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pre Oauth2

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DFFM-maker 2025-12-26 17:48:51 +01:00
parent 956d865500
commit a375a2f76d
7 changed files with 401 additions and 167 deletions
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24
Dockerfile
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@ -1,11 +1,25 @@
FROM python:3.10-slim
FROM python:3.11-slim
WORKDIR /app
# Installa dipendenze sistema
RUN apt-get update && apt-get install -y \
gcc \
postgresql-client \
&& rm -rf /var/lib/apt/lists/*
# Copia requirements e installa
COPY requirements.txt .
RUN pip install -r requirements.txt --no-cache-dir
RUN pip install --no-cache-dir -r requirements.txt
# Copy the application code into the container
COPY . .
# Copia applicazione
COPY app.py .
COPY init_db.py .
CMD ["chainlit", "run", "app.py", "--host", "0.0.0.0", "--port", "8000"]
# Crea directory necessarie
RUN mkdir -p /app/workspaces /app/public /app/.files
EXPOSE 8000
# Script di avvio con inizializzazione DB
CMD python init_db.py && chainlit run app.py --host 0.0.0.0 --port 8000

328
app.py
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@ -1,61 +1,59 @@
import os
import chainlit as cl
import re
from datetime import datetime
import shutil
import uuid
import shutil
from datetime import datetime
from typing import Optional
import chainlit as cl
import ollama
from qdrant_client import AsyncQdrantClient
from qdrant_client.models import PointStruct, Distance, VectorParams
from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
# --- CONFIGURAZIONE DATABASE (PostgreSQL) ---
# Assicurati che user/password coincidano con il tuo docker-compose.yml
# Sintassi: postgresql+asyncpg://user:password@host:port/dbname
DATABASE_URL = "postgresql+asyncpg://user:password@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")
# Attiviamo il salvataggio su DB
cl.data_layer = SQLAlchemyDataLayer(conn_string=DATABASE_URL)
# -
# --- CONFIGURAZIONE HARD-CODED ---
OLLAMA_URL = "http://192.168.1.243:11434"
# ---------------------------------
USER_ROLES = {
'moglie@esempio.com': 'business',
'ingegnere@esempio.com': 'engineering',
'architetto@esempio.com': 'architecture',
'admin@esempio.com': 'admin'
}
# === INIZIALIZZAZIONE DATA LAYER ===
# IMPORTANTE: Deve essere inizializzato PRIMA di definire gli handlers
try:
data_layer = SQLAlchemyDataLayer(conninfo=DATABASE_URL)
cl.data_layer = data_layer
print("✅ SQLAlchemyDataLayer initialized successfully")
except Exception as e:
print(f"❌ Failed to initialize data layer: {e}")
cl.data_layer = None
WORKSPACES_DIR = "./workspaces"
USER_ROLE = "admin"
def create_workspace(user_role):
# === UTILITY FUNCTIONS ===
def create_workspace(user_role: str):
"""Crea directory workspace se non esiste"""
workspace_path = os.path.join(WORKSPACES_DIR, user_role)
if not os.path.exists(workspace_path):
os.makedirs(workspace_path)
os.makedirs(workspace_path, exist_ok=True)
return workspace_path
def save_code_to_file(code, user_role):
timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
def save_code_to_file(code: str, user_role: str) -> str:
"""Salva blocco codice come file .py"""
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
file_name = f"code_{timestamp}.py"
file_path = os.path.join(WORKSPACES_DIR, user_role, file_name)
with open(file_path, "w") as file:
file.write(code)
with open(file_path, "w", encoding="utf-8") as f:
f.write(code)
return file_path
def limit_history(history):
if len(history) > 20:
history = history[-20:]
return history
async def connect_to_qdrant():
client = AsyncQdrantClient(url="http://qdrant:6333")
# === QDRANT FUNCTIONS ===
async def get_qdrant_client() -> AsyncQdrantClient:
"""Connessione a Qdrant"""
client = AsyncQdrantClient(url=QDRANT_URL)
collection_name = "documents"
# Check if collection exists
# Crea collection se non esiste
if not await client.collection_exists(collection_name):
await client.create_collection(
collection_name=collection_name,
@ -64,147 +62,203 @@ async def connect_to_qdrant():
return client
async def get_embeddings(text):
async def get_embeddings(text: str) -> list:
"""Genera embeddings con Ollama"""
client = ollama.Client(host=OLLAMA_URL)
# Limite di sicurezza per evitare errori 500 su Ollama
limit = 2000
if len(text) > limit:
text = text[:limit]
# Limita lunghezza per evitare errori
max_length = 2000
if len(text) > max_length:
text = text[:max_length]
try:
response = client.embed(model='nomic-embed-text', input=text)
if 'embeddings' in response:
return response['embeddings'][0]
return response.get('embedding')
return response.get('embedding', [])
except Exception as e:
print(f"Errore Embedding: {e}")
print(f"❌ Errore Embedding: {e}")
return []
async def search_qdrant(query_text, user_role):
"""Cerca documenti pertinenti su Qdrant"""
async def index_document(file_name: str, content: str) -> bool:
"""Indicizza documento su Qdrant"""
try:
qdrant_client = await connect_to_qdrant()
embeddings = await get_embeddings(content)
if not embeddings:
return False
qdrant_client = await get_qdrant_client()
point_id = str(uuid.uuid4())
point = PointStruct(
id=point_id,
vector=embeddings,
payload={
"file_name": file_name,
"content": content,
"indexed_at": datetime.now().isoformat()
}
)
await qdrant_client.upsert(collection_name="documents", points=[point])
return True
except Exception as e:
print(f"❌ Errore indicizzazione: {e}")
return False
async def search_qdrant(query_text: str, limit: int = 3) -> str:
"""Ricerca documenti rilevanti"""
try:
qdrant_client = await get_qdrant_client()
query_embedding = await get_embeddings(query_text)
if not query_embedding:
return ""
# Usa query_points (nuova API per AsyncClient)
search_result = await qdrant_client.query_points(
collection_name="documents",
query=query_embedding,
limit=3
limit=limit
)
hits = search_result.points
contexts = []
if hits:
for hit in hits:
try:
if hit.payload:
# --- FIX IMPORTANTE: Recupera il contenuto reale ---
nome_file = hit.payload.get('file_name', 'Sconosciuto')
contenuto = hit.payload.get('content', '')
contexts.append(f"--- Documento: {nome_file} ---\n{contenuto}\n----------------")
except Exception as e:
print(f"Error parsing hit: {e}")
for hit in search_result.points:
if hit.payload:
file_name = hit.payload.get('file_name', 'Unknown')
content = hit.payload.get('content', '')
score = hit.score if hasattr(hit, 'score') else 0
contexts.append(
f"📄 **{file_name}** (relevance: {score:.2f})\n"
f"``````"
)
return "\n".join(contexts)
return "\n\n".join(contexts) if contexts else ""
except Exception as e:
print(f"Errore ricerca Qdrant: {e}")
print(f"❌ Errore ricerca Qdrant: {e}")
return ""
# === CHAINLIT HANDLERS ===
@cl.on_chat_start
async def chat_start():
user_email = "admin@esempio.com"
user_role = USER_ROLES.get(user_email, 'guest')
async def on_chat_start():
"""Inizializzazione chat"""
create_workspace(USER_ROLE)
create_workspace(user_role)
# Imposta variabili sessione
cl.user_session.set("role", USER_ROLE)
cl.user_session.set("history", [])
cl.user_session.set("role", user_role)
# Verifica persistenza
persistence_status = "✅ Attiva" if cl.data_layer else "⚠️ Disattivata"
welcome_msg = f"Welcome, {user_role.capitalize()}!"
await cl.Message(content=welcome_msg).send()
await cl.Message(
content=f"🚀 **AI Station Ready** - Workspace: `{USER_ROLE}`\n\n"
f"📤 Upload `.txt` files per indicizzarli nel RAG\n"
f"💾 Persistenza conversazioni: {persistence_status}\n"
f"🤖 Modello: `qwen2.5-coder:7b` @ {OLLAMA_URL}"
).send()
@cl.on_message
async def message(message):
user_role = cl.user_session.get("role", 'guest')
if not user_role:
await cl.Message(content="User role not found").send()
return
async def on_message(message: cl.Message):
"""Gestione messaggi utente"""
user_role = cl.user_session.get("role", "guest")
try:
client = ollama.Client(host=OLLAMA_URL)
history = cl.user_session.get("history", [])
history = limit_history(history)
# --- PASSO 1: Gestione Upload e Indexing (PRIMA della ricerca) ---
# === STEP 1: Gestione Upload ===
if message.elements:
uploaded_files = []
for element in message.elements:
try:
dest_path = os.path.join(WORKSPACES_DIR, user_role, element.name)
with open(element.path, 'rb') as src, open(dest_path, 'wb') as dst:
shutil.copyfileobj(src, dst)
if element.name.endswith('.txt'):
# Encoding utf-8 per sicurezza
with open(dest_path, 'r', encoding='utf-8') as f:
content = f.read()
embeddings = await get_embeddings(content)
if embeddings:
qdrant_client = await connect_to_qdrant()
point_id = str(uuid.uuid4())
# --- FIX IMPORTANTE: Salviamo anche il contenuto nel payload ---
point = PointStruct(
id=point_id,
vector=embeddings,
payload={
"file_name": element.name,
"content": content
}
)
await qdrant_client.upsert(collection_name="documents", points=[point])
await cl.Message(content=f"Documento '{element.name}' indicizzato.").send()
uploaded_files.append(element.name)
except Exception as e:
await cl.Message(content=f"Error saving {element.name}: {e}").send()
if uploaded_files:
await cl.Message(content=f"Files saved: {', '.join(uploaded_files)}").send()
# --- PASSO 2: Cerca nei documenti ---
context_text = await search_qdrant(message.content, user_role)
await handle_file_uploads(message.elements, user_role)
# === STEP 2: RAG Search ===
context_text = await search_qdrant(message.content, limit=3)
# === STEP 3: Preparazione Prompt ===
messages = []
if context_text:
system_prompt = f"Usa esclusivamente il seguente contesto per rispondere alla domanda. Se la risposta non è nel contesto, dillo.\n\nContesto:\n{context_text}"
history.insert(0, {"role": "system", "content": system_prompt})
system_prompt = (
"Sei un assistente AI esperto. Usa ESCLUSIVAMENTE il seguente contesto "
"per rispondere. Se la risposta non è nel contesto, dillo chiaramente.\n\n"
f"**CONTESTO:**\n{context_text}"
)
messages.append({"role": "system", "content": system_prompt})
history.append({"role": "user", "content": message.content})
messages.append({"role": "user", "content": message.content})
# --- PASSO 3: Chat Generation ---
response = client.chat(model='qwen2.5-coder:7b', messages=history)
# === STEP 4: Chiamata Ollama con Streaming ===
client = ollama.Client(host=OLLAMA_URL)
# Code Extraction
code_blocks = re.findall(r"```python(.*?)```", response['message']['content'], re.DOTALL)
msg = cl.Message(content="")
await msg.send()
stream = client.chat(
model='qwen2.5-coder:7b',
messages=messages,
stream=True
)
full_response = ""
for chunk in stream:
content = chunk['message']['content']
full_response += content
await msg.stream_token(content)
await msg.update()
# === STEP 5: Estrai e Salva Codice ===
code_blocks = re.findall(r"``````", full_response, re.DOTALL)
elements = []
if code_blocks:
elements = []
for code in code_blocks:
file_path = save_code_to_file(code, user_role)
elements.append(cl.File(name=os.path.basename(file_path), path=file_path))
history.append({"role": "assistant", "content": response['message']['content']})
cl.user_session.set("history", history)
await cl.Message(content=response['message']['content'], elements=elements).send()
file_path = save_code_to_file(code.strip(), user_role)
elements.append(
cl.File(
name=os.path.basename(file_path),
path=file_path,
display="inline"
)
)
await cl.Message(
content=f"💾 Codice salvato in `{user_role}/`",
elements=elements
).send()
except Exception as e:
await cl.Message(content=f"Error: {e}").send()
await cl.Message(content=f"❌ **Errore:** {str(e)}").send()
async def handle_file_uploads(elements, user_role: str):
"""Gestisce upload e indicizzazione file"""
for element in elements:
try:
# Salva file
dest_path = os.path.join(WORKSPACES_DIR, user_role, element.name)
shutil.copy(element.path, dest_path)
# Indicizza solo .txt
if element.name.endswith('.txt'):
with open(dest_path, 'r', encoding='utf-8') as f:
content = f.read()
success = await index_document(element.name, content)
if success:
await cl.Message(
content=f"✅ **{element.name}** indicizzato in Qdrant"
).send()
else:
await cl.Message(
content=f"⚠️ Errore indicizzazione {element.name}"
).send()
else:
await cl.Message(
content=f"📁 **{element.name}** salvato (solo .txt vengono indicizzati)"
).send()
except Exception as e:
await cl.Message(
content=f"❌ Errore con {element.name}: {str(e)}"
).send()

68
docker-compose.yml
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@ -1,44 +1,68 @@
services:
chainlit-app:
build: .
ports:
- "8000:8000"
postgres:
image: postgres:15-alpine
container_name: ai-station-postgres
environment:
- CHAINLIT_AUTH_HEADER=X-Email
- OLLAMA_API_BASE=http://192.168.1.243:11434
POSTGRES_DB: ai_station
POSTGRES_USER: ai_user
POSTGRES_PASSWORD: secure_password_here
volumes:
- ./:/app
depends_on:
- qdrant
- postgres
- postgres_data:/var/lib/postgresql/data
ports:
- "5432:5432"
networks:
- ai-station-net
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ai_user -d ai_station"] # <- AGGIUNGI -d ai_station
interval: 10s
timeout: 5s
retries: 5
restart: unless-stopped
qdrant:
image: qdrant/qdrant:latest
container_name: ai-station-qdrant
volumes:
- qdrant_data:/qdrant/storage
ports:
- "6333:6333"
volumes:
- qdrant-data:/qdrant/db
- "6334:6334"
networks:
- ai-station-net
restart: unless-stopped
postgres:
image: postgres:16
environment:
POSTGRES_DB: ai_station
POSTGRES_USER: user
POSTGRES_PASSWORD: password
chainlit-app:
build: .
container_name: ai-station-app
ports:
- "5432:5432"
- "8000:8000"
environment:
- 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
- CHAINLIT_AUTH_SECRET=your-secret-key-here
volumes:
- postgres-data:/var/lib/postgresql/data
- ./workspaces:/app/workspaces
- ./public:/app/public
networks:
- ai-station-net
depends_on:
postgres:
condition: service_healthy
qdrant:
condition: service_started
command: chainlit run app.py --host 0.0.0.0 --port 8000
restart: unless-stopped
volumes:
qdrant-data:
postgres-data:
postgres_data:
driver: local
qdrant_data:
driver: local
networks:
ai-station-net:
driver: bridge
ipam:
config:
- subnet: 172.28.0.0/16

100
docker.logs Normal file
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@ -0,0 +1,100 @@
ai-station-qdrant | _ _
ai-station-postgres |
ai-station-postgres | PostgreSQL Database directory appears to contain a database; Skipping initialization
ai-station-postgres |
ai-station-app | 2025-12-26 16:21:52 - Created default config file at /app/.chainlit/config.toml
ai-station-postgres | 2025-12-26 16:21:40.238 UTC [1] LOG: starting PostgreSQL 15.15 on x86_64-pc-linux-musl, compiled by gcc (Alpine 15.2.0) 15.2.0, 64-bit
ai-station-app | 2025-12-26 16:21:52 - Created default translation directory at /app/.chainlit/translations
ai-station-postgres | 2025-12-26 16:21:40.238 UTC [1] LOG: listening on IPv4 address "0.0.0.0", port 5432
ai-station-qdrant | __ _ __| |_ __ __ _ _ __ | |_
ai-station-postgres | 2025-12-26 16:21:40.239 UTC [1] LOG: listening on IPv6 address "::", port 5432
ai-station-qdrant | / _` |/ _` | '__/ _` | '_ \| __|
ai-station-postgres | 2025-12-26 16:21:40.259 UTC [1] LOG: listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432"
ai-station-postgres | 2025-12-26 16:21:40.285 UTC [29] LOG: database system was shut down at 2025-12-26 16:16:26 UTC
ai-station-postgres | 2025-12-26 16:21:40.313 UTC [1] LOG: database system is ready to accept connections
ai-station-postgres | 2025-12-26 16:26:40.323 UTC [27] LOG: checkpoint starting: time
ai-station-qdrant | | (_| | (_| | | | (_| | | | | |_
ai-station-qdrant | \__, |\__,_|_| \__,_|_| |_|\__|
ai-station-qdrant | |_|
ai-station-postgres | 2025-12-26 16:26:40.397 UTC [27] LOG: checkpoint complete: wrote 3 buffers (0.0%); 0 WAL file(s) added, 0 removed, 0 recycled; write=0.020 s, sync=0.010 s, total=0.075 s; sync files=2, longest=0.006 s, average=0.005 s; distance=0 kB, estimate=0 kB
ai-station-qdrant |
ai-station-qdrant | Version: 1.16.3, build: bd49f45a
ai-station-qdrant | Access web UI at http://localhost:6333/dashboard
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/hi.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/en-US.json
ai-station-qdrant |
ai-station-qdrant | 2025-12-26T16:21:39.943731Z INFO storage::content_manager::consensus::persistent: Loading raft state from ./storage/raft_state.json
ai-station-qdrant | 2025-12-26T16:21:39.964037Z INFO storage::content_manager::toc: Loading collection: documents
ai-station-qdrant | 2025-12-26T16:21:40.041342Z INFO collection::shards::local_shard: Recovering shard ./storage/collections/documents/0: 0/1 (0%)
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/bn.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/ta.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/te.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/gu.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/zh-CN.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/ml.json
ai-station-qdrant | 2025-12-26T16:21:40.115966Z INFO collection::shards::local_shard: Recovered collection documents: 1/1 (100%)
ai-station-qdrant | 2025-12-26T16:21:40.117838Z INFO qdrant: Distributed mode disabled
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/kn.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/he-IL.json
ai-station-app | 2025-12-26 16:21:52 - Created default translation file at /app/.chainlit/translations/mr.json
ai-station-app | 2025-12-26 16:22:00 - SQLAlchemyDataLayer storage client is not initialized and elements will not be persisted!
ai-station-app | ✅ SQLAlchemyDataLayer initialized successfully
ai-station-app | 2025-12-26 16:22:00 - Created default chainlit markdown file at /app/chainlit.md
ai-station-app | 2025-12-26 16:22:00 - Your app is available at http://0.0.0.0:8000
ai-station-app | 2025-12-26 16:32:21 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:32:28 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:32:29 - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
ai-station-app | 2025-12-26 16:32:46 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:32:49 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:32:49 - HTTP Request: GET http://qdrant:6333/collections/documents/exists "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:32:50 - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 200 OK"
ai-station-qdrant | 2025-12-26T16:21:40.117951Z INFO qdrant: Telemetry reporting enabled, id: 0b179897-a2e7-448d-97f2-1fe4e1915faa
ai-station-qdrant | 2025-12-26T16:21:40.191722Z INFO qdrant::actix: TLS disabled for REST API
ai-station-qdrant | 2025-12-26T16:21:40.192047Z INFO qdrant::actix: Qdrant HTTP listening on 6333
ai-station-qdrant | 2025-12-26T16:21:40.192122Z INFO actix_server::builder: starting 15 workers
ai-station-qdrant | 2025-12-26T16:21:40.192211Z INFO actix_server::server: Actix runtime found; starting in Actix runtime
ai-station-app | 2025-12-26 16:32:50 - HTTP Request: POST http://qdrant:6333/collections/documents/points/query "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:32:52 - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:04 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:33:09 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:33:18 - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
ai-station-qdrant | 2025-12-26T16:21:40.192262Z INFO actix_server::server: starting service: "actix-web-service-0.0.0.0:6333", workers: 15, listening on: 0.0.0.0:6333
ai-station-qdrant | 2025-12-26T16:21:40.212492Z INFO qdrant::tonic: Qdrant gRPC listening on 6334
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: GET http://qdrant:6333/collections/documents/exists "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: PUT http://qdrant:6333/collections/documents/points?wait=true "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: GET http://qdrant:6333/collections/documents/exists "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: POST http://qdrant:6333/collections/documents/points/query "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:33:19 - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:34:13 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:34:16 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:34:16 - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
ai-station-app | 2025-12-26 16:34:30 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:34:42 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:34:43 - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
ai-station-app | 2025-12-26 16:35:04 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: GET http://qdrant:6333/collections/documents/exists "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: PUT http://qdrant:6333/collections/documents/points?wait=true "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: GET http://qdrant:6333 "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: GET http://qdrant:6333/collections/documents/exists "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: POST http://192.168.1.243:11434/api/embed "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: POST http://qdrant:6333/collections/documents/points/query "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:13 - HTTP Request: POST http://192.168.1.243:11434/api/chat "HTTP/1.1 200 OK"
ai-station-app | 2025-12-26 16:35:24 - Translation file for it-IT not found. Using default translation en-US.
ai-station-app | 2025-12-26 16:35:24 - Translated markdown file for it-IT not found. Defaulting to chainlit.md.
ai-station-app | 2025-12-26 16:35:37 - Translation file for it-IT not found. Using default translation en-US.
ai-station-qdrant | 2025-12-26T16:21:40.212579Z INFO qdrant::tonic: TLS disabled for gRPC API
ai-station-qdrant | 2025-12-26T16:32:49.529256Z INFO actix_web::middleware::logger: 172.28.0.4 "GET /collections/documents/exists HTTP/1.1" 200 81 "-" "python-client/1.16.2 python/3.11.14" 0.001330
ai-station-qdrant | 2025-12-26T16:32:50.646822Z INFO actix_web::middleware::logger: 172.28.0.4 "POST /collections/documents/points/query HTTP/1.1" 200 4905 "-" "python-client/1.16.2 python/3.11.14" 0.014367
ai-station-qdrant | 2025-12-26T16:33:19.146916Z INFO actix_web::middleware::logger: 172.28.0.4 "GET /collections/documents/exists HTTP/1.1" 200 82 "-" "python-client/1.16.2 python/3.11.14" 0.000969
ai-station-qdrant | 2025-12-26T16:33:19.179182Z INFO actix_web::middleware::logger: 172.28.0.4 "PUT /collections/documents/points?wait=true HTTP/1.1" 200 91 "-" "python-client/1.16.2 python/3.11.14" 0.021875
ai-station-qdrant | 2025-12-26T16:33:19.309152Z INFO actix_web::middleware::logger: 172.28.0.4 "GET /collections/documents/exists HTTP/1.1" 200 82 "-" "python-client/1.16.2 python/3.11.14" 0.000935
ai-station-qdrant | 2025-12-26T16:33:19.416423Z INFO actix_web::middleware::logger: 172.28.0.4 "POST /collections/documents/points/query HTTP/1.1" 200 5732 "-" "python-client/1.16.2 python/3.11.14" 0.011009
ai-station-qdrant | 2025-12-26T16:35:13.413627Z INFO actix_web::middleware::logger: 172.28.0.4 "GET /collections/documents/exists HTTP/1.1" 200 82 "-" "python-client/1.16.2 python/3.11.14" 0.000958
ai-station-qdrant | 2025-12-26T16:35:13.438965Z INFO actix_web::middleware::logger: 172.28.0.4 "PUT /collections/documents/points?wait=true HTTP/1.1" 200 91 "-" "python-client/1.16.2 python/3.11.14" 0.015794
ai-station-qdrant | 2025-12-26T16:35:13.576292Z INFO actix_web::middleware::logger: 172.28.0.4 "GET /collections/documents/exists HTTP/1.1" 200 82 "-" "python-client/1.16.2 python/3.11.14" 0.000953
ai-station-qdrant | 2025-12-26T16:35:13.672837Z INFO actix_web::middleware::logger: 172.28.0.4 "POST /collections/documents/points/query HTTP/1.1" 200 5705 "-" "python-client/1.16.2 python/3.11.14" 0.010556

13
dockerignore Normal file
View File

@ -0,0 +1,13 @@
__pycache__
*.pyc
*.pyo
*.pyd
.git
.gitignore
.venv
venv/
workspaces/
*.db
.env
*.log
.DS_Store

27
init_db.py Normal file
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@ -0,0 +1,27 @@
import asyncio
from sqlalchemy import create_engine, text
from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
DATABASE_URL = "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station"
async def init_database():
"""Inizializza le tabelle per Chainlit"""
print("🔧 Inizializzazione database...")
try:
# Crea data layer
data_layer = SQLAlchemyDataLayer(conninfo=DATABASE_URL)
# Forza creazione tabelle
if hasattr(data_layer, '_create_database'):
await data_layer._create_database()
print("✅ Database inizializzato con successo")
else:
print("⚠️ Metodo _create_database non disponibile")
print("ℹ️ Le tabelle verranno create automaticamente al primo utilizzo")
except Exception as e:
print(f"❌ Errore: {e}")
if __name__ == "__main__":
asyncio.run(init_database())

8
requirements.txt
View File

@ -1,9 +1,11 @@
chainlit==1.3.2
pydantic==2.9.2
ollama
asyncpg
asyncpg>=0.29.0
psycopg2-binary
qdrant-client>=1.10.0
sqlalchemy
sqlalchemy>=2.0.0
greenlet>=3.0.0
sniffio
aiohttp
aiohttp
alembic