Chroma db filter by metadata. Modified 7 months ago.

Chroma db filter by metadata 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. This is still an open issue in their repo as far as I can see. Ensure that each item in your collection has relevant metadata. Viewed 6k times 0 . To see all available qualifiers, File metadata and controls. 3, Updating Metadata: Metadata is crucial for effective filtering and searching within collections. 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. Chroma provides two types of filters: Metadata - filter documents based on metadata using where clause in either Collection. Sources. g. 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. Cosine similarity, which is just the dot product, Chroma recasts as cosine distance by subtracting it from one. 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). Use saved searches to filter your results more quickly. 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. . vectorstores import Chroma db = Chroma. By leveraging schema filtering techniques, users can effectively narrow down their queries to retrieve only the most relevant data. Filter by Metadata The where parameter lets you filter documents based on their associated metadata. Chroma uses some funky distance metrics. get() Document - filter documents based on # Embed data into ChromaDB vectordb = Chroma. This section delves into effective strategies for filtering results using metadata in Chroma DB. 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. from_documents(docs, embeddings, persist_directory='db') db. 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. Chroma Cloud. Here's how you can achieve this: This section delves into effective strategies for filtering results using metadata in Chroma DB. Chroma DB is an open-source vector storage system, also known as a vector database, created to store and retrieve vector embeddings. Chroma is the open-source AI application database. So, where you would Chroma DB does not currently create indices on metadata. Github. I'm working with LangChain's Chroma VectorStore, and I'm trying to filter documents based on a list of document names. db = Chroma. The filter parameter allows you to filter the collection based on metadata. Multiple Filters using Chroma(). 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. This metadata is typically stored in a database-like structure that can be indexed and queried. openai import OpenAIEmbeddings # for embedding text from langchain. Metadata¶ Metadata is a dictionary of key-value pairs that can be associated with an embedding. Discord. 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. contains(key) Clearing Data. Here’s a detailed look at how to effectively utilize metadata filters in your similarity search workflows. openai import OpenAIEmbeddings embeddings = OpenAIEmbeddings() from langchain. Chroma allows for filtering over metadata. 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. text_splitter import # Check if specific key exists in the collection # exists = chroma_db. Preview. Println (err) return} // do something with result fmt. Raw. ingest_data: Data: The data to ingest into the vector store (list of Data objects). All in one place. Overview: Metadata provides essential context that can refine search results. chroma import Chroma # for storing and retrieving vectors from langchain. If you have any further questions or need additional assistance, feel free to ask! Details. Explore how Chroma database enhances Filtering: Narrowing down results based on metadata. A workaround is to apply filtering manually after performing vector search. 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. embeddings. By incorporating Croma DB. as_retriever(search_kwargs={'k': 10}) Documents are raw chunks of text that are associated with an embedding. Chroma distance is the L2 norm squared so, in a unit hypersphere (vectors normed to unity) you could conceivably have distance = 4. 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. 1. modify(name="new_name") to change the name of the collection; metadata: A dictionary of metadata associated with the collection. similarity_search_with_score``` - Describe the problem. 59 KB. Contribute to chroma-core/chroma development by creating an account on GitHub. Modified 7 months ago. Let’s explore how we can leverage these query types for more complex use cases. Metadata is stored in the database and can be queried for. 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. 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. These filters can be based on metadata, vector similarity, or a combination of both. general setup as below: import libs. Each vector within the database can have a variety of metadata attached to it. I want to only search for documents between 2 dates. 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. 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. Documents are stored in the database and can be queried for. clear() Limitations ChromaDB offers a robust solution for managing and querying vector data efficiently. Chroma allows for various filtering options that can be applied to your data queries. 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. query(query_embeddings=[[1. These filters allow you to refine your similarity search based on metadata or specific document content. 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. Adding and Filtering Based on Metadata. 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. 1, 2. if you want to search for specific string or filter based on some metadata field you can use Metadata Filtering Process. allowing you to store embeddings and their trying to use RetrievalQA with Chromadb to create a Q&A bot on our company's documents. To implement ChromaDB effectively, it is essential to understand its filtering methods and how they can enhance data retrieval processes. Personally I would advise using Milvus or Pinecone for non-trivially-sized collections. Additionally, Chroma supports multi-modal embedding functions. This approach should help you filter documents based on multiple lists of metadata effectively. I would like to grab the top n data using a different sorting criteria (such as date in the metadata field). Embeddings, vector search, document storage, full-text search, metadata filtering, and multi-modal. Retrieval that just works. I had similar performance issues with only ~50K documents. Metadata values can be of the following types: strings the AI-native open-source embedding database. # Filter on metadata using where filter collection. Keys can be strings, values can be strings, integers, floats, or booleans. As it should The path parameter specifies the directory where Chroma will store its database files on disk. query() or Collection. games and movies. Code. Name. 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. from langchain. 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. 149 lines (149 loc) · 4. I started freaking out when I got values greater than one. 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. base_retriever = chroma_db. Alongside each vector, Chroma DB stores metadata. Query. it will return top n_results document for each query. from_documents (documents=all_documents, embedding=embeddings, persist_directory="chroma_db") When I run: vectordb. vectorstores. search_query: String: The query to search for in the vector store. Blame. Understanding Filters in Chroma. Hybrid Search: Combining text similarity with metadata filtering. 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. Docs. from_documents(texts, embeddings) It works like this: qa = ConversationalRetrievalChain. The metadata is a dictionary of key-value pairs. Skip to content. Batteries included. chroma. Loading. similarity_search``` takes a ```filter``` input parameter but do not forward it to ```langchain. 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. When working with Chroma, a powerful vector database, leveraging these techniques can significantly improve the efficiency of your queries. 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. lseohtfhs wkf uzmx rycq sgxsmga fcee gmdxj xdzwuq mueog digz