import os import chainlit as cl import re from datetime import datetime import shutil import uuid import ollama from qdrant_client import QdrantClient from qdrant_client.http.models import PointStruct # --- CONFIGURAZIONE HARD-CODED PER ROMPERE IL BLOCCO 127.0.0.1 --- OLLAMA_URL = "http://192.168.1.243:11434" # ----------------------------------------------------------------------------- # Define user roles mapping USER_ROLES = { 'moglie@esempio.com': 'business', 'ingegnere@esempio.com': 'engineering', 'architetto@esempio.com': 'architecture', 'admin@esempio.com': 'admin' } # Define the path for workspaces WORKSPACES_DIR = "./workspaces" def create_workspace(user_role): workspace_path = os.path.join(WORKSPACES_DIR, user_role) if not os.path.exists(workspace_path): os.makedirs(workspace_path) def save_code_to_file(code, user_role): timestamp = datetime.now().strftime("%Y%m%d%H%M%S") file_name = f"code_{timestamp}.py" file_path = os.path.join(WORKSPACES_DIR, user_role, file_name) with open(file_path, "w") as file: file.write(code) return file_path def limit_history(history): if len(history) > 20: history = history[-20:] return history async def connect_to_qdrant(): client = QdrantClient("http://qdrant:6333") collection_name = "documents" try: client.get_collection(collection_name) except Exception: client.create_collection( collection_name=collection_name, vectors_config={"size": 768, "distance": "Cosine"} ) return client async def get_embeddings(text): # --- FIX: Splitto Host e Port per evitare confusione --- client = ollama.Client(host=OLLAMA_URL) # Uso l'URL intero # Controllo lunghezza testo if len(text) > 12000: text = text[:12000] try: response = client.embed(model='nomic-embed-text', input=text) # Gestione risposta if 'embeddings' in response: return response['embeddings'][0] return response.get('embedding') except Exception as e: print(f"Errore Embedding: {e}") return [] async def search_qdrant(query_text, user_role): """Cerca documenti pertinenti su Qdrant""" try: qdrant_client = await connect_to_qdrant() query_embedding = await get_embeddings(query_text) # Se non trova embedding (errore connessione), non cercare if not query_embedding: return "" # Cerca i 3 documenti più simili search_result = qdrant_client.search( collection_name="documents", query_vector=query_embedding, limit=3 ) contexts = [] # FIX: controllo sicurezza per evitare 'list index out of range' if search_result: for hit in search_result: try: if 'payload' in hit and 'file_name' in hit['payload']: contexts.append(f"Documento: {hit['payload']['file_name']}") except Exception: pass return "\n".join(contexts) except Exception as e: print(f"Errore ricerca Qdrant: {e}") return "" @cl.on_chat_start async def chat_start(): # Hardcode per test user_email = "admin@esempio.com" user_role = USER_ROLES.get(user_email, 'guest') create_workspace(user_role) cl.user_session.set("history", []) cl.user_session.set("role", user_role) if user_role == 'admin': await cl.Message(content="Welcome, Admin!").send() elif user_role == 'engineering': await cl.Message(content="Welcome, Engineer!").send() elif user_role == 'business': await cl.Message(content="Welcome, Business User!").send() elif user_role == 'architecture': await cl.Message(content="Welcome, Architect!").send() else: await cl.Message(content="Welcome, Guest!").send() @cl.on_message async def message(message): user_role = cl.user_session.get("role", 'guest') if not user_role: await cl.Message(content="User role not found").send() return try: # Client Ollama URL Hardcoded client = ollama.Client(host=OLLAMA_URL) # History & Sliding Window history = cl.user_session.get("history", []) history = limit_history(history) # --- RAG STEP 1: Cerca nei documenti --- context_text = await search_qdrant(message.content, user_role) # Se trova contesto, iniettalo if context_text: system_prompt = f"Contexto dai documenti:\n{context_text}\n\nRispondi usando questo contesto." history.insert(0, {"role": "system", "content": system_prompt}) history.append({"role": "user", "content": message.content}) # Gestione Upload e Indexing if message.elements: uploaded_files = [] for element in message.elements: try: dest_path = os.path.join(WORKSPACES_DIR, user_role, element.name) with open(element.path, 'rb') as src, open(dest_path, 'wb') as dst: shutil.copyfileobj(src, dst) if element.name.endswith('.txt'): with open(dest_path, 'r') as f: content = f.read() # Indexing embeddings = await get_embeddings(content) if embeddings: qdrant_client = await connect_to_qdrant() point_id = uuid.uuid4() point = PointStruct(id=point_id, vector=embeddings, payload={"file_name": element.name}) qdrant_client.upsert(collection_name="documents", points=[point]) await cl.Message(content=f"Documento '{element.name}' indicizzato.").send() uploaded_files.append(element.name) except Exception as e: await cl.Message(content=f"Error saving {element.name}: {e}").send() if uploaded_files: await cl.Message(content=f"Files saved: {', '.join(uploaded_files)}").send() # Chat response = client.chat(model='qwen2.5-coder:7b', messages=history) # Code Extracting code_blocks = re.findall(r"```python(.*?)```", response['message']['content'], re.DOTALL) elements = [] if code_blocks: for code in code_blocks: file_path = save_code_to_file(code, user_role) elements.append(cl.File(name=os.path.basename(file_path), path=file_path)) history.append({"role": "assistant", "content": response['message']['content']}) cl.user_session.set("history", history) await cl.Message(content=response['message']['content'], elements=elements).send() except Exception as e: await cl.Message(content=f"Error: {e}").send()