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<p align="center">
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<a href="https://trychroma.com"><img src="https://user-images.githubusercontent.com/891664/227103090-6624bf7d-9524-4e05-9d2c-c28d5d451481.png" alt="Chroma logo"></a>
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</p>
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<p align="center">
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<b>Chroma - the open-source embedding database</b>. <br />
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The fastest way to build Python or JavaScript LLM apps with memory!
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</p>
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<p align="center">
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<a href="https://discord.gg/MMeYNTmh3x" target="_blank">
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<img src="https://img.shields.io/discord/1073293645303795742?cacheSeconds=3600" alt="Discord">
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</a> |
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<a href="https://github.com/chroma-core/chroma/blob/master/LICENSE" target="_blank">
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<img src="https://img.shields.io/static/v1?label=license&message=Apache 2.0&color=white" alt="License">
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</a> |
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<a href="https://docs.trychroma.com/" target="_blank">
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Docs
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</a> |
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<a href="https://www.trychroma.com/" target="_blank">
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Homepage
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</a>
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</p>
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```bash
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pip install chromadb # python client
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# for javascript, npm install chromadb!
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# for client-server mode, chroma run --path /chroma_db_path
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```
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The core API is only 4 functions (run our [💡 Google Colab](https://colab.research.google.com/drive/1QEzFyqnoFxq7LUGyP1vzR4iLt9PpCDXv?usp=sharing) or [Replit template](https://replit.com/@swyx/BasicChromaStarter?v=1)):
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```python
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import chromadb
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# setup Chroma in-memory, for easy prototyping. Can add persistence easily!
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client = chromadb.Client()
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# Create collection. get_collection, get_or_create_collection, delete_collection also available!
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collection = client.create_collection("all-my-documents")
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# Add docs to the collection. Can also update and delete. Row-based API coming soon!
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collection.add(
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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
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metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
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ids=["doc1", "doc2"], # unique for each doc
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)
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# Query/search 2 most similar results. You can also .get by id
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results = collection.query(
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query_texts=["This is a query document"],
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n_results=2,
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# where={"metadata_field": "is_equal_to_this"}, # optional filter
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# where_document={"$contains":"search_string"} # optional filter
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)
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```
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## Features
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- __Simple__: Fully-typed, fully-tested, fully-documented == happiness
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- __Integrations__: [`🦜️🔗 LangChain`](https://blog.langchain.dev/langchain-chroma/) (python and js), [`🦙 LlamaIndex`](https://twitter.com/atroyn/status/1628557389762007040) and more soon
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- __Dev, Test, Prod__: the same API that runs in your python notebook, scales to your cluster
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- __Feature-rich__: Queries, filtering, density estimation and more
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- __Free & Open Source__: Apache 2.0 Licensed
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## Use case: ChatGPT for ______
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For example, the `"Chat your data"` use case:
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1. Add documents to your database. You can pass in your own embeddings, embedding function, or let Chroma embed them for you.
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2. Query relevant documents with natural language.
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3. Compose documents into the context window of an LLM like `GPT3` for additional summarization or analysis.
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## Embeddings?
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What are embeddings?
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- [Read the guide from OpenAI](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings)
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- __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.
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- __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.
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- __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.
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- __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.
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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](https://docs.trychroma.com/guides/embeddings#default:-all-minilm-l6-v2) to embed for you but you can also use OpenAI embeddings, Cohere (multilingual) embeddings, or your own.
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## Get involved
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Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.
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- [Join the conversation on Discord](https://discord.gg/MMeYNTmh3x) - `#contributing` channel
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- [Review the 🛣️ Roadmap and contribute your ideas](https://docs.trychroma.com/roadmap)
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- [Grab an issue and open a PR](https://github.com/chroma-core/chroma/issues) - [`Good first issue tag`](https://github.com/chroma-core/chroma/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22)
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- [Read our contributing guide](https://docs.trychroma.com/contributing)
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**Release Cadence**
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We currently release new tagged versions of the `pypi` and `npm` packages on Mondays. Hotfixes go out at any time during the week.
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## License
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[Apache 2.0](./LICENSE)
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