Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Template for building your own custom ChatGPT style doc search powered by Next.js, OpenAI, and Supabase.
| Date | Stars |
|---|---|
| 2026-07-24 | 1729 |
| 2026-07-25 | 1729 |
| 2026-07-28 | 1729 |
| 2026-07-30 | 1729 |
| 2026-08-06 | 1729 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Next.js OpenAI Doc Search Starter
This starter takes all the `.mdx` files in the `pages` directory and processes them to use as custom context within [OpenAI Text Completion](https://platform.openai.com/docs/guides/completion) prompts.
## Deploy
Deploy this starter to Vercel. The Supabase integration will automatically set the required environment variables and configure your [Database Schema](./supabase/migrations/20230406025118_init.sql). All you have to do is set your `OPENAI_KEY` and you're ready to go!
[
## Technical Details
Building your own custom ChatGPT involves four steps:
1. [👷 Build time] Pre-process the knowledge base (your `.mdx` files in your `pages` folder).
2. [👷 Build time] Store embeddings in Postgres with [pgvector](https://supabase.com/docs/guides/database/extensions/pgvector).
3. [🏃 Runtime] Perform vector similarity search to find the content that's relevant to the question.
4. [🏃 Runtime] Inject content into OpenAI GPT-3 text completion prompt and stream response to the client.
## 👷 Build time
Step 1. and 2. happen at build time, e.g. when Vercel builds your Next.js app. During this time the [`generate-embeddings`](./lib/generate-embeddings.ts) script is being executed which performs the following tasks:
```mermaid
sequenceDiagram
participant Vercel
participant DB (pgvector)
participant OpenAI (API)
loop 1. Pre-process the knowledge base
Vercel->>Vercel: Chunk .mdx pages into sections
loop 2. Create & store embeddings
Vercel->>OpenAI (API): create embedding for page section
OpenAI (API)->>Vercel: embedding vector(1536)
Vercel->>DB (pgvector): store embedding for page section
end
end
```
In addition to storing the embeddings, this script generates a checksum for each of your `.mdx` files and stores this in another database table to make sure the embeddings are only regenerated when the file has changed.
## 🏃 Runtime
Step 3. and 4. happen at runtime, anytime the user submits a question. When this happens, the following sequence of tasks is performed:
```mermaid
sequenceDiagram
participant Client
participant Edge Function
participant DB (pgvector)
participant OpenAI (API)
Client->>Edge Function: { query: lorem ispum }
critical 3. Perform vector similarity search
Edge Function->>OpenAI (API): create embedding for query
OpenAI (API)->>Edge Function: embedding vector(1536)
Edge Function->>DB (pgvector): vector similarity search
DB (pgvector)->>Edge Function: relevant docs content
end
critical 4. Inject content into prompt
Edge Function->>OpenAI (API): completion request prompt: query + relevant docs content
OpenAI (API)-->>Client: text/event-stream: completions response
end
```
The relevant files for this are the [`SearchDialog` (Client)](./components/SearchDialog.tsx) component and the [`vector-search` (Edge Function)](./pages/api/vector-search.ts).
The initialization of the database, including Excerpt of 5,806 characters
Read on GitHub
Thor 雷神 Schaeff · Google DeepMind · United States
12
Greg Richardson · Supabase
11
10
4
2
2
2
1
1
1
Aditya Tripathi · @Climactic · India
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ec78245d193c3553, topic:vector-search, readme:vector search, readme:similarity search
matched fp:ec78245d193c3553, topic:chatgpt