main.py

# This is a version of the main.py file found in ../../server/main.py that also gives ChatGPT access to the upsert endpoint
# (allowing it to save information from the chat back to the vector) database.
# Copy and paste this into the main file at ../../server/main.py if you choose to give the model access to the upsert endpoint
# and want to access the openapi.json when you run the app locally at http://0.0.0.0:8000/sub/openapi.json.
import os
from typing import Optional
import uvicorn
from fastapi import FastAPI, File, Form, HTTPException, Depends, Body, UploadFile
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from fastapi.staticfiles import StaticFiles
from loguru import logger

from models.api import (
    DeleteRequest,
    DeleteResponse,
    QueryRequest,
    QueryResponse,
    UpsertRequest,
    UpsertResponse,
)
from datastore.factory import get_datastore
from services.file import get_document_from_file

from models.models import DocumentMetadata, Source


bearer_scheme = HTTPBearer()
BEARER_TOKEN = os.environ.get("BEARER_TOKEN")
assert BEARER_TOKEN is not None


def validate_token(credentials: HTTPAuthorizationCredentials = Depends(bearer_scheme)):
    if credentials.scheme != "Bearer" or credentials.credentials != BEARER_TOKEN:
        raise HTTPException(status_code=401, detail="Invalid or missing token")
    return credentials


app = FastAPI()
app.mount("/.well-known", StaticFiles(directory=".well-known"), name="static")

# Create a sub-application, in order to access just the upsert and query endpoints in the OpenAPI schema, found at http://0.0.0.0:8000/sub/openapi.json when the app is running locally
sub_app = FastAPI(
    title="Retrieval Plugin API",
    description="A retrieval API for querying and filtering documents based on natural language queries and metadata",
    version="1.0.0",
    servers=[{"url": "https://your-app-url.com"}],
    dependencies=[Depends(validate_token)],
)
app.mount("/sub", sub_app)


@app.post(
    "/upsert-file",
    response_model=UpsertResponse,
)
async def upsert_file(
    file: UploadFile = File(...),
    metadata: Optional[str] = Form(None),
):
    try:
        metadata_obj = (
            DocumentMetadata.parse_raw(metadata)
            if metadata
            else DocumentMetadata(source=Source.file)
        )
    except:
        metadata_obj = DocumentMetadata(source=Source.file)

    document = await get_document_from_file(file, metadata_obj)

    try:
        ids = await datastore.upsert([document])
        return UpsertResponse(ids=ids)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail=f"str({e})")


@app.post(
    "/upsert",
    response_model=UpsertResponse,
)
async def upsert_main(
    request: UpsertRequest = Body(...),
    token: HTTPAuthorizationCredentials = Depends(validate_token),
):
    try:
        ids = await datastore.upsert(request.documents)
        return UpsertResponse(ids=ids)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail="Internal Service Error")


@sub_app.post(
    "/upsert",
    response_model=UpsertResponse,
    # NOTE: We are describing the shape of the API endpoint input due to a current limitation in parsing arrays of objects from OpenAPI schemas. This will not be necessary in the future.
    description="Save chat information. Accepts an array of documents with text (potential questions + conversation text), metadata (source 'chat' and timestamp, no ID as this will be generated). Confirm with the user before saving, ask for more details/context.",
)
async def upsert(
    request: UpsertRequest = Body(...),
    token: HTTPAuthorizationCredentials = Depends(validate_token),
):
    try:
        ids = await datastore.upsert(request.documents)
        return UpsertResponse(ids=ids)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail="Internal Service Error")


@app.post(
    "/query",
    response_model=QueryResponse,
)
async def query_main(
    request: QueryRequest = Body(...),
    token: HTTPAuthorizationCredentials = Depends(validate_token),
):
    try:
        results = await datastore.query(
            request.queries,
        )
        return QueryResponse(results=results)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail="Internal Service Error")


@sub_app.post(
    "/query",
    response_model=QueryResponse,
    # NOTE: We are describing the shape of the API endpoint input due to a current limitation in parsing arrays of objects from OpenAPI schemas. This will not be necessary in the future.
    description="Accepts search query objects array each with query and optional filter. Break down complex questions into sub-questions. Refine results by criteria, e.g. time / source, don't do this often. Split queries if ResponseTooLargeError occurs.",
)
async def query(
    request: QueryRequest = Body(...),
    token: HTTPAuthorizationCredentials = Depends(validate_token),
):
    try:
        results = await datastore.query(
            request.queries,
        )
        return QueryResponse(results=results)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail="Internal Service Error")


@app.delete(
    "/delete",
    response_model=DeleteResponse,
)
async def delete(
    request: DeleteRequest = Body(...),
    token: HTTPAuthorizationCredentials = Depends(validate_token),
):
    if not (request.ids or request.filter or request.delete_all):
        raise HTTPException(
            status_code=400,
            detail="One of ids, filter, or delete_all is required",
        )
    try:
        success = await datastore.delete(
            ids=request.ids,
            filter=request.filter,
            delete_all=request.delete_all,
        )
        return DeleteResponse(success=success)
    except Exception as e:
        logger.error(e)
        raise HTTPException(status_code=500, detail="Internal Service Error")


@app.on_event("startup")
async def startup():
    global datastore
    datastore = await get_datastore()


def start():
    uvicorn.run("server.main:app", host="0.0.0.0", port=8000, reload=True)