DSPy Multi Document Agents

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

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