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FCS RAG Bot - Proof of Concept

AI-powered technical support assistant for Vena platform using Retrieval Augmented Generation (RAG).

🎯 Objective

Prove that a RAG system can accurately retrieve and synthesize answers to complex Vena technical questions from a curated knowledge base.

📁 Project Structure

RAG Bot POC/
├── src/                    # Source code
│   ├── config.py          # Configuration settings
│   ├── ingestion.py       # Document ingestion pipeline
│   ├── retrieval.py       # RAG query pipeline
│   └── app.py             # Streamlit chat interface
│
├── knowledge_base/         # Vena documentation
│   ├── issue_resolutions/ # Historical problems + solutions
│   ├── patterns/          # VenaQL code patterns
│   ├── concepts/          # Concept explainers
│   └── troubleshooting/   # Debugging guides
│
├── data/                   # ChromaDB vector storage
├── requirements.txt        # Python dependencies
└── .env                    # API keys (not in git)

🚀 Quick Start

1. Set up Python environment

python -m venv venv
venv\Scripts\activate  # Windows
# or: source venv/bin/activate  # Mac/Linux

pip install -r requirements.txt

2. Configure API key

cp .env.example .env
# Edit .env and add your OpenAI API key

3. Ingest knowledge base

python -m src.ingestion

4. Run the chat interface

streamlit run src/app.py

📊 Success Criteria

Metric Target
Retrieval Relevance ≥80%
Answer Accuracy ≥70%
Response Time <5 seconds

🛠️ Tech Stack

  • LLM: OpenAI GPT-4o
  • Embeddings: OpenAI text-embedding-3-small
  • Vector DB: ChromaDB
  • Backend: Python + FastAPI
  • Frontend: Streamlit

📅 Timeline

2-3 week POC starting December 24, 2024

👥 Team

  • Developer: Miles Waite
  • Stakeholder: Martin Bruwer

Part of the Vena RAG-Based Technical Support System initiative