to only documents with a specific metadata field value. document_id_key] = result. Additionally, Dosubot requested more specific information about the context in which the system processes user questions to match text chunks. I am following various tutorials on LangChain, and am now trying to figure out how to use a subset of the documents in the vectorstore instead of the whole database. prompts import ChatPromptTemplate, MessagesPlaceholder # Define a custom prompt to provide instructions and any additional context. Nov 29, 2023 · In this example, a filter is added to check if the "question" key exists in the metadata. Here is a small example: Jul 17, 2023 · From what I understand, you opened this issue to seek guidance on modifying code to use multiple categories in metadata filtering for a ConversationalRetrievalChain. embeddings_redundant_filter. MongoDB database name. Parameters. The default metadata_payload_key is "metadata". -- P reviously, I discussed how to perform metadata filtering using Langchain and Pinecone. The broad and deep Neo4j integration allows for vector search, cypher generation and database Ensemble Retriever. LLMChainFilter. OpenSearch was built on the large-scale distributed search engine developed by Alibaba. Tags: langchain. After this your filter should work has intended. Xata has a native vector type, which can be added to any table, and supports similarity search. Thank you. This function is designed to return documents and their scores that are most similar to a given query. documents import Document from langchain_community. as_retriver(), you can pass like this. This is because the from_documents method extracts the page_content from each document to create the texts list, which is then passed to the from_texts method. - RedisTag: Filter by the exact match against string-based categorical metadata fields. This allows the retriever to not only May 30, 2023 · I'm using langchain library to save the information of my company in a Vector Database, and when I query for information the results are great, but need a way to Aug 7, 2023 · Since most vector stores support a metadata filter, we can easily filter records based on metadata, for example, the year of the movie being 1980. By default, Neo4j vector index implementation in LangChain represents the documents using the Chunk node label, where the text property stores the text of the document, and the embedding property holds the vector representation of the text. Filter and qdrant_client. To display the page number found in the Document's metadata when retrieving information from a chain, you would need to include the Feb 13, 2024 · In this example, the filter parameter is used to filter the search results based on the metadata. documents Filter out metadata types that are not Sep 13, 2023 · I've started using Langchain and ChromaDB a few days ago, but I'm facing an issue I cannot solve. collection ( Collection[MongoDBDocumentType]) – MongoDB collection to add the texts to. http. Today, we’ll explore the world of Hierarchical Navigable Small World graphs, better Construct Filters. from_llm( OpenAI( Metadata Filtering . 4 days ago · filter (Optional[Dict[str, str]]) – Filter by metadata. May 21, 2023 · Learn how to use Metadata Filter on Pinecone to retrieve documents based on key value pairs using Flowise. Nov 17, 2023 · 1. This method returns a list of tuples, where each tuple contains a Document object and a score. I was trying this but no luck: Pinecone lets you attach metadata key-value pairs to vectors in an index, and specify filter expressions when you query the index. Let me clarify this for you. data: Dict[str, Any] Below is a table that illustrates some evens that might be emitted by various chains. Chroma. 4 days ago · Defaults to 4. Jan 24, 2024 · To filter by the "received_date" metadata in a specific time window using Qdrant, you can use the filter parameter in the search methods. _qdrant_filter_from_dict(filter), # change this line to query_filter = filter. similarity_search ( query_document, k=n_results, filter= { 'category': 'science' }) This would return the n_results most similar documents to query_document that also have 'science' as their 'category' metadata. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. Published on: 7月 19, 2023. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. models. Splits On: How this text splitter splits text. Adds Metadata: Whether or not this text splitter adds metadata about where each Jul 21, 2023 · Here's how I would use it in code: vectordb. Defaults to 4. The load_file method in the DirectoryLoader class only loads the content of the file into a Document object and does not extract or store any metadata about the file. The code lives in an integration package called: langchain_postgres. xq mc ln ls kk gw ym lw sz az