---
title: EUDR Chabo Generator
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit
---

# ChatFed Generator - MCP Server

A language model-based generation service designed for ChatFed RAG (Retrieval-Augmented Generation) pipelines. This module serves as an **MCP (Model Context Protocol) server** that generates contextual responses using configurable LLM providers with support for retrieval result processing.

## MCP Endpoint

The main MCP function is `generate` which provides context-aware text generation using configurable LLM providers when properly configured with API credentials.

**Parameters**:
- `query` (str, required): The question or query to be answered
- `context` (str|list, required): Context for answering - can be plain text or list of retrieval result dictionaries

**Returns**: String containing the generated answer based on the provided context and query.

**Example usage**:
```python
from gradio_client import Client

client = Client("ENTER CONTAINER URL / SPACE ID")
result = client.predict(
		query="What are the key findings?",
		context="Your relevant documents or context here...",
		api_name="/generate"
)
print(result)
```

## Configuration

### LLM Provider Configuration
1. Set your preferred inference provider in `params.cfg`
2. Configure the model and generation parameters  
3. Set the required API key environment variable
4. [Optional] Adjust temperature and max_tokens settings
5. Run the app:

```bash
docker build -t chatfed-generator .
docker run -p 7860:7860 chatfed-generator
```

## Environment Variables Required

# Make sure to set the appropriate environment variables:
# - OpenAI: `OPENAI_API_KEY`
# - Anthropic: `ANTHROPIC_API_KEY`
# - Cohere: `COHERE_API_KEY`
# - HuggingFace: `HF_TOKEN`