init

app/main.py

@@ -136,9 +136,13 @@ def retrieve_documents(query: str, top_k: int = 3) -> List[Dict]:
     # Extract documents
     documents = []
     for match in results['matches']:
+        # Ensure page_number is always an integer (Pinecone may return float)
+        page_num = match['metadata'].get('page_number', 0)
+        page_num = int(page_num) if isinstance(page_num, (int, float)) else 0
+
         documents.append({
             'pdf_name': match['metadata'].get('pdf_name', 'unknown.pdf'),
-            'page_number': match['metadata'].get('page_number', 0),
+            'page_number': page_num,
             'content': match['metadata'].get('text', ''),
             'score': match.get('score', 0.0)
         })
@@ -174,7 +178,7 @@ Sual: {query}
 
 Cavab verərkən:
 1. Dəqiq faktlar yazın
-2. Hər faktı mənbə ilə göstərin: (PDF: fayl_adı.pdf, Səhifə: X)
+2. Hər faktı mənbə ilə göstərin: (PDF: fayl_adı.pdf, Səhifə: X) - səhifə nömrəsini tam ədəd (integer) olaraq yazın, məsələn "Səhifə: 11" (11.0 yox)
 3. Kontekstdə olmayan məlumat əlavə etməyin"""
 
     try:
@@ -228,7 +232,7 @@ async def health():
 
 
 @app.post("/llm")
-async def llm_endpoint(request: QuestionRequest | ChatRequest):
+async def llm_endpoint(request: Request):
     """
     LLM chatbot endpoint for SOCAR historical documents.
 
@@ -248,23 +252,32 @@ async def llm_endpoint(request: QuestionRequest | ChatRequest):
     2. ChatRequest: {"messages": [{"role": "user", "content": "..."}], ...}
     """
     try:
-        # Handle both request formats
-        if isinstance(request, QuestionRequest):
-            query = request.question
-            temperature = request.temperature
-            max_tokens = request.max_tokens
-        else:  # ChatRequest
-            # Extract the user's question (last message)
-            if not request.messages:
+        # Parse request body
+        body = await request.json()
+
+        # Determine request format and extract query
+        if "question" in body:
+            # QuestionRequest format
+            query = body.get("question")
+            temperature = body.get("temperature", 0.2)
+            max_tokens = body.get("max_tokens", 1000)
+            is_simple_format = True
+        elif "messages" in body:
+            # ChatRequest format
+            messages = body.get("messages", [])
+            if not messages:
                 raise HTTPException(status_code=400, detail="No messages provided")
 
-            user_messages = [msg for msg in request.messages if msg.role == "user"]
+            user_messages = [msg for msg in messages if msg.get("role") == "user"]
             if not user_messages:
                 raise HTTPException(status_code=400, detail="No user message found")
 
-            query = user_messages[-1].content
-            temperature = request.temperature
-            max_tokens = request.max_tokens
+            query = user_messages[-1].get("content")
+            temperature = body.get("temperature", 0.2)
+            max_tokens = body.get("max_tokens", 1000)
+            is_simple_format = False
+        else:
+            raise HTTPException(status_code=400, detail="Invalid request format. Expected 'question' or 'messages' field.")
 
         # Retrieve relevant documents
         documents = retrieve_documents(query, top_k=3)
@@ -288,7 +301,7 @@ async def llm_endpoint(request: QuestionRequest | ChatRequest):
         ]
 
         # Return appropriate response format
-        if isinstance(request, QuestionRequest):
+        if is_simple_format:
             return AnswerResponse(
                 answer=answer,
                 sources=sources,

app/templates/index.html

@@ -4,6 +4,14 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <title>SOCAR Historical Documents AI System</title>
+
+    <!-- Favicon -->
+    <link rel="icon" type="image/x-icon" href="{{ url_for('static', path='/favicon/favicon.ico') }}">
+    <link rel="apple-touch-icon" sizes="180x180" href="{{ url_for('static', path='/favicon/apple-touch-icon.png') }}">
+    <link rel="icon" type="image/png" sizes="192x192" href="{{ url_for('static', path='/favicon/icon-192.png') }}">
+    <link rel="icon" type="image/png" sizes="512x512" href="{{ url_for('static', path='/favicon/icon-512.png') }}">
+
+    <!-- Stylesheet -->
     <link rel="stylesheet" href="{{ url_for('static', path='/css/style.css') }}">
 </head>
 <body>

docker-compose.yml

@@ -1,11 +1,11 @@
 version: '3.8'
 
 services:
-  llm-api:
+  socar-ai-system:
     build:
       context: .
       dockerfile: Dockerfile
-    container_name: socar-llm-api
+    container_name: socar-ai-system
     ports:
       - "8000:8000"
     env_file:
@@ -24,6 +24,10 @@ services:
       start_period: 40s
     networks:
       - socar-network
+    labels:
+      - "com.socar.description=SOCAR Historical Documents AI System"
+      - "com.socar.features=OCR,LLM,Frontend"
+      - "com.socar.version=1.0.0"
 
 networks:
   socar-network:

test_llm.json

@@ -0,0 +1 @@
+{"question":"Neft hasilatı haqqında nə məlumat var?"}