DugBot

DugBot is an advanced AI-powered research assistant designed to help researchers discover and understand biomedical studies through natural language queries. Built on LangGraph and LangChain frameworks, it provides intelligent search capabilities over the NHLBI-BioData Catalyst research studies using both vector-based similarity search and knowledge graph traversal.

Overview

DugBot employs Retrieval Augmented Generation (RAG) to provide accurate, grounded responses about biomedical studies, variables, and their relationships. The system processes 200+ studies from the BioData Catalyst database and enables researchers to find relevant information through conversational queries.

Architecture

Multi-Agent System

• Intent Agent: Analyzes user queries and extracts intent/preferences
• Supervisor Agent: Routes queries to appropriate lookup mechanisms
• KG Lookup Agent: Retrieves information through knowledge graph traversal
• QV Lookup Agent: Performs vector-based similarity search

Dual Retrieval Mechanisms

1. Vector-Based Retrieval: Semantic similarity search over pre-generated questions
2. Knowledge Graph Retrieval: Concept-based graph traversal for entity relationships

Getting Started

Prerequisites

• Python 3.8+
• Docker (optional, for containerized deployment)
• Ollama (for local LLM inference)
• Qdrant (vector database)
• Redis (knowledge graph storage)

Usage

Server Deployment

The system provides multiple server endpoints for different use cases:

1. Agent Server (Multi-agent routing)

python src/agent_server.py
# Serves on port 8099

2. Knowledge Graph Server (KG-only queries)

python src/kg_app_server.py
# Serves on port 8094

3. Combined QVKG Server (Vector + KG)

python src/qvkg_app_server.py
# Serves on port 8005

API Endpoints

Agent Server

• POST /agent/invoke - Full agent routing with intent analysis
• POST /agent/stream - Streaming responses
• GET /agent/score/{trace_id}/{score} - Feedback scoring

Knowledge Graph Server

• POST /kg-app/invoke - Knowledge graph-based queries
• POST /kg-app/stream - Streaming KG responses

Combined Server

• POST /qvkg-app/invoke - Combined vector + KG queries
• POST /qvkg-app/stream - Streaming combined responses

Data Processing

Question Generation

The system includes tools for generating training questions from study abstracts:

# Generate questions from study abstracts
python src/core.py

Database Population

# Create embeddings for question-answer pairs
python db_builder/create_embeddings.py

# Load data into Qdrant
python db_builder/qdrant_loader.py

Project Structure

Koios-develop/
├── src/
│   ├── agents/                 # Multi-agent implementations
│   │   ├── intent_agent_graph.py
│   │   ├── route_agentic_graph.py
│   │   ├── combined_context_graph.py
│   │   └── supervisor.py
│   ├── chains/                 # Processing chains
│   │   ├── kg_chain.py
│   │   ├── question_lookup_chain.py
│   │   ├── qvkg_chain.py
│   │   └── user_intent_chain.py
│   ├── models/                 # Data models
│   │   ├── agent_state.py
│   │   └── user_question.py
│   ├── databases/              # Database connectors
│   │   ├── qdrant.py
│   │   └── redis_graph.py
│   ├── guardrails/             # Input validation
│   │   └── input_guard.py
│   └── util/                   # Utility functions
├── db_builder/                 # Database setup tools
├── prompts/                    # Prompt templates
├── ragas_benchmark/            # Evaluation framework
└── requirements.txt

License

This project is licensed under the MIT License