Hybrid Support Agent

An agentic AI system designed to act as a support engineer for any GitHub repository. It uses a Hybrid GraphRAG approach, combining semantic documentation search (Qdrant) with historical issue/bug relationship mapping (Neo4j) to provide grounded, cited answers.

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🏗️ Architecture Overview

The system follows a multi-layered agentic architecture built with LangGraph:

1. The Ingestion Pipeline (ETL):
   • Vector DB (Qdrant): Stores embeddings of the repository docs with feature tags and a neo4j_id bridge.
   • Graph DB (Neo4j): Stores Issue and Feature nodes linked by AFFECTS relationships.
2. The Agentic Core:
   • Analyzer: Identifies the relevant feature/component in the user query.
   • Hybrid Retriever: Queries Qdrant and, when possible, Neo4j using the doc feature bridge or detected feature.
   • Critic Node: Verifies grounding and retries retrieval up to 3 times if hallucination is detected.
3. Interaction Layer:
   • FastAPI Backend: Orchestrates the LangGraph state machine.
   • React Frontend: Chat interface on Vercel that formats citations and links GitHub issues.
   • Auth0: GitHub social login with JWT validation on the backend.

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🚀 Setup Instructions

1. Prerequisites

• Python 3.13+
• Neo4j Aura Account (required)
• Qdrant Account (optional; in-memory fallback exists)
• Groq API Key (default model via LiteLLM)
• OpenAI API Key (optional, only for evaluation via RAGAS)

2. Clone and Install

git clone https://github.com/aditya345-coder/Hybrid-Support-Engineer.git
cd Hybrid-Support-Engineer

# Install dependencies
pip install -e backend/

3. Environment Configuration

Create a .env file in the root directory (see .env.example) and populate it with your keys:

# LLM
LLM_MODEL=groq/llama-3.1-8b-instant
GROQ_API_KEY=your_groq_key

# Logging
LOG_LEVEL=INFO
# Local only: write logs to file (stdout always enabled)
LOG_TO_FILE=true
LOG_FILE=logs/app.log

# Qdrant
QDRANT_URL=your_qdrant_url
QDRANT_API_KEY=your_qdrant_api_key

# Neo4j
NEO4J_URI=neo4j+s://your_instance.databases.neo4j.io
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=your_password

# GitHub (for ingestion)
GITHUB_TOKEN=your_github_pat
# Set to any GitHub repo (owner/name). This example uses FastAPI.
TARGET_REPO=tiangolo/fastapi

# Evaluation (optional)
OPENAI_API_KEY=your_openai_key

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🛠️ How to Run

Phase 1: Ingest Data (Do this once)

First, populate your databases with the documentation and GitHub issues:

# Ingest Documentation into Qdrant
python backend/src/ingestion/docs_loader.py

# Ingest GitHub Issues into Neo4j (last 20 closed issues)
python backend/src/ingestion/github_loader.py

Phase 2: Start the System

You need to run both the backend API and the frontend UI:

1. Start the Backend API:

   python backend/src/main.py

2. Start the React Frontend (dev mode):

   cd frontend
   cmd /c npm install    # first time only
   cmd /c npm run dev    # Vite dev server on :5173

Notes:

• Neo4j configuration is required; the app will error if it is missing.

Phase 3: (Optional) Run Evaluation

python backend/src/evaluation/evaluate.py

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📺 Demo

A video demo or screenshot gallery will be placed here soon.

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🛡️ Key Features

• Grounded Answers With Citations: Responses require [Source: ...] tags and the UI formats links to GitHub issues.
• Hybrid Retrieval: Qdrant doc chunks plus Neo4j issues, bridged by neo4j_id or detected feature.
• Verification Loop: The LangGraph critic retries retrieval up to 3 times when hallucination is detected.
• Evaluation Harness: RAGAS-based evaluation using src/evaluation/golden_dataset.json.