RAG Agriculture Knowledge Assistant

A smart document retrieval system that converts agricultural documents into an interactive form for building an intelligence base. The retrieval-augmented generation system is used to ask the system any question the users wish to know with the answer being referenced and accurate.

Overview

Key Features

• Document Upload/Indexing: Automatically indexes PDF files, cleans text using PyMuPDF, and generates vector representations for semantic searching.
• Natural Language Queries: Ask questions in natural, everyday language and receive answers that take context into consideration.
• Source Attribution: Each answer contains references to the source documents, making them traceable and verifiable.
• Vector Similarity Search: Based on ChromaDB for efficient semantic document searches by meaning, not keyword.
• Integration of LLM: Using Ollama for local inference with Qwen2:1.5B, preserving privacy and avoiding API charges.
• Document Management: See, list, and delete indexed documents via a clean API.

Technical Architecture

• Backend: FastAPI with async support.
• Vector Database: ChromaDB for persistent embedding storage.
• Embeddings: Default ChromaDB embedding function.
• LLM: Ollama (local inference) with Qwen2:1.5B.
• Document Processing: PyMuPDF (fitz) for text extraction, LangChain for recursive chunking.
• Containerization: Docker + Docker Compose managed via Makefile.

System Performance

• Fast Indexing: Processes complex PDFs quickly using high-performance C-based extraction (fitz).
• Sub-second Retrieval: Retrieves relevant context chunks in <100ms via vector search.
• Streaming Responses: Real-time token generation for a smooth user experience.
• Scalable Storage: Persistent vector database ensures knowledge remains available across restarts.

Installation & Setup

The project is fully containerized. Use the provided Makefile to manage the application:

# Build and start the entire stack (Ollama, Backend, Frontend)
make

# Stop all services
make down

# View live logs
make logs

API Endpoints

POST /upload # Upload and index PDF documents

GET /documents # List all indexed documents

DELETE /documents/{filename} # Remove document from knowledge base

POST /chat?query=… # Ask questions and get answers

POST /search?query=… # Vector search without LLM generation

Use Cases

• Agricultural Research: Query farming best practices, crop disease information, and irrigation guidelines
• Knowledge Management: Convert static document libraries into interactive Q&A systems
• Decision Support: Get quick answers from policy documents, regulations, and technical manuals
• Educational Tools: Create study assistants from textbooks and research papers

Technical Highlights

• Recursive Chunking: Uses a 1500-character window to preserve semantic meaning better than fixed-size blocks.
• Source-Grounded Responses: LLM is constrained to answer only from provided context, preventing hallucinations
• Containerized Orchestration: Seamless deployment of AI, Backend, and Frontend services using Docker.
• Local Data Privacy: Entire pipeline runs locally; no data ever leaves your machine.

Project Learnings

This project demonstrates expertise in:

• Information Retrieval: Vector embeddings, semantic search, and similarity metrics
• NLP Engineering: Text preprocessing, chunking strategies, and prompt engineering
• System Design: Building production-ready APIs with proper error handling and validation
• AI/ML Integration: Combining multiple AI components (embeddings, vector DBs, LLMs) into a cohesive system

Future Enhancements

• Multi-modal support (images, tables from PDFs)
• Conversational memory for follow-up questions
• Batch document processing
• Advanced filtering (by date, document type, custom metadata)

This project showcases the practical application of RAG architecture for domain-specific knowledge extraction, demonstrating how modern AI can make large document collections instantly queryable.