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

Currently, the application provides the following main API endpoints:

/api/answer

It's a POST request that sends a JSON in body with 4 values. It will receive an answer for a user provided question. Here is a JavaScript fetch 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))

In response you will get a json document like this one:

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

/api/docs_check

It will make sure documentation is loaded on a server (just run it every time user is switching between libraries (documentations)). It's a POST request that sends a JSON in body with 1 value. Here is a JavaScript fetch example:

// 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))

In response you will get a json document like this one:

{
  "status": "exists"
}

/api/combine

Provides json that tells UI which vectors are available and where they are located with a simple get request.

Response will include: date, description, docLink, fullName, language, location (local or docshub), model, name, version.

Example of json in Docshub and local:

/api/upload

Uploads file that needs to be trained, response is json with task id, which can be used to check on tasks progress 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:

{
  "status": "ok",
  "task_id": "b2684988-9047-428b-bd47-08518679103c"
}

/api/task_status

Gets task status (task_id) from /api/upload:

// Task status (Get http://127.0.0.1:5000/api/task_status)
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))

Responses: There are two types of responses:

1. while task it still running, where "current" 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"
}

/api/delete_old

Deletes old vectorstores:

// 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:

{ "status": "ok" }