126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
"""FastAPI app creation, logger configuration and main API routes."""
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import sys
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from typing import Any
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import llama_index
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from fastapi import FastAPI
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from fastapi.openapi.utils import get_openapi
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from loguru import logger
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from private_gpt.paths import docs_path
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from private_gpt.server.chat.chat_router import chat_router
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from private_gpt.server.chunks.chunks_router import chunks_router
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from private_gpt.server.completions.completions_router import completions_router
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from private_gpt.server.embeddings.embeddings_router import embeddings_router
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from private_gpt.server.health.health_router import health_router
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from private_gpt.server.ingest.ingest_router import ingest_router
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from private_gpt.settings.settings import settings
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# Remove pre-configured logging handler
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logger.remove(0)
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# Create a new logging handler same as the pre-configured one but with the extra
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# attribute `request_id`
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logger.add(
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sys.stdout,
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level="INFO",
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format=(
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"<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | "
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"<level>{level: <8}</level> | "
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"<cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> | "
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"ID: {extra[request_id]} - <level>{message}</level>"
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),
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)
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# Add LlamaIndex simple observability
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llama_index.set_global_handler("simple")
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# Start the API
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with open(docs_path / "description.md") as description_file:
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description = description_file.read()
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tags_metadata = [
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{
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"name": "Ingestion",
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"description": "High-level APIs covering document ingestion -internally "
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"managing document parsing, splitting,"
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"metadata extraction, embedding generation and storage- and ingested "
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"documents CRUD."
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"Each ingested document is identified by an ID that can be used to filter the "
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"context"
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"used in *Contextual Completions* and *Context Chunks* APIs.",
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},
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{
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"name": "Contextual Completions",
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"description": "High-level APIs covering contextual Chat and Completions. They "
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"follow OpenAI's format, extending it to "
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"allow using the context coming from ingested documents to create the "
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"response. Internally"
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"manage context retrieval, prompt engineering and the response generation.",
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},
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{
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"name": "Context Chunks",
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"description": "Low-level API that given a query return relevant chunks of "
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"text coming from the ingested"
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"documents.",
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},
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{
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"name": "Embeddings",
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"description": "Low-level API to obtain the vector representation of a given "
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"text, using an Embeddings model."
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"Follows OpenAI's embeddings API format.",
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},
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{
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"name": "Health",
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"description": "Simple health API to make sure the server is up and running.",
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},
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]
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app = FastAPI()
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def custom_openapi() -> dict[str, Any]:
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if app.openapi_schema:
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return app.openapi_schema
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openapi_schema = get_openapi(
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title="PrivateGPT",
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description=description,
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version="0.1.0",
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summary="PrivateGPT is a production-ready AI project that allows you to "
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"ask questions to your documents using the power of Large Language "
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"Models (LLMs), even in scenarios without Internet connection. "
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"100% private, no data leaves your execution environment at any point.",
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contact={
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"url": "https://github.com/imartinez/privateGPT",
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},
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license_info={
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"name": "Apache 2.0",
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"url": "https://www.apache.org/licenses/LICENSE-2.0.html",
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},
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routes=app.routes,
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tags=tags_metadata,
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)
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openapi_schema["info"]["x-logo"] = {
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"url": "https://lh3.googleusercontent.com/drive-viewer"
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"/AK7aPaD_iNlMoTquOBsw4boh4tIYxyEuhz6EtEs8nzq3yNkNAK00xGj"
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"E1KUCmPJSk3TYOjcs6tReG6w_cLu1S7L_gPgT9z52iw=s2560"
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}
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app.openapi_schema = openapi_schema
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return app.openapi_schema
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app.openapi = custom_openapi # type: ignore[method-assign]
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app.include_router(completions_router)
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app.include_router(chat_router)
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app.include_router(chunks_router)
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app.include_router(ingest_router)
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app.include_router(embeddings_router)
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app.include_router(health_router)
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if settings.ui.enabled:
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from private_gpt.ui.ui import mount_in_app
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mount_in_app(app)
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