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DSPy-Multi-Document-Agents

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An advanced distributed knowledge fabric for intelligent document processing, featuring multi-document agents, optimized query handling, and semantic understanding.

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# DSPy-Multi-Document-Agents

A multi-agent document processing system built with DSPy. Partitions documents into per-document agents that independently evaluate and answer queries, coordinated by a master agent with query planning and reranking.

## Architecture

```
User query
  -> QueryPlanner (selects relevant document agents)
    -> DocumentAgents (evaluate relevance, extract answers in parallel)
      -> RerankModule (reorders results by score)
        -> MasterAgent (returns top answer with citations)
```

## Components

| Component | Description |
|---|---|
| `MasterAgent` | Orchestrates query flow across document agents |
| `QueryPlanner` | DSPy ChainOfThought module that selects which agents to involve |
| `DocumentAgent` | Per-document agent that evaluates queries, extracts answers, and generates responses via Claude |
| `RerankModule` | Reranks initial retrieval scores using Qdrant context |
| `RerankingOptimizer` | Uses DSPy's BootstrapFewShotWithRandomSearch to optimize reranking |

## Dependencies

| Dependency | Purpose |
|---|---|
| DSPy | LLM orchestration and prompt optimization |
| Qdrant | Vector database for document embeddings |
| LlamaIndex | Document loading and vector store indexing |
| sentence-transformers (all-MiniLM-L6-v2) | Query encoding |
| Anthropic Claude (claude-3-haiku) | LLM for query planning, evaluation, answer generation |
| unstructured | Document parsing |

## Requirements

- Python >= 3.8
- Running Qdrant instance (default: localhost:6333)
- `ANTHROPIC_API_KEY` environment variable

## Setup

```bash
git clone https://github.com/jmanhype/DSPy-Multi-Document-Agents.git
cd DSPy-Multi-Document-Agents
pip install -r requirements.txt
# Start Qdrant (e.g., via Docker)
docker run -p 6333:6333 qdrant/qdrant
export ANTHROPIC_API_KEY="your-key"
python main.py
```

By default, `main.py` loads documents from `docs/latest.md` (configurable via `DOCUMENT_PATH` env var).

## How queries are processed

1. Documents are loaded, partitioned, embedded, and stored in Qdrant
2. A `DocumentAgent` is created for each document partition
3. `QueryPlanner` uses ChainOfThought to select relevant agents
4. Selected agents evaluate the query against their content
5. `RerankModule` adjusts scores using retrieved context
6. `MasterAgent` picks the top-scoring agent and asks it to generate a full answer with citations

## Limitations

- The reranking logic is simplistic (adds context length to initial score)
- Relevance scoring uses keyword heuristics ("not relevant" / "partially relevant" string matching), not a trained classifier
- Query decomposition splits on "and"/"or" keywords only
- Similarity search uses Jaccard similarity, not semantic embeddings
- No web UI; runs as a CLI script
- The Nextra documentation site (`pages/`) describes the architecture but the docs app requires separate `npm install && npm run dev`

## Documentation site

The `pages/` directory contains a Nextra (Next.js) documentation site:

```bash
npm install
npm run dev
# Open http://localhost:3000
```

## License

MIT

DSPy-Multi-Document-Agents

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