Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory!
pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, docker-compose up -d --buildThe core API is only 4 functions (run our 💡 Google Colab):
import chromadb
# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
client = chromadb.Client()
# Create collection. get_collection, get_or_create_collection, delete_collection also available!
collection = client.create_collection("all-my-documents")
# Add docs to the collection. Can also update and delete. Row-based API coming soon!
collection.add(
documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatically. You can skip that and add your own embeddings as well
metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
ids=["doc1", "doc2"], # unique for each doc
)
# Query/search 2 most similar results. You can also .get by id
results = collection.query(
query_texts=["This is a query document"],
n_results=2,
# where={"metadata_field": "is_equal_to_this"}, # optional filter
# where_document={"$contains":"search_string"} # optional filter
)- Simple: Fully-typed, fully-tested, fully-documented == happiness
- Integrations:
🦜️🔗 LangChain(python and js),🦙 LlamaIndexand more soon - Dev, Test, Prod: the same API that runs in your python notebook, scales to your cluster
- Feature-rich: Queries, filtering, density estimation and more
- Free & Open Source: Apache 2.0 Licensed
For example, the "Chat your data" use case:
- Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.
- Query relevant documents with natural language.
- Compose documents into the context window of an LLM like
GPT3for additional summarization or analysis.
What are embeddings?
- Read the guide from OpenAI
- Literal: Embedding something turns it from image/text/audio into a list of numbers. 🖼️ or 📄 =>
[1.2, 2.1, ....]. This process makes documents "understandable" to a machine learning model. - By analogy: An embedding represents the essence of a document. This enables documents and queries with the same essence to be "near" each other and therefore easy to find.
- Technical: An embedding is the latent-space position of a document at a layer of a deep neural network. For models trained specifically to embed data, this is the last layer.
- A small example: If you search your photos for "famous bridge in San Francisco". By embedding this query and comparing it to the embeddings of your photos and their metadata - it should return photos of the Golden Gate Bridge.
Embeddings databases (also known as vector databases) store embeddings and allow you to search by nearest neighbors rather than by substrings like a traditional database. By default, Chroma uses Sentence Transformers to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.
Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.