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Chroma

The AI Engineer presents Chroma

Overview

Chroma is the open-source embedding database that makes knowledge, facts & skills pluggable for LLMs. It simplifies building LLM apps with tools to store, embed, search vectors and analyze data. Supports Python, JavaScript, integrates with LangChain & more.

Description

Chroma is an open-source embedding database designed to make knowledge, facts and skills pluggable for large language models (LLMs). It dramatically simplifies building custom LLM applications by providing developer-friendly tools to store, embed, search and analyze vectorized data.

Key Highlights

👩‍💻 Intuitive SDKs for Python and JavaScript/TypeScript for productivity

📡 Client-server architecture that scales from notebooks to production clusters

⚡ Blazing fast performance powered by state-of-the-art indexing

🔎 Comprehensive tooling around vector search, analysis, filtering etc.

🤝 Integrations with LangChain, LlamaIndex and more

🚀 Feature-rich yet simple API with full typing and docs

Whether you want to build a conversational search engine, "ChatGPT for X" or any other LLM application powered by vectors, Chroma provides the reliable and scalable data layer to make it shine. Its batteries-included capabilities allow you to focus on high-level logic instead of infrastructure.

With support for custom embedding functions and a fully managed backend handling provisioning/scaling, Chroma delivers a delightful developer experience. Check out the documentation to get started!

🤔 Why should The AI Engineer care about Chroma?

  1. 📡 Simplicity - Zero config vector search and storage lets engineers focus on apps not infra.
  2. 🔌 Modularity - Plugs right into LangChain, LlamaIndex and other libraries with consistent API.
  3. ⚡ Performance - Designed for low latency querying needed in real-time LLM loops.
  4. 👥 Community - Be part of innovators building the future conversational interfaces.
  5. 🔬 Observability - Upcoming relevance scoring gives insight into model functionality.

In summary, Chroma provides an essential and painless building block for engineers to integrate vector search into large language model applications. By abstracting complexity, it amplifies leverage for creating the next generation of AI-powered software.

Chroma Stats

  • 👷🏽‍♀️ Builders: Jeff Huber, Hammad Bashir, Luke VanderHart, Anton Troynikov, Trayan Azarov
  • 💾 Used in 10.7k repositories
  • 👩🏽‍💻 Contributors: 82
  • 💫 GitHub Stars: 9.7k
  • 🍴 Forks: 776
  • 👁️ Watch: 70
  • 🪪 License: Apache-2.0
  • 🔗 Links: Below 👇🏽

🖇️ Chroma Links


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