DocuTalk: RAG-Based Technical Document Assistant

🚀 Overview

DocuTalk is an intelligent conversational agent designed to interact with technical documentation (PDFs). It leverages Retrieval-Augmented Generation (RAG) to provide accurate, context-aware answers based on uploaded documents.

Unlike standard LLM interactions, this project focuses on reducing hallucinations by grounding the model's responses in specific external data sources.

🛠️ Tech Stack

• Core: Python 3.10+
• LLM Orchestration: LangChain
• Interface: Streamlit
• Vector Store: FAISS (Facebook AI Similarity Search) - Chosen for efficient dense vector clustering.
• Embeddings: OpenAI Embeddings / HuggingFace
• Data Analysis: Pandas (for conversation logging and performance metrics)

🧮 Mathematical Concept

The retrieval system is based on Cosine Similarity between high-dimensional vectors. Given a query vector $A$ and a document vector $B$, relevance is calculated as:

$$\text{similarity} = \cos(\theta) = \frac{A \cdot B}{|A| |B|}$$

🔮 Future Improvements (GraphRAG)

Currently, the retrieval is based on vector similarity chunks. The next roadmap step is to implement a Knowledge Graph approach (using Neo4j or NetworkX) to map relationships between entities in the document, allowing for multi-hop reasoning – leveraging my background in Graph Theory.

📊 Analytics

The application logs user interactions to a CSV file to monitor:

• Response latency.
• Token usage.
• User feedback loops.