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rag improvements

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Aljaz Ceru
2025-09-21 18:44:02 +02:00
parent f58a76ac59
commit a2ee959ec9
3 changed files with 401 additions and 86 deletions
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79
backend/modules/chatbot/main.py
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@@ -265,7 +265,6 @@ class ChatbotModule(BaseModule):
async def chat_completion(self, request: ChatRequest, user_id: str, db: Session) -> ChatResponse:
"""Generate chat completion response"""
logger.info("=== CHAT COMPLETION METHOD CALLED ===")
# Get chatbot configuration from database
db_chatbot = db.query(DBChatbotInstance).filter(DBChatbotInstance.id == request.chatbot_id).first()
@@ -364,11 +363,10 @@ class ChatbotModule(BaseModule):
metadata={"error": str(e), "fallback": True}
)
async def _generate_response(self, message: str, db_messages: List[DBMessage],
async def _generate_response(self, message: str, db_messages: List[DBMessage],
config: ChatbotConfig, context: Optional[Dict] = None, db: Session = None) -> tuple[str, Optional[List]]:
"""Generate response using LLM with optional RAG"""
logger.info("=== _generate_response METHOD CALLED ===")
# Lazy load dependencies if not available
await self._ensure_dependencies()
@@ -397,8 +395,8 @@ class ChatbotModule(BaseModule):
for i, result in enumerate(rag_results)]
# Build full RAG context from all results
rag_context = "\\n\\nRelevant information from knowledge base:\\n" + "\\n\\n".join([
f"[Document {i+1}]:\\n{result.document.content}" for i, result in enumerate(rag_results)
rag_context = "\n\nRelevant information from knowledge base:\n" + "\n\n".join([
f"[Document {i+1}]:\n{result.document.content}" for i, result in enumerate(rag_results)
])
# Detailed RAG logging - ALWAYS log for debugging
@@ -407,14 +405,14 @@ class ChatbotModule(BaseModule):
logger.info(f"Collection: {qdrant_collection_name}")
logger.info(f"Number of results: {len(rag_results)}")
for i, result in enumerate(rag_results):
logger.info(f"\\n--- RAG Result {i+1} ---")
logger.info(f"\n--- RAG Result {i+1} ---")
logger.info(f"Score: {getattr(result, 'score', 'N/A')}")
logger.info(f"Document ID: {getattr(result.document, 'id', 'N/A')}")
logger.info(f"Full Content ({len(result.document.content)} chars):")
logger.info(f"{result.document.content}")
if hasattr(result.document, 'metadata'):
logger.info(f"Metadata: {result.document.metadata}")
logger.info(f"\\n=== RAG CONTEXT BEING ADDED TO PROMPT ({len(rag_context)} chars) ===")
logger.info(f"\n=== RAG CONTEXT BEING ADDED TO PROMPT ({len(rag_context)} chars) ===")
logger.info(rag_context)
logger.info("=== END RAG SEARCH RESULTS ===")
else:
@@ -428,11 +426,6 @@ class ChatbotModule(BaseModule):
logger.warning(f"RAG search traceback: {traceback.format_exc()}")
# Build conversation context (includes the current message from db_messages)
logger.info(f"=== CRITICAL DEBUG ===")
logger.info(f"rag_context length: {len(rag_context)}")
logger.info(f"rag_context empty: {not rag_context}")
logger.info(f"rag_context preview: {rag_context[:200] if rag_context else 'EMPTY'}")
logger.info(f"=== END CRITICAL DEBUG ===")
messages = self._build_conversation_messages(db_messages, config, rag_context, context)
# Note: Current user message is already included in db_messages from the query
@@ -452,9 +445,9 @@ class ChatbotModule(BaseModule):
if config.use_rag and rag_context:
logger.info(f"RAG context added: {len(rag_context)} characters")
logger.info(f"RAG sources: {len(sources) if sources else 0} documents")
logger.info("\\n=== COMPLETE MESSAGES SENT TO LLM ===")
logger.info("\n=== COMPLETE MESSAGES SENT TO LLM ===")
for i, msg in enumerate(messages):
logger.info(f"\\n--- Message {i+1} ---")
logger.info(f"\n--- Message {i+1} ---")
logger.info(f"Role: {msg['role']}")
logger.info(f"Content ({len(msg['content'])} chars):")
# Truncate long content for logging (full RAG context can be very long)
@@ -518,38 +511,34 @@ class ChatbotModule(BaseModule):
# Return fallback if available
return "I'm currently unable to process your request. Please try again later.", None
def _build_conversation_messages(self, db_messages: List[DBMessage], config: ChatbotConfig,
def _build_conversation_messages(self, db_messages: List[DBMessage], config: ChatbotConfig,
rag_context: str = "", context: Optional[Dict] = None) -> List[Dict]:
"""Build messages array for LLM completion"""
messages = []
logger.info(f"DEBUG: _build_conversation_messages called. rag_context length: {len(rag_context)}")
# System prompt - keep it clean without RAG context
# System prompt
system_prompt = config.system_prompt
if rag_context:
# Add explicit instruction to use RAG context
system_prompt += "\n\nIMPORTANT: Use the following information from the knowledge base to answer the user's question. " \
"This information is directly relevant to their query and should be your primary source:\n" + rag_context
if context and context.get('additional_instructions'):
system_prompt += f"\n\nAdditional instructions: {context['additional_instructions']}"
messages.append({"role": "system", "content": system_prompt})
logger.info(f"Building messages from {len(db_messages)} database messages")
# Conversation history (messages are already limited by memory_length in the query)
# Reverse to get chronological order
# Include ALL messages - the current user message is needed for the LLM to respond!
for idx, msg in enumerate(reversed(db_messages)):
logger.info(f"Processing message {idx}: role={msg.role}, content_preview={msg.content[:50] if msg.content else 'None'}...")
if msg.role in ["user", "assistant"]:
# For user messages, prepend RAG context if available
content = msg.content
if msg.role == "user" and rag_context and idx == 0:
# Add RAG context to the current user message (first in reversed order)
content = f"Relevant information from knowledge base:\n{rag_context}\n\nQuestion: {msg.content}"
logger.info("Added RAG context to user message")
messages.append({
"role": msg.role,
"content": content
"content": msg.content
})
logger.info(f"Added message with role {msg.role} to LLM messages")
else:
@@ -690,10 +679,9 @@ class ChatbotModule(BaseModule):
return router
# API Compatibility Methods
async def chat(self, chatbot_config: Dict[str, Any], message: str,
async def chat(self, chatbot_config: Dict[str, Any], message: str,
conversation_history: List = None, user_id: str = "anonymous") -> Dict[str, Any]:
"""Chat method for API compatibility"""
logger.info("=== CHAT METHOD (API COMPATIBILITY) CALLED ===")
logger.info(f"Chat method called with message: {message[:50]}... by user: {user_id}")
# Lazy load dependencies
@@ -723,20 +711,21 @@ class ChatbotModule(BaseModule):
fallback_responses=chatbot_config.get("fallback_responses", [])
)
# For API compatibility, create a temporary DBMessage for the current message
# so RAG context can be properly added
from app.models.chatbot import ChatbotMessage as DBMessage
# Generate response using internal method
# Create a temporary message object for the current user message
temp_messages = [
DBMessage(
id=0,
conversation_id=0,
role="user",
content=message,
timestamp=datetime.utcnow(),
metadata={}
)
]
# Create a temporary user message with the current message
temp_user_message = DBMessage(
conversation_id="temp_conversation",
role=MessageRole.USER.value,
content=message
)
# Generate response using internal method with the current message included
response_content, sources = await self._generate_response(
message, [temp_user_message], config, None, db
message, temp_messages, config, None, db
)
return {