End-to-End RAG Application with AstraDB & Llama 3.1

The challenge

A technology-driven organization aimed to enhance information retrieval and knowledge accessibility by implementing an end-to-end Retrieval-Augmented Generation (RAG) solution. The objective was to build a scalable AI system capable of retrieving contextual information from multiple sources and generating accurate, real-time responses using open-source Large Language Models (LLMs). The solution was designed to combine document-based retrieval, live web search, and intelligent agent orchestration to deliver reliable and up-to-date responses across diverse information domains.

Key challenges

• Managing large-scale storage and retrieval of embeddings efficiently
• Combining document-based knowledge with real-time external information sources
• Ensuring response accuracy and contextual relevance across multiple APIs
• Orchestrating multiple tools and retrieval pipelines effectively
• Maintaining scalability and low-latency performance for enterprise usage

Technology used

• Models Used: Llama 3.1 and Gemma2-9B-it for intelligent response generation
• Database: AstraDB (Apache Cassandra) for vector storage and retrieval
• Frameworks: LangChain and LangGraph for orchestration and workflow management
• APIs Integrated: Groq API, SERP API, and Hugging Face API for live search and model capabilities
• Data Wrappers: Arxiv API Wrapper and Wikipedia API Wrapper for enriched knowledge retrieval

What we built

• Data Ingestion & Embedding: Processes PDFs and web pages to generate and store embeddings in AstraDB
• Hybrid Retrieval Engine: Combines vector search with live API-based information retrieval
• Agent Orchestration: Uses LangChain and LangGraph for intelligent query routing and workflow execution
• Response Generation: Consolidates retrieved context and generates accurate responses using Llama 3.1
• Automation & Scalability: Supports enterprise-grade deployment with modular architecture

Objectives

• Build a scalable Retrieval-Augmented Generation (RAG) system
• Enable efficient storage and retrieval of embeddings at scale
• Integrate open-source LLMs for intelligent response generation
• Combine static document retrieval with live web search capabilities
• Improve answer accuracy through agent orchestration and contextual retrieval
• Deliver up-to-date and reliable responses across multiple knowledge sources

Outcomes

• Scalable vector storage enabling fast and efficient retrieval at enterprise scale
• Improved response quality through hybrid retrieval combining embeddings and live search APIs
• Better contextual accuracy with orchestrated multi-source knowledge retrieval
• Always up-to-date responses through integration with real-time external sources
• Modular and extensible architecture for future AI enhancements
• Faster access to reliable information with intelligent response generation