add design md

.chainlit/config.toml

@@ -1,68 +1,161 @@
 [project]
+# List of environment variables to be provided by each user to use the app.
 user_env = []
+
+# Duration (in seconds) during which the session is saved when the connection is lost
 session_timeout = 3600
-user_session_timeout = 1296000
+
+# Duration (in seconds) of the user session expiry
+user_session_timeout = 1296000  # 15 days
+
+# Enable third parties caching (e.g., LangChain cache)
 cache = false
+
+# Whether to persist user environment variables (API keys) to the database
+# Set to true to store user env vars in DB, false to exclude them for security
 persist_user_env = false
+
+# Whether to mask user environment variables (API keys) in the UI with password type
+# Set to true to show API keys as ***, false to show them as plain text
 mask_user_env = false
+
+# Authorized origins
 allow_origins = ["*"]
 
 [features]
+# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
 unsafe_allow_html = true
+
+# Process and display mathematical expressions. This can clash with "$" characters in messages.
 latex = false
+
+# Autoscroll new user messages at the top of the window
 user_message_autoscroll = true
+
+# Automatically tag threads with the current chat profile (if a chat profile is used)
 auto_tag_thread = true
+
+# Allow users to edit their own messages
 edit_message = true
+
+# Allow users to share threads (backend + UI). Requires an app-defined on_shared_thread_view callback.
 allow_thread_sharing = false
 
+[features.slack]
+# Add emoji reaction when message is received (requires reactions:write OAuth scope)
+reaction_on_message_received = false
+
+# Authorize users to spontaneously upload files with messages
 [features.spontaneous_file_upload]
-enabled = true
-accept = ["*"]
-max_files = 20
-max_size_mb = 500
+    enabled = true
+    # Define accepted file types using MIME types
+    # Examples:
+    # 1. For specific file types:
+    #    accept = ["image/jpeg", "image/png", "application/pdf"]
+    # 2. For all files of certain type:
+    #    accept = ["image/*", "audio/*", "video/*"]
+    # 3. For specific file extensions:
+    #    accept = { "application/octet-stream" = [".xyz", ".pdb"] }
+    # Note: Using "*/*" is not recommended as it may cause browser warnings
+    accept = ["*"]
+    max_files = 20
+    max_size_mb = 500
 
 [features.audio]
-enabled = false
-sample_rate = 24000
+    # Enable audio features
+    enabled = false
+    # Sample rate of the audio
+    sample_rate = 24000
 
 [features.mcp]
-enabled = false
+    # Enable Model Context Protocol (MCP) features
+    enabled = false
+
+[features.mcp.sse]
+    enabled = true
+
+[features.mcp.streamable-http]
+    enabled = true
+
+[features.mcp.stdio]
+    enabled = true
+    # Only the executables in the allow list can be used for MCP stdio server.
+    # Only need the base name of the executable, e.g. "npx", not "/usr/bin/npx".
+    # Please don't comment this line for now, we need it to parse the executable name.
+    allowed_executables = [ "npx", "uvx" ]
 
 [UI]
+# Name of the assistant.
 name = "Ai Station DFFM"
+
 default_theme = "dark"
+
 layout = "wide"
+
 default_sidebar_state = "open"
 
-# Più “SaaS”: evita di mostrare troppo ragionamento di default
-cot = "tool_call"
+# Description of the assistant. This is used for HTML tags.
+# description = ""
 
-custom_css = "/public/ui-s-tier.css"
+# Chain of Thought (CoT) display mode. Can be "hidden", "tool_call" or "full".
+cot = "full"
 
+# Specify a CSS file that can be used to customize the user interface.
+# The CSS file can be served from the public directory or via an external link.
+# custom_css = "/public/test.css"
+
+# CSS personalizzato
+custom_css = "/public/custom.css"
+
+# Logo custom
 [UI.theme]
-primary_color = "#0066CC"
+primary_color = "#0066CC"  # Colore brand
 background_color = "#1a1a1a"
-alert_style = "modern"
 
-# Brand assets (metti i file in /public/brand/)
-logo_file_url = "/public/brand/logo-header.png"
-default_avatar_file_url = "/public/brand/avatar.png"
-login_page_image = "/public/brand/login.jpg"
-login_page_image_filter = "brightness-50 grayscale"
+# Specify additional attributes for a custom CSS file
+# custom_css_attributes = "media=\"print\""
 
-[[UI.header_links]]
-name = "Docs"
-display_name = "Docs"
-icon_url = "/public/brand/icon-32.png"
-url = "https://ai.dffm.it"
-target = "_blank"
+# Specify a JavaScript file that can be used to customize the user interface.
+# The JavaScript file can be served from the public directory.
+# custom_js = "/public/test.js"
 
-[[UI.header_links]]
-name = "Support"
-display_name = "Support"
-icon_url = "/public/brand/icon-32.png"
-url = "mailto:support@dffm.it"
-target = "_blank"
+# The style of alert boxes. Can be "classic" or "modern".
+alert_style = "classic"
+
+# Specify additional attributes for custom JS file
+# custom_js_attributes = "async type = \"module\""
+
+# Custom login page image, relative to public directory or external URL
+# login_page_image = "/public/custom-background.jpg"
+
+# Custom login page image filter (Tailwind internal filters, no dark/light variants)
+# login_page_image_filter = "brightness-50 grayscale"
+# login_page_image_dark_filter = "contrast-200 blur-sm"
+
+# Specify a custom meta URL (used for meta tags like og:url)
+# custom_meta_url = "https://github.com/Chainlit/chainlit"
+
+# Specify a custom meta image url.
+# custom_meta_image_url = "https://chainlit-cloud.s3.eu-west-3.amazonaws.com/logo/chainlit_banner.png"
+
+# Load assistant logo directly from URL.
+logo_file_url = ""
+
+# Load assistant avatar image directly from URL.
+default_avatar_file_url = ""
+
+# Specify a custom build directory for the frontend.
+# This can be used to customize the frontend code.
+# Be careful: If this is a relative path, it should not start with a slash.
+# custom_build = "./public/build"
+
+# Specify optional one or more custom links in the header.
+# [[UI.header_links]]
+#     name = "Issues"
+#     display_name = "Report Issue"
+#     icon_url = "https://avatars.githubusercontent.com/u/128686189?s=200&v=4"
+#     url = "https://github.com/Chainlit/chainlit/issues"
+#     target = "_blank" (default)  # Optional: "_self", "_parent", "_top".
 
 [meta]
 generated_by = "2.8.3"

.files/fe508c80-f90f-471d-a7f8-fc98d4a018d6/a98d6f7b-5275-452d-92c1-7e3968dcb526/app.py

@@ -0,0 +1,463 @@
+import os
+import re
+import uuid
+import shutil
+import requests
+import time
+import json
+from datetime import datetime
+from typing import Optional, Dict, List, Any
+import chainlit as cl
+import ollama
+from docling.document_converter import DocumentConverter
+from qdrant_client import AsyncQdrantClient
+# CORREZIONE IMPORT: Importiamo le classi necessarie direttamente dalla libreria
+from qdrant_client.models import PointStruct, Distance, VectorParams, SparseVectorParams, Prefetch
+from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
+from chainlit.types import ThreadDict
+from functools import lru_cache
+
+# === FIX IMPORT ROBUSTO ===
+try:
+    from chainlit.data.storage_clients import BaseStorageClient
+except ImportError:
+    try:
+        from chainlit.data.base import BaseStorageClient
+    except ImportError:
+        from chainlit.data.storage_clients.base import BaseStorageClient
+
+# === CONFIGURAZIONE ===
+DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
+QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
+BGE_API_URL = os.getenv("BGE_API_URL", "http://192.168.1.243:8001/embed") 
+
+VISION_MODEL = "minicpm-v"
+DEFAULT_TEXT_MODEL = "glm-4.6:cloud"
+
+WORKSPACES_DIR = "./workspaces"
+STORAGE_DIR = "./.files"
+
+os.makedirs(STORAGE_DIR, exist_ok=True)
+os.makedirs(WORKSPACES_DIR, exist_ok=True)
+
+# === MAPPING UTENTI ===
+USER_PROFILES = {
+    "giuseppe@defranceschi.pro": { "role": "admin", "name": "Giuseppe", "workspace": "admin_workspace", "rag_collection": "admin_docs", "capabilities": ["debug", "all"], "show_code": True },
+    "giuseppe.defranceschi@gmail.com": { "role": "admin", "name": "Giuseppe", "workspace": "admin_workspace", "rag_collection": "admin_docs", "capabilities": ["debug", "all"], "show_code": True },
+    "federica.tecchio@gmail.com": { "role": "business", "name": "Federica", "workspace": "business_workspace", "rag_collection": "contabilita", "capabilities": ["basic_chat"], "show_code": False },
+    "riccardob545@gmail.com": { "role": "engineering", "name": "Riccardo", "workspace": "engineering_workspace", "rag_collection": "engineering_docs", "capabilities": ["code"], "show_code": True },
+    "giuliadefranceschi05@gmail.com": { "role": "architecture", "name": "Giulia", "workspace": "architecture_workspace", "rag_collection": "architecture_manuals", "capabilities": ["visual"], "show_code": False }
+}
+
+# === STORAGE CLIENT ===
+class LocalStorageClient(BaseStorageClient):
+    def __init__(self, storage_path: str):
+        self.storage_path = storage_path
+        os.makedirs(storage_path, exist_ok=True)
+    async def upload_file(self, object_key: str, data: bytes, mime: str = "application/octet-stream", overwrite: bool = True) -> Dict[str, str]:
+        file_path = os.path.join(self.storage_path, object_key)
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, "wb") as f: f.write(data)
+        return {"object_key": object_key, "url": f"/files/{object_key}"}
+    async def get_read_url(self, object_key: str) -> str: return f"/files/{object_key}"
+    async def delete_file(self, object_key: str) -> bool:
+        path = os.path.join(self.storage_path, object_key)
+        if os.path.exists(path): os.remove(path); return True
+        return False
+    async def close(self): pass
+
+@cl.data_layer
+def get_data_layer():
+    return SQLAlchemyDataLayer(conninfo=DATABASE_URL, storage_provider=LocalStorageClient(STORAGE_DIR))
+
+# === OAUTH & UTILS ===
+@cl.oauth_callback
+def oauth_callback(provider_id: str, token: str, raw_user_data: Dict[str, str], default_user: cl.User) -> Optional[cl.User]:
+    if provider_id == "google":
+        email = raw_user_data.get("email", "").lower()
+        profile = USER_PROFILES.get(email, USER_PROFILES.get("guest", {"role": "guest", "name": "Guest", "workspace": "guest", "rag_collection": "public", "show_code": False}))
+        default_user.metadata.update({"role": profile["role"], "workspace": profile["workspace"], "rag_collection": profile["rag_collection"], "show_code": profile["show_code"], "display_name": profile["name"]})
+        return default_user
+    return default_user
+
+def create_workspace(workspace_name: str) -> str:
+    path = os.path.join(WORKSPACES_DIR, workspace_name)
+    os.makedirs(path, exist_ok=True)
+    return path
+
+
+# === CORE: DOCLING ===
+def process_file_with_docling(file_path: str) -> str:
+    try:
+        converter = DocumentConverter()
+        result = converter.convert(file_path)
+        return result.document.export_to_markdown()
+    except Exception as e:
+        print(f"❌ Docling Error: {e}")
+        return ""
+
+# === CORE: BGE-M3 CLIENT ===
+def get_bge_embeddings(text: str) -> Optional[Dict[str, Any]]:
+    try:
+        payload = {"texts": [text[:8000]]} 
+        response = requests.post(BGE_API_URL, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json().get("data", [])
+        if data:
+            return data[0]
+        return None
+    except Exception as e:
+        print(f"❌ BGE API Error: {e}")
+        return None
+
+# === CORE: QDRANT ===
+async def ensure_collection(collection_name: str):
+    client = AsyncQdrantClient(url=QDRANT_URL)
+    if not await client.collection_exists(collection_name):
+        await client.create_collection(
+            collection_name=collection_name,
+            vectors_config={"dense": VectorParams(size=1024, distance=Distance.COSINE)},
+            sparse_vectors_config={"sparse": SparseVectorParams()}
+        )
+
+async def index_document(file_name: str, content: str, collection_name: str):
+    await ensure_collection(collection_name)
+    client = AsyncQdrantClient(url=QDRANT_URL)
+    
+    chunk_size = 2000
+    overlap = 200
+    
+    points = []
+    for i in range(0, len(content), chunk_size - overlap):
+        chunk = content[i : i + chunk_size]
+        embedding_data = get_bge_embeddings(chunk)
+        
+        if embedding_data:
+            points.append(PointStruct(
+                id=str(uuid.uuid4()),
+                vector={
+                    "dense": embedding_data["dense"],
+                    "sparse": embedding_data["sparse"]
+                },
+                payload={
+                    "file_name": file_name,
+                    "content": chunk,
+                    "indexed_at": datetime.now().isoformat()
+                }
+            ))
+            
+    if points:
+        await client.upsert(collection_name=collection_name, points=points)
+        return len(points)
+    return 0
+
+async def search_hybrid(query: str, collection_name: str, limit: int = 4) -> str:
+    client = AsyncQdrantClient(url=QDRANT_URL)
+    if not await client.collection_exists(collection_name): return ""
+    
+    query_emb = get_bge_embeddings(query)
+    if not query_emb: return ""
+    
+    # CORREZIONE QUI: Usiamo l'oggetto Prefetch importato correttamente
+    results = await client.query_points(
+        collection_name=collection_name,
+        prefetch=[
+            Prefetch(
+                query=query_emb["sparse"],
+                using="sparse",
+                limit=limit * 2
+            )
+        ],
+        query=query_emb["dense"],
+        using="dense",
+        limit=limit
+    )
+    
+    context = []
+    for hit in results.points:
+        context.append(f"--- DA {hit.payload['file_name']} ---\n{hit.payload['content']}")
+    
+    return "\n\n".join(context)
+
+# === Caching Embeddings ===
+@lru_cache(maxsize=1000)
+def get_bge_embeddings_cached(text: str):
+    """Cache per query ripetute"""
+    return get_bge_embeddings(text)
+
+# === CHAINLIT HANDLERS ===
+@cl.on_chat_start
+async def start():
+    # 1. Profilo utente
+    user = cl.user_session.get("user")
+    email = user.identifier if user else "guest"
+    profile = USER_PROFILES.get(email, USER_PROFILES["giuseppe@defranceschi.pro"])
+    
+    cl.user_session.set("profile", profile)
+    create_workspace(profile["workspace"])
+
+    # 2. Badge HTML personalizzato
+    role_color = {
+        "admin": "#e74c3c",
+        "engineering": "#3498db",
+        "business": "#2ecc71",
+        "architecture": "#9b59b6",
+    }.get(profile["role"], "#95a5a6")
+    
+    badge_html = f"""
+    <div style="background:{role_color}; padding:8px; border-radius:8px; margin-bottom:16px;">
+        👤 <b>{profile['name']}</b> | 🔧 {profile['role'].upper()} | 📁 {profile['workspace']}
+    </div>
+    """
+    await cl.Message(content=badge_html).send()
+
+    # 3. Settings UI
+    settings = await cl.ChatSettings(
+        [
+            cl.input_widget.Slider(
+                id="top_k",
+                label="Numero Documenti RAG",
+                initial=4,
+                min=1,
+                max=10,
+                step=1,
+            ),
+            cl.input_widget.Select(
+                id="vision_detail",
+                label="Dettaglio Analisi Immagini",
+                values=["auto", "low", "high"],
+                initial_value="auto",
+            ),
+            cl.input_widget.TextInput(
+                id="system_instruction",
+                label="Istruzione Sistema Custom (opzionale)",
+                initial="",
+                placeholder="Es: Rispondi sempre in formato tecnico...",
+            ),
+            cl.input_widget.Select(
+                id="model",
+                label="Modello di Ragionamento",
+                values=[DEFAULT_TEXT_MODEL, "llama3.2", "mistral", "qwen2.5-coder:32b"],
+                initial_value=DEFAULT_TEXT_MODEL,
+            ),
+            cl.input_widget.Slider(
+                id="temperature",
+                label="Creatività (Temperatura)",
+                initial=0.3,
+                min=0,
+                max=1,
+                step=0.1,
+            ),
+            cl.input_widget.Switch(
+                id="rag_enabled",
+                label="Usa Conoscenza Documenti (RAG)",
+                initial=True,
+            ),
+        ]
+    ).send()
+
+    cl.user_session.set("settings", settings)
+
+    # 4. Messaggio iniziale (opzionale)
+    await cl.Message(
+        content=(
+            f"🚀 **Vision-RAG Hybrid System Online**\n"
+            f"Utente: {profile['name']} | Workspace: {profile['workspace']}\n"
+            f"Engine: Docling + BGE-M3 + {VISION_MODEL}"
+        )
+    ).send()
+
+    
+    cl.user_session.set("settings", settings)
+    
+    await cl.Message(f"🚀 **Vision-RAG Hybrid System Online**\nUtente: {profile['name']} | Workspace: {profile['workspace']}\nEngine: Docling + BGE-M3 + {VISION_MODEL}").send()
+
+@cl.on_settings_update
+async def setup_agent(settings):
+    cl.user_session.set("settings", settings)
+    await cl.Message(content=f"✅ Impostazioni aggiornate: Modello {settings['model']}").send()
+
+async def log_metrics(metrics: dict):
+    # Versione minima: log su stdout
+    print("[METRICS]", metrics)
+
+    # In futuro puoi:
+    # - salvarle in Postgres
+    # - mandarle a Prometheus / Grafana
+    # - scriverle su file JSON per analisi settimanale
+
+# - Resume Chat Handler
+
+@cl.on_chat_resume
+async def on_chat_resume(thread: ThreadDict):
+    """
+    Viene chiamato quando l'utente clicca 'Riprendi' su una chat archiviata.
+    Chainlit carica già i messaggi nella UI, qui puoi solo ripristinare la sessione.
+    """
+    # Se vuoi, puoi recuperare l'identifier dell’utente dal thread
+    user_identifier = thread.get("userIdentifier")
+    profile = USER_PROFILES.get(
+        user_identifier,
+        USER_PROFILES["giuseppe@defranceschi.pro"],
+    )
+    cl.user_session.set("profile", profile)
+
+    # Puoi anche ripristinare eventuale stato custom (es: impostazioni di default)
+    # oppure semplicemente salutare l’utente
+    await cl.Message(
+        content="👋 Bentornato! Possiamo riprendere da questa conversazione."
+    ).send()
+
+@cl.on_message
+async def main(message: cl.Message):
+    start_time = time.time()
+
+    profile = cl.user_session.get("profile")
+    settings = cl.user_session.get("settings", {})
+
+    selected_model = settings.get("model", DEFAULT_TEXT_MODEL)
+    temperature = settings.get("temperature", 0.3)
+    rag_enabled = settings.get("rag_enabled", True)
+
+    workspace = create_workspace(profile["workspace"])
+
+    images_for_vision = []
+    doc_context = ""
+    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)

.gitignore

@@ -6,4 +6,3 @@ workspaces/*
 qdrant_storage/.files/
 __pycache__/
 .env
-.files/

Dockerfile

@@ -3,9 +3,12 @@ FROM python:3.11-slim
 WORKDIR /app
 
 # Installa dipendenze sistema
+# Aggiunte libgl1 e libglib2.0-0 per il supporto Docling/CV2
 RUN apt-get update && apt-get install -y \
     gcc \
     postgresql-client \
+    libgl1 \
+    libglib2.0-0 \
     && rm -rf /var/lib/apt/lists/*
 
 # Copia requirements e installa

README.md

@@ -1,325 +1,342 @@
-# AI Station - Document Analysis Platform
+# AI Station DFFM - Vision-RAG Hybrid System
 
-## 📋 Overview
+Sistema AI multi-utente con supporto RAG (Retrieval-Augmented Generation), analisi immagini e gestione documenti avanzata, basato su Chainlit, Ollama e BGE-M3.
 
-**AI Station** è una piattaforma di analisi documentale basata su AI che utilizza **Retrieval-Augmented Generation (RAG)** per analizzare PDF e documenti testuali con il modello **GLM-4.6:Cloud**.
+## 🌟 Features
+
+### Core AI
+- **RAG Hybrid Search** con BGE-M3 (dense + sparse embeddings)
+- **Vision Analysis** tramite MiniCPM-V per OCR e descrizione immagini
+- **Document Processing** con Docling (PDF, DOCX) con preservazione tabelle/formule
+- **Multi-Model Support** (Ollama locale + cloud models)
+- **Streaming Responses** con latenza ridotta
+
+### Multi-Utente
+- **OAuth2 Google** con profili personalizzati per ruolo
+- **Workspace isolati** per utente/team
+- **RAG Collections dedicate** per knowledge base separate
+- **Permessi granulari** (admin, engineering, business, architecture)
+
+### UI/UX
+- **Badge ruolo personalizzato** con colori dedicati
+- **Settings dinamici** (temperatura, top_k RAG, modello, istruzioni custom)
+- **Chat history persistente** con ripresa conversazioni
+- **Auto-save codice Python** estratto dalle risposte
+- **Metriche real-time** (response time, RAG hits, errori)
+
+### Performance
+- **Caching embeddings** (LRU cache 1000 query)
+- **Chunking intelligente** (2000 char con overlap 200)
+- **Async operations** su Qdrant e Ollama
+- **PostgreSQL** per persistenza thread e metadata
+
+## 🏗️ Architettura
+
+### Hardware Setup
+
+#### AI-SRV (Chainlit VM)
+- **IP**: 192.168.1.244
+- **CPU**: 16 core (QEMU Virtual)
+- **RAM**: 64 GB
+- **Storage**: 195 GB
+- **Ruolo**: Host Chainlit app + PostgreSQL + Qdrant
+
+#### AI-Server (GPU Workstation)
+- **IP**: 192.168.1.243
+- **CPU**: Intel Core Ultra 7 265 (20 core, max 6.5 GHz)
+- **RAM**: 32 GB
+- **GPU**: NVIDIA RTX A1000 (8 GB VRAM)
+- **Storage**: 936 GB NVMe
+- **Ruolo**: Ollama models + BGE-M3 embeddings service
 
 ### Stack Tecnologico
-- **Backend**: Python + Chainlit (LLM UI framework)
-- **LLM**: GLM-4.6:Cloud (via Ollama Cloud)
-- **Vector DB**: Qdrant (semantic search)
-- **PDF Processing**: PyMuPDF (fitz)
-- **Database**: PostgreSQL + SQLAlchemy ORM
-- **Containerization**: Docker Compose
-- **Embeddings**: nomic-embed-text (via Ollama local)
 
----
+┌─────────────────────────────────────────┐
+│ Chainlit UI (ai-srv) │
+│ Badge + Settings + Chat History │
+└──────────────┬──────────────────────────┘
+│
+┌──────────────▼──────────────────────────┐
+│ Python Backend (app.py) │
+│ - OAuth2 Google │
+│ - Multi-user profiles │
+│ - File processing orchestration │
+└─┬────────┬──────────┬──────────┬────────┘
+│ │ │ │
+▼ ▼ ▼ ▼
+┌─────┐ ┌─────┐ ┌────────┐ ┌──────────┐
+│ PG │ │Qdrant│ │ Ollama │ │ BGE API │
+│ │ │Vector│ │ GPU │ │ CPU │
+│ │ │ DB │ │ Server │ │ Server │
+└─────┘ └─────┘ └────────┘ └──────────┘
+ai-srv ai-srv ai-server ai-server
 
-## 🚀 Quick Start
+text
 
-### Prerequisites
-- Docker & Docker Compose
-- Ollama installed locally (for embeddings)
-- Ollama Cloud account (for glm-4.6:cloud)
+## 📋 Requisiti
 
-### 1️⃣ Clone & Setup
+### Sistema
+- Docker 24.x+ con Docker Compose
+- Accesso a Google Cloud Console (per OAuth2)
+- 2 server (o VM) con networking condiviso
+
+### Modelli Ollama (da installare su ai-server)
 ```bash
-git clone git@github.com:your-username/ai-station.git
+ollama pull minicpm-v              # Vision model (5.5 GB)
+ollama pull glm-4.6:cloud          # Cloud reasoning
+ollama pull qwen2.5-coder:32b      # Code generation (9 GB)
+ollama pull llama3.2               # Fast general purpose (4.7 GB)
+🚀 Installazione
+1. Clone Repository
+bash
+git clone <your-repo>
 cd ai-station
+2. Configurazione Ambiente
+Crea .env:
 
-# Configure environment
-cat > .env << 'EOF'
-DATABASE_URL=postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station
+bash
+# Database
+DATABASE_URL=postgresql+asyncpg://ai_user:CHANGE_ME@postgres:5432/ai_station
+
+# AI Services
 OLLAMA_URL=http://192.168.1.243:11434
 QDRANT_URL=http://qdrant:6333
-EOF
-```
+BGE_API_URL=http://192.168.1.243:8001/embed
 
-### 2️⃣ Authenticate Ollama Cloud
-```bash
-ollama signin
-# Follow the link to authenticate with your Ollama account
-```
+# OAuth Google
+OAUTH_GOOGLE_CLIENT_ID=your-client-id.apps.googleusercontent.com
+OAUTH_GOOGLE_CLIENT_SECRET=your-secret
+CHAINLIT_AUTH_SECRET=$(openssl rand -base64 32)
 
-### 3️⃣ Start Services
-```bash
-docker compose up -d
+3. Configurazione OAuth Google
+Vai su Google Cloud Console
+
+Crea nuovo progetto → API e servizi → Credenziali
+
+Crea "ID client OAuth 2.0"
+
+Aggiungi URI autorizzati:
+
+https://ai.dffm.it/auth/oauth/google/callback
+
+http://localhost:8000/auth/oauth/google/callback (dev)
+
+Copia Client ID e Secret in .env
+
+4. Personalizza Utenti
+Modifica app.py → USER_PROFILES:
+
+python
+USER_PROFILES = {
+    "tuo.email@example.com": {
+        "role": "admin",
+        "name": "Nome",
+        "workspace": "workspace_name",
+        "rag_collection": "docs_collection",
+        "capabilities": ["debug", "all"],
+        "show_code": True,
+    },
+    # ... altri utenti
+}
+5. Deploy
+bash
+# Build e avvio
+docker compose up -d --build
+
+# Verifica logs
 docker compose logs -f chainlit-app
+
+# Dovresti vedere:
+# ✅ Tutte le tabelle create con successo.
+# Your app is available at http://localhost:8000
+6. Setup BGE-M3 Service (su ai-server)
+bash
+# Installa dependencies
+pip install fastapi uvicorn FlagEmbedding torch
+
+# Salva il file bge_service.py (vedi docs/)
+python bge_service.py
+# Listening on http://0.0.0.0:8001
+🎯 Utilizzo
+Login
+Accedi via browser: https://ai.dffm.it (o http://localhost:8000)
+
+Click su "Continue with Google"
+
+Autorizza con account configurato in USER_PROFILES
+
+Chat con RAG
+Carica PDF/DOCX → Sistema li indicizza automaticamente
+
+Fai domande → Risposta con contesto dai documenti
+
+Regola top_k (numero documenti) via settings
+
+Analisi Immagini
+Carica screenshot/diagrammi
+
+Il sistema:
+
+Estrae testo (OCR)
+
+Descrive grafici/tabelle
+
+Usa descrizione come contesto per rispondere
+
+Settings Disponibili
+Numero Documenti RAG (1-10): Quanti chunk recuperare
+
+Modello: Scegli tra locale/cloud
+
+Temperatura (0-1): Creatività risposta
+
+RAG Enabled: On/Off recupero documenti
+
+Istruzione Custom: Prompt system personalizzato
+
+Ripresa Chat
+Sidebar → Chat History
+
+Click su conversazione → "Riprendi"
+
+Continua da dove avevi lasciato
+
+📊 Metriche
+Ogni risposta logga (stdout):
+
+json
+{
+  "response_time": 18.65,
+  "rag_hits": 4,
+  "model": "glm-4.6:cloud",
+  "user_role": "admin",
+  "error": null
+}
+Raccogli con:
+
+bash
+docker logs ai-station-app | grep METRICS > metrics.log
+🔧 Troubleshooting
+RAG non trova documenti
+Verifica collection name in USER_PROFILES[email]["rag_collection"]
+
+Controlla Qdrant: curl http://localhost:6333/collections
+
+Badge HTML non si vede
+Abilita in .chainlit/config.toml:
 ```
 
-### 4️⃣ Access UI
-Navigate to: **http://localhost:8000**
-
----
-
-## 📁 Project Structure
+```text
+[features]
+unsafe_allow_html = true
+Modello Ollama non risponde
+bash
+# Testa connessione
+curl http://192.168.1.243:11434/api/tags
 
+# Verifica modello disponibile
+ollama list
+BGE embeddings fail
 ```
+```bash
+# Testa API
+curl -X POST http://192.168.1.243:8001/embed \
+  -H "Content-Type: application/json" \
+  -d '{"texts": ["test"]}'
+```
+
+📁 Struttura Progetto
+```bash
 ai-station/
-├── app.py                 # Main Chainlit application
-├── requirements.txt       # Python dependencies
-├── docker-compose.yml     # Docker services config
-├── .env                   # Environment variables (gitignored)
-├── workspaces/           # User workspace directories
-│   └── admin/            # Admin user files
-└── README.md             # This file
+├── app.py                    # Main Chainlit app
+├── init_db.py               # Database schema init
+├── requirements.txt         # Python deps
+├── Dockerfile              # Container config
+├── docker-compose.yaml     # Multi-service orchestration
+├── .chainlit/
+│   └── config.toml         # UI/features config
+├── public/
+│   └── custom.css          # Custom styling
+├── workspaces/             # User file storage (volume)
+│   ├── admin_workspace/
+│   ├── engineering_workspace/
+│   └── ...
+└── .files/                 # Chainlit storage (volume)
 ```
-
----
-
-## 🔧 Features
-
-### ✅ Implemented
-- **PDF Upload & Processing**: Extract text from PDF documents using PyMuPDF
-- **Document Indexing**: Automatic chunking and semantic indexing via Qdrant
-- **RAG Search**: Retrieve relevant document chunks based on semantic similarity
-- **Intelligent Analysis**: GLM-4.6:Cloud analyzes documents with full context
-- **Code Extraction**: Automatically save Python code blocks from responses
-- **Chat History**: Persistent conversation storage via SQLAlchemy
-- **Streaming Responses**: Real-time token streaming via Chainlit
-
-### 🔄 Workflow
-1. User uploads PDF or TXT file
-2. System extracts text and creates semantic chunks
-3. Chunks indexed in Qdrant vector database
-4. User asks questions about documents
-5. RAG retrieves relevant chunks
-6. GLM-4.6:Cloud analyzes with full context
-7. Streaming response to user
-
----
-
-## 📊 Technical Details
-
-### Document Processing Pipeline
-
-```
-PDF Upload
-    ↓
-PyMuPDF Text Extraction
-    ↓
-Text Chunking (1500 chars, 200 char overlap)
-    ↓
-nomic-embed-text Embeddings (Ollama local)
-    ↓
-Qdrant Vector Storage
-    ↓
-Semantic Search on User Query
-    ↓
-GLM-4.6:Cloud Analysis with RAG Context
-    ↓
-Chainlit Streaming Response
-```
-
-### Key Functions
-
-| Function | Purpose |
-|----------|---------|
-| `extract_text_from_pdf()` | Convert PDF to text using PyMuPDF |
-| `chunk_text()` | Split text into overlapping chunks |
-| `get_embeddings()` | Generate embeddings via Ollama |
-| `index_document()` | Store chunks in Qdrant |
-| `search_qdrant()` | Retrieve relevant context |
-| `on_message()` | Process user queries with RAG |
-
----
-
-## 🔐 Environment Variables
-
-```env
-DATABASE_URL=postgresql+asyncpg://user:pass@postgres:5432/ai_station
-OLLAMA_URL=http://192.168.1.243:11434          # Local Ollama for embeddings
-QDRANT_URL=http://qdrant:6333                  # Vector database
-```
-
-**Note**: GLM-4.6:Cloud authentication is handled automatically via `ollama signin`
-
----
-
-## 🐳 Docker Services
-
-| Service | Port | Purpose |
-|---------|------|---------|
-| `chainlit-app` | 8000 | Chainlit UI & API |
-| `postgres` | 5432 | Conversation persistence |
-| `qdrant` | 6333 | Vector database |
-| `ollama` | 11434 | Local embeddings (external) |
-
-Start/Stop:
+🔐 Sicurezza
 ```bash
-docker compose up -d      # Start all services
-docker compose down       # Stop all services
-docker compose logs -f    # View logs
-docker compose restart    # Restart services
+OAuth2 obbligatorio (no accesso anonimo)
+
+Workspace isolation (file separati per utente)
+
+HTML sanitization (configurable via unsafe_allow_html)
+
+Environment secrets (.env mai committato)
+
+PostgreSQL passwords cambiate da default
 ```
 
----
 
-## 📝 Usage Examples
-
-### Example 1: Analyze Tax Document
-```
-User: "Qual è l'importo totale del documento?"
-AI Station: 
-  ✅ Extracts PDF content
-  ✅ Searches relevant sections
-  ✅ Analyzes with GLM-4.6:Cloud
-  📄 Returns: "Based on the document, the total amount is..."
-```
-
-### Example 2: Multi-Document Analysis
-```
-1. Upload multiple PDFs (invoices, contracts)
-2. All documents automatically indexed
-3. Query across all documents simultaneously
-4. RAG retrieves most relevant chunks
-5. GLM-4.6:Cloud synthesizes answer
-```
-
----
-
-## 🛠️ Development
-
-### Install Dependencies
+🚦 Roadmap
 ```bash
-pip install -r requirements.txt
+ Re-ranking con cross-encoder
+
+ Query expansion automatica
+
+ Feedback loop (👍👎 su risposte)
+
+ Export conversazioni PDF/Markdown
+
+ Multi-query RAG parallelo
+
+ Prometheus/Grafana monitoring
+
+ Adaptive chunking per tipo documento
+
+ Audio input support
 ```
 
-### Requirements
-```
-chainlit==1.3.2
-pydantic==2.9.2
-ollama>=0.1.0
-asyncpg>=0.29.0
-psycopg2-binary
-qdrant-client>=1.10.0
-sqlalchemy>=2.0.0
-greenlet>=3.0.0
-sniffio
-aiohttp
-alembic
-pymupdf
-python-dotenv
+
+## 📝 Licenza
+```tect
+MIT License - vedi file [LICENSE](LICENSE) per dettagli.
+Crea file LICENSE nella root del progetto
+text
+MIT License
+
+Copyright (c) 2026 DFFM / Giuseppe De Franceschi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
 ```
 
-### Local Testing (without Docker)
-```bash
-# Start Ollama, PostgreSQL, Qdrant manually
-ollama serve &
-chainlit run app.py
-```
+👥 Contributors
+Giuseppe De Franceschi - @defranceschi
 
----
+🙏 Credits
+Chainlit - UI framework
 
-## 🔄 Model Details
+Ollama - LLM runtime
 
-### GLM-4.6:Cloud
-- **Provider**: Zhipu AI via Ollama Cloud
-- **Capabilities**: Long context, reasoning, multilingual
-- **Cost**: Free tier available
-- **Authentication**: Device key (automatic via `ollama signin`)
+Qdrant - Vector DB
 
-### nomic-embed-text
-- **Local embedding model** for chunking/retrieval
-- **Dimensions**: 768
-- **Speed**: Fast, runs locally
-- **Used for**: RAG semantic search
+BGE-M3 - Embeddings
 
----
+Docling - Document processing
 
-## 📈 Monitoring & Logs
-
-### Check Service Health
-```bash
-# View all logs
-docker compose logs
-
-# Follow live logs
-docker compose logs -f chainlit-app
-
-# Check specific container
-docker inspect ai-station-chainlit-app
-```
-
-### Common Issues
-| Issue | Solution |
-|-------|----------|
-| `unauthorized` error | Run `ollama signin` on server |
-| Database connection failed | Check PostgreSQL is running |
-| Qdrant unavailable | Verify `docker-compose up` completed |
-| PDF not extracted | Ensure PyMuPDF installed: `pip install pymupdf` |
-
----
-
-## 🚀 Deployment
-
-### Production Checklist
-- [ ] Set secure PostgreSQL credentials in `.env`
-- [ ] Enable SSL/TLS for Chainlit endpoints
-- [ ] Configure CORS for frontend
-- [ ] Setup log aggregation (ELK, Datadog, etc.)
-- [ ] Implement rate limiting
-- [ ] Add API authentication
-- [ ] Configure backup strategy for Qdrant
-
-### Cloud Deployment Options
-- **AWS**: ECS + RDS + VectorDB
-- **Google Cloud**: Cloud Run + Cloud SQL
-- **DigitalOcean**: App Platform + Managed Databases
-
----
-
-## 📚 API Reference
-
-### REST Endpoints (via Chainlit)
-- `POST /api/chat` - Send message with context
-- `GET /api/threads` - List conversations
-- `POST /api/upload` - Upload document
-
-### WebSocket
-- Real-time streaming responses via Chainlit protocol
-
----
-
-## 🔮 Future Features
-
-- [ ] OAuth2 Google authentication
-- [ ] Document metadata extraction (dates, amounts, entities)
-- [ ] Advanced search filters (type, date range, language)
-- [ ] Export results (PDF, CSV, JSON)
-- [ ] Analytics dashboard
-- [ ] Multi-language support
-- [ ] Document versioning
-- [ ] Compliance reporting (GDPR, audit trails)
-
----
-
-## 📞 Support
-
-### Troubleshooting
-1. Check logs: `docker compose logs chainlit-app`
-2. Verify Ollama authentication: `ollama show glm-4.6:cloud`
-3. Test Qdrant connection: `curl http://localhost:6333/health`
-4. Inspect PostgreSQL: `docker compose exec postgres psql -U ai_user -d ai_station`
-
-### Performance Tips
-- Increase chunk overlap for better context retrieval
-- Adjust embedding model based on latency requirements
-- Monitor Qdrant memory usage for large document sets
-- Implement caching for frequent queries
-
----
-
-## 📄 License
-
-MIT License - See LICENSE file
-
-## 👤 Author
-
-AI Station Team
-
----
-
-**Last Updated**: December 26, 2025
-**Version**: 1.0.0
-**Status**: Production Ready ✅
+## **Status**: 🔨 Pre-Production | **Last Update**: 2026-01-01

app.py

@@ -4,27 +4,20 @@ import uuid
 import shutil
 import requests
 import time
+import json
 from datetime import datetime
-from typing import Optional, Dict, Any, List
-
+from typing import Optional, Dict, List, Any
 import chainlit as cl
 import ollama
-
 from docling.document_converter import DocumentConverter
 from qdrant_client import AsyncQdrantClient
-from qdrant_client.models import (
-    PointStruct,
-    Distance,
-    VectorParams,
-    SparseVectorParams,
-    Prefetch,
-)
-
+# CORREZIONE IMPORT: Importiamo le classi necessarie direttamente dalla libreria
+from qdrant_client.models import PointStruct, Distance, VectorParams, SparseVectorParams, Prefetch
 from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
 from chainlit.types import ThreadDict
 from functools import lru_cache
 
-# === FIX IMPORT ROBUSTO Storage Client ===
+# === FIX IMPORT ROBUSTO ===
 try:
     from chainlit.data.storage_clients import BaseStorageClient
 except ImportError:
@@ -33,207 +26,68 @@ except ImportError:
     except ImportError:
         from chainlit.data.storage_clients.base import BaseStorageClient
 
-
-# =========================
-# CONFIG
-# =========================
-DATABASE_URL = os.getenv(
-    "DATABASE_URL",
-    "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station",
-)
+# === CONFIGURAZIONE ===
+DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
 OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
 QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
 BGE_API_URL = os.getenv("BGE_API_URL", "http://192.168.1.243:8001/embed") 
 
 VISION_MODEL = "minicpm-v"
-
 DEFAULT_TEXT_MODEL = "glm-4.6:cloud"
-MINIMAX_MODEL = "minimax-m2.1:cloud"
-
-MODEL_CHOICES = [
-    DEFAULT_TEXT_MODEL,
-    MINIMAX_MODEL,
-    "llama3.2",
-    "mistral",
-    "qwen2.5-coder:32b",
-]
 
 WORKSPACES_DIR = "./workspaces"
 STORAGE_DIR = "./.files"
+
 os.makedirs(STORAGE_DIR, exist_ok=True)
 os.makedirs(WORKSPACES_DIR, exist_ok=True)
 
-# =========================
-# USER PROFILES
-# =========================
+# === MAPPING UTENTI ===
 USER_PROFILES = {
-    "giuseppe@defranceschi.pro": {
-        "role": "admin",
-        "name": "Giuseppe",
-        "workspace": "admin_workspace",
-        "rag_collection": "admin_docs",
-        "capabilities": ["debug", "all"],
-        "show_code": True,
-    },
-    "federica.tecchio@gmail.com": {
-        "role": "business",
-        "name": "Federica",
-        "workspace": "business_workspace",
-        "rag_collection": "contabilita",
-        "capabilities": ["basic_chat"],
-        "show_code": False,
-    },
-    "riccardob545@gmail.com": {
-        "role": "engineering",
-        "name": "Riccardo",
-        "workspace": "engineering_workspace",
-        "rag_collection": "engineering_docs",
-        "capabilities": ["code"],
-        "show_code": True,
-    },
-    "giuliadefranceschi05@gmail.com": {
-        "role": "architecture",
-        "name": "Giulia",
-        "workspace": "architecture_workspace",
-        "rag_collection": "architecture_manuals",
-        "capabilities": ["visual"],
-        "show_code": False,
-    },
-    "giuseppe.defranceschi@gmail.com": {
-        "role": "architecture",
-        "name": "Giuseppe",
-        "workspace": "architecture_workspace",
-        "rag_collection": "architecture_manuals",
-        "capabilities": ["visual"],
-        "show_code": False,
-    },
+    "giuseppe@defranceschi.pro": { "role": "admin", "name": "Giuseppe", "workspace": "admin_workspace", "rag_collection": "admin_docs", "capabilities": ["debug", "all"], "show_code": True },
+    "federica.tecchio@gmail.com": { "role": "business", "name": "Federica", "workspace": "business_workspace", "rag_collection": "contabilita", "capabilities": ["basic_chat"], "show_code": False },
+    "riccardob545@gmail.com": { "role": "engineering", "name": "Riccardo", "workspace": "engineering_workspace", "rag_collection": "engineering_docs", "capabilities": ["code"], "show_code": True },
+    "giuliadefranceschi05@gmail.com": { "role": "architecture", "name": "Giulia", "workspace": "architecture_workspace", "rag_collection": "architecture_manuals", "capabilities": ["visual"], "show_code": False },
+    "giuseppe.defranceschi@gmail.com": { "role": "architecture", "name": "Giuseppe", "workspace": "architecture_workspace", "rag_collection": "architecture_manuals", "capabilities": ["visual"], "show_code": False }
 }
 
-GUEST_PROFILE = {
-    "role": "guest",
-    "name": "Guest",
-    "workspace": "guest",
-    "rag_collection": "public",
-    "capabilities": ["basic_chat"],
-    "show_code": False,
-}
-
-# Sensible defaults per ruolo (S-Tier: thoughtful defaults) [file:3]
-ROLE_DEFAULTS = {
-    "admin": {
-        "model": DEFAULT_TEXT_MODEL,
-        "top_k": 6,
-        "temperature": 0.3,
-        "rag_enabled": True,
-        "vision_detail": "high",
-    },
-    "engineering": {
-        "model": MINIMAX_MODEL,
-        "top_k": 5,
-        "temperature": 0.3,
-        "rag_enabled": True,
-        "vision_detail": "low",
-    },
-    "business": {
-        "model": DEFAULT_TEXT_MODEL,
-        "top_k": 4,
-        "temperature": 0.2,
-        "rag_enabled": True,
-        "vision_detail": "auto",
-    },
-    "architecture": {
-        "model": DEFAULT_TEXT_MODEL,
-        "top_k": 4,
-        "temperature": 0.3,
-        "rag_enabled": True,
-        "vision_detail": "high",
-    },
-    "guest": {
-        "model": DEFAULT_TEXT_MODEL,
-        "top_k": 3,
-        "temperature": 0.2,
-        "rag_enabled": False,
-        "vision_detail": "auto",
-    },
-}
-
-
-# =========================
-# STORAGE
-# =========================
+# === STORAGE CLIENT ===
 class LocalStorageClient(BaseStorageClient):
     def __init__(self, storage_path: str):
         self.storage_path = storage_path
         os.makedirs(storage_path, exist_ok=True)
-
-    async def upload_file(
-        self,
-        object_key: str,
-        data: bytes,
-        mime: str = "application/octet-stream",
-        overwrite: bool = True,
-    ) -> Dict[str, str]:
+    async def upload_file(self, object_key: str, data: bytes, mime: str = "application/octet-stream", overwrite: bool = True) -> Dict[str, str]:
         file_path = os.path.join(self.storage_path, object_key)
         os.makedirs(os.path.dirname(file_path), exist_ok=True)
-        with open(file_path, "wb") as f:
-            f.write(data)
+        with open(file_path, "wb") as f: f.write(data)
         return {"object_key": object_key, "url": f"/files/{object_key}"}
-
-    async def get_read_url(self, object_key: str) -> str:
-        return f"/files/{object_key}"
-
+    async def get_read_url(self, object_key: str) -> str: return f"/files/{object_key}"
     async def delete_file(self, object_key: str) -> bool:
         path = os.path.join(self.storage_path, object_key)
-        if os.path.exists(path):
-            os.remove(path)
-            return True
+        if os.path.exists(path): os.remove(path); return True
         return False
-
-    async def close(self):
-        pass
-
+    async def close(self): pass
 
 @cl.data_layer
 def get_data_layer():
     return SQLAlchemyDataLayer(conninfo=DATABASE_URL, storage_provider=LocalStorageClient(STORAGE_DIR))
 
-
-# =========================
-# OAUTH
-# =========================
+# === OAUTH & UTILS ===
 @cl.oauth_callback
-def oauth_callback(
-    provider_id: str,
-    token: str,
-    raw_user_data: Dict[str, str],
-    default_user: cl.User,
-) -> Optional[cl.User]:
+def oauth_callback(provider_id: str, token: str, raw_user_data: Dict[str, str], default_user: cl.User) -> Optional[cl.User]:
     if provider_id == "google":
         email = raw_user_data.get("email", "").lower()
-        profile = USER_PROFILES.get(email, GUEST_PROFILE)
-
-        default_user.metadata.update(
-            {
-                "role": profile["role"],
-                "workspace": profile["workspace"],
-                "rag_collection": profile["rag_collection"],
-                "show_code": profile["show_code"],
-                "display_name": profile["name"],
-            }
-        )
+        profile = USER_PROFILES.get(email, USER_PROFILES.get("guest", {"role": "guest", "name": "Guest", "workspace": "guest", "rag_collection": "public", "show_code": False}))
+        default_user.metadata.update({"role": profile["role"], "workspace": profile["workspace"], "rag_collection": profile["rag_collection"], "show_code": profile["show_code"], "display_name": profile["name"]})
         return default_user
     return default_user
 
-
 def create_workspace(workspace_name: str) -> str:
     path = os.path.join(WORKSPACES_DIR, workspace_name)
     os.makedirs(path, exist_ok=True)
     return path
 
 
-# =========================
-# CORE: DOCLING
-# =========================
+# === CORE: DOCLING ===
 def process_file_with_docling(file_path: str) -> str:
     try:
         converter = DocumentConverter()
@@ -243,10 +97,7 @@ def process_file_with_docling(file_path: str) -> str:
         print(f"❌ Docling Error: {e}")
         return ""
 
-
-# =========================
-# CORE: BGE-M3 embeddings
-# =========================
+# === CORE: BGE-M3 CLIENT ===
 def get_bge_embeddings(text: str) -> Optional[Dict[str, Any]]:
     try:
         payload = {"texts": [text[:8000]]} 
@@ -260,364 +111,353 @@ def get_bge_embeddings(text: str) -> Optional[Dict[str, Any]]:
         print(f"❌ BGE API Error: {e}")
         return None
 
-
-@lru_cache(maxsize=1000)
-def get_bge_embeddings_cached(text: str):
-    return get_bge_embeddings(text)
-
-
-# =========================
-# CORE: QDRANT
-# =========================
+# === CORE: QDRANT ===
 async def ensure_collection(collection_name: str):
     client = AsyncQdrantClient(url=QDRANT_URL)
     if not await client.collection_exists(collection_name):
         await client.create_collection(
             collection_name=collection_name,
             vectors_config={"dense": VectorParams(size=1024, distance=Distance.COSINE)},
-            sparse_vectors_config={"sparse": SparseVectorParams()},
+            sparse_vectors_config={"sparse": SparseVectorParams()}
         )
 
-
-async def index_document(file_name: str, content: str, collection_name: str) -> int:
+async def index_document(file_name: str, content: str, collection_name: str):
     await ensure_collection(collection_name)
     client = AsyncQdrantClient(url=QDRANT_URL)
     
     chunk_size = 2000
     overlap = 200
-    points: List[PointStruct] = []
     
+    points = []
     for i in range(0, len(content), chunk_size - overlap):
         chunk = content[i : i + chunk_size]
         embedding_data = get_bge_embeddings(chunk)
+        
         if embedding_data:
-            points.append(
-                PointStruct(
+            points.append(PointStruct(
                 id=str(uuid.uuid4()),
-                    vector={"dense": embedding_data["dense"], "sparse": embedding_data["sparse"]},
+                vector={
+                    "dense": embedding_data["dense"],
+                    "sparse": embedding_data["sparse"]
+                },
                 payload={
                     "file_name": file_name,
                     "content": chunk,
-                        "indexed_at": datetime.now().isoformat(),
-                    },
-                )
-            )
+                    "indexed_at": datetime.now().isoformat()
+                }
+            ))
             
     if points:
         await client.upsert(collection_name=collection_name, points=points)
         return len(points)
-
     return 0
 
-
 async def search_hybrid(query: str, collection_name: str, limit: int = 4) -> str:
     client = AsyncQdrantClient(url=QDRANT_URL)
-
-    if not await client.collection_exists(collection_name):
-        return ""
+    if not await client.collection_exists(collection_name): return ""
     
     query_emb = get_bge_embeddings(query)
-    if not query_emb:
-        return ""
+    if not query_emb: return ""
     
+    # CORREZIONE QUI: Usiamo l'oggetto Prefetch importato correttamente
     results = await client.query_points(
         collection_name=collection_name,
-        prefetch=[Prefetch(query=query_emb["sparse"], using="sparse", limit=limit * 2)],
+        prefetch=[
+            Prefetch(
+                query=query_emb["sparse"],
+                using="sparse",
+                limit=limit * 2
+            )
+        ],
         query=query_emb["dense"],
         using="dense",
-        limit=limit,
+        limit=limit
     )
     
     context = []
     for hit in results.points:
         context.append(f"--- DA {hit.payload['file_name']} ---\n{hit.payload['content']}")
+    
     return "\n\n".join(context)
 
+# === Caching Embeddings ===
+@lru_cache(maxsize=1000)
+def get_bge_embeddings_cached(text: str):
+    """Cache per query ripetute"""
+    return get_bge_embeddings(text)
 
-# =========================
-# UX HELPERS (S-Tier: clarity, consistency)
-# =========================
-def role_to_badge_class(role: str) -> str:
-    allowed = {"admin", "engineering", "business", "architecture", "guest"}
-    return f"dfm-badge--{role}" if role in allowed else "dfm-badge--guest"
-
-
-def build_system_prompt(system_instruction: str, has_rag: bool, has_files: bool) -> str:
-    base = (
-        "Sei un assistente tecnico esperto.\n"
-        "Obiettivo: rispondere in modo chiaro, preciso e operativo.\n"
-        "- Se mancano dettagli, fai 1-2 domande mirate.\n"
-        "- Se scrivi codice, includi snippet piccoli e verificabili.\n"
-    )
-    if has_rag:
-        base += "- Usa il contesto RAG come fonte primaria quando presente.\n"
-    if has_files:
-        base += "- Se sono presenti file/immagini, sfrutta le informazioni estratte.\n"
-
-    if system_instruction.strip():
-        base += "\nIstruzione aggiuntiva (utente): " + system_instruction.strip() + "\n"
-
-    return base
-
-
-def extract_code_blocks(text: str) -> List[str]:
-    return re.findall(r"```(?:\w+)?\n(.*?)```", text, re.DOTALL)
-
-
-async def log_metrics(metrics: dict):
-    # Mantieni semplice: stdout (come nella tua versione) [file:6]
-    print("METRICS:", metrics)
-
-
-# =========================
-# CHAINLIT HANDLERS
-# =========================
+# === CHAINLIT HANDLERS ===
 @cl.on_chat_start
 async def start():
-    # 1) Profilo utente
+    # 1. Profilo utente
     user = cl.user_session.get("user")
     email = user.identifier if user else "guest"
+    profile = USER_PROFILES.get(email, USER_PROFILES["giuseppe@defranceschi.pro"])
     
-    profile = USER_PROFILES.get(email, GUEST_PROFILE)
     cl.user_session.set("profile", profile)
-
     create_workspace(profile["workspace"])
 
-    role = profile.get("role", "guest")
-    defaults = ROLE_DEFAULTS.get(role, ROLE_DEFAULTS["guest"])
-    cl.user_session.set("role_defaults", defaults)
+    # 2. Badge HTML personalizzato
+    role_color = {
+        "admin": "#e74c3c",
+        "engineering": "#3498db",
+        "business": "#2ecc71",
+        "architecture": "#9b59b6",
+    }.get(profile["role"], "#95a5a6")
     
-    # 2) Badge (HTML controllato; stile via CSS)
     badge_html = f"""
-    <div class="dfm-badge {role_to_badge_class(role)}">
-      <span><b>{profile['name']}</b></span>
-      <span style="opacity:.8">{role.upper()}</span>
-      <span style="opacity:.7">· {profile['workspace']}</span>
+    <div style="background:{role_color}; padding:8px; border-radius:8px; margin-bottom:16px;">
+        👤 <b>{profile['name']}</b> | 🔧 {profile['role'].upper()} | 📁 {profile['workspace']}
     </div>
     """
     await cl.Message(content=badge_html).send()
 
-    # 3) Settings UI (Clarity + sensible defaults)
+    # 3. Settings UI
     settings = await cl.ChatSettings(
         [
-            cl.input_widget.Switch(
-                id="rag_enabled",
-                label="📚 Usa Conoscenza Documenti",
-                initial=bool(defaults["rag_enabled"]),
-                description="Attiva la ricerca nei documenti caricati (consigliato).",
-            ),
             cl.input_widget.Slider(
                 id="top_k",
-                label="Profondità Ricerca (documenti)",
-                initial=int(defaults["top_k"]),
+                label="Numero Documenti RAG",
+                initial=4,
                 min=1,
                 max=10,
                 step=1,
-                description="Più documenti = risposta più completa ma più lenta.",
-            ),
-            cl.input_widget.Select(
-                id="model",
-                label="🤖 Modello AI",
-                values=MODEL_CHOICES,
-                initial_value=str(defaults["model"]),
-            ),
-            cl.input_widget.Slider(
-                id="temperature",
-                label="Creatività",
-                initial=float(defaults["temperature"]),
-                min=0,
-                max=1,
-                step=0.1,
-                description="Bassa = più precisione (consigliato per codice).",
             ),
             cl.input_widget.Select(
                 id="vision_detail",
-                label="🔍 Dettaglio Analisi Immagini",
+                label="Dettaglio Analisi Immagini",
                 values=["auto", "low", "high"],
-                initial_value=str(defaults["vision_detail"]),
+                initial_value="auto",
             ),
             cl.input_widget.TextInput(
                 id="system_instruction",
-                label="✏️ Istruzione Sistema (opzionale)",
+                label="Istruzione Sistema Custom (opzionale)",
                 initial="",
-                placeholder="es: Rispondi con bullet points e includi esempi",
-                description="Personalizza stile/format delle risposte.",
+                placeholder="Es: Rispondi sempre in formato tecnico...",
+            ),
+            cl.input_widget.Select(
+                id="model",
+                label="Modello di Ragionamento",
+                values=[DEFAULT_TEXT_MODEL, "llama3.2", "mistral", "qwen2.5-coder:32b"],
+                initial_value=DEFAULT_TEXT_MODEL,
+            ),
+            cl.input_widget.Slider(
+                id="temperature",
+                label="Creatività (Temperatura)",
+                initial=0.3,
+                min=0,
+                max=1,
+                step=0.1,
+            ),
+            cl.input_widget.Switch(
+                id="rag_enabled",
+                label="Usa Conoscenza Documenti (RAG)",
+                initial=True,
             ),
         ]
     ).send()
 
     cl.user_session.set("settings", settings)
 
+    # 4. Messaggio iniziale (opzionale)
     await cl.Message(
         content=(
-            "✅ Ai Station online.\n"
-            f"• Workspace: `{profile['workspace']}`\n"
-            f"• Default modello: `{defaults['model']}`\n"
-            f"• Vision: `{VISION_MODEL}`"
+            f"🚀 **Vision-RAG Hybrid System Online**\n"
+            f"Utente: {profile['name']} | Workspace: {profile['workspace']}\n"
+            f"Engine: Docling + BGE-M3 + {VISION_MODEL}"
         )
     ).send()
 
     
-@cl.on_settings_update
-async def setupagentsettings(settings):
     cl.user_session.set("settings", settings)
     
-    await cl.Message(
-        content=(
-            "✅ Impostazioni aggiornate:\n"
-            f"• Modello: `{settings.get('model')}`\n"
-            f"• RAG: {'ON' if settings.get('rag_enabled') else 'OFF'} · top_k={settings.get('top_k')}\n"
-            f"• Creatività: {settings.get('temperature')}\n"
-            f"• Vision detail: `{settings.get('vision_detail')}`"
-        )
-    ).send()
+    await cl.Message(f"🚀 **Vision-RAG Hybrid System Online**\nUtente: {profile['name']} | Workspace: {profile['workspace']}\nEngine: Docling + BGE-M3 + {VISION_MODEL}").send()
 
+@cl.on_settings_update
+async def setup_agent(settings):
+    cl.user_session.set("settings", settings)
+    await cl.Message(content=f"✅ Impostazioni aggiornate: Modello {settings['model']}").send()
+
+async def log_metrics(metrics: dict):
+    # Versione minima: log su stdout
+    print("[METRICS]", metrics)
+
+    # In futuro puoi:
+    # - salvarle in Postgres
+    # - mandarle a Prometheus / Grafana
+    # - scriverle su file JSON per analisi settimanale
+
+# - Resume Chat Handler
 
 @cl.on_chat_resume
 async def on_chat_resume(thread: ThreadDict):
+    """
+    Viene chiamato quando l'utente clicca 'Riprendi' su una chat archiviata.
+    Chainlit carica già i messaggi nella UI, qui puoi solo ripristinare la sessione.
+    """
+    # Se vuoi, puoi recuperare l'identifier dell’utente dal thread
     user_identifier = thread.get("userIdentifier")
-    profile = USER_PROFILES.get(user_identifier, GUEST_PROFILE)
+    profile = USER_PROFILES.get(
+        user_identifier,
+        USER_PROFILES["giuseppe@defranceschi.pro"],
+    )
     cl.user_session.set("profile", profile)
-    create_workspace(profile["workspace"])
-    await cl.Message(content="Bentornato! Riprendiamo da qui.").send()
 
+    # Puoi anche ripristinare eventuale stato custom (es: impostazioni di default)
+    # oppure semplicemente salutare l’utente
+    await cl.Message(
+        content="👋 Bentornato! Possiamo riprendere da questa conversazione."
+    ).send()
 
 @cl.on_message
 async def main(message: cl.Message):
     start_time = time.time()
 
-    profile = cl.user_session.get("profile", GUEST_PROFILE)
+    profile = cl.user_session.get("profile")
     settings = cl.user_session.get("settings", {})
 
     selected_model = settings.get("model", DEFAULT_TEXT_MODEL)
-    temperature = float(settings.get("temperature", 0.3))
-    rag_enabled = bool(settings.get("rag_enabled", True))
-    top_k = int(settings.get("top_k", 4))
-    vision_detail = settings.get("vision_detail", "auto")
-    system_instruction = (settings.get("system_instruction", "") or "").strip()
+    temperature = settings.get("temperature", 0.3)
+    rag_enabled = settings.get("rag_enabled", True)
 
     workspace = create_workspace(profile["workspace"])
 
-    # 1) Gestione upload (immagini / pdf / docx)
-    images_for_vision: List[str] = []
+    images_for_vision = []
     doc_context = ""
+    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)
 
-            # Immagini
-            if "image" in (element.mime or ""):
+            if "image" in element.mime:
                 images_for_vision.append(file_path)
-                msg_img = cl.Message(content=f"🖼️ Analizzo immagine `{element.name}` con `{VISION_MODEL}`...")
+                msg_img = cl.Message(
+                    content=f"👁️ Analizzo immagine **{element.name}** con {VISION_MODEL}..."
+                )
                 await msg_img.send()
 
-                try:
-                    with open(file_path, "rb") as imgfile:
-                        imgbytes = imgfile.read()
+                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=[
-                            {
+                    messages=[{
                         "role": "user",
                         "content": (
-                                    "Analizza questa immagine tecnica. "
-                                    "Trascrivi testi/codici e descrivi diagrammi o tabelle in dettaglio. "
-                                    f"Dettaglio richiesto: {vision_detail}."
+                            "Analizza questa immagine tecnica. Trascrivi testi, codici "
+                            "e descrivi diagrammi o tabelle in dettaglio."
                         ),
-                                "images": [imgbytes],
-                            }
-                        ],
+                        "images": [img_bytes],
+                    }],
                 )
-                    desc = res.get("message", {}).get("content", "")
-                    doc_context += f"\n\n## DESCRIZIONE IMMAGINE: {element.name}\n{desc}\n"
-                    msg_img.content = f"✅ Immagine analizzata: {desc[:300]}..."
-                    await msg_img.update()
-                except Exception as e:
-                    msg_img.content = f"❌ Errore analisi immagine: {e}"
+                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()
 
-            # Documenti (pdf/docx)
-            elif element.name.lower().endswith((".pdf", ".docx")):
-                msg_doc = cl.Message(content=f"📄 Leggo `{element.name}` con Docling (tabelle/formule)...")
+            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"])
-                    doc_context += f"\n\n## CONTENUTO FILE: {element.name}\n{markdown_content[:2000]}\n"
-                    msg_doc.content = f"✅ `{element.name}` convertito e indicizzato ({chunks} chunks)."
+                    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}`."
+                    msg_doc.content = f"❌ Errore lettura {element.name}"
                 await msg_doc.update()
 
-    # 2) RAG retrieval (solo se attivo e senza immagini-only flow)
-    rag_context = ""
+    # 2. RAG RETRIEVAL
     if rag_enabled and not images_for_vision:
-        rag_context = await search_hybrid(message.content, profile["rag_collection"], limit=top_k)
-
-    has_rag = bool(rag_context.strip())
-    has_files = bool(doc_context.strip())
-
-    # 3) Prompt building
-    system_prompt = build_system_prompt(system_instruction, has_rag=has_rag, has_files=has_files)
+        rag_context = await search_hybrid(
+            message.content, profile["rag_collection"]
+        )
 
     final_context = ""
-    if has_rag:
-        final_context += "\n\n# CONTESTO RAG\n" + rag_context
-    if has_files:
-        final_context += "\n\n# CONTESTO FILE SESSIONE\n" + doc_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."
+    )
 
-    # 4) Generazione (stream)
     msg = cl.Message(content="")
     await msg.send()
 
-    error: Optional[str] = None
+    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{final_context}"},
+                {
+                    "role": "user",
+                    "content": f"Domanda: {message.content}\n\n{final_context}",
+                },
             ],
             options={"temperature": temperature},
             stream=True,
         )
-        async for chunk in stream:
-            content = chunk.get("message", {}).get("content", "")
-            if content:
-                await msg.stream_token(content)
 
+        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"\n\n❌ Errore AI: {error}")
+        await msg.stream_token(f"❌ Errore AI: {error}")
         await msg.update()
 
-    # 5) Salvataggio code blocks (solo per profili con show_code)
-    if profile.get("show_code", False) and msg.content:
-        codeblocks = extract_code_blocks(msg.content)
-        if codeblocks:
-            for i, code in enumerate(codeblocks):
+    # 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"
-                try:
-                    with open(os.path.join(workspace, fname), "w", encoding="utf-8") as f:
+                with open(os.path.join(workspace, fname), "w") as f:
                     f.write(code.strip())
-                    await cl.Message(content=f"💾 Script salvato: `{fname}`").send()
-                except Exception as e:
-                    await cl.Message(content=f"❌ Errore salvataggio `{fname}`: {e}").send()
+                await cl.Message(
+                    content=f"💾 Script salvato: `{fname}`"
+                ).send()
 
-    # 6) Metriche
+    # 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_context.count("--- DA ") if rag_context else 0,
+        "rag_hits": rag_hits,
         "model": selected_model,
-        "user_role": profile.get("role", "unknown"),
+        "user_role": profile["role"],
         "error": error,
     }
+
     await log_metrics(metrics)

bck/app-bck212.py

@@ -0,0 +1,344 @@
+import os
+import re
+import uuid
+import shutil
+from datetime import datetime
+from typing import Optional, Dict, List
+import chainlit as cl
+import ollama
+import fitz  # PyMuPDF
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.models import PointStruct, Distance, VectorParams
+from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
+
+# === FIX IMPORT ROBUSTO ===
+# Gestisce le differenze tra le versioni di Chainlit 2.x
+try:
+    from chainlit.data.storage_clients import BaseStorageClient
+except ImportError:
+    try:
+        from chainlit.data.base import BaseStorageClient
+    except ImportError:
+        from chainlit.data.storage_clients.base import BaseStorageClient
+
+# === CONFIGURAZIONE ===
+DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
+QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
+WORKSPACES_DIR = "./workspaces"
+STORAGE_DIR = "./.files"
+
+os.makedirs(STORAGE_DIR, exist_ok=True)
+os.makedirs(WORKSPACES_DIR, exist_ok=True)
+
+# === MAPPING UTENTI E RUOLI ===
+USER_PROFILES = {
+    "giuseppe@defranceschi.pro": {
+        "role": "admin",
+        "name": "Giuseppe",
+        "workspace": "admin_workspace",
+        "rag_collection": "admin_docs",
+        "capabilities": ["debug", "system_prompts", "user_management", "all_models"],
+        "show_code": True
+    },
+    "federica.tecchio@gmail.com": {
+        "role": "business",
+        "name": "Federica",
+        "workspace": "business_workspace",
+        "rag_collection": "contabilita",
+        "capabilities": ["pdf_upload", "basic_chat"],
+        "show_code": False
+    },
+    "giuseppe.defranceschi@gmail.com": {
+        "role": "admin",
+        "name": "Giuseppe",
+        "workspace": "admin_workspace",
+        "rag_collection": "admin_docs",
+        "capabilities": ["debug", "system_prompts", "user_management", "all_models"],
+        "show_code": True
+    },
+        "riccardob545@gmail.com": {
+        "role": "engineering",
+        "name": "Riccardo",
+        "workspace": "engineering_workspace",
+        "rag_collection": "engineering_docs",
+        "capabilities": ["code_execution", "data_viz", "advanced_chat"],
+        "show_code": True
+    },
+    "giuliadefranceschi05@gmail.com": {
+        "role": "architecture",
+        "name": "Giulia",
+        "workspace": "architecture_workspace",
+        "rag_collection": "architecture_manuals",
+        "capabilities": ["visual_chat", "pdf_upload", "image_gen"],
+        "show_code": False
+    }
+}
+
+# === CUSTOM LOCAL STORAGE CLIENT (FIXED) ===# Questa classe ora implementa tutti i metodi astratti richiesti da Chainlit 2.8.3
+class LocalStorageClient(BaseStorageClient):
+    """Storage locale su filesystem per file/elementi"""
+    
+    def __init__(self, storage_path: str):
+        self.storage_path = storage_path
+        os.makedirs(storage_path, exist_ok=True)
+    
+    async def upload_file(
+        self,
+        object_key: str,
+        data: bytes,
+        mime: str = "application/octet-stream",
+        overwrite: bool = True,
+    ) -> Dict[str, str]:
+        file_path = os.path.join(self.storage_path, object_key)
+        os.makedirs(os.path.dirname(file_path), exist_ok=True)
+        with open(file_path, "wb") as f:
+            f.write(data)
+        return {"object_key": object_key, "url": f"/files/{object_key}"}
+
+    # Implementazione metodi obbligatori mancanti nella versione precedente
+    async def get_read_url(self, object_key: str) -> str:
+        return f"/files/{object_key}"
+
+    async def delete_file(self, object_key: str) -> bool:
+        file_path = os.path.join(self.storage_path, object_key)
+        if os.path.exists(file_path):
+            os.remove(file_path)
+            return True
+        return False
+
+    async def close(self):
+        pass
+
+# === DATA LAYER ===
+@cl.data_layer
+def get_data_layer():
+    return SQLAlchemyDataLayer(
+        conninfo=DATABASE_URL,
+        user_thread_limit=1000,
+        storage_provider=LocalStorageClient(storage_path=STORAGE_DIR)
+    )
+
+# === OAUTH CALLBACK ===
+@cl.oauth_callback
+def oauth_callback(
+    provider_id: str,
+    token: str,
+    raw_user_data: Dict[str, str],
+    default_user: cl.User,
+) -> Optional[cl.User]:
+    if provider_id == "google":
+        email = raw_user_data.get("email", "").lower()
+        
+        # Verifica se utente è autorizzato (opzionale: blocca se non in lista)
+        # if email not in USER_PROFILES:
+        #    return None
+        
+        # Recupera profilo o usa default Guest
+        profile = USER_PROFILES.get(email, get_user_profile("guest"))
+        
+        default_user.metadata.update({
+            "picture": raw_user_data.get("picture", ""),
+            "role": profile["role"],
+            "workspace": profile["workspace"],
+            "rag_collection": profile["rag_collection"],
+            "capabilities": profile["capabilities"],
+            "show_code": profile["show_code"],
+            "display_name": profile["name"]
+        })
+        return default_user
+    return default_user
+
+# === UTILITY FUNCTIONS ===
+def get_user_profile(user_email: str) -> Dict:
+    return USER_PROFILES.get(user_email.lower(), {
+        "role": "guest",
+        "name": "Ospite",
+        "workspace": "guest_workspace",
+        "rag_collection": "documents",
+        "capabilities": [],
+        "show_code": False
+    })
+
+def create_workspace(workspace_name: str) -> str:
+    path = os.path.join(WORKSPACES_DIR, workspace_name)
+    os.makedirs(path, exist_ok=True)
+    return path
+
+def save_code_to_file(code: str, workspace: str) -> str:
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    file_name = f"code_{timestamp}.py"
+    file_path = os.path.join(WORKSPACES_DIR, workspace, file_name)
+    with open(file_path, "w", encoding="utf-8") as f:
+        f.write(code)
+    return file_path
+
+def extract_text_from_pdf(pdf_path: str) -> str:
+    try:
+        doc = fitz.open(pdf_path)
+        text = "\n".join([page.get_text() for page in doc])
+        doc.close()
+        return text
+    except Exception:
+        return ""
+
+# === QDRANT FUNCTIONS ===
+async def get_qdrant_client() -> AsyncQdrantClient:
+    return AsyncQdrantClient(url=QDRANT_URL)
+
+async def ensure_collection(collection_name: str):
+    client = await get_qdrant_client()
+    if not await client.collection_exists(collection_name):
+        await client.create_collection(
+            collection_name=collection_name,
+            vectors_config=VectorParams(size=768, distance=Distance.COSINE)
+        )
+
+async def get_embeddings(text: str) -> list:
+    client = ollama.Client(host=OLLAMA_URL)
+    try:
+        response = client.embed(model='nomic-embed-text', input=text[:2000])
+        if 'embeddings' in response: return response['embeddings'][0]
+        return response.get('embedding', [])
+    except: return []
+
+async def index_document(file_name: str, content: str, collection_name: str) -> bool:
+    try:
+        await ensure_collection(collection_name)
+        embedding = await get_embeddings(content)
+        if not embedding: return False
+        
+        qdrant = await get_qdrant_client()
+        await qdrant.upsert(
+            collection_name=collection_name,
+            points=[PointStruct(
+                id=str(uuid.uuid4()),
+                vector=embedding,
+                payload={"file_name": file_name, "content": content[:3000], "indexed_at": datetime.now().isoformat()}
+            )]
+        )
+        return True
+    except: return False
+
+async def search_qdrant(query: str, collection: str) -> str:
+    try:
+        client = await get_qdrant_client()
+        if not await client.collection_exists(collection): return ""
+        emb = await get_embeddings(query)
+        if not emb: return ""
+        res = await client.query_points(collection_name=collection, query=emb, limit=3)
+        return "\n\n".join([hit.payload['content'] for hit in res.points if hit.payload])
+    except: return ""
+
+# === CHAINLIT HANDLERS ===
+
+@cl.on_chat_start
+async def on_chat_start():
+    user = cl.user_session.get("user")
+    
+    if not user:
+        # Fallback locale se non c'è auth
+        user_email = "guest@local"
+        profile = get_user_profile(user_email)
+    else:
+        user_email = user.identifier
+        # I metadati sono già popolati dalla callback oauth
+        profile = USER_PROFILES.get(user_email, get_user_profile("guest"))
+
+    # Salva in sessione
+    cl.user_session.set("email", user_email)
+    cl.user_session.set("role", profile["role"])
+    cl.user_session.set("workspace", profile["workspace"])
+    cl.user_session.set("rag_collection", profile["rag_collection"])
+    cl.user_session.set("show_code", profile["show_code"])
+    
+    create_workspace(profile["workspace"])
+
+    # === SETTINGS WIDGETS ===
+    settings_widgets = [
+        cl.input_widget.Select(
+            id="model",
+            label="Modello AI",
+            values=["glm-4.6:cloud", "llama3.2", "mistral", "qwen2.5-coder:32b"],
+            initial_value="glm-4.6:cloud",
+        ),
+        cl.input_widget.Slider(
+            id="temperature",
+            label="Temperatura",
+            initial=0.7, min=0, max=2, step=0.1,
+        ),
+    ]
+    if profile["role"] == "admin":
+        settings_widgets.append(cl.input_widget.Switch(id="rag_enabled", label="Abilita RAG", initial=True))
+    
+    await cl.ChatSettings(settings_widgets).send()
+    
+    await cl.Message(
+        content=f"👋 Ciao **{profile['name']}**!\n"
+                f"Ruolo: `{profile['role']}` | Workspace: `{profile['workspace']}`\n"
+    ).send()
+
+@cl.on_settings_update
+async def on_settings_update(settings):
+    cl.user_session.set("settings", settings)
+    await cl.Message(content="✅ Impostazioni aggiornate").send()
+
+@cl.on_message
+async def on_message(message: cl.Message):
+    workspace = cl.user_session.get("workspace")
+    rag_collection = cl.user_session.get("rag_collection")
+    user_role = cl.user_session.get("role")
+    show_code = cl.user_session.get("show_code")
+    
+    settings = cl.user_session.get("settings", {})
+    model = settings.get("model", "glm-4.6:cloud")
+    temperature = settings.get("temperature", 0.7)
+    rag_enabled = settings.get("rag_enabled", True) if user_role == "admin" else True
+
+    # 1. GESTIONE FILE
+    if message.elements:
+        for element in message.elements:
+            dest = os.path.join(WORKSPACES_DIR, workspace, element.name)
+            shutil.copy(element.path, dest)
+            if element.name.endswith(".pdf"):
+                text = extract_text_from_pdf(dest)
+                if text:
+                    await index_document(element.name, text, rag_collection)
+                    await cl.Message(content=f"✅ **{element.name}** indicizzato.").send()
+
+    # 2. RAG
+    context = ""
+    if rag_enabled:
+        context = await search_qdrant(message.content, rag_collection)
+    
+    system_prompt = "Sei un assistente esperto."
+    if context: system_prompt += f"\n\nCONTESTO:\n{context}"
+    
+    # 3. GENERAZIONE
+    client = ollama.AsyncClient(host=OLLAMA_URL)
+    msg = cl.Message(content="")
+    await msg.send()
+    
+    stream = await client.chat(
+        model=model,
+        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": message.content}],
+        options={"temperature": temperature},
+        stream=True
+    )
+    
+    full_resp = ""
+    async for chunk in stream:
+        token = chunk['message']['content']
+        full_resp += token
+        await msg.stream_token(token)
+    await msg.update()
+
+    # 4. SALVATAGGIO CODICE
+    if show_code:
+        blocks = re.findall(r"``````", full_resp, re.DOTALL)
+        elements = []
+        for code in blocks:
+            path = save_code_to_file(code.strip(), workspace)
+            elements.append(cl.File(name=os.path.basename(path), path=path, display="inline"))
+        if elements:
+            await cl.Message(content="💾 Codice salvato", elements=elements).send()

bck/cpu.txt

@@ -0,0 +1,39 @@
+Architecture:                         x86_64
+CPU op-mode(s):                       32-bit, 64-bit
+Address sizes:                        40 bits physical, 48 bits virtual
+Byte Order:                           Little Endian
+CPU(s):                               16
+On-line CPU(s) list:                  0-15
+Vendor ID:                            GenuineIntel
+Model name:                           QEMU Virtual CPU version 2.5+
+CPU family:                           15
+Model:                                107
+Thread(s) per core:                   1
+Core(s) per socket:                   4
+Socket(s):                            4
+Stepping:                             1
+BogoMIPS:                             4999.99
+Flags:                                fpu de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx lm constant_tsc nopl xtopology cpuid tsc_known_freq pni ssse3 cx16 sse4_1 sse4_2 x2apic popcnt aes hypervisor lahf_lm cpuid_fault pti
+Hypervisor vendor:                    KVM
+Virtualization type:                  full
+L1d cache:                            512 KiB (16 instances)
+L1i cache:                            512 KiB (16 instances)
+L2 cache:                             64 MiB (16 instances)
+L3 cache:                             64 MiB (4 instances)
+NUMA node(s):                         1
+NUMA node0 CPU(s):                    0-15
+Vulnerability Gather data sampling:   Not affected
+Vulnerability Itlb multihit:          KVM: Mitigation: VMX unsupported
+Vulnerability L1tf:                   Mitigation; PTE Inversion
+Vulnerability Mds:                    Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
+Vulnerability Meltdown:               Mitigation; PTI
+Vulnerability Mmio stale data:        Unknown: No mitigations
+Vulnerability Reg file data sampling: Not affected
+Vulnerability Retbleed:               Not affected
+Vulnerability Spec rstack overflow:   Not affected
+Vulnerability Spec store bypass:      Vulnerable
+Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
+Vulnerability Spectre v2:             Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Retpoline
+Vulnerability Srbds:                  Not affected
+Vulnerability Tsx async abort:        Not affected
+Vulnerability Vmscape:                Not affected

bck/design-principles-example.md

@@ -0,0 +1,129 @@
+# S-Tier SaaS Dashboard Design Checklist (Inspired by Stripe, Airbnb, Linear)
+
+## I. Core Design Philosophy & Strategy
+
+*   [ ] **Users First:** Prioritize user needs, workflows, and ease of use in every design decision.
+*   [ ] **Meticulous Craft:** Aim for precision, polish, and high quality in every UI element and interaction.
+*   [ ] **Speed & Performance:** Design for fast load times and snappy, responsive interactions.
+*   [ ] **Simplicity & Clarity:** Strive for a clean, uncluttered interface. Ensure labels, instructions, and information are unambiguous.
+*   [ ] **Focus & Efficiency:** Help users achieve their goals quickly and with minimal friction. Minimize unnecessary steps or distractions.
+*   [ ] **Consistency:** Maintain a uniform design language (colors, typography, components, patterns) across the entire dashboard.
+*   [ ] **Accessibility (WCAG AA+):** Design for inclusivity. Ensure sufficient color contrast, keyboard navigability, and screen reader compatibility.
+*   [ ] **Opinionated Design (Thoughtful Defaults):** Establish clear, efficient default workflows and settings, reducing decision fatigue for users.
+
+## II. Design System Foundation (Tokens & Core Components)
+
+*   [ ] **Define a Color Palette:**
+    *   [ ] **Primary Brand Color:** User-specified, used strategically.
+    *   [ ] **Neutrals:** A scale of grays (5-7 steps) for text, backgrounds, borders.
+    *   [ ] **Semantic Colors:** Define specific colors for Success (green), Error/Destructive (red), Warning (yellow/amber), Informational (blue).
+    *   [ ] **Dark Mode Palette:** Create a corresponding accessible dark mode palette.
+    *   [ ] **Accessibility Check:** Ensure all color combinations meet WCAG AA contrast ratios.
+*   [ ] **Establish a Typographic Scale:**
+    *   [ ] **Primary Font Family:** Choose a clean, legible sans-serif font (e.g., Inter, Manrope, system-ui).
+    *   [ ] **Modular Scale:** Define distinct sizes for H1, H2, H3, H4, Body Large, Body Medium (Default), Body Small/Caption. (e.g., H1: 32px, Body: 14px/16px).
+    *   [ ] **Font Weights:** Utilize a limited set of weights (e.g., Regular, Medium, SemiBold, Bold).
+    *   [ ] **Line Height:** Ensure generous line height for readability (e.g., 1.5-1.7 for body text).
+*   [ ] **Define Spacing Units:**
+    *   [ ] **Base Unit:** Establish a base unit (e.g., 8px).
+    *   [ ] **Spacing Scale:** Use multiples of the base unit for all padding, margins, and layout spacing (e.g., 4px, 8px, 12px, 16px, 24px, 32px).
+*   [ ] **Define Border Radii:**
+    *   [ ] **Consistent Values:** Use a small set of consistent border radii (e.g., Small: 4-6px for inputs/buttons; Medium: 8-12px for cards/modals).
+*   [ ] **Develop Core UI Components (with consistent states: default, hover, active, focus, disabled):**
+    *   [ ] Buttons (primary, secondary, tertiary/ghost, destructive, link-style; with icon options)
+    *   [ ] Input Fields (text, textarea, select, date picker; with clear labels, placeholders, helper text, error messages)
+    *   [ ] Checkboxes & Radio Buttons
+    *   [ ] Toggles/Switches
+    *   [ ] Cards (for content blocks, multimedia items, dashboard widgets)
+    *   [ ] Tables (for data display; with clear headers, rows, cells; support for sorting, filtering)
+    *   [ ] Modals/Dialogs (for confirmations, forms, detailed views)
+    *   [ ] Navigation Elements (Sidebar, Tabs)
+    *   [ ] Badges/Tags (for status indicators, categorization)
+    *   [ ] Tooltips (for contextual help)
+    *   [ ] Progress Indicators (Spinners, Progress Bars)
+    *   [ ] Icons (use a single, modern, clean icon set; SVG preferred)
+    *   [ ] Avatars
+
+## III. Layout, Visual Hierarchy & Structure
+
+*   [ ] **Responsive Grid System:** Design based on a responsive grid (e.g., 12-column) for consistent layout across devices.
+*   [ ] **Strategic White Space:** Use ample negative space to improve clarity, reduce cognitive load, and create visual balance.
+*   [ ] **Clear Visual Hierarchy:** Guide the user's eye using typography (size, weight, color), spacing, and element positioning.
+*   [ ] **Consistent Alignment:** Maintain consistent alignment of elements.
+*   [ ] **Main Dashboard Layout:**
+    *   [ ] Persistent Left Sidebar: For primary navigation between modules.
+    *   [ ] Content Area: Main space for module-specific interfaces.
+    *   [ ] (Optional) Top Bar: For global search, user profile, notifications.
+*   [ ] **Mobile-First Considerations:** Ensure the design adapts gracefully to smaller screens.
+
+## IV. Interaction Design & Animations
+
+*   [ ] **Purposeful Micro-interactions:** Use subtle animations and visual feedback for user actions (hovers, clicks, form submissions, status changes).
+    *   [ ] Feedback should be immediate and clear.
+    *   [ ] Animations should be quick (150-300ms) and use appropriate easing (e.g., ease-in-out).
+*   [ ] **Loading States:** Implement clear loading indicators (skeleton screens for page loads, spinners for in-component actions).
+*   [ ] **Transitions:** Use smooth transitions for state changes, modal appearances, and section expansions.
+*   [ ] **Avoid Distraction:** Animations should enhance usability, not overwhelm or slow down the user.
+*   [ ] **Keyboard Navigation:** Ensure all interactive elements are keyboard accessible and focus states are clear.
+
+## V. Specific Module Design Tactics
+
+### A. Multimedia Moderation Module
+
+*   [ ] **Clear Media Display:** Prominent image/video previews (grid or list view).
+*   [ ] **Obvious Moderation Actions:** Clearly labeled buttons (Approve, Reject, Flag, etc.) with distinct styling (e.g., primary/secondary, color-coding). Use icons for quick recognition.
+*   [ ] **Visible Status Indicators:** Use color-coded Badges for content status (Pending, Approved, Rejected).
+*   [ ] **Contextual Information:** Display relevant metadata (uploader, timestamp, flags) alongside media.
+*   [ ] **Workflow Efficiency:**
+    *   [ ] Bulk Actions: Allow selection and moderation of multiple items.
+    *   [ ] Keyboard Shortcuts: For common moderation actions.
+*   [ ] **Minimize Fatigue:** Clean, uncluttered interface; consider dark mode option.
+
+### B. Data Tables Module (Contacts, Admin Settings)
+
+*   [ ] **Readability & Scannability:**
+    *   [ ] Smart Alignment: Left-align text, right-align numbers.
+    *   [ ] Clear Headers: Bold column headers.
+    *   [ ] Zebra Striping (Optional): For dense tables.
+    *   [ ] Legible Typography: Simple, clean sans-serif fonts.
+    *   [ ] Adequate Row Height & Spacing.
+*   [ ] **Interactive Controls:**
+    *   [ ] Column Sorting: Clickable headers with sort indicators.
+    *   [ ] Intuitive Filtering: Accessible filter controls (dropdowns, text inputs) above the table.
+    *   [ ] Global Table Search.
+*   [ ] **Large Datasets:**
+    *   [ ] Pagination (preferred for admin tables) or virtual/infinite scroll.
+    *   [ ] Sticky Headers / Frozen Columns: If applicable.
+*   [ ] **Row Interactions:**
+    *   [ ] Expandable Rows: For detailed information.
+    *   [ ] Inline Editing: For quick modifications.
+    *   [ ] Bulk Actions: Checkboxes and contextual toolbar.
+    *   [ ] Action Icons/Buttons per Row: (Edit, Delete, View Details) clearly distinguishable.
+
+### C. Configuration Panels Module (Microsite, Admin Settings)
+
+*   [ ] **Clarity & Simplicity:** Clear, unambiguous labels for all settings. Concise helper text or tooltips for descriptions. Avoid jargon.
+*   [ ] **Logical Grouping:** Group related settings into sections or tabs.
+*   [ ] **Progressive Disclosure:** Hide advanced or less-used settings by default (e.g., behind "Advanced Settings" toggle, accordions).
+*   [ ] **Appropriate Input Types:** Use correct form controls (text fields, checkboxes, toggles, selects, sliders) for each setting.
+*   [ ] **Visual Feedback:** Immediate confirmation of changes saved (e.g., toast notifications, inline messages). Clear error messages for invalid inputs.
+*   [ ] **Sensible Defaults:** Provide default values for all settings.
+*   [ ] **Reset Option:** Easy way to "Reset to Defaults" for sections or entire configuration.
+*   [ ] **Microsite Preview (If Applicable):** Show a live or near-live preview of microsite changes.
+
+## VI. CSS & Styling Architecture
+
+*   [ ] **Choose a Scalable CSS Methodology:**
+    *   [ ] **Utility-First (Recommended for LLM):** e.g., Tailwind CSS. Define design tokens in config, apply via utility classes.
+    *   [ ] **BEM with Sass:** If not utility-first, use structured BEM naming with Sass variables for tokens.
+    *   [ ] **CSS-in-JS (Scoped Styles):** e.g., Stripe's approach for Elements.
+*   [ ] **Integrate Design Tokens:** Ensure colors, fonts, spacing, radii tokens are directly usable in the chosen CSS architecture.
+*   [ ] **Maintainability & Readability:** Code should be well-organized and easy to understand.
+*   [ ] **Performance:** Optimize CSS delivery; avoid unnecessary bloat.
+
+## VII. General Best Practices
+
+*   [ ] **Iterative Design & Testing:** Continuously test with users and iterate on designs.
+*   [ ] **Clear Information Architecture:** Organize content and navigation logically.
+*   [ ] **Responsive Design:** Ensure the dashboard is fully functional and looks great on all device sizes (desktop, tablet, mobile).
+*   [ ] **Documentation:** Maintain clear documentation for the design system and components.

bck/error.log

@@ -0,0 +1,717 @@
+ai-station-app       | [INFO] 2025-12-31 10:20:23,724 [RapidOCR] download_file.py:82: Download size: 25.67MB
+ai-station-app       | [INFO] 2025-12-31 10:20:27,451 [RapidOCR] download_file.py:95: Successfully saved to: /usr/local/lib/python3.11/site-packages/rapidocr/models/ch_PP-OCRv4_rec_infer.pth
+ai-station-app       | [INFO] 2025-12-31 10:20:27,460 [RapidOCR] main.py:50: Using /usr/local/lib/python3.11/site-packages/rapidocr/models/ch_PP-OCRv4_rec_infer.pth
+ai-station-app       | 2025-12-31 10:20:28 - Auto OCR model selected rapidocr with torch.
+ai-station-app       | 2025-12-31 10:20:28 - Loading plugin 'docling_defaults'
+ai-station-app       | 2025-12-31 10:20:28 - Registered layout engines: ['docling_layout_default', 'docling_experimental_table_crops_layout']
+ai-station-app       | 2025-12-31 10:20:28 - Accelerator device: 'cpu'
+ai-station-app       | 2025-12-31 10:20:59 - Loading plugin 'docling_defaults'
+ai-station-app       | 2025-12-31 10:20:59 - Registered table structure engines: ['docling_tableformer']
+ai-station-app       | 2025-12-31 10:22:00 - Accelerator device: 'cpu'
+ai-station-app       | 2025-12-31 10:22:02 - Processing document esempio manuale Omron.pdf
+ai-station-app       | 2025-12-31 10:23:07 - Finished converting document esempio manuale Omron.pdf in 173.75 sec.
+ai-station-app       | 2025-12-31 10:23:07 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
+ai-station-app       | 2025-12-31 10:23:07 - An unexpected error occurred:
+ai-station-app       | 2025-12-31 10:23:07 - Translation file for it-IT not found. Using default translation en-US.
+ai-station-qdrant    | 2025-12-31T10:23:07.266024Z  INFO actix_web::middleware::logger: 172.18.0.4 "GET /collections/admin_docs/exists HTTP/1.1" 200 69 "-" "python-client/1.16.2 python/3.11.14" 0.001423
+ai-station-app       | 2025-12-31 10:23:07 - HTTP Request: GET http://qdrant:6333/collections/admin_docs/exists "HTTP/1.1 200 OK"
+ai-station-qdrant    | 2025-12-31T10:23:07.422109Z  INFO storage::content_manager::toc::collection_meta_ops: Creating collection admin_docs
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-426' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-429' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-432' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-434' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-437' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-439' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-441' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-443' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-446' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-448' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-450' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-452' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-454' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-457' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-459' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-461' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-463' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-465' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-467' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-469' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-471' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-474' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-478' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-480' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-482' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-484' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-486' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-488' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-490' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-492' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-494' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-496' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-498' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-501' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-503' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | 2025-12-31 10:23:07 - Task exception was never retrieved
+ai-station-app       | future: <Task finished name='Task-505' coro=<AsyncServer._handle_event_internal() done, defined at /usr/local/lib/python3.11/site-packages/socketio/async_server.py:605> exception=ValueError('Session not found')>
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/asyncio/tasks.py", line 277, in __step
+ai-station-app       |     result = coro.send(None)
+ai-station-app       |              ^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 607, in _handle_event_internal
+ai-station-app       |     r = await server._trigger_event(data[0], namespace, sid, *data[1:])
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/socketio/async_server.py", line 634, in _trigger_event
+ai-station-app       |     ret = await handler(*args)
+ai-station-app       |           ^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/socket.py", line 323, in window_message
+ai-station-app       |     session = WebsocketSession.require(sid)
+ai-station-app       |               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/session.py", line 354, in require
+ai-station-app       |     raise ValueError("Session not found")
+ai-station-app       | ValueError: Session not found
+ai-station-app       | post request handler error
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/async_server.py", line 306, in handle_request
+ai-station-app       |     await socket.handle_post_request(environ)
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/async_socket.py", line 109, in handle_post_request
+ai-station-app       |     p = payload.Payload(encoded_payload=body)
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/payload.py", line 13, in __init__
+ai-station-app       |     self.decode(encoded_payload)
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/payload.py", line 44, in decode
+ai-station-app       |     raise ValueError('Too many packets in payload')
+ai-station-app       | ValueError: Too many packets in payload
+ai-station-app       | 2025-12-31 10:23:07 - post request handler error
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/async_server.py", line 306, in handle_request
+ai-station-app       |     await socket.handle_post_request(environ)
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/async_socket.py", line 109, in handle_post_request
+ai-station-app       |     p = payload.Payload(encoded_payload=body)
+ai-station-app       |         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/payload.py", line 13, in __init__
+ai-station-app       |     self.decode(encoded_payload)
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/engineio/payload.py", line 44, in decode
+ai-station-app       |     raise ValueError('Too many packets in payload')
+ai-station-app       | ValueError: Too many packets in payload
+ai-station-qdrant    | 2025-12-31T10:23:07.898915Z  INFO actix_web::middleware::logger: 172.18.0.4 "PUT /collections/admin_docs HTTP/1.1" 200 57 "-" "python-client/1.16.2 python/3.11.14" 0.478796
+ai-station-app       | 2025-12-31 10:23:07 - HTTP Request: PUT http://qdrant:6333/collections/admin_docs "HTTP/1.1 200 OK"
+ai-station-app       | 2025-12-31 10:23:08 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
+ai-station-qdrant    | 2025-12-31T10:23:12.256007Z  INFO actix_web::middleware::logger: 172.18.0.4 "PUT /collections/admin_docs/points?wait=true HTTP/1.1" 200 84 "-" "python-client/1.16.2 python/3.11.14" 0.128378
+ai-station-app       | 2025-12-31 10:23:12 - HTTP Request: PUT http://qdrant:6333/collections/admin_docs/points?wait=true "HTTP/1.1 200 OK"
+ai-station-app       | 2025-12-31 10:23:12 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
+ai-station-qdrant    | 2025-12-31T10:23:12.413564Z  INFO actix_web::middleware::logger: 172.18.0.4 "GET /collections/admin_docs/exists HTTP/1.1" 200 68 "-" "python-client/1.16.2 python/3.11.14" 0.006418
+ai-station-app       | 2025-12-31 10:23:12 - HTTP Request: GET http://qdrant:6333/collections/admin_docs/exists "HTTP/1.1 200 OK"
+ai-station-app       | 2025-12-31 10:23:12 - module 'chainlit.data' has no attribute 'qdrant_client'
+ai-station-app       | Traceback (most recent call last):
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/utils.py", line 57, in wrapper
+ai-station-app       |     return await user_function(**params_values)
+ai-station-app       |            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/usr/local/lib/python3.11/site-packages/chainlit/callbacks.py", line 161, in with_parent_id
+ai-station-app       |     await func(message)
+ai-station-app       |   File "/app/app.py", line 250, in main
+ai-station-app       |     rag_context = await search_hybrid(message.content, profile["rag_collection"])
+ai-station-app       |                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       |   File "/app/app.py", line 166, in search_hybrid
+ai-station-app       |     cl.data.qdrant_client.models.Prefetch(
+ai-station-app       |     ^^^^^^^^^^^^^^^^^^^^^
+ai-station-app       | AttributeError: module 'chainlit.data' has no attribute 'qdrant_client'
+ai-station-app       | 2025-12-31 10:23:28 - Translation file for it-IT not found. Using default translation en-US.
+ai-station-postgres  | 2025-12-31 10:23:35.822 UTC [28] LOG:  checkpoint starting: time
+ai-station-postgres  | 2025-12-31 10:23:37.312 UTC [28] LOG:  checkpoint complete: wrote 17 buffers (0.1%); 0 WAL file(s) added, 0 removed, 0 recycled; write=1.428 s, sync=0.019 s, total=1.491 s; sync files=11, longest=0.009 s, average=0.002 s; distance=68 kB, estimate=68 kB
+^C
+giuseppe@ai-srv:~/ai-station$

docker-compose.yml

@@ -10,7 +10,7 @@ services:
       - DATABASE_URL=postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station
       - OLLAMA_URL=http://192.168.1.243:11434
       - QDRANT_URL=http://qdrant:6333
-      - BGE_API_URL=http://192.168.1.243:8001
+      - BGE_API_URL=http://192.168.1.243:8001/embed
     volumes:
       - ./workspaces:/app/workspaces
       - ./public:/app/public  # ⬅️ VERIFICA QUESTO

error.log

@@ -1,133 +0,0 @@
-qdrant-1  |            _                 _    
-chainlit-app-1  | 2025-12-25 18:05:12 - INFO - chainlit - Your app is available at http://0.0.0.0:8000
-postgres-1      | 
-postgres-1      | PostgreSQL Database directory appears to contain a database; Skipping initialization
-postgres-1      | 
-postgres-1      | 2025-12-25 16:38:01.071 UTC [1] LOG:  starting PostgreSQL 18.1 (Debian 18.1-1.pgdg13+2) on x86_64-pc-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit
-qdrant-1        |   __ _  __| |_ __ __ _ _ __ | |_  
-qdrant-1        |  / _` |/ _` | '__/ _` | '_ \| __| 
-qdrant-1        | | (_| | (_| | | | (_| | | | | |_  
-qdrant-1        |  \__, |\__,_|_|  \__,_|_| |_|\__| 
-postgres-1      | 2025-12-25 16:38:01.072 UTC [1] LOG:  listening on IPv4 address "0.0.0.0", port 5432
-postgres-1      | 2025-12-25 16:38:01.072 UTC [1] LOG:  listening on IPv6 address "::", port 5432
-postgres-1      | 2025-12-25 16:38:01.093 UTC [1] LOG:  listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432"
-postgres-1      | 2025-12-25 16:38:01.126 UTC [32] LOG:  database system was shut down at 2025-12-25 14:34:55 UTC
-postgres-1      | 2025-12-25 16:38:01.155 UTC [1] LOG:  database system is ready to accept connections
-qdrant-1        |     |_|                           
-qdrant-1        | 
-qdrant-1        | Access web UI at https://ui.qdrant.tech/?v=v1.0.0
-qdrant-1        | 
-qdrant-1        | [2025-12-25T16:38:00.816Z INFO  storage::content_manager::consensus::persistent] Initializing new raft state at ./storage/raft_state
-qdrant-1        | [2025-12-25T16:38:00.861Z INFO  qdrant] Distributed mode disabled
-qdrant-1        | [2025-12-25T16:38:00.861Z INFO  qdrant] Telemetry reporting enabled, id: e6113e43-627c-471d-8374-0f1b61799d76
-qdrant-1        | [2025-12-25T16:38:00.872Z INFO  qdrant::tonic] Qdrant gRPC listening on 6334
-qdrant-1        | [2025-12-25T16:38:00.890Z INFO  actix_server::builder] Starting 3 workers
-qdrant-1        | [2025-12-25T16:38:00.890Z INFO  actix_server::server] Actix runtime found; starting in Actix runtime
-qdrant-1        | [2025-12-25T16:39:02.504Z INFO  actix_server::server] SIGTERM received; starting graceful shutdown
-qdrant-1        | [2025-12-25T16:39:02.505Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        | [2025-12-25T16:39:02.508Z INFO  actix_server::accept] Accept thread stopped
-qdrant-1        | [2025-12-25T16:39:02.508Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        | [2025-12-25T16:39:02.508Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        |            _                 _    
-qdrant-1        |   __ _  __| |_ __ __ _ _ __ | |_  
-qdrant-1        |  / _` |/ _` | '__/ _` | '_ \| __| 
-qdrant-1        | | (_| | (_| | | | (_| | | | | |_  
-postgres-1      | 2025-12-25 16:39:02.495 UTC [1] LOG:  received fast shutdown request
-postgres-1      | 2025-12-25 16:39:02.505 UTC [1] LOG:  aborting any active transactions
-postgres-1      | 2025-12-25 16:39:02.521 UTC [1] LOG:  background worker "logical replication launcher" (PID 35) exited with exit code 1
-postgres-1      | 2025-12-25 16:39:02.521 UTC [30] LOG:  shutting down
-postgres-1      | 2025-12-25 16:39:02.533 UTC [30] LOG:  checkpoint starting: shutdown immediate
-chainlit-app-1  | 2025-12-25 18:05:25 - INFO - httpx - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
-chainlit-app-1  | /app/app.py:43: UserWarning: Qdrant client version 1.16.2 is incompatible with server version 1.0.0. Major versions should match and minor version difference must not exceed 1. Set check_compatibility=False to skip version check.
-chainlit-app-1  |   return QdrantClient(url=QDRANT_URL)
-qdrant-1        |  \__, |\__,_|_|  \__,_|_| |_|\__| 
-chainlit-app-1  | 2025-12-25 18:05:25 - INFO - httpx - HTTP Request: GET http://qdrant:6333/collections/documents "HTTP/1.1 404 Not Found"
-chainlit-app-1  | 2025-12-25 18:06:08 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:06:10 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-qdrant-1        |     |_|                           
-qdrant-1        | 
-qdrant-1        | Access web UI at https://ui.qdrant.tech/?v=v1.0.0
-qdrant-1        | 
-qdrant-1        | [2025-12-25T16:43:53.592Z INFO  storage::content_manager::consensus::persistent] Loading raft state from ./storage/raft_state
-qdrant-1        | [2025-12-25T16:43:53.612Z INFO  qdrant] Distributed mode disabled
-postgres-1      | 2025-12-25 16:39:02.601 UTC [30] LOG:  checkpoint complete: wrote 0 buffers (0.0%), wrote 3 SLRU buffers; 0 WAL file(s) added, 0 removed, 0 recycled; write=0.019 s, sync=0.009 s, total=0.079 s; sync files=2, longest=0.005 s, average=0.005 s; distance=0 kB, estimate=0 kB; lsn=0/1BEF980, redo lsn=0/1BEF980
-postgres-1      | 2025-12-25 16:39:02.644 UTC [1] LOG:  database system is shut down
-postgres-1      | 
-postgres-1      | PostgreSQL Database directory appears to contain a database; Skipping initialization
-postgres-1      | 
-postgres-1      | 2025-12-25 16:43:53.946 UTC [1] LOG:  starting PostgreSQL 18.1 (Debian 18.1-1.pgdg13+2) on x86_64-pc-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit
-postgres-1      | 2025-12-25 16:43:53.947 UTC [1] LOG:  listening on IPv4 address "0.0.0.0", port 5432
-postgres-1      | 2025-12-25 16:43:53.947 UTC [1] LOG:  listening on IPv6 address "::", port 5432
-qdrant-1        | [2025-12-25T16:43:53.612Z INFO  qdrant] Telemetry reporting enabled, id: 2a83356a-9770-47d3-a0bd-638f75769522
-qdrant-1        | [2025-12-25T16:43:53.615Z INFO  qdrant::tonic] Qdrant gRPC listening on 6334
-qdrant-1        | [2025-12-25T16:43:53.616Z INFO  actix_server::builder] Starting 3 workers
-postgres-1      | 2025-12-25 16:43:53.965 UTC [1] LOG:  listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432"
-postgres-1      | 2025-12-25 16:43:53.990 UTC [32] LOG:  database system was shut down at 2025-12-25 16:39:02 UTC
-postgres-1      | 2025-12-25 16:43:54.013 UTC [1] LOG:  database system is ready to accept connections
-postgres-1      | 2025-12-25 16:48:54.089 UTC [30] LOG:  checkpoint starting: time
-postgres-1      | 2025-12-25 16:48:54.175 UTC [30] LOG:  checkpoint complete: wrote 0 buffers (0.0%), wrote 3 SLRU buffers; 0 WAL file(s) added, 0 removed, 0 recycled; write=0.036 s, sync=0.009 s, total=0.088 s; sync files=2, longest=0.005 s, average=0.005 s; distance=0 kB, estimate=0 kB; lsn=0/1BEFA88, redo lsn=0/1BEFA30
-postgres-1      | 2025-12-25 16:56:42.002 UTC [1] LOG:  received fast shutdown request
-postgres-1      | 2025-12-25 16:56:42.018 UTC [1] LOG:  aborting any active transactions
-postgres-1      | 2025-12-25 16:56:42.026 UTC [1] LOG:  background worker "logical replication launcher" (PID 35) exited with exit code 1
-postgres-1      | 2025-12-25 16:56:42.030 UTC [30] LOG:  shutting down
-postgres-1      | 2025-12-25 16:56:42.039 UTC [30] LOG:  checkpoint starting: shutdown immediate
-postgres-1      | 2025-12-25 16:56:42.086 UTC [30] LOG:  checkpoint complete: wrote 0 buffers (0.0%), wrote 0 SLRU buffers; 0 WAL file(s) added, 0 removed, 0 recycled; write=0.004 s, sync=0.001 s, total=0.057 s; sync files=0, longest=0.000 s, average=0.000 s; distance=0 kB, estimate=0 kB; lsn=0/1BEFB38, redo lsn=0/1BEFB38
-postgres-1      | 2025-12-25 16:56:42.131 UTC [1] LOG:  database system is shut down
-postgres-1      | 
-postgres-1      | PostgreSQL Database directory appears to contain a database; Skipping initialization
-postgres-1      | 
-chainlit-app-1  | 2025-12-25 18:06:10 - WARNING - chainlit - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
-chainlit-app-1  | 2025-12-25 18:06:13 - INFO - chainlit - Missing custom logo. Falling back to default logo.
-chainlit-app-1  | 2025-12-25 18:06:21 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:06:21 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-qdrant-1        | [2025-12-25T16:43:53.617Z INFO  actix_server::server] Actix runtime found; starting in Actix runtime
-qdrant-1        | [2025-12-25T16:56:42.005Z INFO  actix_server::server] SIGTERM received; starting graceful shutdown
-qdrant-1        | [2025-12-25T16:56:42.006Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        | [2025-12-25T16:56:42.006Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        | [2025-12-25T16:56:42.007Z INFO  actix_server::worker] Shutting down idle worker
-qdrant-1        | [2025-12-25T16:56:42.007Z INFO  actix_server::accept] Accept thread stopped
-qdrant-1        |            _                 _    
-qdrant-1        |   __ _  __| |_ __ __ _ _ __ | |_  
-qdrant-1        |  / _` |/ _` | '__/ _` | '_ \| __| 
-qdrant-1        | | (_| | (_| | | | (_| | | | | |_  
-qdrant-1        |  \__, |\__,_|_|  \__,_|_| |_|\__| 
-qdrant-1        |     |_|                           
-qdrant-1        | 
-qdrant-1        | Access web UI at https://ui.qdrant.tech/?v=v1.0.0
-qdrant-1        | 
-qdrant-1        | [2025-12-25T16:56:52.790Z INFO  storage::content_manager::consensus::persistent] Loading raft state from ./storage/raft_state
-qdrant-1        | [2025-12-25T16:56:52.796Z INFO  qdrant] Distributed mode disabled
-qdrant-1        | [2025-12-25T16:56:52.796Z INFO  qdrant] Telemetry reporting enabled, id: f821b8ea-9ee5-497e-a172-dfebf253f7b1
-qdrant-1        | [2025-12-25T16:56:52.797Z INFO  qdrant::tonic] Qdrant gRPC listening on 6334
-qdrant-1        | [2025-12-25T16:56:52.798Z INFO  actix_server::builder] Starting 3 workers
-qdrant-1        | [2025-12-25T16:56:52.798Z INFO  actix_server::server] Actix runtime found; starting in Actix runtime
-qdrant-1        | [2025-12-25T18:05:25.183Z INFO  actix_web::middleware::logger] 172.18.0.4 "GET /collections/documents HTTP/1.1" 404 110 "-" "python-client/1.16.2 python/3.10.19" 0.007704
-qdrant-1        | [2025-12-25T18:05:30.499Z INFO  actix_web::middleware::logger] 172.18.0.4 "PUT /collections/documents HTTP/1.1" 200 71 "-" "python-client/1.16.2 python/3.10.19" 5.311157
-qdrant-1        | [2025-12-25T18:06:22.662Z INFO  actix_web::middleware::logger] 172.18.0.4 "GET /collections/documents HTTP/1.1" 200 413 "-" "python-client/1.16.2 python/3.10.19" 0.005606
-postgres-1      | 2025-12-25 16:56:43.530 UTC [1] LOG:  starting PostgreSQL 18.1 (Debian 18.1-1.pgdg13+2) on x86_64-pc-linux-gnu, compiled by gcc (Debian 14.2.0-19) 14.2.0, 64-bit
-postgres-1      | 2025-12-25 16:56:43.532 UTC [1] LOG:  listening on IPv4 address "0.0.0.0", port 5432
-postgres-1      | 2025-12-25 16:56:43.532 UTC [1] LOG:  listening on IPv6 address "::", port 5432
-postgres-1      | 2025-12-25 16:56:43.552 UTC [1] LOG:  listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432"
-postgres-1      | 2025-12-25 16:56:43.585 UTC [32] LOG:  database system was shut down at 2025-12-25 16:56:42 UTC
-postgres-1      | 2025-12-25 16:56:43.616 UTC [1] LOG:  database system is ready to accept connections
-postgres-1      | 2025-12-25 17:01:43.645 UTC [30] LOG:  checkpoint starting: time
-postgres-1      | 2025-12-25 17:01:43.712 UTC [30] LOG:  checkpoint complete: wrote 0 buffers (0.0%), wrote 3 SLRU buffers; 0 WAL file(s) added, 0 removed, 0 recycled; write=0.019 s, sync=0.009 s, total=0.068 s; sync files=2, longest=0.005 s, average=0.005 s; distance=0 kB, estimate=0 kB; lsn=0/1BEFC40, redo lsn=0/1BEFBE8
-chainlit-app-1  | 2025-12-25 18:06:21 - WARNING - chainlit - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
-chainlit-app-1  | 2025-12-25 18:06:22 - INFO - httpx - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:06:22 - INFO - httpx - HTTP Request: GET http://qdrant:6333/collections/documents "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:06:28 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:06:36 - INFO - httpx - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:06:37 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 500 Internal Server Error"
-chainlit-app-1  | 2025-12-25 18:06:39 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:06:48 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:07:02 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:07:16 - INFO - httpx - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:07:22 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 500 Internal Server Error"
-chainlit-app-1  | 2025-12-25 18:07:22 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:07:49 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:07:54 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:08:15 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:08:30 - WARNING - chainlit - Translation file for it-IT not found. Using parent translation it.
-chainlit-app-1  | 2025-12-25 18:08:57 - INFO - httpx - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
-chainlit-app-1  | 2025-12-25 18:09:03 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 500 Internal Server Error"
-chainlit-app-1  | 2025-12-25 18:09:03 - INFO - httpx - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"

public/custom.css

@@ -1,36 +1,12 @@
-/* dFm AI Station - Perplexity Clean Style */
-:root {
-  --bg-color: #0B0F1A;
-  --card-color: #161B2C;
-  --accent-color: #6366F1;
+.user-badge {
+    background: linear-gradient(45deg, #667eea 0%, #764ba2 100%);
+    padding: 4px 12px;
+    border-radius: 12px;
+    font-size: 0.85em;
 }
 
-body { background-color: var(--bg-color) !important; color: #F1F5F9 !important; }
-
-/* Header e Logo */
-header { 
-  background: rgba(11, 15, 26, 0.8) !important; 
-  backdrop-filter: blur(8px); 
-  border-bottom: 1px solid #23293F !important; 
-}
-
-/* Sidebar */
-.MuiDrawer-paper { 
-  background-color: var(--bg-color) !important; 
-  border-right: 1px solid #23293F !important; 
-}
-
-/* Messaggi */
-div[class*="user"] { 
-  background: #1E253A !important; 
-  border-radius: 12px !important; 
-  border-left: 4px solid var(--accent-color) !important;
-}
-
-/* Input Area */
-form { 
-  background: var(--card-color) !important; 
-  border-radius: 20px !important; 
-  border: 1px solid #334155 !important;
-  box-shadow: 0 4px 20px rgba(0,0,0,0.5) !important;
+/* Evidenzia codice */
+.message pre {
+    background: #2d2d2d;
+    border-left: 4px solid #0066CC;
 }

script/test_vision.py

@@ -0,0 +1,23 @@
+import httpx
+import base64
+import sys
+
+# Configurazione
+OLLAMA_URL = "http://192.168.1.243:11434"
+MODEL = "minicpm-v"
+
+print(f"👁️ Test Visione su {OLLAMA_URL} con modello {MODEL}...")
+
+# 1. Controlla se il modello è caricato
+try:
+    r = httpx.get(f"{OLLAMA_URL}/api/tags")
+    models = [m['name'] for m in r.json()['models']]
+    if MODEL not in str(models):
+        print(f"❌ Errore: Il modello {MODEL} non è stato trovato su Ollama!")
+        sys.exit(1)
+    print(f"✅ Modello {MODEL} trovato.")
+except Exception as e:
+    print(f"❌ Errore connessione Ollama: {e}")
+    sys.exit(1)
+
+print("🚀 Tutto pronto per l'implementazione!")