A naukri.com initiative
Hackernoon
2w
302
Image Credit: Hackernoon
How to Build a Smart Documentation - Based on OpenAI Embeddings (Chunking, Indexing, and Searching)
- The article discusses building a 'smart documentation' chatbot by indexing documentation into manageable chunks, generating embeddings with OpenAI, and performing similarity search.
- The purpose is to create a chatbot that can provide answers from documentation based on user queries, using Markdown files as an example.
- The solution involves three main parts: reading documentation files, indexing the documentation through chunking and embedding, and searching the documentation.
- Documentation files can be scanned from a folder or fetched from a database or CMS.
- Indexing involves chunking documents, generating vector embeddings for each chunk, and storing embeddings locally.
- Chunking is vital to prevent data exceeding model limits, while overlap ensures context continuity between chunks.
- Vector embeddings from OpenAI are used for similarity searches between user queries and document chunks.
- Cosine similarity is calculated to filter relevant document chunks based on user queries.
- A small Express.js endpoint integrates OpenAI's Chat API to generate responses based on the most relevant document chunks.
- The article provides code snippets and explains the process step by step, offering a template for building a chatbot with a chat-like interface.
Read Full Article
18 Likes
For uninterrupted reading, download the app