README.md

---
title: EUDR Retriever
emoji: 🐠
colorFrom: yellow
colorTo: pink
sdk: docker
pinned: false
---


# ChatFed Retriever - MCP Server

A semantic document retrieval and reranking service designed for ChatFed RAG (Retrieval-Augmented Generation) pipelines. This module serves as an **MCP (Model Context Protocol) server** that retrieves semantically similar documents from vector databases with optional cross-encoder reranking.

## MCP Endpoint

The main MCP function is `retrieve_mcp` which provides a top_k retrieval and reranking function when properly connected to an external vector database.

**Parameters**:
- `query` (str, required): The search query text
- `reports_filter` (str, optional): Comma-separated list of specific report filenames
- `sources_filter` (str, optional): Filter by document source type
- `subtype_filter` (str, optional): Filter by document subtype  
- `year_filter` (str, optional): Comma-separated list of years to filter by

**Returns**: List of dictionaries containing:
- `answer`: Document content
- `answer_metadata`: Document metadata
- `score`: Relevance score [disabled when reranker used]

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

client = Client("ENTER CONTAINER URL / SPACE ID")
result = client.predict(
		query="...",
		reports_filter="",
		sources_filter="",
		subtype_filter="",
		year_filter="",
		api_name="/retrieve_mcp"
)
print(result)
```

## Configuration

### Vector Store Configuration
1. Set your data source according to the provider
2. Set the embedding model to match the data source
3. Set the retriever parameters
4. [Optional] Set the reranker parameters
5. Run the app:

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