code: """FastAPI app creation, logger configuration and main API routes."""
  import sys
  from typing import Any
  
  import llama_index
  from fastapi import FastAPI
  from fastapi.openapi.utils import get_openapi
  from loguru import logger
  
  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
  
  # Remove pre-configured logging handler
  logger.remove(0)
  # Create a new logging handler same as the pre-configured one but with the extra
  # attribute `request_id`
  logger.add(
      sys.stdout,
      level="INFO",
      format=(
          "<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | "
          "<level>{level: <8}</level> | "
          "<cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> | "
          "ID: {extra[request_id]} - <level>{message}</level>"
      ),
  )
  
  # Add LlamaIndex simple observability
  llama_index.set_global_handler("simple")
  
  # 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.",
      },
  ]
  
  app = FastAPI()
  
  
  def custom_openapi() -> dict[str, Any]:
      if app.openapi_schema:
          return app.openapi_schema
      openapi_schema = get_openapi(
          title="Momentus GPT",
          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)
  
  
  if settings.ui.enabled:
      from private_gpt.ui.ui import mount_in_app
  
      mount_in_app(app)
  
apis: llama_index.set_global_handler
extract_api: [((774, 790), 'loguru.logger.remove', 'logger.remove', (['(0)'], {}), '(0)\n', (787, 790), False, 'from loguru import logger\n'), ((897, 1147), 'loguru.logger.add', 'logger.add', (['sys.stdout'], {'level': '"""INFO"""', 'format': '"""<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> | ID: {extra[request_id]} - <level>{message}</level>"""'}), "(sys.stdout, level='INFO', format=\n    '<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> | ID: {extra[request_id]} - <level>{message}</level>'\n    )\n", (907, 1147), False, 'from loguru import logger\n'), ((1241, 1281), 'llama_index.set_global_handler', 'llama_index.set_global_handler', (['"""simple"""'], {}), "('simple')\n", (1271, 1281), False, 'import llama_index\n'), ((2819, 2828), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (2826, 2828), False, 'from fastapi import FastAPI\n'), ((2953, 3521), 'fastapi.openapi.utils.get_openapi', 'get_openapi', ([], {'title': '"""Momentus GPT"""', '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':\n    'https://www.apache.org/licenses/LICENSE-2.0.html'}", 'routes': 'app.routes', 'tags': 'tags_metadata'}), "(title='Momentus GPT', description=description, version='0.1.0',\n    summary=\n    '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.'\n    , contact={'url': 'https://github.com/imartinez/privateGPT'},\n    license_info={'name': 'Apache 2.0', 'url':\n    'https://www.apache.org/licenses/LICENSE-2.0.html'}, routes=app.routes,\n    tags=tags_metadata)\n", (2964, 3521), False, 'from fastapi.openapi.utils import get_openapi\n'), ((4319, 4336), 'private_gpt.ui.ui.mount_in_app', 'mount_in_app', (['app'], {}), '(app)\n', (4331, 4336), False, 'from private_gpt.ui.ui import mount_in_app\n')]