mirror of
https://github.com/arc53/DocsGPT
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350 lines
7.9 KiB
Markdown
350 lines
7.9 KiB
Markdown
# API Endpoints Documentation
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*Currently, the application provides the following main API endpoints:*
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### 1. /api/answer
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**Description:**
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This endpoint is used to request answers to user-provided questions.
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**Request:**
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**Method**: `POST`
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**Headers**: Content-Type should be set to `application/json; charset=utf-8`
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**Request Body**: JSON object with the following fields:
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* `question` — The user's question.
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* `history` — (Optional) Previous conversation history.
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* `api_key`— Your API key.
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* `embeddings_key` — Your embeddings key.
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* `active_docs` — The location of active documentation.
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Here is a JavaScript Fetch Request example:
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```js
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// answer (POST http://127.0.0.1:5000/api/answer)
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fetch("http://127.0.0.1:5000/api/answer", {
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"method": "POST",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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"body": JSON.stringify({"question":"Hi","history":null,"api_key":"OPENAI_API_KEY","embeddings_key":"OPENAI_API_KEY",
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"active_docs": "javascript/.project/ES2015/openai_text-embedding-ada-002/"})
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})
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.then((res) => res.text())
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.then(console.log.bind(console))
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```
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**Response**
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In response, you will get a JSON document containing the `answer`, `query` and `result`:
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```json
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{
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"answer": "Hi there! How can I help you?\n",
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"query": "Hi",
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"result": "Hi there! How can I help you?\nSOURCES:"
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}
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```
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### 2. /api/docs_check
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**Description:**
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This endpoint will make sure documentation is loaded on the server (just run it every time user is switching between libraries (documentations)).
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**Request:**
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**Method**: `POST`
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**Headers**: Content-Type should be set to `application/json; charset=utf-8`
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**Request Body**: JSON object with the field:
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* `docs` — The location of the documentation:
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```js
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// docs_check (POST http://127.0.0.1:5000/api/docs_check)
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fetch("http://127.0.0.1:5000/api/docs_check", {
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"method": "POST",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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"body": JSON.stringify({"docs":"javascript/.project/ES2015/openai_text-embedding-ada-002/"})
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})
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.then((res) => res.text())
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.then(console.log.bind(console))
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```
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**Response:**
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In response, you will get a JSON document like this one indicating whether the documentation exists or not:
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```json
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{
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"status": "exists"
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}
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```
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### 3. /api/combine
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**Description:**
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This endpoint provides information about available vectors and their locations with a simple GET request.
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**Request:**
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**Method**: `GET`
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**Response:**
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Response will include:
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* `date`
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* `description`
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* `docLink`
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* `fullName`
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* `language`
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* `location` (local or docshub)
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* `model`
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* `name`
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* `version`
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Example of JSON in Docshub and local:
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<img width="295" alt="image" src="https://user-images.githubusercontent.com/15183589/224714085-f09f51a4-7a9a-4efb-bd39-798029bb4273.png">
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### 4. /api/upload
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**Description:**
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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.
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**Request:**
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**Method**: `POST`
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**Request Body**: A multipart/form-data form with file upload and additional fields, including `user` and `name`.
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HTML example:
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```html
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<form action="/api/upload" method="post" enctype="multipart/form-data" class="mt-2">
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<input type="file" name="file" class="py-4" id="file-upload">
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<input type="text" name="user" value="local" hidden>
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<input type="text" name="name" placeholder="Name:">
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<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">
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Upload
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</button>
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</form>
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```
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**Response:**
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JSON response with a status and a task ID that can be used to check the task's progress.
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### 5. /api/task_status
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**Description:**
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This endpoint is used to get the status of a task (`task_id`) from `/api/upload`
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**Request:**
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**Method**: `GET`
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**Query Parameter**: `task_id` (task ID to check)
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**Sample JavaScript Fetch Request:**
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```js
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// Task status (Get http://127.0.0.1:5000/api/task_status)
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fetch("http://localhost:5001/api/task_status?task_id=YOUR_TASK_ID", {
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"method": "GET",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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})
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.then((res) => res.text())
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.then(console.log.bind(console))
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```
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**Response:**
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There are two types of responses:
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1. While the task is still running, the 'current' value will show progress from 0 to 100.
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```json
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{
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"result": {
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"current": 1
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},
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"status": "PROGRESS"
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}
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```
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2. When task is completed:
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```json
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{
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"result": {
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"directory": "temp",
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"filename": "install.rst",
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"formats": [
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".rst",
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".md",
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".pdf"
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],
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"name_job": "somename",
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"user": "local"
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},
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"status": "SUCCESS"
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}
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```
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### 6. /api/delete_old
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**Description:**
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This endpoint is used to delete old Vector Stores.
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**Request:**
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**Method**: `GET`
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**Query Parameter**: `task_id`
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**Sample JavaScript Fetch Request:**
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```js
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// delete_old (GET http://127.0.0.1:5000/api/delete_old)
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fetch("http://localhost:5001/api/delete_old?task_id=YOUR_TASK_ID", {
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"method": "GET",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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})
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.then((res) => res.text())
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.then(console.log.bind(console))
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```
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**Response:**
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JSON response indicating the status of the operation:
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```json
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{ "status": "ok" }
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```
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### 7. /api/get_api_keys
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**Description:**
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The endpoint retrieves a list of API keys for the user.
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**Request:**
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**Method**: `GET`
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**Sample JavaScript Fetch Request:**
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```js
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// get_api_keys (GET http://127.0.0.1:5000/api/get_api_keys)
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fetch("http://localhost:5001/api/get_api_keys", {
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"method": "GET",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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})
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.then((res) => res.text())
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.then(console.log.bind(console))
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```
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**Response:**
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JSON response with a list of created API keys:
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```json
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[
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{
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"id": "string",
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"name": "string",
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"key": "string",
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"source": "string"
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},
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...
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]
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```
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### 8. /api/create_api_key
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**Description:**
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Create a new API key for the user.
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**Request:**
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**Method**: `POST`
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**Headers**: Content-Type should be set to `application/json; charset=utf-8`
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**Request Body**: JSON object with the following fields:
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* `name` — A name for the API key.
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* `source` — The source documents that will be used.
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* `prompt_id` — The prompt ID.
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* `chunks` — The number of chunks used to process an answer.
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Here is a JavaScript Fetch Request example:
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```js
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// create_api_key (POST http://127.0.0.1:5000/api/create_api_key)
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fetch("http://127.0.0.1:5000/api/create_api_key", {
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"method": "POST",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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"body": JSON.stringify({"name":"Example Key Name",
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"source":"Example Source",
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"prompt_id":"creative",
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"chunks":"2"})
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})
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.then((res) => res.json())
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.then(console.log.bind(console))
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```
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**Response**
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In response, you will get a JSON document containing the `id` and `key`:
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```json
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{
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"id": "string",
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"key": "string"
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}
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```
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### 9. /api/delete_api_key
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**Description:**
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Delete an API key for the user.
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**Request:**
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**Method**: `POST`
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**Headers**: Content-Type should be set to `application/json; charset=utf-8`
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**Request Body**: JSON object with the field:
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* `id` — The unique identifier of the API key to be deleted.
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Here is a JavaScript Fetch Request example:
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```js
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// delete_api_key (POST http://127.0.0.1:5000/api/delete_api_key)
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fetch("http://127.0.0.1:5000/api/delete_api_key", {
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"method": "POST",
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"headers": {
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"Content-Type": "application/json; charset=utf-8"
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},
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"body": JSON.stringify({"id":"API_KEY_ID"})
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})
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.then((res) => res.json())
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.then(console.log.bind(console))
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```
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**Response:**
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In response, you will get a JSON document indicating the status of the operation:
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```json
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{
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"status": "ok"
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}
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``` |