This article outlines a method for building a 'smart documentation' chatbot, aimed at improving information retrieval from various documentation formats. The approach involves reading documentation files, indexing them by splitting into smaller chunks, generating embeddings using OpenAI, and enabling a similarity search for user queries. This chatbot can answer frequently asked questions, facilitate document searches, and capture user queries. The solution is particularly applicable to Markdown files, but is adaptable for other text formats, ensuring an efficient and user-friendly information access system.
Creating a smart documentation chatbot involves indexing documentation into manageable sections and using OpenAI for embeddings, enabling users to efficiently retrieve specific information.
The chatbot can be versatile, providing quick answers to FAQs, searching docs akin to Algolia, and capturing user queries for better support.
Document files are dynamically read from a specified folder using tools like glob, avoiding hardcoded text to ensure scalability and adaptability.
By splitting documentation into chunks and applying embeddings, the chatbot enhances the user experience by quickly connecting them to relevant information.
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