Context Enrichment Window Around Chunks Using LlamaIndex

This project enhances AI-powered document retrieval by integrating LlamaIndex, FAISS, and OpenAI embeddings. It improves search accuracy by using sentence windows, ensuring contextually rich and relevant responses.

• Enhances context-aware document retrieval by providing full context instead of isolated sentences.

• Uses FAISS vector search for fast and scalable information retrieval.

• Breaks down long PDFs into structured, searchable text chunks.

• Allows customizable query processing for fine-tuned search results.

• Improves AI-powered Q&A systems with more relevant, context-rich answers.

• Demonstrates context-enriched search vs. standard retrieval for better accuracy.

• Helps organizations manage internal documents and knowledge bases efficiently.

• Optimizes data storage and retrieval using vector embeddings.

• Enables quick document summarization for faster insights.

• Can be integrated with LLMs like GPT-4o for AI-driven search applications.