内容摘录
!Chroma
!Chroma
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<b>Chroma - the open-source search engine for AI</b>. <br />
The fastest way to build Python or JavaScript LLM apps that search over your data!
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<a href="https://discord.gg/MMeYNTmh3x" target="_blank">
<img src="https://img.shields.io/discord/1073293645303795742?cacheSeconds=3600" alt="Discord">
</a> |
<a href="https://github.com/chroma-core/chroma/blob/master/LICENSE" target="_blank">
<img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License">
</a> |
<a href="https://docs.trychroma.com/" target="_blank">
Docs
</a> |
<a href="https://www.trychroma.com/" target="_blank">
Homepage
</a>
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Chroma Cloud
Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.
Get started with Chroma Cloud
API
The core API is only 4 functions (run our 💡 Google Colab):
Learn about all features on our Docs
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, regex and more
__Free & Open Source__: Apache 2.0 Licensed
Use case: ChatGPT for ______
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 GPT4 for additional summarization or analysis.
Embeddings?
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.
Chroma allows you to store these vectors or embeddings and 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.
Get involved
Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.
Join the conversation on Discord - #contributing channel
Review the 🛣️ Roadmap and contribute your ideas
Grab an issue and open a PR - Good first issue tag
Read our contributing guide
**Release Cadence**
We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.
License
Apache 2.0