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Build a documentation chatbot using Firecrawl for web scraping and KDB.AI for vector storage and retrieval. ➡️ Firecrawl simplifies the web scraping process, allowing efficient extraction of relevant content from documentation websites. ➡️ KDB.AI provides powerful vector storage and querying capabilities, enabling context-aware searches through metadata filtering. ➡️ The integration of these tools with LlamaIndex creates a chatbot that can keep documentation up-to-date, understand query context, and provide relevant responses. Check out the tutorial: https://lnkd.in/gVj-4nb3

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This is a game-changer for efficient and accurate content extraction! The combination of Firecrawl and KDB.AI simplifies the process, making it easier to keep documentation updated. Thanks for sharing this insightful tutorial. #WebScraping #AIIntegration #DocumentationChatbot

Vector storage significantly impacts retrieval by enabling context-aware searches through metadata filtering. This allows for efficient extraction and querying of relevant content, as demonstrated by the integration of Firecrawl and KDB.AI with LlamaIndex to create a documentation chatbot that provides up-to-date and relevant responses.

Kunal Purohit

Leading GenAI Technology Strategy in Travel Distribution Ops at Amadeus | Awarded ESSEC Metalab Prize for Explainable AI Research

2mo

That’s amazing. Retrieval side of RAG GenAI architecture is a crucial & challenging task especially with dynamic website content. I am excited to try this!!

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Ahmad Jawabreh

Building real AI solutions @CMND.ai

2mo
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Rakesh Agrawal

Customer-Obsessed | High Performing Teams | Delightful Products | Generative AI | Data Engineering

2mo

Balu Gopalakrishna Pillai Sanjana Jain

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