464 lines
17 KiB
Python
464 lines
17 KiB
Python
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import os
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import re
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import uuid
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import shutil
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import requests
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import time
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import json
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from datetime import datetime
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from typing import Optional, Dict, List, Any
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import chainlit as cl
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import ollama
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from docling.document_converter import DocumentConverter
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from qdrant_client import AsyncQdrantClient
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# CORREZIONE IMPORT: Importiamo le classi necessarie direttamente dalla libreria
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from qdrant_client.models import PointStruct, Distance, VectorParams, SparseVectorParams, Prefetch
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from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
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from chainlit.types import ThreadDict
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from functools import lru_cache
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# === FIX IMPORT ROBUSTO ===
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try:
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from chainlit.data.storage_clients import BaseStorageClient
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except ImportError:
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try:
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from chainlit.data.base import BaseStorageClient
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except ImportError:
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from chainlit.data.storage_clients.base import BaseStorageClient
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# === CONFIGURAZIONE ===
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DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
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OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
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QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
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BGE_API_URL = os.getenv("BGE_API_URL", "http://192.168.1.243:8001/embed")
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VISION_MODEL = "minicpm-v"
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DEFAULT_TEXT_MODEL = "glm-4.6:cloud"
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WORKSPACES_DIR = "./workspaces"
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STORAGE_DIR = "./.files"
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os.makedirs(STORAGE_DIR, exist_ok=True)
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os.makedirs(WORKSPACES_DIR, exist_ok=True)
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# === MAPPING UTENTI ===
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USER_PROFILES = {
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"giuseppe@defranceschi.pro": { "role": "admin", "name": "Giuseppe", "workspace": "admin_workspace", "rag_collection": "admin_docs", "capabilities": ["debug", "all"], "show_code": True },
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"giuseppe.defranceschi@gmail.com": { "role": "admin", "name": "Giuseppe", "workspace": "admin_workspace", "rag_collection": "admin_docs", "capabilities": ["debug", "all"], "show_code": True },
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"federica.tecchio@gmail.com": { "role": "business", "name": "Federica", "workspace": "business_workspace", "rag_collection": "contabilita", "capabilities": ["basic_chat"], "show_code": False },
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"riccardob545@gmail.com": { "role": "engineering", "name": "Riccardo", "workspace": "engineering_workspace", "rag_collection": "engineering_docs", "capabilities": ["code"], "show_code": True },
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"giuliadefranceschi05@gmail.com": { "role": "architecture", "name": "Giulia", "workspace": "architecture_workspace", "rag_collection": "architecture_manuals", "capabilities": ["visual"], "show_code": False }
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}
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# === STORAGE CLIENT ===
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class LocalStorageClient(BaseStorageClient):
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def __init__(self, storage_path: str):
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self.storage_path = storage_path
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os.makedirs(storage_path, exist_ok=True)
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async def upload_file(self, object_key: str, data: bytes, mime: str = "application/octet-stream", overwrite: bool = True) -> Dict[str, str]:
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file_path = os.path.join(self.storage_path, object_key)
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os.makedirs(os.path.dirname(file_path), exist_ok=True)
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with open(file_path, "wb") as f: f.write(data)
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return {"object_key": object_key, "url": f"/files/{object_key}"}
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async def get_read_url(self, object_key: str) -> str: return f"/files/{object_key}"
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async def delete_file(self, object_key: str) -> bool:
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path = os.path.join(self.storage_path, object_key)
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if os.path.exists(path): os.remove(path); return True
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return False
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async def close(self): pass
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@cl.data_layer
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def get_data_layer():
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return SQLAlchemyDataLayer(conninfo=DATABASE_URL, storage_provider=LocalStorageClient(STORAGE_DIR))
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# === OAUTH & UTILS ===
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@cl.oauth_callback
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def oauth_callback(provider_id: str, token: str, raw_user_data: Dict[str, str], default_user: cl.User) -> Optional[cl.User]:
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if provider_id == "google":
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email = raw_user_data.get("email", "").lower()
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profile = USER_PROFILES.get(email, USER_PROFILES.get("guest", {"role": "guest", "name": "Guest", "workspace": "guest", "rag_collection": "public", "show_code": False}))
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default_user.metadata.update({"role": profile["role"], "workspace": profile["workspace"], "rag_collection": profile["rag_collection"], "show_code": profile["show_code"], "display_name": profile["name"]})
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return default_user
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return default_user
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def create_workspace(workspace_name: str) -> str:
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path = os.path.join(WORKSPACES_DIR, workspace_name)
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os.makedirs(path, exist_ok=True)
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return path
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# === CORE: DOCLING ===
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def process_file_with_docling(file_path: str) -> str:
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try:
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converter = DocumentConverter()
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result = converter.convert(file_path)
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return result.document.export_to_markdown()
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except Exception as e:
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print(f"❌ Docling Error: {e}")
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return ""
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# === CORE: BGE-M3 CLIENT ===
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def get_bge_embeddings(text: str) -> Optional[Dict[str, Any]]:
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try:
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payload = {"texts": [text[:8000]]}
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response = requests.post(BGE_API_URL, json=payload, timeout=30)
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response.raise_for_status()
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data = response.json().get("data", [])
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if data:
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return data[0]
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return None
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except Exception as e:
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print(f"❌ BGE API Error: {e}")
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return None
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# === CORE: QDRANT ===
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async def ensure_collection(collection_name: str):
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client = AsyncQdrantClient(url=QDRANT_URL)
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if not await client.collection_exists(collection_name):
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await client.create_collection(
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collection_name=collection_name,
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vectors_config={"dense": VectorParams(size=1024, distance=Distance.COSINE)},
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sparse_vectors_config={"sparse": SparseVectorParams()}
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)
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async def index_document(file_name: str, content: str, collection_name: str):
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await ensure_collection(collection_name)
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client = AsyncQdrantClient(url=QDRANT_URL)
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chunk_size = 2000
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overlap = 200
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points = []
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for i in range(0, len(content), chunk_size - overlap):
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chunk = content[i : i + chunk_size]
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embedding_data = get_bge_embeddings(chunk)
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if embedding_data:
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points.append(PointStruct(
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id=str(uuid.uuid4()),
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vector={
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"dense": embedding_data["dense"],
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"sparse": embedding_data["sparse"]
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},
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payload={
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"file_name": file_name,
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"content": chunk,
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"indexed_at": datetime.now().isoformat()
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}
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))
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if points:
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await client.upsert(collection_name=collection_name, points=points)
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return len(points)
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return 0
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async def search_hybrid(query: str, collection_name: str, limit: int = 4) -> str:
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client = AsyncQdrantClient(url=QDRANT_URL)
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if not await client.collection_exists(collection_name): return ""
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query_emb = get_bge_embeddings(query)
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if not query_emb: return ""
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# CORREZIONE QUI: Usiamo l'oggetto Prefetch importato correttamente
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results = await client.query_points(
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collection_name=collection_name,
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prefetch=[
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Prefetch(
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query=query_emb["sparse"],
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using="sparse",
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limit=limit * 2
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)
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],
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query=query_emb["dense"],
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using="dense",
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limit=limit
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)
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context = []
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for hit in results.points:
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context.append(f"--- DA {hit.payload['file_name']} ---\n{hit.payload['content']}")
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return "\n\n".join(context)
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# === Caching Embeddings ===
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@lru_cache(maxsize=1000)
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def get_bge_embeddings_cached(text: str):
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"""Cache per query ripetute"""
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return get_bge_embeddings(text)
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# === CHAINLIT HANDLERS ===
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@cl.on_chat_start
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async def start():
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# 1. Profilo utente
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user = cl.user_session.get("user")
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email = user.identifier if user else "guest"
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profile = USER_PROFILES.get(email, USER_PROFILES["giuseppe@defranceschi.pro"])
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cl.user_session.set("profile", profile)
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create_workspace(profile["workspace"])
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# 2. Badge HTML personalizzato
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role_color = {
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"admin": "#e74c3c",
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"engineering": "#3498db",
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"business": "#2ecc71",
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"architecture": "#9b59b6",
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}.get(profile["role"], "#95a5a6")
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badge_html = f"""
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<div style="background:{role_color}; padding:8px; border-radius:8px; margin-bottom:16px;">
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👤 <b>{profile['name']}</b> | 🔧 {profile['role'].upper()} | 📁 {profile['workspace']}
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</div>
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"""
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await cl.Message(content=badge_html).send()
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# 3. Settings UI
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settings = await cl.ChatSettings(
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[
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cl.input_widget.Slider(
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id="top_k",
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label="Numero Documenti RAG",
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initial=4,
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min=1,
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max=10,
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step=1,
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),
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cl.input_widget.Select(
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id="vision_detail",
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label="Dettaglio Analisi Immagini",
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values=["auto", "low", "high"],
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initial_value="auto",
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),
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cl.input_widget.TextInput(
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id="system_instruction",
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label="Istruzione Sistema Custom (opzionale)",
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initial="",
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placeholder="Es: Rispondi sempre in formato tecnico...",
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),
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cl.input_widget.Select(
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id="model",
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label="Modello di Ragionamento",
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values=[DEFAULT_TEXT_MODEL, "llama3.2", "mistral", "qwen2.5-coder:32b"],
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initial_value=DEFAULT_TEXT_MODEL,
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),
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cl.input_widget.Slider(
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id="temperature",
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label="Creatività (Temperatura)",
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initial=0.3,
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min=0,
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max=1,
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step=0.1,
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),
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cl.input_widget.Switch(
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id="rag_enabled",
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label="Usa Conoscenza Documenti (RAG)",
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initial=True,
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),
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]
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).send()
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cl.user_session.set("settings", settings)
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# 4. Messaggio iniziale (opzionale)
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await cl.Message(
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content=(
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f"🚀 **Vision-RAG Hybrid System Online**\n"
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f"Utente: {profile['name']} | Workspace: {profile['workspace']}\n"
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f"Engine: Docling + BGE-M3 + {VISION_MODEL}"
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)
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).send()
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cl.user_session.set("settings", settings)
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await cl.Message(f"🚀 **Vision-RAG Hybrid System Online**\nUtente: {profile['name']} | Workspace: {profile['workspace']}\nEngine: Docling + BGE-M3 + {VISION_MODEL}").send()
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@cl.on_settings_update
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async def setup_agent(settings):
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cl.user_session.set("settings", settings)
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await cl.Message(content=f"✅ Impostazioni aggiornate: Modello {settings['model']}").send()
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async def log_metrics(metrics: dict):
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# Versione minima: log su stdout
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print("[METRICS]", metrics)
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# In futuro puoi:
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# - salvarle in Postgres
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# - mandarle a Prometheus / Grafana
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# - scriverle su file JSON per analisi settimanale
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# - Resume Chat Handler
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@cl.on_chat_resume
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async def on_chat_resume(thread: ThreadDict):
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"""
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Viene chiamato quando l'utente clicca 'Riprendi' su una chat archiviata.
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Chainlit carica già i messaggi nella UI, qui puoi solo ripristinare la sessione.
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"""
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# Se vuoi, puoi recuperare l'identifier dell’utente dal thread
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user_identifier = thread.get("userIdentifier")
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profile = USER_PROFILES.get(
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user_identifier,
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USER_PROFILES["giuseppe@defranceschi.pro"],
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)
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cl.user_session.set("profile", profile)
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# Puoi anche ripristinare eventuale stato custom (es: impostazioni di default)
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# oppure semplicemente salutare l’utente
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await cl.Message(
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content="👋 Bentornato! Possiamo riprendere da questa conversazione."
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).send()
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@cl.on_message
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async def main(message: cl.Message):
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start_time = time.time()
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profile = cl.user_session.get("profile")
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settings = cl.user_session.get("settings", {})
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selected_model = settings.get("model", DEFAULT_TEXT_MODEL)
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temperature = settings.get("temperature", 0.3)
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rag_enabled = settings.get("rag_enabled", True)
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workspace = create_workspace(profile["workspace"])
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images_for_vision = []
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doc_context = ""
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rag_context = "" # ← la inizializzi qui, così esiste sempre
|
|||
|
|
|
|||
|
|
# 1. GESTIONE FILE
|
|||
|
|
if message.elements:
|
|||
|
|
for element in message.elements:
|
|||
|
|
file_path = os.path.join(workspace, element.name)
|
|||
|
|
shutil.copy(element.path, file_path)
|
|||
|
|
|
|||
|
|
if "image" in element.mime:
|
|||
|
|
images_for_vision.append(file_path)
|
|||
|
|
msg_img = cl.Message(
|
|||
|
|
content=f"👁️ Analizzo immagine **{element.name}** con {VISION_MODEL}..."
|
|||
|
|
)
|
|||
|
|
await msg_img.send()
|
|||
|
|
|
|||
|
|
with open(file_path, "rb") as img_file:
|
|||
|
|
img_bytes = img_file.read()
|
|||
|
|
|
|||
|
|
client_sync = ollama.Client(host=OLLAMA_URL)
|
|||
|
|
res = client_sync.chat(
|
|||
|
|
model=VISION_MODEL,
|
|||
|
|
messages=[{
|
|||
|
|
"role": "user",
|
|||
|
|
"content": (
|
|||
|
|
"Analizza questa immagine tecnica. Trascrivi testi, codici "
|
|||
|
|
"e descrivi diagrammi o tabelle in dettaglio."
|
|||
|
|
),
|
|||
|
|
"images": [img_bytes],
|
|||
|
|
}],
|
|||
|
|
)
|
|||
|
|
desc = res["message"]["content"]
|
|||
|
|
doc_context += f"\n\n[DESCRIZIONE IMMAGINE {element.name}]:\n{desc}"
|
|||
|
|
msg_img.content = f"✅ Immagine analizzata:\n{desc[:200]}..."
|
|||
|
|
await msg_img.update()
|
|||
|
|
|
|||
|
|
elif element.name.endswith((".pdf", ".docx")):
|
|||
|
|
msg_doc = cl.Message(
|
|||
|
|
content=f"📄 Leggo **{element.name}** con Docling (tabelle/formule)..."
|
|||
|
|
)
|
|||
|
|
await msg_doc.send()
|
|||
|
|
|
|||
|
|
markdown_content = process_file_with_docling(file_path)
|
|||
|
|
if markdown_content:
|
|||
|
|
chunks = await index_document(
|
|||
|
|
element.name, markdown_content, profile["rag_collection"]
|
|||
|
|
)
|
|||
|
|
msg_doc.content = (
|
|||
|
|
f"✅ **{element.name}**: Convertito e salvato {chunks} "
|
|||
|
|
"frammenti nel DB vettoriale."
|
|||
|
|
)
|
|||
|
|
doc_context += (
|
|||
|
|
f"\n\n[CONTENUTO FILE {element.name}]:\n"
|
|||
|
|
f"{markdown_content[:1000]}..."
|
|||
|
|
)
|
|||
|
|
else:
|
|||
|
|
msg_doc.content = f"❌ Errore lettura {element.name}"
|
|||
|
|
await msg_doc.update()
|
|||
|
|
|
|||
|
|
# 2. RAG RETRIEVAL
|
|||
|
|
if rag_enabled and not images_for_vision:
|
|||
|
|
rag_context = await search_hybrid(
|
|||
|
|
message.content, profile["rag_collection"]
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
final_context = ""
|
|||
|
|
if rag_context:
|
|||
|
|
final_context += f"CONTESTO RAG:\n{rag_context}\n"
|
|||
|
|
if doc_context:
|
|||
|
|
final_context += f"CONTESTO SESSIONE CORRENTE:\n{doc_context}\n"
|
|||
|
|
|
|||
|
|
system_prompt = (
|
|||
|
|
"Sei un assistente tecnico esperto. Usa il contesto fornito "
|
|||
|
|
"(incluso Markdown di tabelle e descrizioni immagini) per "
|
|||
|
|
"rispondere con precisione. Cita i documenti fonte."
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
msg = cl.Message(content="")
|
|||
|
|
await msg.send()
|
|||
|
|
|
|||
|
|
error = None
|
|||
|
|
|
|||
|
|
# 3. GENERAZIONE
|
|||
|
|
try:
|
|||
|
|
client_async = ollama.AsyncClient(host=OLLAMA_URL)
|
|||
|
|
stream = await client_async.chat(
|
|||
|
|
model=selected_model,
|
|||
|
|
messages=[
|
|||
|
|
{"role": "system", "content": system_prompt},
|
|||
|
|
{
|
|||
|
|
"role": "user",
|
|||
|
|
"content": f"Domanda: {message.content}\n\n{final_context}",
|
|||
|
|
},
|
|||
|
|
],
|
|||
|
|
options={"temperature": temperature},
|
|||
|
|
stream=True,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
async for chunk in stream:
|
|||
|
|
content = chunk["message"]["content"]
|
|||
|
|
await msg.stream_token(content)
|
|||
|
|
await msg.update()
|
|||
|
|
except Exception as e:
|
|||
|
|
error = str(e)
|
|||
|
|
await msg.stream_token(f"❌ Errore AI: {error}")
|
|||
|
|
await msg.update()
|
|||
|
|
|
|||
|
|
# 4. SALVATAGGIO CODICE
|
|||
|
|
if profile["show_code"]:
|
|||
|
|
code_blocks = re.findall(r"``````", msg.content, re.DOTALL)
|
|||
|
|
if code_blocks:
|
|||
|
|
for i, code in enumerate(code_blocks):
|
|||
|
|
fname = f"script_{datetime.now().strftime('%H%M%S')}_{i}.py"
|
|||
|
|
with open(os.path.join(workspace, fname), "w") as f:
|
|||
|
|
f.write(code.strip())
|
|||
|
|
await cl.Message(
|
|||
|
|
content=f"💾 Script salvato: `{fname}`"
|
|||
|
|
).send()
|
|||
|
|
|
|||
|
|
# 5. METRICHE (ALLA FINE)
|
|||
|
|
elapsed = time.time() - start_time
|
|||
|
|
|
|||
|
|
# Se rag_context è una stringa concatenata, puoi stimare i "rag_hits"
|
|||
|
|
# contando i separatori che usi in search_hybrid (es. '--- DA ')
|
|||
|
|
if rag_context:
|
|||
|
|
rag_hits = rag_context.count("--- DA ")
|
|||
|
|
else:
|
|||
|
|
rag_hits = 0
|
|||
|
|
|
|||
|
|
metrics = {
|
|||
|
|
"response_time": elapsed,
|
|||
|
|
"rag_hits": rag_hits,
|
|||
|
|
"model": selected_model,
|
|||
|
|
"user_role": profile["role"],
|
|||
|
|
"error": error,
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
await log_metrics(metrics)
|