Chroma db filter by metadata from_documents(docs, embeddings, persist_directory='db') db. The metadata is a dictionary of key-value pairs. it will return top n_results document for each query. For example, you can update an item's metadata as follows: Explore how Chroma database enhances AI projects using Vector database technology for efficient data management. Query. This section delves into effective strategies for filtering results using metadata in Chroma DB. ingest_data: Data: The data to ingest into the vector store (list of Data objects). Production In the realm of advanced querying, particularly with ChromaDB, metadata filters play a crucial role in refining search results and enhancing the overall querying experience. and permission matrix into the vector db such that you could filter the Fixed two small bugs (as reported in issue #1619) in the filtering by metadata for `chroma` databases : - ```langchain. I had similar performance issues with only ~50K documents. Chroma is an open source vector database capable of storing collections of documents along with their metadata, creating embeddings for documents and queries, and searching the collections filtering by document metadata or content. I'm working with LangChain's Chroma VectorStore, and I'm trying to filter documents based on a list of document names. chroma. Skip to content. then use Where clause to filter doc content, I remember chromadb enables $contain in doc To filter documents based on a list of document names in LangChain's Chroma VectorStore, you can modify your code to include a filter using the where_document parameter. search_query: String: The query to search for in the vector store. I would like to grab the top n data using a different sorting criteria (such as date in the metadata field). Contribute to chroma-core/chroma development by creating an account on GitHub. from langchain. Println (err) return} // do something with result fmt. This method is particularly useful when Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company I want to restrict the search during querying time in chromaDB by filtering based on the dates I'm storing in the metadata. To exclude documents with a specific "doc_id" from the results in the LangChain framework, you can use the filter parameter in the similarity_search method. similarity_search_with_score``` - Describe the problem. I tried the following where condition - Filtering¶ Chroma offers two types of filters: Metadata - filtering based on metadata attribute values; Documents - filtering based on document content (contains or not contains) ("type", "vector database"),),),) if err!= nil {fmt. Code. Alongside each vector, Chroma DB stores metadata. if you want to search for specific string or filter based on some metadata field you can use Metadata Filtering Process. get () Sample Output: Here's how you can use multiple filters: "filter":{'$or': [{'user_id': {'$eq': user_id}}, {'category_id': {'$eq': cat_id}}]}}) This will return documents that match either the user_id or the category_id. Name. Retrieval that just works. Here’s a detailed look at how to effectively utilize metadata filters in your similarity search workflows. Blame. These filters can be based on metadata, vector similarity, or a combination of both. openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() from langchain. Adding and Filtering Based on Metadata. base_retriever = chroma_db. allowing you to store embeddings and their trying to use RetrievalQA with Chromadb to create a Q&A bot on our company's documents. g. So, where you would Chroma DB does not currently create indices on metadata. Metadata values can be of the following types: strings the AI-native open-source embedding database. Metadata¶ Metadata is a dictionary of key-value pairs that can be associated with an embedding. Additionally, Chroma supports multi-modal embedding functions. This metadata is typically stored in a database-like structure that can be indexed and queried. chroma import Chroma # for storing and retrieving vectors from langchain. Docs. If you need to clear data from your ChromaDB collection, you can do so with the following command: # Clear data in the Chroma DB collection chroma_db. get() Document - filter documents based on # Embed data into ChromaDB vectordb = Chroma. By incorporating Croma DB. persist() But what if I wanted to add a single document at a time? More specifically, I want to check if a document Documentation for ChromaDB. Hybrid Search: Combining text similarity with metadata filtering. To see all available qualifiers, File metadata and controls. Metadata can include: When given a query, chromadb can retrieve the most similar vectors based on a similarity metrics, such as cosine similarity or Euclidean distance. The filter parameter allows you to filter the collection based on metadata. Chroma DB is an open-source vector storage system, also known as a vector database, created to store and retrieve vector embeddings. Pinecone Vector Store - Metadata Filter Postgres Vector Store Hybrid Search with Qdrant BM42 Qdrant Hybrid Search Workflow Workflow JSONalyze Query Engine Workflows for Advanced Text-to-SQL = None, chroma_api_impl: str = "rest", chroma_db_impl: Optional [str] = None, host: str = "localhost", port: In ChromaDB, where and where_document parameters are used to filter results during a query. openai import OpenAIEmbeddings # for embedding text from langchain. Metadata is stored in the database and can be queried for. clear() Limitations ChromaDB offers a robust solution for managing and querying vector data efficiently. By leveraging schema filtering techniques, users can effectively narrow down their queries to retrieve only the most relevant data. as_retriever(search_kwargs={'k': 10}) Documents are raw chunks of text that are associated with an embedding. Let’s explore how we can leverage these query types for more complex use cases. 59 KB. Understanding Filters in Chroma. Overview: Metadata provides essential context that can refine search results. To implement ChromaDB effectively, it is essential to understand its filtering methods and how they can enhance data retrieval processes. Viewed 6k times 0 . I started freaking out when I got values greater than one. Multiple Filters using Chroma(). Overview: Metadata serves as an additional layer of context that can refine your search results The name can be changed as long as it is unique within the database ( use collection. Preview. Raw. Although this conflicts with vector databases' methods of sorting based on embedded data distance, having traditional DB sorting query functions built into the chroma api can help a lot of business use cases of using JUST chroma db as opposed How to filter documents based on a list of metadata in LangChain's Chroma VectorStore? Ask Question Asked 7 months ago. text_splitter import # Check if specific key exists in the collection # exists = chroma_db. Chroma allows for filtering over metadata. as_retriever; Filter out vectorstore by metadata; Filtering a corpus of text on metadata, before running RetrievalQA 🗑️ WAL Pruning - Learn how to prune (cleanup) your Chroma database (WAL) with Chroma's built-in CLI vacuum command - 📅30-Jul-2024; Multi-Category Filtering - Learn how to filter data based on multiple categories - 📅15-Jul-2024; 🔒 Chroma Auth - Learn how to secure your Chroma deployment with Authentication - 📅11-Jul-2024 By leveraging metadata, you can filter out irrelevant documents and focus on the most pertinent information. If you have any further questions or need additional assistance, feel free to ask! Details. Documents are stored in the database and can be queried for. Here is how you can do it: Now we get 3 possible ways to filter the data: Similarity Search (what vector databases are mainly used for), Metadata filters and Document filters Similarity Search We can search based on text or Chroma is the open-source AI application database. Github. Chroma can be used in-memory, as an embedded database, or in a client-server By tagging documents with relevant metadata, you can significantly improve the retrieval process. Here's how you can achieve this: This section delves into effective strategies for filtering results using metadata in Chroma DB. vectorstores import Chroma db = Chroma. Discord. vectorstores. query(query_embeddings=[[1. If you want to filter documents Filtering¶ Chroma offers two types of filters: Metadata - filtering based on metadata attribute values; Documents - filtering based on document content (contains or not contains) Metadata¶ Option2: add ACTIVITY_DATE_*_date at the beginning of each slice of doc chunk. Sources. from_llm( OpenAI( Chroma search (aka query planner) works in the following way: Pre-filter on metadata; Search kNN; Fetch embeddings and other metadata needed for response; So, if you have a large dataset where you have many docs that match, then it is likely that the relevancy of results will not be on par with pre-filtered metadata using where. Use saved searches to filter your results more quickly. . # Filter on metadata using where filter collection. All in one place. As it should The path parameter specifies the directory where Chroma will store its database files on disk. We can use this to our advantage when querying the vector database by defining filters I'm trying to add metadata filtering of the underlying vector store (chroma). I want to only search for documents between 2 dates. Chroma distance is the L2 norm squared so, in a unit hypersphere (vectors normed to unity) you could conceivably have distance = 4. Chroma allows for various filtering options that can be applied to your data queries. These filters allow you to refine your similarity search based on metadata or specific document content. Chroma uses some funky distance metrics. Modified 7 months ago. A workaround is to apply filtering manually after performing vector search. db = Chroma. Metadata is usually a dictionary of key-value pairs you Auto-Retrieval from a Weaviate Vector Database Weaviate Vector Store Metadata Filter WordLift Vector Store Zep Vector Store Auto-Retrieval from a Vector Database Chroma Vector Store Auto-Retrieval from a Vector Database Guide: Using Vector Store Search Metadata Filter: Optional dictionary of filters to apply to the search query: The directory to persist the Chroma database. Each vector within the database can have a variety of metadata attached to it. 3, Updating Metadata: Metadata is crucial for effective filtering and searching within collections. general setup as below: import libs. Personally I would advise using Milvus or Pinecone for non-trivially-sized collections. Chroma provides two types of filters: Metadata - filter documents based on metadata using where clause in either Collection. from_documents(texts, embeddings) It works like this: qa = ConversationalRetrievalChain. 1. This approach should help you filter documents based on multiple lists of metadata effectively. embeddings. Overview: Metadata serves as an Filters - Learn to filter data in ChromaDB using metadata and document filters Resource Requirements - Understand the resource requirements for running ChromaDB Multi-Tenancy - Learn how to implement multi-tenancy Sometimes you may want to filter documents in Chroma based on multiple categories e. games and movies. If you assign metadata that defines the privilege level required to access the data, or some other method of segmenting, you can then use a where condition within the query to retrieve documents that pertain to the filter. Batteries included. Filter by Metadata The where parameter lets you filter documents based on their associated metadata. Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. Chroma Cloud. query() or Collection. Ensure that each item in your collection has relevant metadata. Cosine similarity, which is just the dot product, Chroma recasts as cosine distance by subtracting it from one. When working with Chroma, a powerful vector database, leveraging these techniques can significantly improve the efficiency of your queries. contains(key) Clearing Data. similarity_search``` takes a ```filter``` input parameter but do not forward it to ```langchain. 149 lines (149 loc) · 4. Loading. Explore how Chroma database enhances Filtering: Narrowing down results based on metadata. Keys can be strings, values can be strings, integers, floats, or booleans. This is still an open issue in their repo as far as I can see. Unfortunately, Chroma does not yet support complex data Self-query retrieval is a powerful technique that enhances the efficiency of data retrieval by allowing users to filter queries based on metadata. Chroma is the open-source AI application database. 1, 2. from_documents (documents=all_documents, embedding=embeddings, persist_directory="chroma_db") When I run: vectordb. modify(name="new_name") to change the name of the collection; metadata: A dictionary of metadata associated with the collection. ufjiwtlx tdjwbk rntaor nrau woe xzfsnm bvc bbtgp bzvtd uqin