360 lines
12 KiB
Python
360 lines
12 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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from datetime import datetime
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from typing import Optional
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import chainlit as cl
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import ollama
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import fitz # PyMuPDF
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from qdrant_client import AsyncQdrantClient
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from qdrant_client.models import PointStruct, Distance, VectorParams
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from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
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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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# === INIZIALIZZAZIONE DATA LAYER ===
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try:
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data_layer = SQLAlchemyDataLayer(conninfo=DATABASE_URL)
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cl.data_layer = data_layer
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print("✅ SQLAlchemyDataLayer initialized successfully")
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except Exception as e:
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print(f"❌ Failed to initialize data layer: {e}")
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cl.data_layer = None
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WORKSPACES_DIR = "./workspaces"
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USER_ROLE = "admin"
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# === UTILITY FUNCTIONS ===
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def create_workspace(user_role: str):
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"""Crea directory workspace se non esiste"""
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workspace_path = os.path.join(WORKSPACES_DIR, user_role)
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os.makedirs(workspace_path, exist_ok=True)
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return workspace_path
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def save_code_to_file(code: str, user_role: str) -> str:
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"""Salva blocco codice come file .py"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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file_name = f"code_{timestamp}.py"
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file_path = os.path.join(WORKSPACES_DIR, user_role, file_name)
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with open(file_path, "w", encoding="utf-8") as f:
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f.write(code)
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return file_path
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def extract_text_from_pdf(pdf_path: str) -> str:
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"""Estrae testo da PDF usando PyMuPDF"""
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try:
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doc = fitz.open(pdf_path)
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text_parts = []
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for page_num in range(len(doc)):
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page = doc[page_num]
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text = page.get_text()
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text_parts.append(f"--- Pagina {page_num + 1} ---\n{text}\n")
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doc.close()
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return "\n".join(text_parts)
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except Exception as e:
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print(f"❌ Errore estrazione PDF: {e}")
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return ""
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# === QDRANT FUNCTIONS ===
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async def get_qdrant_client() -> AsyncQdrantClient:
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"""Connessione a Qdrant"""
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client = AsyncQdrantClient(url=QDRANT_URL)
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collection_name = "documents"
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# Crea collection se non esiste
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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=VectorParams(size=768, distance=Distance.COSINE)
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)
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return client
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async def get_embeddings(text: str) -> list:
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"""Genera embeddings con Ollama"""
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client = ollama.Client(host=OLLAMA_URL)
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# Limita lunghezza per evitare errori
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max_length = 2000
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if len(text) > max_length:
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text = text[:max_length]
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try:
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response = client.embed(model='nomic-embed-text', input=text)
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if 'embeddings' in response:
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return response['embeddings'][0]
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return response.get('embedding', [])
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except Exception as e:
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print(f"❌ Errore Embedding: {e}")
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return []
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async def index_document(file_name: str, content: str) -> bool:
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"""Indicizza documento su Qdrant"""
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try:
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# Suddividi documento lungo in chunks
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chunks = chunk_text(content, max_length=1500)
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qdrant_client = await get_qdrant_client()
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points = []
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for i, chunk in enumerate(chunks):
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embeddings = await get_embeddings(chunk)
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if not embeddings:
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continue
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point_id = str(uuid.uuid4())
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point = PointStruct(
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id=point_id,
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vector=embeddings,
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payload={
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"file_name": file_name,
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"content": chunk,
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"chunk_index": i,
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"total_chunks": len(chunks),
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"indexed_at": datetime.now().isoformat()
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}
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)
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points.append(point)
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if points:
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await qdrant_client.upsert(collection_name="documents", points=points)
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return True
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return False
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except Exception as e:
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print(f"❌ Errore indicizzazione: {e}")
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return False
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def chunk_text(text: str, max_length: int = 1500, overlap: int = 200) -> list:
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"""Divide testo in chunks con overlap"""
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if len(text) <= max_length:
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return [text]
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chunks = []
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start = 0
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while start < len(text):
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end = start + max_length
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# Cerca l'ultimo punto/newline prima del limite
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if end < len(text):
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last_period = text.rfind('.', start, end)
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last_newline = text.rfind('\n', start, end)
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split_point = max(last_period, last_newline)
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if split_point > start:
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end = split_point + 1
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chunks.append(text[start:end].strip())
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start = end - overlap # Overlap per continuità
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return chunks
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async def search_qdrant(query_text: str, limit: int = 5) -> str:
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"""Ricerca documenti rilevanti"""
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try:
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qdrant_client = await get_qdrant_client()
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query_embedding = await get_embeddings(query_text)
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if not query_embedding:
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return ""
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search_result = await qdrant_client.query_points(
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collection_name="documents",
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query=query_embedding,
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limit=limit
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)
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contexts = []
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seen_files = set()
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for hit in search_result.points:
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if hit.payload:
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file_name = hit.payload.get('file_name', 'Unknown')
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content = hit.payload.get('content', '')
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chunk_idx = hit.payload.get('chunk_index', 0)
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score = hit.score if hasattr(hit, 'score') else 0
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# Evita duplicati dello stesso file
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file_key = f"{file_name}_{chunk_idx}"
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if file_key not in seen_files:
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seen_files.add(file_key)
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contexts.append(
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f"📄 **{file_name}** (chunk {chunk_idx+1}, score: {score:.2f})\n"
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f"```\n{content[:600]}...\n```"
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)
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return "\n\n".join(contexts) if contexts else ""
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except Exception as e:
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print(f"❌ Errore ricerca Qdrant: {e}")
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return ""
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# === CHAINLIT HANDLERS ===
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@cl.on_chat_start
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async def on_chat_start():
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"""Inizializzazione chat"""
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create_workspace(USER_ROLE)
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# Imposta variabili sessione
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cl.user_session.set("role", USER_ROLE)
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# Verifica persistenza
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persistence_status = "✅ Attiva" if cl.data_layer else "⚠️ Disattivata"
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await cl.Message(
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content=f"🚀 **AI Station Ready** - Workspace: `{USER_ROLE}`\n\n"
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f"📤 Upload **PDF** o **.txt** per indicizzarli nel RAG\n"
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f"💾 Persistenza conversazioni: {persistence_status}\n"
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f"🤖 Modello: `glm-4.6:cloud` @ {OLLAMA_URL}\n\n"
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f"💡 **Supporto PDF attivo**: Carica fatture, F24, dichiarazioni fiscali!"
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).send()
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@cl.on_message
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async def on_message(message: cl.Message):
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"""Gestione messaggi utente"""
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user_role = cl.user_session.get("role", "guest")
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try:
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# === STEP 1: Gestione Upload ===
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if message.elements:
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await handle_file_uploads(message.elements, user_role)
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# === STEP 2: RAG Search ===
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context_text = await search_qdrant(message.content, limit=5)
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# === STEP 3: Preparazione Prompt ===
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system_prompt = (
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"Sei un assistente AI esperto in analisi documentale e fiscale. "
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"Usa ESCLUSIVAMENTE il seguente contesto per rispondere. "
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"Se la risposta non è nel contesto, dillo chiaramente."
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)
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if context_text:
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full_prompt = f"{system_prompt}\n\n**CONTESTO DOCUMENTI:**\n{context_text}\n\n**DOMANDA UTENTE:**\n{message.content}"
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else:
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full_prompt = f"{system_prompt}\n\n**DOMANDA UTENTE:**\n{message.content}"
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# === STEP 4: Usa glm-4.6:cloud ===
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client = ollama.Client(host=OLLAMA_URL)
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msg = cl.Message(content="")
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await msg.send()
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messages = [{"role": "user", "content": full_prompt}]
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stream = client.chat(
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model='glm-4.6:cloud',
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messages=messages,
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stream=True
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)
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full_response = ""
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for chunk in stream:
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content = chunk['message']['content']
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full_response += content
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await msg.stream_token(content)
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await msg.update()
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# === STEP 5: Estrai e Salva Codice ===
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code_blocks = re.findall(r"```python\n(.*?)```", full_response, re.DOTALL)
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if code_blocks:
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elements = []
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for code in code_blocks:
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file_path = save_code_to_file(code.strip(), user_role)
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elements.append(
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cl.File(
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name=os.path.basename(file_path),
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path=file_path,
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display="inline"
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)
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)
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await cl.Message(
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content=f"💾 Codice salvato in `{user_role}/`",
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elements=elements
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).send()
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except Exception as e:
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await cl.Message(content=f"❌ **Errore:** {str(e)}").send()
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async def handle_file_uploads(elements, user_role: str):
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"""Gestisce upload e indicizzazione file (TXT e PDF)"""
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for element in elements:
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try:
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# Salva file
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dest_path = os.path.join(WORKSPACES_DIR, user_role, element.name)
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shutil.copy(element.path, dest_path)
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content = None
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# Estrai testo in base al tipo di file
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if element.name.lower().endswith('.pdf'):
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await cl.Message(content=f"📄 Elaborazione PDF **{element.name}**...").send()
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content = extract_text_from_pdf(dest_path)
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if not content:
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await cl.Message(
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content=f"⚠️ **{element.name}**: PDF vuoto o non leggibile"
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).send()
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continue
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elif element.name.lower().endswith('.txt'):
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with open(dest_path, 'r', encoding='utf-8') as f:
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content = f.read()
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else:
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await cl.Message(
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content=f"📁 **{element.name}** salvato (supportati: .pdf, .txt)"
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).send()
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continue
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# Indicizza su Qdrant
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if content:
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success = await index_document(element.name, content)
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if success:
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word_count = len(content.split())
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await cl.Message(
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content=f"✅ **{element.name}** indicizzato in Qdrant\n"
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f"📊 Parole estratte: {word_count:,}"
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).send()
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else:
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await cl.Message(
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content=f"⚠️ Errore indicizzazione **{element.name}**"
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).send()
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except Exception as e:
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await cl.Message(
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content=f"❌ Errore con **{element.name}**: {str(e)}"
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).send()
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