DocsGPT/docs/pages/Developing/API-docs.md

API Endpoints Documentation

Currently, the application provides the following main API endpoints:

1. /api/answer

Description:

This endpoint is used to request answers to user-provided questions.

Request:

Method: POST

Headers: Content-Type should be set to application/json; charset=utf-8

Request Body: JSON object with the following fields:

• question — The user's question.
• history — (Optional) Previous conversation history.
• api_key— Your API key.
• embeddings_key — Your embeddings key.
• active_docs — The location of active documentation.

Here is a JavaScript Fetch Request example:

// answer (POST http://127.0.0.1:5000/api/answer)
fetch("http://127.0.0.1:5000/api/answer", {
      "method": "POST",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
      "body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
      "active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
})
.then((res) => res.text())
.then(console.log.bind(console))

Response

In response, you will get a JSON document containing the answer, query and result:

{
  "answer": "Hi there! How can I help you?\n",
  "query": "Hi",
  "result": "Hi there! How can I help you?\nSOURCES:"
}

2. /api/docs_check

Description:

This endpoint will make sure documentation is loaded on the server (just run it every time user is switching between libraries (documentations)).

Request:

Headers: Content-Type should be set to application/json; charset=utf-8

Request Body: JSON object with the field:

• docs — The location of the documentation:

// answer (POST http://127.0.0.1:5000/api/docs_check)
fetch("http://127.0.0.1:5000/api/docs_check", {
      "method": "POST",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
      "body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
})
.then((res) => res.text())
.then(console.log.bind(console))

Response:

In response, you will get a JSON document like this one indicating whether the documentation exists or not:

{
  "status": "exists"
}

3. /api/combine

Description:

This endpoint provides information about available vectors and their locations with a simple GET request.

Request:

Method: GET

Response:

Response will include:

• date
• description
• docLink
• fullName
• language
• location (local or docshub)
• model
• name
• version

Example of JSON in Docshub and local:

4. /api/upload

Description:

This endpoint is used to upload a file that needs to be trained, response is JSON with task ID, which can be used to check on task's progress.

Request:

Method: POST Request Body: A multipart/form-data form with file upload and additional fields, including user and name.

HTML example:

<form action="/api/upload" method="post" enctype="multipart/form-data" class="mt-2">
    <input type="file" name="file" class="py-4" id="file-upload">
    <input type="text" name="user" value="local" hidden>
    <input type="text" name="name" placeholder="Name:">
    
    <button type="submit" class="py-2 px-4 text-white bg-purple-30 rounded-md hover:bg-purple-30 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-purple-30">
        Upload
    </button>
</form>

Response:

JSON response with a status and a task ID that can be used to check the task's progress.

5. /api/task_status

Description:

This endpoint is used to get the status of a task (task_id) from /api/upload

Request: Method: GET Query Parameter: task_id (task ID to check)

Sample JavaScript Fetch Request:

// Task status (Get http://127.0.0.1:5000/api/task_status)
fetch("http://localhost:5001/api/task_status?task_id=YOUR_TASK_ID", {
      "method": "GET",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
})
.then((res) => res.text())
.then(console.log.bind(console))

Response:

There are two types of responses:

1. While the task is still running, the 'current' value will show progress from 0 to 100.

   {
     "result": {
       "current": 1
     },
     "status": "PROGRESS"
   }
   

2. When task is completed:

   {
     "result": {
       "directory": "temp",
       "filename": "install.rst",
       "formats": [
         ".rst",
         ".md",
         ".pdf"
       ],
       "name_job": "somename",
       "user": "local"
     },
     "status": "SUCCESS"
   }
   

6. /api/delete_old

Description:

This endpoint is used to delete old Vector Stores.

Request:

Method: GET

// Task status (GET http://127.0.0.1:5000/api/docs_check)
fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
      "method": "GET",
      "headers": {
            "Content-Type": "application/json; charset=utf-8"
      },
})
.then((res) => res.text())
.then(console.log.bind(console))

Response:

JSON response indicating the status of the operation:

{ "status": "ok" }