implementazione BGE-M3 Dense

app.py

@@ -2,17 +2,19 @@ import os
 import re
 import uuid
 import shutil
+import pandas as pd # NUOVO: Gestione Excel
+import httpx        # NUOVO: Chiamate API Remote
 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 qdrant_client import models
+from qdrant_client.models import PointStruct, Distance, VectorParams, SparseVectorParams, SparseIndexParams
 from chainlit.data.sql_alchemy import SQLAlchemyDataLayer
 
-# === FIX IMPORT ROBUSTO ===
+# === FIX IMPORT ===
-# Gestisce le differenze tra le versioni di Chainlit 2.x
 try:
     from chainlit.data.storage_clients import BaseStorageClient
 except ImportError:
@@ -23,8 +25,11 @@ except ImportError:
 
 # === CONFIGURAZIONE ===
 DATABASE_URL = os.getenv("DATABASE_URL", "postgresql+asyncpg://ai_user:secure_password_here@postgres:5432/ai_station")
+# PUNTANO AL SERVER .243 (Il "Cervello")
 OLLAMA_URL = os.getenv("OLLAMA_URL", "http://192.168.1.243:11434")
+BGE_API_URL = os.getenv("BGE_API_URL", "http://192.168.1.243:8001")
 QDRANT_URL = os.getenv("QDRANT_URL", "http://qdrant:6333")
+
 WORKSPACES_DIR = "./workspaces"
 STORAGE_DIR = "./.files"
 
@@ -75,178 +80,172 @@ USER_PROFILES = {
     }
 }
 
-# === CUSTOM LOCAL STORAGE CLIENT (FIXED) ===# Questa classe ora implementa tutti i metodi astratti richiesti da Chainlit 2.8.3
+# === STORAGE CLIENT ===
 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(
+    async def upload_file(self, object_key: str, data: bytes, mime: str = "application/octet-stream", overwrite: bool = True) -> Dict[str, str]:
-        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:
+        with open(file_path, "wb") as f: f.write(data)
-            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 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)
+        path = os.path.join(self.storage_path, object_key)
-        if os.path.exists(file_path):
+        if os.path.exists(path): os.remove(path); return True
-            os.remove(file_path)
-            return True
         return False
+    async def close(self): pass
 
-    async def close(self):
-        pass
-
-# === DATA LAYER ===
 @cl.data_layer
 def get_data_layer():
-    return SQLAlchemyDataLayer(
+    return SQLAlchemyDataLayer(conninfo=DATABASE_URL, user_thread_limit=1000, storage_provider=LocalStorageClient(STORAGE_DIR))
-        conninfo=DATABASE_URL,
-        user_thread_limit=1000,
-        storage_provider=LocalStorageClient(storage_path=STORAGE_DIR)
-    )
 
-# === OAUTH CALLBACK ===
+# === OAUTH ===
 @cl.oauth_callback
-def oauth_callback(
+def oauth_callback(provider_id: str, token: str, raw_user_data: Dict[str, str], default_user: cl.User) -> Optional[cl.User]:
-    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"],
+            "role": profile["role"], "workspace": profile["workspace"],
-            "workspace": profile["workspace"],
+            "rag_collection": profile["rag_collection"], "capabilities": profile["capabilities"],
-            "rag_collection": profile["rag_collection"],
+            "show_code": profile["show_code"], "display_name": profile["name"]
-            "capabilities": profile["capabilities"],
-            "show_code": profile["show_code"],
-            "display_name": profile["name"]
         })
         return default_user
     return default_user
 
-# === UTILITY FUNCTIONS ===
+def get_user_profile(email: str) -> Dict:
-def get_user_profile(user_email: str) -> Dict:
+    return USER_PROFILES.get(email.lower(), {"role": "guest", "name": "Ospite", "workspace": "guest_workspace", "rag_collection": "documents", "show_code": False})
-    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:
+def create_workspace(name: str) -> str:
-    path = os.path.join(WORKSPACES_DIR, workspace_name)
+    path = os.path.join(WORKSPACES_DIR, 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")
+    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-    file_name = f"code_{timestamp}.py"
+    path = os.path.join(WORKSPACES_DIR, workspace, f"code_{ts}.py")
-    file_path = os.path.join(WORKSPACES_DIR, workspace, file_name)
+    with open(path, "w", encoding="utf-8") as f: f.write(code)
-    with open(file_path, "w", encoding="utf-8") as f:
+    return path
-        f.write(code)
-    return file_path
 
-def extract_text_from_pdf(pdf_path: str) -> str:
+# === PARSING DOCUMENTI ===
+def extract_text_from_pdf(path: str) -> str:
     try:
-        doc = fitz.open(pdf_path)
+        doc = fitz.open(path)
-        text = "\n".join([page.get_text() for page in doc])
+        return "\n".join([page.get_text() for page in doc])
-        doc.close()
+    except: return ""
-        return text
+
-    except Exception:
+def extract_text_from_excel(path: str) -> str:
+    """Estrae testo da Excel convertendo i fogli in Markdown"""
+    try:
+        xl = pd.read_excel(path, sheet_name=None)
+        text_content = []
+        for sheet, df in xl.items():
+            text_content.append(f"\n--- Foglio Excel: {sheet} ---\n")
+            # Pulisce NaN e converte in stringa
+            clean_df = df.fillna("").astype(str)
+            text_content.append(clean_df.to_markdown(index=False))
+        return "\n".join(text_content)
+    except Exception as e:
+        print(f"❌ Errore Excel: {e}")
         return ""
 
-# === QDRANT FUNCTIONS ===
+# === AI & EMBEDDINGS (Remoto) ===
-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])
+        async with httpx.AsyncClient(timeout=30.0) as client:
-        if 'embeddings' in response: return response['embeddings'][0]
+            resp = await client.post(
-        return response.get('embedding', [])
+                f"{BGE_API_URL}/embed",
-    except: return []
+                json={"texts": [text], "normalize": True}
-
-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()}
-            )]
             )
+            if resp.status_code == 200:
+                data = resp.json()
+                # Gestisce sia il vecchio formato (lista diretta) che il nuovo (dict)
+                if isinstance(data, list): return data[0] # Vecchia API
+                if "dense" in data: return data["dense"][0] # Nuova API Hybrid
+                if "embeddings" in data: return data["embeddings"][0] # API precedente
+    except Exception as e:
+        print(f"⚠️ Errore Embedding: {e}")
+    return []
+
+async def ensure_collection(name: str):
+    client = AsyncQdrantClient(url=QDRANT_URL)
+    if not await client.collection_exists(name):
+        # Creiamo una collezione ottimizzata
+        await client.create_collection(
+            collection_name=name,
+            vectors_config={
+                "bge_dense": VectorParams(size=1024, distance=Distance.COSINE)
+            }
+            # Se in futuro abilitiamo lo sparse output dal .243, aggiungeremo:
+            # sparse_vectors_config={"bge_sparse": SparseVectorParams(index=SparseIndexParams(on_disk=False))}
+        )
+
+async def index_document(filename: str, content: str, collection: str) -> bool:
+    try:
+        await ensure_collection(collection)
+        chunks = [content[i:i+3000] for i in range(0, len(content), 3000)]
+
+        qdrant = AsyncQdrantClient(url=QDRANT_URL)
+        points = []
+
+        for i, chunk in enumerate(chunks):
+            # Ottieni embedding (assume che get_embeddings ritorni la lista float)
+            # Nota: Se hai aggiornato l'API .243 per ritornare un dict {"dense": ...},
+            # devi aggiornare get_embeddings per estrarre ["dense"]!
+
+            # Vedere funzione get_embeddings aggiornata sotto
+            emb = await get_embeddings(chunk)
+
+            if emb:
+                points.append(PointStruct(
+                    id=str(uuid.uuid4()),
+                    # Vettori nominati
+                    vector={"bge_dense": emb},
+                    payload={"file_name": filename, "content": chunk, "chunk_id": i}
+                ))
+
+        if points:
+            await qdrant.upsert(collection_name=collection, points=points)
             return True
-    except: return False
+    except Exception as e:
+        print(f"Index Error: {e}")
+    return False
 
 async def search_qdrant(query: str, collection: str) -> str:
     try:
-        client = await get_qdrant_client()
+        client = AsyncQdrantClient(url=QDRANT_URL)
         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])
+        # Ricerca mirata sul vettore BGE
+        res = await client.query_points(
+            collection_name=collection,
+            query=emb,
+            using="bge_dense", # Specifica quale indice usare
+            limit=5
+        )
+        return "\n\n".join([f"📄 {hit.payload['file_name']}:\n{hit.payload['content']}" for hit in res.points if hit.payload])
     except: return ""
 
-# === CHAINLIT HANDLERS ===
+# === CHAT LOGIC ===
-
 @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
+        email = "guest@local"
-        user_email = "guest@local"
+        profile = get_user_profile(email)
-        profile = get_user_profile(user_email)
     else:
-        user_email = user.identifier
+        email = user.identifier
-        # I metadati sono già popolati dalla callback oauth
+        profile = USER_PROFILES.get(email, get_user_profile("guest"))
-        profile = USER_PROFILES.get(user_email, get_user_profile("guest"))
 
-    # Salva in sessione
+    cl.user_session.set("email", email)
-    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"])
@@ -254,91 +253,65 @@ async def on_chat_start():
     
     create_workspace(profile["workspace"])
 
-    # === SETTINGS WIDGETS ===
+    settings = [
-    settings_widgets = [
+        cl.input_widget.Select(id="model", label="Modello", values=["glm-4.6:cloud", "llama3"], initial_value="glm-4.6:cloud"),
-        cl.input_widget.Select(
+        cl.input_widget.Slider(id="temp", label="Temperatura", initial=0.5, min=0, max=1, step=0.1)
-            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))
+        settings.append(cl.input_widget.Switch(id="rag", label="RAG Attivo", initial=True))
     
-    await cl.ChatSettings(settings_widgets).send()
+    await cl.ChatSettings(settings).send()
-    
+    await cl.Message(content=f"👋 Ciao **{profile['name']}**! Pronto per l'automazione.").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):
+async def on_settings_update(s): cl.user_session.set("settings", s)
-    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")
+    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
+    # 1. FILE UPLOAD (PDF & EXCEL)
     if message.elements:
-        for element in message.elements:
+        for el in message.elements:
-            dest = os.path.join(WORKSPACES_DIR, workspace, element.name)
+            dest = os.path.join(WORKSPACES_DIR, workspace, el.name)
-            shutil.copy(element.path, dest)
+            shutil.copy(el.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
+            content = ""
-    context = ""
+            if el.name.endswith(".pdf"):
-    if rag_enabled:
+                content = extract_text_from_pdf(dest)
-        context = await search_qdrant(message.content, rag_collection)
+            elif el.name.endswith((".xlsx", ".xls")):
+                await cl.Message(content=f"📊 Analisi Excel **{el.name}**...").send()
+                content = extract_text_from_excel(dest)
             
-    system_prompt = "Sei un assistente esperto."
+            if content:
-    if context: system_prompt += f"\n\nCONTESTO:\n{context}"
+                ok = await index_document(el.name, content, rag_collection)
+                icon = "✅" if ok else "❌"
+                await cl.Message(content=f"{icon} **{el.name}** elaborato.").send()
+
+    # 2. RAG & GENERATION
+    rag_active = settings.get("rag", True) if role == "admin" else True
+    context = await search_qdrant(message.content, rag_collection) if rag_active else ""
+    
+    prompt = "Sei un esperto di automazione industriale."
+    if context: prompt += f"\n\nUSA QUESTO CONTESTO (Manuali/Excel):\n{context}"
     
-    # 3. GENERAZIONE
-    client = ollama.AsyncClient(host=OLLAMA_URL)
     msg = cl.Message(content="")
     await msg.send()
     
+    try:
+        client = ollama.AsyncClient(host=OLLAMA_URL)
         stream = await client.chat(
-        model=model,
+            model=settings.get("model", "glm-4.6:cloud"),
-        messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": message.content}],
+            messages=[{"role": "system", "content": prompt}, {"role": "user", "content": message.content}],
-        options={"temperature": temperature},
+            options={"temperature": settings.get("temp", 0.5)},
             stream=True
         )
-    
-    full_resp = ""
         async for chunk in stream:
-        token = chunk['message']['content']
+            await msg.stream_token(chunk['message']['content'])
-        full_resp += token
+    except Exception as e:
-        await msg.stream_token(token)
+        await msg.stream_token(f"Errore connessione AI: {e}")
-    await msg.update()
     
-    # 4. SALVATAGGIO CODICE
+    await msg.update()
-    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()

requirements.txt

@@ -27,3 +27,7 @@ sniffio
 aiohttp
 boto3>=1.28.0
 azure-storage-file-datalake>=12.14.0
+# NUOVI PER EXCEL
+pandas
+openpyxl
+tabulate