I designed and deployed a production-ready RAG system that enables accurate, low-latency querying of private PDF documents by grounding LLM responses in verified document context.
The Problem
Organizations store critical knowledge in unstructured documents such as PDFs, manuals, and reports. Retrieving precise answers from these documents is slow and inefficient, while naïve LLM usage often leads to hallucinations or irrelevant responses.
Traditional keyword search fails to capture semantic meaning, and generic chatbots lack grounding in source data.
The challenge: build a system that delivers fast, accurate, and context-aware answers — grounded strictly in the uploaded business documents.
The Solution
DocQuery is a Retrieval-Augmented Generation (RAG) assistant that combines semantic search with ultra-fast LLM reasoning to answer questions directly from private documents.
The system retrieves the most relevant document chunks using vector similarity search and injects them into a structured prompt pipeline. This ensures responses are both accurate and explainable.
System Architecture
- Users upload one or more PDF documents
- Documents are parsed, chunked, and embedded using MiniLM
- Embeddings are stored in a Pinecone vector database
- User queries are embedded and matched via top-K similarity search
- Retrieved context is injected into an LCEL-style prompt
- Llama 3 (served via Groq API) generates grounded responses
- Results are delivered through a clean chat interface
This architecture cleanly separates retrieval, prompt design & orchestration, and response generation, making the system extensible and production-ready.
Key Engineering Decisions
Vector Database: Pinecone
Chosen for managed scalability, low-latency similarity search, and reliability in production semantic retrieval workloads.
Embeddings: all-MiniLM-L6-v2
Selected to balance embedding quality, speed, and cost — ideal for real-time document querying.
LLM Inference: Llama 3 via Groq
Groq’s ultra-fast inference significantly reduces latency, enabling smooth, interactive user experiences rather than batch-style responses.
Prompt Orchestration: LCEL-Style Chaining
Structured message composition ensures clear separation between system instructions, retrieved context, and user input — reducing hallucinations and improving consistency.
Core Capabilities
- Semantic retrieval across multiple PDF documents
- Top-K chunk selection for highly relevant context
- Persistent conversational memory across sessions
- Retry mechanism to explore alternative grounded responses
- Clean, professional chat interface optimized for real usage
Outcome & Impact
- Delivered a complete end-to-end RAG pipeline from ingestion to inference
- Achieved fast response times suitable for interactive knowledge access
- Built a reusable architecture applicable to internal knowledge bases, research assistants, and support tools
This project demonstrates applied AI engineering beyond experimentation — with real deployment constraints in mind.
Live Demo
▶ Try the system live on Hugging Face
An interactive demo running in a production-style environment.
Skills Demonstrated
- Retrieval-Augmented Generation (RAG)
- Vector Databases & Semantic Search
- LLM Prompt Orchestration (LCEL)
- Cloud-Based AI Deployment
- System Design & Trade-Off Analysis
Planned Improvements
- Document-level metadata filtering
- Hybrid semantic + keyword retrieval
- Retrieval quality evaluation metrics
- User authentication and document isolation
Why This Project Matters
Many AI projects fail after the demo stage due to unreliable behavior and rising operational costs.
This system was designed to avoid those problems by prioritizing deployability, maintainability, and predictable performance from day one.