README.md

metadata

title: SuperExpert
emoji: 📉
colorFrom: pink
colorTo: yellow
sdk: docker
pinned: false
app_port: 8000
app_file: chat.py

Super Expert

A project for versatile AI agents that can run with proprietary models or completely open-source. The meta expert has two agents: a basic Meta Agent, and Jar3d, a more sophisticated and versatile agent.

Act as an open source perplexity.

Thanks John Adeojo, who brings this wonderful project to open source community!

Tech Stack

• LLM(openai, claude, llama)
• Frontend(Chainlit - chain of thought reasoning)
• Backend
  • python
  • docker
  • Hugging Face deploy

TODO

[] fix "/end" meta expert 503 error,maybe we should "Retry". [] deploy to Huggingface

[] Long-term memory. [] Full Ollama and vLLM integration. [] Integrations to RAG platforms for more intelligent document processing and faster RAG.

PMF - What problem this project has solved?

Business Logics

LLM Application Workflow

1. User Query: The user initiates the interaction by submitting a query or request for information.
2. Agent Accesses the Internet: The agent retrieves relevant information from various online sources, such as web pages, articles, and databases.
3. Document Chunking: The retrieved URLs are processed to break down the content into smaller, manageable documents or chunks. This step ensures that the information is more digestible and can be analyzed effectively.(tools\legacy\offline_graph_rag_tool copy.py run_rag)
4. Vectorization: Each document chunk is then transformed into a multi-dimensional embedding using vectorization techniques. This process captures the semantic meaning of the text, allowing for nuanced comparisons between different pieces of information.
5. Similarity Search: A similarity search is performed using cosine similarity (or another appropriate metric) to identify and rank the most relevant document chunks in relation to the original user query. This step helps in finding the closest matches based on the embeddings generated earlier.
6. Response Generation: Finally, the most relevant chunks are selected, and the LLM synthesizes them into a coherent response that directly addresses the user's query.

Bullet points

FAQ

1. How this system work? Please draw its workflow

2. How

3. How hybrid-retrieval work? In offline_graph_rag file, we combine similarity search with

Table of Contents

1. Core Concepts
2. Prerequisites
3. Configuration
   • API Key Configuration
   • Endpoints Configuration
4. Setup for Basic Meta Agent
5. Setup for Jar3d
   • Docker Setup for Jar3d
   • Interacting with Jar3d
6. Roadmap for Jar3d

Core Concepts

This project leverages four core concepts:

1. Meta prompting: For more information, refer to the paper on Meta-Prompting (source). Read our notes on Meta-Prompting Overview for a more concise overview.
2. Chain of Reasoning: For Jar3d, we also leverage an adaptation of Chain-of-Reasoning
3. Jar3d uses retrieval augmented generation, which isn't used within the Basic Meta Agent. Read our notes on Overview of Agentic RAG.
4. Jar3d can generate knowledge graphs from web-pages allowing it to produce more comprehensive outputs.

Prerequisites

Environment Setup

1. Install Anaconda:
   Download Anaconda from https://www.anaconda.com/.

2. Create a Virtual Environment:

   conda create -n agent_env python=3.11 pip
   

3. Activate the Virtual Environment:

   conda activate agent_env
   

Repository Setup

1. Clone the Repository:

   git clone https://github.com/JarvisChan666/SuperExpert
   

2. Navigate to the Repository:

   cd /path/to/your-repo/meta_expert
   

3. Install Requirements:

   pip install -r requirements.txt
   

4. You will need Docker and Docker Composed installed to get the project up and running:

   • Docker
   • Docker Compose

5. If you wish to use Hybrid Retrieval, you will need to create a Free Neo4j Aura Account:

   • Neo4j Aura

Configuration

1. Navigate to the Repository:

   cd /path/to/your-repo/SuperExpert
   

2. Open the config.yaml file:

   nano config/config.yaml
   

If you want to use hyhbrid search, please open settings and choose "Graph and Dense".

API Key Configuration

Enter API Keys for your choice of LLM provider:

• Serper API Key: Get it from https://serper.dev/
• OpenAI API Key: Get it from https://openai.com/
• Gemini API Key: Get it from https://ai.google.dev/gemini-api
• Claude API Key: Get it from https://docs.anthropic.com/en/api/getting-started
• Groq API Key: Get it from https://console.groq.com/keys

For Hybrid retrieval, you will require a Claude API key

Endpoints Configuration

Set the LLM_SERVER variable to choose your inference provider. Possible values are:

• openai
• mistral
• claude
• gemini (Not currently supported)
• ollama (Not currently supported)
• groq
• vllm (Not currently supported)

Example:

LLM_SERVER: claude

Remember to keep your config.yaml file private as it contains sensitive information.

Setup for Basic Meta Agent

The basic meta agent is an early iteration of the project. It demonstrates meta prompting rather than being a useful tool for research. It uses a naive approach of scraping the entirety of a web page and feeding that into the context of the meta agent, who either continues the task or delivers a final answer.

Run Your Query in Shell

python -m agents.meta_agent

Then enter your query.

Setup for Jar3d

Jar3d is a more sophisticated agent that uses RAG, Chain-of-Reasoning, and Meta-Prompting to complete long-running research tasks.

Note: Currently, the best results are with Claude 3.5 Sonnet and Llama 3.1 70B. Results with GPT-4 are inconsistent

Try Jar3d with:

• Writing a newsletter - Example
• Writing a literature review
• As a holiday assistant

Jar3d is in active development, and its capabilities are expected to improve with better models. Feedback is greatly appreciated.

Docker Setup for Jar3d

Jar3d can be run using Docker for easier setup and deployment.

Prerequisites

• Docker
• Docker Compose

Quick Start

1. Clone the repository:

   git clone https://github.com/brainqub3/meta_expert.git
   cd meta_expert
   

2. Build and start the containers:

   docker-compose up --build
   

3. Access Jar3d: Once running, access the Jar3d web interface at http://localhost:8000.

You can end your docker session by pressing Ctrl + C or Cmd + C in your terminal and running:

docker-compose down

Notes

• The Docker setup includes Jar3d and the NLM-Ingestor service.
• Playwright and its browser dependencies are included for web scraping capabilities.
• Ollama is not included in this Docker setup. If needed, set it up separately and configure in config.yaml.
• Configuration is handled through config.yaml, not environment variables in docker-compose.

For troubleshooting, check the container logs:

docker-compose logs

Refer to the project's GitHub issues for common problems and solutions.

Interacting with Jar3d

Once you're set up, Jar3d will proceed to introduce itself and ask some questions. The questions are designed to help you refine your requirements. When you feel you have provided all the relevant information to Jar3d, you can end the questioning part of the workflow by typing /end.