README.md

---
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](Docs/Meta-Prompting%20Overview.MD), and [Jar3d](Docs/Introduction%20to%20Jar3d.MD), 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

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv


## TODO
[] fix local docker bugs,local test
[] 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?

2. 



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

## Table of Contents

1. [Core Concepts](#core-concepts)
2. [Prerequisites](#prerequisites)
3. [Configuration](#configuration)
   - [API Key Configuration](#api-key-configuration)
   - [Endpoints Configuration](#endpoints-configuration)
4. [Setup for Basic Meta Agent](#setup-for-basic-meta-agent)
5. [Setup for Jar3d](#setup-for-jar3d)
   - [Docker Setup for Jar3d](#docker-setup-for-jar3d)
   - [Interacting with Jar3d](#interacting-with-jar3d)
6. [Roadmap for Jar3d](#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](https://arxiv.black/pdf/2401.12954)). Read our notes on [Meta-Prompting Overview](Docs/Meta-Prompting%20Overview.MD) for a more concise overview.
2. **Chain of Reasoning**: For [Jar3d](#setup-for-jar3d), we also leverage an adaptation of [Chain-of-Reasoning](https://github.com/ProfSynapse/Synapse_CoR)
3. [Jar3d](#setup-for-jar3d) uses retrieval augmented generation, which isn't used within the [Basic Meta Agent](#setup-for-basic-meta-agent). Read our notes on [Overview of Agentic RAG](Docs/Overview%20of%20Agentic%20RAG.MD). 
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/](https://www.anaconda.com/).

2. **Create a Virtual Environment:**
   ```bash
   conda create -n agent_env python=3.11 pip
   ```
   
3. **Activate the Virtual Environment:**
   ```bash
   conda activate agent_env
   ```

## Repository Setup
1. **Clone the Repository:**
   ```bash
   git clone https://github.com/JarvisChan666/SuperExpert
   ```

2. **Navigate to the Repository:**
   ```bash
   cd /path/to/your-repo/meta_expert
   ```

3. **Install Requirements:**
   ```bash
   pip install -r requirements.txt
   ```

4. You will need Docker and Docker Composed installed to get the project up and running:
   - [Docker](https://www.docker.com/get-started)
   - [Docker Compose](https://docs.docker.com/compose/install/)

5. **If you wish to use Hybrid Retrieval, you will need to create a Free Neo4j Aura Account:**
   - [Neo4j Aura](https://neo4j.com/)

## Configuration

1. Navigate to the Repository:
   ```bash
   cd /path/to/your-repo/SuperExpert
   ```

2. Open the `config.yaml` file:
   ```bash
   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/](https://serper.dev/)
- **OpenAI API Key:** Get it from [https://openai.com/](https://openai.com/)
- **Gemini API Key:** Get it from [https://ai.google.dev/gemini-api](https://ai.google.dev/gemini-api)
- **Claude API Key:** Get it from [https://docs.anthropic.com/en/api/getting-started](https://docs.anthropic.com/en/api/getting-started)
- **Groq API Key:** Get it from [https://console.groq.com/keys](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:

```yaml
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

```bash
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](Docs/Example%20Outputs/Llama%203.1%20Newsletter.MD)
- 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](https://www.docker.com/get-started)
- [Docker Compose](https://docs.docker.com/compose/install/)

#### Quick Start

1. Clone the repository:
   ```bash
   git clone https://github.com/brainqub3/meta_expert.git
   cd meta_expert
   ```

2. Build and start the containers:
   ```bash
   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:
```bash
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:

```bash
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`.