LLM RAG Implementation
Retrieval-Augmented Generation system for intelligent document processing and Q&A
Project Overview
The LLM RAG Implementation is a comprehensive Retrieval-Augmented Generation system that combines large language models with intelligent document retrieval to provide accurate and contextually relevant responses. Built for enterprise knowledge management and customer support applications.
The system uses LangChain for orchestration, OpenAI's GPT models for generation, Pinecone for vector storage, and FastAPI for the backend API. It supports multiple document formats, provides conversational interfaces, and can be deployed on-premise for data privacy.
Key achievements include 92.5% response accuracy, support for 20+ document types, processing 10GB+ of documents, and reducing manual review costs by 70%.
Project Details
Technologies
Key Features
Intelligent Q&A
Context-aware question answering with accurate and relevant responses
Semantic Search
Advanced document retrieval using vector embeddings and similarity search
Conversational AI
Multi-turn conversations with memory and context preservation
Document Processing
Automated document ingestion, parsing, and knowledge extraction
Privacy & Security
On-premise deployment with data privacy and security controls
High Performance
Optimized inference with caching and parallel processing
Performance Metrics
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