Chroma - the open-source embedding database.
The fastest way to build Python or JavaScript LLM apps with memory!

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pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, docker-compose up -d --build

The core API is only 4 functions (run our 💡 Google Colab or Replit template):

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
)

Features

• Simple: Fully-typed, fully-tested, fully-documented == happiness
• Integrations: 🦜️🔗 LangChain (python and js), 🦙 LlamaIndex and 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

Use case: ChatGPT for ______

For example, the "Chat your data" use case:

1. Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.
2. Query relevant documents with natural language.
3. Compose documents into the context window of an LLM like GPT3 for additional summarization or analysis.

Embeddings?