private_gpt/launcher.py

"""FastAPI app creation, logger configuration and main API routes."""
import logging
from typing import Any

from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.openapi.utils import get_openapi
from injector import Injector

from private_gpt.paths import docs_path
from private_gpt.server.chat.chat_router import chat_router
from private_gpt.server.chunks.chunks_router import chunks_router
from private_gpt.server.completions.completions_router import completions_router
from private_gpt.server.embeddings.embeddings_router import embeddings_router
from private_gpt.server.health.health_router import health_router
from private_gpt.server.ingest.ingest_router import ingest_router
from private_gpt.settings.settings import Settings

logger = logging.getLogger(__name__)


def create_app(root_injector: Injector) -> FastAPI:

    # Start the API
    with open(docs_path / "description.md") as description_file:
        description = description_file.read()

        tags_metadata = [
            {
                "name": "Ingestion",
                "description": "High-level APIs covering document ingestion -internally "
                "managing document parsing, splitting,"
                "metadata extraction, embedding generation and storage- and ingested "
                "documents CRUD."
                "Each ingested document is identified by an ID that can be used to filter the "
                "context"
                "used in *Contextual Completions* and *Context Chunks* APIs.",
            },
            {
                "name": "Contextual Completions",
                "description": "High-level APIs covering contextual Chat and Completions. They "
                "follow OpenAI's format, extending it to "
                "allow using the context coming from ingested documents to create the "
                "response. Internally"
                "manage context retrieval, prompt engineering and the response generation.",
            },
            {
                "name": "Context Chunks",
                "description": "Low-level API that given a query return relevant chunks of "
                "text coming from the ingested"
                "documents.",
            },
            {
                "name": "Embeddings",
                "description": "Low-level API to obtain the vector representation of a given "
                "text, using an Embeddings model."
                "Follows OpenAI's embeddings API format.",
            },
            {
                "name": "Health",
                "description": "Simple health API to make sure the server is up and running.",
            },
        ]

        async def bind_injector_to_request(request: Request) -> None:
            request.state.injector = root_injector

        app = FastAPI(dependencies=[Depends(bind_injector_to_request)])

        def custom_openapi() -> dict[str, Any]:
            if app.openapi_schema:
                return app.openapi_schema
            openapi_schema = get_openapi(
                title="PrivateGPT",
                description=description,
                version="0.1.0",
                summary="PrivateGPT is a production-ready AI project that allows you to "
                "ask questions to your documents using the power of Large Language "
                "Models (LLMs), even in scenarios without Internet connection. "
                "100% private, no data leaves your execution environment at any point.",
                contact={
                    "url": "https://github.com/imartinez/privateGPT",
                },
                license_info={
                    "name": "Apache 2.0",
                    "url": "https://www.apache.org/licenses/LICENSE-2.0.html",
                },
                routes=app.routes,
                tags=tags_metadata,
            )
            openapi_schema["info"]["x-logo"] = {
                "url": "https://lh3.googleusercontent.com/drive-viewer"
                "/AK7aPaD_iNlMoTquOBsw4boh4tIYxyEuhz6EtEs8nzq3yNkNAK00xGj"
                "E1KUCmPJSk3TYOjcs6tReG6w_cLu1S7L_gPgT9z52iw=s2560"
            }

            app.openapi_schema = openapi_schema
            return app.openapi_schema

        app.openapi = custom_openapi  # type: ignore[method-assign]

        app.include_router(completions_router)
        app.include_router(chat_router)
        app.include_router(chunks_router)
        app.include_router(ingest_router)
        app.include_router(embeddings_router)
        app.include_router(health_router)

        settings = root_injector.get(Settings)
        if settings.server.cors.enabled:
            logger.debug("Setting up CORS middleware")
            app.add_middleware(
                CORSMiddleware,
                allow_credentials=settings.server.cors.allow_credentials,
                allow_origins=settings.server.cors.allow_origins,
                allow_origin_regex=settings.server.cors.allow_origin_regex,
                allow_methods=settings.server.cors.allow_methods,
                allow_headers=settings.server.cors.allow_headers,
            )

        if settings.ui.enabled:
            logger.debug("Importing the UI module")
            from private_gpt.ui.ui import PrivateGptUi

            ui = root_injector.get(PrivateGptUi)
            ui.mount_in_app(app, settings.ui.path)

        return app