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# 中国法律AI助手

法律文件出处：https://github.com/LawRefBook/Laws
法律文件来源：https://github.com/LawRefBook/Laws

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.
项目模板：https://github.com/supabase-community/nextjs-openai-doc-search

## Deploy
这个项目从 `pages` 目录中获取所有的 `.mdx` 文件，并将其处理成自定义上下文，以在[OpenAI 文本自动补全](https://platform.openai.com/docs/guides/completion)提示中使用。

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!
## 部署

部署此starter到Vercel。Supabase集成将自动设置所需的环境变量并配置您的[数据库概要](./supabase/migrations/20230406025118_init.sql)。您只需要设置 `OPENAI_KEY`，然后就可以开始了！

[![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_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
## 技术细节

Building your own custom ChatGPT involves four steps:
构建您自己的自定义ChatGPT涉及四个步骤：

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.
1. [👷 构建时间] 预处理知识库（您的 `pages` 文件夹中的 `.mdx` 文件）。
2. [👷 构建时间] 在PostgreSQL中使用 [pgvector](https://supabase.com/docs/guides/database/extensions/pgvector) 存储嵌入向量。
3. [🏃 运行时] 执行向量相似性搜索，查找与问题相关的内容。
4. [🏃 运行时] 将内容注入到OpenAI GPT-3文本自动补全中，并将响应流式传输到客户端。

## 👷 Build time
## 👷 构建时间

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:
步骤1和2发生在构建时间，例如当Vercel构建您的Next.js应用程序时。此时执行 [`generate-embeddings`](./lib/generate-embeddings.ts) 脚本，该脚本执行以下任务：

```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
loop 1. 预处理知识库
Vercel->>Vercel: 将 .mdx 页面划分为部分
loop 2. 创建并存储嵌入
Vercel->>OpenAI (API): 为页面部分创建嵌入
OpenAI (API)->>Vercel: 嵌入向量(1536)
Vercel->>DB (pgvector): 存储页面部分的嵌入
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.
除了存储嵌入向量之外，此脚本还为每个 `.mdx` 文件生成一个校验和，并将其存储在另一个数据库表中，以确保仅当文件更改时才重新生成嵌入向量。

## 🏃 Runtime
## 🏃 运行时

Step 3. and 4. happen at runtime, anytime the user submits a question. When this happens, the following sequence of tasks is performed:
步骤3和4在运行时发生，即用户提交问题时。发生这种情况时，执行以下一系列任务：

```mermaid
sequenceDiagram
Expand All @@ -51,55 +53,57 @@ sequenceDiagram
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
critical 3. 执行向量相似性搜索
Edge Function->>OpenAI (API): 为查询创建嵌入
OpenAI (API)->>Edge Function: 嵌入向量(1536)
Edge Function->>DB (pgvector): 向量相似性搜索
DB (pgvector)->>Edge Function: 相关文档内容
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
critical 4. 将内容注入到提示中
Edge Function->>OpenAI (API): 完成请求提示：查询+相关文档内容
OpenAI (API)-->>Client: text/event-stream：自动补全响应
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).
此为 [`SearchDialog（客户端）`](./components/SearchDialog.tsx)组件和[`vector-search（边缘函数）`](./pages/api/vector-search.ts)负责的相关文件。

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`.
数据库的初始化，包括 `pgvector` 扩展的设置存储在 [`supabase/migrations`文件夹](./supabase/migrations/)中，并在运行 `supabase start` 时自动应用于本地PostgreSQL实例。

## Local Development
## 本地开发

### Configuration
### 配置

- `cp .env.example .env`
- Set your `OPENAI_KEY` in the newly created `.env` file.
- 在新创建的 `.env` 文件中设置 `OPENAI_KEY`。

### Start Supabase
### 启动 Supabase

Make sure you have Docker installed and running locally. Then run
确保已安装并在本地运行 Docker。然后运行

```bash
supabase start
```

### Start the Next.js App
### 启动 Next.js 应用程序

In a new terminal window, run
在新的终端窗口中运行

```bash
pnpm dev
```

## Deploy
## 部署

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!
仅需将此 starter 部署到 Vercel。Supabase集成将自动设置所需的环境变量并配置您的[数据库Schema](./supabase/migrations/20230406025118_init.sql)。您只需设置 `OPENAI_KEY` 并开始使用即可！

[![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_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
## 了解更多

- 阅读我们如何建立[Supabase文档的ChatGPT](https://supabase.com/blog/chatgpt-supabase-docs)的博客帖子。
- [[Docs] pgvector：嵌入和向量相似性](https://supabase.com/docs/guides/database/extensions/pgvector)函数。
- 观看[Greg](https://twitter.com/ggrdson) 关于[Rabbit Hole Syndrome YouTube Channel](https://www.youtube.com/@RabbitHoleSyndrome)的 "How I built this" [video](https://youtu.be/Yhtjd7yGGGA):

- 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)

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