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-blue-500 rounded-md hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500">
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:
- while task it still running, where "current" will show progress from 0 to 100
{
"result": {
"current": 1
},
"status": "PROGRESS"
}
- 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" }