Context Enrichment Window Around Chunks Using LlamaIndex
Optimize document retrieval with AI using FAISS, OpenAI embeddings & context windows for smarter knowledge management & Q&A systems.
$20 USD
$10.00 USD
Project Outcomes
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.
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Enhances context-aware document retrieval by providing full context instead of isolated sentences.
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Uses FAISS vector search for fast and scalable information retrieval.
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Breaks down long PDFs into structured, searchable text chunks.
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Allows customizable query processing for fine-tuned search results.
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Improves AI-powered Q&A systems with more relevant, context-rich answers.
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Demonstrates context-enriched search vs. standard retrieval for better accuracy.
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Helps organizations manage internal documents and knowledge bases efficiently.
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Optimizes data storage and retrieval using vector embeddings.
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Enables quick document summarization for faster insights.
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Can be integrated with LLMs like GPT-4o for AI-driven search applications.
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