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304
docker_svc/agent/app/main.py
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docker_svc/agent/app/main.py
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from fastapi import FastAPI, File, UploadFile, HTTPException, Request, BackgroundTasks
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from fastapi.responses import JSONResponse, StreamingResponse
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.openapi.utils import get_openapi
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from llama_index.core import SimpleDirectoryReader
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from llama_index.embeddings.ollama import OllamaEmbedding
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from llama_index.llms.ollama import Ollama
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from typing import Literal, List
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from pydantic import BaseModel
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from langdetect import DetectorFactory
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from qdrant_client import QdrantClient
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import os
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from typing import List
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import uuid
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import yaml
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from dotenv import load_dotenv
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import logging
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import asyncio
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import json
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from libs.check_medical import is_medical_query
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import libs.manage_languages as manage_languages
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import libs.qdrant_helper as qdrant_helper
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from libs.models import ChatMessage, ChatRequest
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import libs.prompt_helper as prompt_helper
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from libs.log_prompts import log_prompt_to_db
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# Set seed for reproducibility of language detection
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DetectorFactory.seed = 0
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# Configure logging
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logging.basicConfig(
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level=logging.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger(__name__)
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# Load environment variables from .env file
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load_dotenv()
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# Initialize FastAPI app
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app = FastAPI(
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docs_url="/docs",
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redoc_url="/redoc",
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max_request_body_size=100 * 1024 * 1024 # 100MB
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)
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# Get CORS origins from environment or use default
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cors_origins = os.getenv("CORS_ORIGINS", "http://localhost:3000,http://127.0.0.1:3000").split(",")
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# Add CORS middleware with proper configuration
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app.add_middleware(
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CORSMiddleware,
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allow_origins=cors_origins,
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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expose_headers=["Content-Type", "X-Content-Type-Options"],
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max_age=600, # 10 minutes for preflight cache
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)
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# Load custom OpenAPI schema
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def load_custom_openapi():
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with open("openapi.json", "r") as f:
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custom_openapi = yaml.safe_load(f)
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default_openapi = get_openapi(
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title=app.title,
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version=app.version,
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openapi_version=app.openapi_version,
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description=app.description,
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routes=app.routes,
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)
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default_openapi["info"] = custom_openapi.get("info", default_openapi["info"])
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default_openapi["paths"].update(custom_openapi.get("paths", {}))
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return default_openapi
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app.openapi = load_custom_openapi
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with open("prompts.yaml", "r") as f:
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prompts = yaml.safe_load(f)
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SYSTEM_PROMPT_TEMPLATE = prompts["system_prompt"]
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LANGUAGE_PROMPTS = prompts["languages"]
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PERSONALITY_PROMPTS = prompts["personalities"]
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# Configuration of models and services using .env variables
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OLLAMA_BASE_URL = os.getenv("OLLAMA_BASE_URL", "https://ollama.kube-ext.isc.heia-fr.ch")
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logger.info(f"Starting application with OLLAMA_BASE_URL: {OLLAMA_BASE_URL}")
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# Embedding model using Ollama
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embed_model = OllamaEmbedding(
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model_name=os.getenv("EMBED_MODEL_NAME", "mxbai-embed-large"),
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base_url=OLLAMA_BASE_URL,
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request_timeout=os.getenv("TIMEOUT_REQUEST_EMBED", 20.0)
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)
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logger.info("OllamaEmbedding initialized with model: " + os.getenv("EMBED_MODEL_NAME", "mxbai-embed-large"))
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# Direct inference model
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llm = Ollama(
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model=os.getenv("LLM_MODEL_NAME", "llama3"),
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base_url=OLLAMA_BASE_URL,
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temperature=float(os.getenv("TEMPERATURE", "0.7")),
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request_timeout=os.getenv("TIMEOUT_REQUEST_CHAT_DIRECT", 30.0)
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)
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logger.info(f"Ollama LLM initialized with model: {llm.model} "
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f"with temperature: {llm.temperature}")
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# Reasoning model
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llm_reasoning = Ollama(
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model=os.getenv("LLM_MODEL_NAME_THINKING", "deepseek-r1:14b"),
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base_url=OLLAMA_BASE_URL,
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temperature=float(os.getenv("TEMPERATURE", "0.7")),
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request_timeout=os.getenv("TIMEOUT_REQUEST_CHAT_REASON", 60.0)
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)
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logger.info(f"Ollama reasoning LLM initialized with model: {llm_reasoning.model} "
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f"with temperature: {llm_reasoning.temperature}")
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# Qdrant configuration
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qdrant_client = QdrantClient(
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host=os.getenv("QDRANT_HOST", "localhost"),
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port=int(os.getenv("QDRANT_PORT", "6333"))
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)
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collection_name = os.getenv("COLLECTION_NAME", "default_collection")
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vector_size = int(os.getenv("VECTOR_SIZE", "1024"))
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logger.info(f"Qdrant client initialized with host: {os.getenv('QDRANT_HOST')} and collection: {collection_name}")
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# Ensure Qdrant collection exists
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qdrant_helper.ensure_collection_exists(qdrant_client, collection_name, vector_size)
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# Endpoint to upload PDFs
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@app.post("/upload")
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async def upload_pdfs(files: List[UploadFile] = File(...)):
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logger.info("Received upload request")
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try:
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uploaded_files_count = len(files)
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logger.debug(f"Number of files to upload: {uploaded_files_count}")
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for file in files:
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file_id = str(uuid.uuid4())
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file_path = f"./pdfs/{file_id}.pdf"
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logger.debug(f"Processing file: {file.filename}, saving as {file_path}")
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with open(file_path, "wb") as f:
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f.write(await file.read())
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logger.debug(f"File {file.filename} saved successfully")
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documents = SimpleDirectoryReader(input_files=[file_path]).load_data()
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logger.debug(f"Loaded {len(documents)} documents from {file.filename}")
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qdrant_helper.index_documents(qdrant_client, collection_name, embed_model, documents)
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return {"message": f"{uploaded_files_count} files processed and indexed successfully"}
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except Exception as e:
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logger.error(f"Error in upload endpoint: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"Error processing files: {str(e)}")
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# Chat endpoint with language, temperature, and reasoning support
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@app.post("/chat")
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async def chat_inference(chat_request: ChatRequest, http_request: Request, background_tasks: BackgroundTasks):
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logger.info("Received chat request")
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try:
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if not chat_request.messages:
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logger.warning("No messages provided in the request")
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raise HTTPException(status_code=400, detail="No messages provided")
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# Log the complete request object to inspect its contents
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logger.debug(f"Complete chat request object: {chat_request.dict()}")
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logger.debug(f"Request messages: {chat_request.messages}")
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logger.debug(f"Requested language: {chat_request.language}")
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logger.debug(f"Requested temperature: {chat_request.temperature}")
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logger.debug(f"Requested reasoning: {chat_request.reasoning}")
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logger.debug(f"Requested streaming: {chat_request.stream}")
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logger.debug(f"Requested personality: {chat_request.personality}")
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# Validate language
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manage_languages.validate_language(chat_request.language)
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# Log più dettagliato della personalità
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logger.info(f"Processing request with personality: {chat_request.personality}")
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# Validate personality
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if chat_request.personality not in ["cool", "cynical", "supportive"]:
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logger.warning(f"Invalid personality: {chat_request.personality}, using 'supportive' as default")
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chat_request.personality = "supportive"
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# Validate temperature
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if not (0 < chat_request.temperature < 1):
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raise HTTPException(status_code=400, detail="Temperature must be between 0 and 1 (exclusive)")
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# Prepare message data
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current_message = chat_request.messages[-1].content.lower()
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history = chat_request.messages[:-1]
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logger.debug(f"Current user message: {current_message}")
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logger.debug(f"Message history: {history}")
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# Prepare full conversation history as a concatenated string
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conversation_history = "\n".join([f"{msg.role}: {msg.content}" for msg in chat_request.messages])
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logger.debug(f"Full conversation history: {conversation_history}")
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# Detect language if "auto"
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if chat_request.language == "auto":
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chat_request.language = manage_languages.detect_language(current_message)
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logger.info(f"Detected language using inference: {chat_request.language}")
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# Check if the query is medical-related
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is_medical = is_medical_query(current_message)
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logger.debug(f"Is medical-related query? {is_medical}")
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# Select LLM and set temperature
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selected_llm = prompt_helper.select_llm(llm, llm_reasoning, chat_request.reasoning)
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selected_llm.temperature = chat_request.temperature
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logger.info(f"Using LLM model: {selected_llm.model} with temperature: {selected_llm.temperature}")
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# Retrieve documents from Qdrant
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retrieved_docs = qdrant_helper.retrieve_documents(qdrant_client, collection_name, embed_model, current_message)
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# Format system prompt with personality - verifico passaggio corretto
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system_message_content = prompt_helper.format_system_prompt(
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SYSTEM_PROMPT_TEMPLATE,
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LANGUAGE_PROMPTS,
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chat_request.language,
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retrieved_docs,
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is_medical,
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chat_request.personality, # Confermo passaggio personalità
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PERSONALITY_PROMPTS # Confermo passaggio dizionario personalità
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)
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# Decidiamo se utilizzare lo streaming o la risposta sincrona
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if chat_request.stream:
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# Streaming response
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logger.info("Using streaming response")
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async def generate():
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full_response = ""
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async for content in prompt_helper.perform_inference_streaming(
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selected_llm,
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system_message_content,
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history,
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chat_request.messages[-1].content
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):
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if content:
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full_response += content
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# Formato SSE standard con \n\n alla fine per delimitare gli eventi
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yield f"data: {json.dumps({'content': content, 'full': full_response})}\n\n"
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# Log the full conversation and response
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background_tasks.add_task(
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log_prompt_to_db,
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None, # TODO: User ID not available yet
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http_request.client.host, # Client's IP address
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conversation_history, # Full conversation history
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full_response # AI-generated response
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)
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# Signal the end of the stream con formato SSE consistente
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yield f"data: {json.dumps({'done': True})}\n\n"
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return StreamingResponse(
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generate(),
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media_type="text/event-stream; charset=utf-8",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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"Content-Type": "text/event-stream; charset=utf-8",
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}
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)
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else:
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# Non-streaming response
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logger.info("Using non-streaming response")
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response_content = prompt_helper.perform_inference(
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selected_llm,
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system_message_content,
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history,
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chat_request.messages[-1].content,
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stream=False
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)
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# Log the full conversation and response in the background
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background_tasks.add_task(
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log_prompt_to_db,
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None, # TODO: User ID not available yet
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http_request.client.host, # Client's IP address
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conversation_history, # Full conversation history
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response_content # AI-generated response
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)
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return {"response": response_content}
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except Exception as e:
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logger.error(f"Error in chat inference: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"Error processing chat: {str(e)}")
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# Add new DELETE endpoint to clear all documents
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@app.delete("/docs")
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async def delete_all_docs():
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logger.info("Received request to delete all documents")
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try:
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qdrant_helper.delete_all_documents(qdrant_client, collection_name, vector_size)
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return {"message": "All documents have been deleted from the database"}
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except Exception as e:
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logger.error(f"Error in delete endpoint: {str(e)}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"Error deleting documents: {str(e)}")
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