mirror of
https://github.com/arc53/DocsGPT
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154 lines
4.0 KiB
Markdown
154 lines
4.0 KiB
Markdown
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App currently has two main api endpoints:
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### /api/answer
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Its a POST request that sends a JSON in body with 4 values. Here is a JavaScript fetch example
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It will recieve an answer for a user provided question
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```
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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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In response you will get a json document like this one:
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```
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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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### /api/docs_check
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It will make sure documentation is loaded on a server (just run it everytime user is switching between libraries (documentations)
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Its a POST request that sends a JSON in body with 1 value. Here is a JavaScript fetch example
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```
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// answer (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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In response you will get a json document like this one:
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```
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{
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"status": "exists"
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}
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```
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### /api/combine
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Provides json that tells UI which vectors are available and where they are located with a simple get request
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Respsonse will include:
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date, description, docLink, fullName, language, location (local or docshub), model, name, 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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### /api/upload
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Uploads file that needs to be trained, response is json with task id, which can be used to check on tasks progress
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HTML example:
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```
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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-blue-500 rounded-md hover:bg-blue-600 focus:outline-none focus:ring-2 focus:ring-offset-2 focus:ring-blue-500">
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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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```
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{
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"status": "ok",
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"task_id": "b2684988-9047-428b-bd47-08518679103c"
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}
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```
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### /api/task_status
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Gets task status (task_id) from /api/upload
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```
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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=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
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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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Responses:
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There are two types of repsonses:
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1. while task it still running, where "current" will show progress from 0 - 100
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```
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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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```
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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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### /api/delete_old
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deletes old vecotstores
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```
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// Task status (GET http://127.0.0.1:5000/api/docs_check)
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fetch("http://localhost:5001/api/task_status?task_id=b2d2a0f4-387c-44fd-a443-e4fe2e7454d1", {
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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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```
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{"status": 'ok'}
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```
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