Nextjs-Openai-Doc-Search

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nextjs-openai-doc-search is an Open Source customer support & CX tool that provides a template for building custom ChatGPT-style documentation search. It leverages Next.js, OpenAI, and Supabase to create AI-powered search functionality.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is nextjs-openai-doc-search?

nextjs-openai-doc-search offers a robust template for developers to create their own ChatGPT-style documentation search engines. This open-source project integrates Next.js for the frontend, OpenAI for AI capabilities, and Supabase for database management, including pgvector for embedding storage. It processes .mdx files to generate custom context for OpenAI's text completion prompts, enabling highly relevant search results. The tool handles both build-time processing of knowledge bases and runtime vector similarity searches, making it ideal for providing intelligent, context-aware answers to user queries based on specific documentation.

Best used for

Ideal for developers and technical teams who need to build custom AI-powered search experiences for their documentation, integrate OpenAI's text completion, and leverage Supabase for backend services. Especially valuable for creating intelligent knowledge bases that provide contextually relevant answers to user queries.

Common actions

build AI search

create doc chatbots

implement vector search

integrate OpenAI

develop custom AI tools

collaborationautomated workflowopen-sourcelow-code/no-codedeepfakeworkflowsgithub copilotface swapping"AI Agents"

Capabilities

Key features

ChatGPT-style doc search
Next.js, OpenAI, Supabase
MDX file processing
Embeddings with pgvector
Vector similarity search
Streamed AI responses

Target Audience

developerstechnical writersdocumentation managers

Integrations

openaisupabasenext.js

Pricing & Plans

Open Source

Free

FAQs

What technologies are used in nextjs-openai-doc-search?

This template is built using Next.js for the frontend, OpenAI for AI capabilities like text completion and embeddings, and Supabase for the backend, including PostgreSQL with the pgvector extension for storing and querying vector embeddings.

How does the tool process documentation for search?

It processes .mdx files from your project's pages directory at build time. These files are chunked into sections, and embeddings are created for each section using OpenAI's API. These embeddings are then stored in a Supabase Postgres database with pgvector.

Can I use my own custom documentation format?

By default, the tool is configured to work with .mdx files. While it's possible to adapt it for other formats, the current setup specifically processes .mdx files for generating embeddings and context for the AI search.

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Voice & Call Center Email & Chat Support Customer Feedback Community Management Customer Success Multilingual Support

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