https://github.com/alexander-zuev/kollektiv
The open source chat powered by LLMs with RAG. Kollektiv makes it easy to sync your custom data sources and get accurate, contextual replies.
https://github.com/alexander-zuev/kollektiv
chatbot llm rag
Last synced: 2 months ago
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The open source chat powered by LLMs with RAG. Kollektiv makes it easy to sync your custom data sources and get accurate, contextual replies.
- Host: GitHub
- URL: https://github.com/alexander-zuev/kollektiv
- Owner: alexander-zuev
- License: apache-2.0
- Created: 2024-08-24T14:11:38.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-28T09:01:07.000Z (4 months ago)
- Last Synced: 2025-04-02T16:06:39.019Z (3 months ago)
- Topics: chatbot, llm, rag
- Language: Python
- Homepage: https://www.thekollektiv.ai
- Size: 57.5 MB
- Stars: 82
- Watchers: 4
- Forks: 4
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# 🚀 Kollektiv - LLMs + Up-to-date knowledge
[](https://www.python.org/downloads/)
[](https://github.com/astral-sh/ruff)
[](https://github.com/alexander-zuev/kollektiv/actions/workflows/ci_pipeline.yml)
[](https://codecov.io/github/alexander-zuev/kollektiv)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/alexander-zuev/kollektiv/graphs/commit-activity)
## 🌟 Overview
Kollektiv is a Retrieval-Augmented Generation (RAG) system designed for one purpose - allow you to chat with your
favorite docs (of libraries, frameworks, tools primarily) easily.
This project aims to allow LLMs to tap into the most up-to-date knowledge in 2 clicks so that you don't have to
worry about incorrect replies, hallucinations or inaccuracies when working with the best LLMs.
## ❓Why?
This project was born out of a **personal itch** - whenever a new feature of my favorite library comes up, I know I
can't rely on the LLM to help me build with it - because it simply doesn't know about it!
**The root cause** - LLMs lack access to the most recent documentation or private knowledge, as they are trained on a
set of data that was accumulated way back (sometimes more than a year ago).
**The impact** - hallucinations in answers, inaccurate, incorrect or outdated information, which directly decreases
productivity and usefulness of using LLMs
**But there is a better way...**
What if LLMs could tap into a source of up-to-date information on libraries, tools, frameworks you are building with?
Imagine your LLM could intelligently decide when it needs to check the documentation source and always provide an
accurate reply?
## 🎯 Goal
Meet Kollektiv -> an open-source RAG app that helps you easily:
- parse the docs of your favorite libraries
- efficiently stores and embeds them in a local vector storage
- sets up an LLM chat which you can rely on
**Note** this is v.0.1.6 and reliability of the system can be characterized as following:
- in 50% of the times it works every time!
So do let me know if you are experiencing issues and I'll try to fix them.
## ⚙️ Key Features
- **🕷️ Intelligent Web Crawling**: Utilizes FireCrawl API to efficiently crawl and extract content from specified documentation websites.
- **🧠 Advanced Document Processing**: Implements custom chunking strategies to optimize document storage and retrieval.
- **🔍 Vector Search**: Employs Chroma DB for high-performance similarity search of document chunks.
- **🔄 Multi-Query Expansion**: Enhances search accuracy by generating multiple relevant queries for each user input.
- **📊 Smart Re-ranking**: Utilizes Cohere's re-ranking API to improve relevancy of search results
- **🤖 AI-Powered Responses**: Integrates with Claude 3.5 Sonnet to generate human-like, context-aware responses.
- **🧠 Dynamic system prompt**: Automatically summarizes the embedded documentation to improve RAG decision-making.
## 🛠️ Technical Stack
- **Backend**: Python/FastAPI
- **Storage**:
- Supabase (auth/data)
- ChromaDB (vectors)
- Redis (queues/real-time)
- **AI/ML**:
- OpenAI text-embedding-3-small (embeddings)
- Anthropic Claude 3.5 Sonnet (chat)
- Cohere (re-ranking)
- **Additional**: tiktoken, pydantic, pytest, ruff
## 🚀 Quick Start
1. **Clone the repository:**
```bash
git clone https://github.com/alexander-zuev/kollektiv.git
cd kollektiv
```
2. **Set up environment variables:**
Create a `.env` file in the project root with the following:
```bash
FIRECRAWL_API_KEY="your_firecrawl_api_key"
OPENAI_API_KEY="your_openai_api_key"
ANTHROPIC_API_KEY="your_anthropic_api_key"
COHERE_API_KEY="your_cohere_api_key"
```
3. **Install dependencies:**
```bash
poetry install
```
4. **Start the application and Redis:**
```bash
poetry run kollektiv
```
This command will start the FastAPI application and a Redis server using Docker Compose. The `docker-compose.yml` file is located at `scripts/external_deps/docker-compose.yml`.
## 💡 Usage
1. **Start the Application:**
```bash
# Run both API and Chainlit UI
poetry run kollektiv
# Or run only Chainlit UI
chainlit run main.py
```
2. **Add Documentation:**
```bash
@docs add https://your-docs-url.com
```
The system will guide you through:
- Setting crawling depth
- Adding exclude patterns (optional)
- Processing and embedding content
3. **Manage Documents:**
```bash
@docs list # List all documents
@docs remove [ID] # Remove a document
@help # Show all commands
```
4. **Chat with Documentation:**
Simply ask questions in natural language. The system will:
- Search relevant documentation
- Re-rank results for accuracy
- Generate contextual responses
## ❤️🩹 Current Limitations
- Image content not supported (text-only embeddings)
- No automatic re-indexing of documentation
- URL validation limited to common formats
- Exclude patterns must start with `/`
## 🛣️ Roadmap
For a brief roadmap please check out [project wiki page](https://github.com/alexander-zuev/kollektiv/wiki).
## 📈 Performance Metrics
Evaluation is currently done using `ragas` library. There are 2 key parts assessed:
1. End-to-end generation
- Faithfulness
- Answer relevancy
- Answer correctness
2. Retriever (TBD)
- Context recall
- Context precision
## 📜 License
Kollektiv is licensed under a modified version of the Apache License 2.0. While it allows for free use, modification,
and distribution for non-commercial purposes, any commercial use requires explicit permission from the copyright owner.
- For non-commercial use: You are free to use, modify, and distribute this software under the terms of the Apache License 2.0.
- For commercial use: Please contact [email protected] to obtain a commercial license.
See the [LICENSE](LICENSE.md) file for the full license text and additional conditions.
## Project Renaming Notice
The project has been renamed from **OmniClaude** to **Kollektiv** to:
- avoid confusion / unintended copyright infringement of Anthropic
- emphasize the goal to become a tool to enhance collaboration through simplifying access to knowledge
- overall cool name (isn't it?)
If you have any questions regarding the renaming, feel free to reach out.
## 🙏 Acknowledgements
- [FireCrawl](https://firecrawl.dev/) for superb web crawling
- [Chroma DB](https://www.trychroma.com/) for easy vector storage and retrieval
- [Anthropic](https://www.anthropic.com/) for Claude 3.5 Sonnet
- [OpenAI](https://openai.com/) for text embeddings
- [Cohere](https://cohere.ai/) for re-ranking capabilities
## 📞 Support
For any questions or issues, please [open an issue](https://github.com/alexander-zuev/kollektiv/issues)
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