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https://github.com/jimmylv/chatvox

"Chat With Any Video" project in 24 hours, challenge myself to complete in @Supabase's AI Hackathon.
https://github.com/jimmylv/chatvox

ai chatgpt openai supabase video whisper

Last synced: 28 days ago
JSON representation

"Chat With Any Video" project in 24 hours, challenge myself to complete in @Supabase's AI Hackathon.

Host: GitHub
URL: https://github.com/jimmylv/chatvox
Owner: JimmyLv
License: gpl-3.0
Created: 2023-04-16T05:54:19.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-06-02T04:39:50.000Z (7 months ago)
Last Synced: 2024-11-16T19:55:49.848Z (about 1 month ago)
Topics: ai, chatgpt, openai, supabase, video, whisper
Language: TypeScript
Homepage: https://chatvox.aitodo.co
Size: 1.84 MB
Stars: 100
Watchers: 6
Forks: 15
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE.txt

Awesome Lists containing this project

README

        # ChatVox - "Chat With Any Video"

> based on Supabase's [Next.js OpenAI Doc Search Starter](https://github.com/supabase-community/nextjs-openai-doc-search?og=v2)

![ChatVox Cover Image](/public/og-image.png)

ChatVox · Chat With Any Video: This project takes the YouTube/Podcast link you enter or the local Video/Audio file (WIP) you upload on the page, then you can chat with the video, just like ChatGPT! 🤩

Talking to a video is essentially talking to this person, It meant "chat with wisdom" 🤣

I'm updating AI Hackathon on Twitter in real-time, This is the showcase of the whole process. https://twitter.com/Jimmy_JingLv/status/1647483720608415744?s=20

The technology behind it is to process them to use as the custom context within [OpenAI Text Completion](https://platform.openai.com/docs/guides/completion) prompts and LangChain to handle the document searching and Q&A answering.

## 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_API_KEY` and you're ready to go!

[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=Next.js%20OpenAI%20Doc%20Search%20Starter&demo-description=Template%20for%20building%20your%20own%20custom%20ChatGPT%20style%20doc%20search%20powered%20by%20Next.js%2C%20OpenAI%2C%20and%20Supabase.&demo-url=https%3A%2F%2Fsupabase.com%2Fdocs&demo-image=%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F1OntM6THNEUvlUsYy6Bjmf%2F475e39dbc84779538c8ed47c63a37e0e%2Fnextjs_openai_doc_search_og.png&project-name=Next.js%20OpenAI%20Doc%20Search%20Starter&repository-name=nextjs-openai-doc-search-starter&repository-url=https%3A%2F%2Fgithub.com%2FJimmyLv%2FChatVox%2F&from=github&integration-ids=oac_jUduyjQgOyzev1fjrW83NYOv&env=OPENAI_API_KEY&envDescription=Get%20your%20OpenAI%20API%20key%3A&envLink=https%3A%2F%2Fplatform.openai.com%2Faccount%2Fapi-keys&teamCreateStatus=hidden&external-id=nextjs-open-ai-doc-search)

## 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 the setup of the `pgvector` extension is stored in the [`supabase/migrations` folder](./supabase/migrations/) which is automatically applied to your local Postgres instance when running `supabase start`.

## Local Development

### Configuration

- `cp .env.example .env`

- Set your `OPENAI_API_KEY` in the newly created `.env` file.

### Start Supabase

Make sure you have Docker installed and running locally. Then run

```bash

supabase start

```

### Start the Next.js App

In a new terminal window, run

```bash

pnpm dev

```

## 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_API_KEY` and you're ready to go!

[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?demo-title=Next.js%20OpenAI%20Doc%20Search%20Starter&demo-description=Template%20for%20building%20your%20own%20custom%20ChatGPT%20style%20doc%20search%20powered%20by%20Next.js%2C%20OpenAI%2C%20and%20Supabase.&demo-url=https%3A%2F%2Fsupabase.com%2Fdocs&demo-image=%2F%2Fimages.ctfassets.net%2Fe5382hct74si%2F1OntM6THNEUvlUsYy6Bjmf%2F475e39dbc84779538c8ed47c63a37e0e%2Fnextjs_openai_doc_search_og.png&project-name=Next.js%20OpenAI%20Doc%20Search%20Starter&repository-name=nextjs-openai-doc-search-starter&repository-url=https%3A%2F%2Fgithub.com%2Fsupabase-community%2Fnextjs-openai-doc-search%2F&from=github&integration-ids=oac_jUduyjQgOyzev1fjrW83NYOv&env=OPENAI_API_KEY&envDescription=Get%20your%20OpenAI%20API%20key%3A&envLink=https%3A%2F%2Fplatform.openai.com%2Faccount%2Fapi-keys&teamCreateStatus=hidden&external-id=nextjs-open-ai-doc-search)

## Learn More

- Read the blogpost on how we built [ChatGPT for the Supabase Docs](https://supabase.com/blog/chatgpt-supabase-docs).

- [[Docs] pgvector: Embeddings and vector similarity](https://supabase.com/docs/guides/database/extensions/pgvector)

- Watch [Greg's](https://twitter.com/ggrdson) "How I built this" [video](https://youtu.be/Yhtjd7yGGGA) on the [Rabbit Hole Syndrome YouTube Channel](https://www.youtube.com/@RabbitHoleSyndrome):

[![Video: How I Built Supabase’s OpenAI Doc Search](https://img.youtube.com/vi/Yhtjd7yGGGA/0.jpg)](https://www.youtube.com/watch?v=Yhtjd7yGGGA)