fixed bugs

app/routers/chat.py

@@ -21,9 +21,9 @@ def _get_public_chatbot(chatbot_id: str, supabase) -> dict:
 
 @router.post("/chat/{chatbot_id}", response_model=ChatResponse)
 async def chat(
-    chatbot_id: str,
+        chatbot_id: str,
-    message: ChatMessage,
+        message: ChatMessage,
-    user=Depends(get_optional_user),
+        user=Depends(get_optional_user),
 ):
     supabase = get_supabase()
     chatbot = _get_public_chatbot(chatbot_id, supabase)
@@ -97,9 +97,9 @@ async def chat(
 
 @router.get("/chat/{chatbot_id}/history/{session_id}", response_model=List[MessageResponse])
 async def get_chat_history(
-    chatbot_id: str,
+        chatbot_id: str,
-    session_id: str,
+        session_id: str,
-    user=Depends(get_optional_user),
+        user=Depends(get_optional_user),
 ):
     supabase = get_supabase()
 
@@ -114,7 +114,7 @@ async def get_chat_history(
     conv_id = conversation.data[0]["id"]
     messages = supabase.table("messages").select("*") \
         .eq("conversation_id", conv_id) \
-        .order("created_at", asc=True) \
+        .order("created_at", desc=False) \
         .execute()
 
     return [
@@ -140,7 +140,8 @@ async def get_analytics(chatbot_id: str, user=Depends(get_current_user)):
     if not company.data:
         raise HTTPException(status_code=404, detail="Company not found")
 
-    chatbot = supabase.table("chatbots").select("id").eq("id", chatbot_id).eq("company_id", company.data[0]["id"]).execute()
+    chatbot = supabase.table("chatbots").select("id").eq("id", chatbot_id).eq("company_id",
+                                                                              company.data[0]["id"]).execute()
     if not chatbot.data:
         raise HTTPException(status_code=404, detail="Chatbot not found")
 
@@ -159,11 +160,11 @@ async def get_analytics(chatbot_id: str, user=Depends(get_current_user)):
 # ── Helpers ───────────────────────────────────────────────────────────────────
 
 def _get_or_create_conversation(
-    chatbot_id: str,
+        chatbot_id: str,
-    session_id: str,
+        session_id: str,
-    user_id: Optional[str],
+        user_id: Optional[str],
-    language: str,
+        language: str,
-    supabase,
+        supabase,
 ) -> dict:
     existing = supabase.table("conversations").select("*") \
         .eq("chatbot_id", chatbot_id) \
@@ -186,21 +187,29 @@ def _get_or_create_conversation(
 
 
 def _get_conversation_history(conversation_id: str, supabase) -> List[dict]:
+    """
+    FIX: Changed from desc=True to desc=False (ascending/chronological order).
+
+    The conversation history MUST be in chronological order (oldest first)
+    for the LLM to correctly understand the conversation flow.
+    Previously, messages were returned newest-first, which reversed the
+    conversation and confused the model.
+    """
     messages = supabase.table("messages").select("role, content") \
         .eq("conversation_id", conversation_id) \
-        .order("created_at", desc=True) \
+        .order("created_at", desc=False) \
         .limit(20) \
         .execute()
     return messages.data or []
 
 
 def _save_message(
-    conversation_id: str,
+        conversation_id: str,
-    role: str,
+        role: str,
-    content: str,
+        content: str,
-    supabase,
+        supabase,
-    sources: Optional[list] = None,
+        sources: Optional[list] = None,
-    model: str = "",
+        model: str = "",
 ):
     supabase.table("messages").insert({
         "id": str(uuid.uuid4()),
@@ -209,4 +218,4 @@ def _save_message(
         "content": content,
         "sources": sources,
         "model": model,
-    }).execute()
+    }).execute()

app/services/rag.py

@@ -11,11 +11,11 @@ RAG_SYSTEM_PROMPT = """You are a helpful AI assistant for {company_name}.
 Your role is to answer questions based on the provided context from company documents.
 
 IMPORTANT RULES:
-1. Only answer based on the provided context
+1. Answer based on the provided context below
-2. If information is not in the context, say "I don't have information about that in my knowledge base"
+2. If the context does not contain enough information, say so, but also try to be helpful with what IS available
 3. Be concise and helpful
 4. Always maintain a professional, friendly tone
-5. If asked about topics outside the context, politely redirect to relevant topics
+5. If asked about topics completely outside the context, politely redirect to relevant topics
 
 {custom_instructions}
 
@@ -47,8 +47,9 @@ class RAGEngine:
         # Step 1: Embed the query
         try:
             query_embedding = self.embedding_svc.embed_text(query)
+            logger.info(f"[RAG] Query embedded successfully. Vector length: {len(query_embedding)}")
         except Exception as e:
-            logger.error(f"Embedding error: {e}")
+            logger.error(f"[RAG] Embedding error: {e}", exc_info=True)
             return {
                 "response": "I'm having trouble processing your request. Please try again.",
                 "sources": [],
@@ -57,13 +58,22 @@ class RAGEngine:
             }
 
         # Step 2: Retrieve relevant chunks
+        # FIX: Lowered score_threshold from 0.3 to 0.1 to avoid filtering out
+        # all results. With cosine similarity, 0.3 can be too aggressive for
+        # many document types and query patterns.
         retrieved = self.vector_svc.search(
             collection_name=collection_name,
             query_vector=query_embedding,
             limit=5,
-            score_threshold=0.3,
+            score_threshold=0.1,  # FIX: was 0.3, now 0.1 to avoid over-filtering
         )
 
+        logger.info(f"[RAG] Retrieved {len(retrieved)} chunks from collection '{collection_name}'")
+        for i, item in enumerate(retrieved):
+            score = item.get("score", 0)
+            text_preview = item.get("payload", {}).get("text", "")[:80]
+            logger.info(f"[RAG]   Chunk {i+1}: score={score:.4f}, preview='{text_preview}...'")
+
         # Step 3: Build sources
         sources = []
         context_parts = []
@@ -84,7 +94,12 @@ class RAGEngine:
                     )
                 )
 
-        context = "\n\n---\n\n".join(context_parts) if context_parts else "No relevant information found."
+        if context_parts:
+            context = "\n\n---\n\n".join(context_parts)
+            logger.info(f"[RAG] Built context from {len(context_parts)} chunks ({len(context)} chars)")
+        else:
+            context = "No relevant information found in the knowledge base."
+            logger.warning(f"[RAG] No context found for query: '{query}' in collection '{collection_name}'")
 
         # Step 4: Build messages
         system_prompt = RAG_SYSTEM_PROMPT.format(
@@ -95,13 +110,18 @@ class RAGEngine:
 
         messages = [{"role": "system", "content": system_prompt}]
 
-        # Add conversation history (last 10 messages)
+        # FIX: Conversation history must be in CHRONOLOGICAL order (oldest first).
-        for msg in conversation_history[-10:]:
+        # The history should already come sorted ascending from the chat router.
+        # We take the last 10 messages for context window management.
+        history_to_use = conversation_history[-10:] if conversation_history else []
+        for msg in history_to_use:
             messages.append({"role": msg["role"], "content": msg["content"]})
 
         # Add current query
         messages.append({"role": "user", "content": query})
 
+        logger.info(f"[RAG] Sending {len(messages)} messages to LLM (model: {chatbot_config.get('model')})")
+
         # Step 5: Generate response
         model = chatbot_config.get("model", "accounts/fireworks/models/kimi-k2-instruct-0905")
         try:
@@ -111,6 +131,7 @@ class RAGEngine:
                 max_tokens=chatbot_config.get("max_tokens", 1000),
                 temperature=chatbot_config.get("temperature", 0.7),
             )
+            logger.info(f"[RAG] LLM response generated. Tokens used: {result.get('tokens_used', 0)}")
             return {
                 "response": result["content"],
                 "sources": sources,
@@ -118,7 +139,7 @@ class RAGEngine:
                 "model": result.get("model", model),
             }
         except Exception as e:
-            logger.error(f"LLM generation error: {e}")
+            logger.error(f"[RAG] LLM generation error: {e}", exc_info=True)
             return {
                 "response": "I'm having trouble generating a response. Please try again later.",
                 "sources": sources,
@@ -127,4 +148,4 @@ class RAGEngine:
             }
 
 
-rag_engine = RAGEngine()
+rag_engine = RAGEngine()

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "qdrant-client>=1.17.0",
     "sentry-sdk>=2.53.0",
     "spglib>=2.7.0",
+    "stripe>=14.3.0",
     "supabase>=2.28.0",
     "uvicorn>=0.41.0",
 ]

uv.lock

@@ -202,6 +202,7 @@ dependencies = [
     { name = "qdrant-client" },
     { name = "sentry-sdk" },
     { name = "spglib" },
+    { name = "stripe" },
     { name = "supabase" },
     { name = "uvicorn" },
 ]
@@ -214,6 +215,7 @@ requires-dist = [
     { name = "qdrant-client", specifier = ">=1.17.0" },
     { name = "sentry-sdk", specifier = ">=2.53.0" },
     { name = "spglib", specifier = ">=2.7.0" },
+    { name = "stripe", specifier = ">=14.3.0" },
     { name = "supabase", specifier = ">=2.28.0" },
     { name = "uvicorn", specifier = ">=0.41.0" },
 ]
@@ -1448,6 +1450,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/96/7c/a81ef5ef10978dd073a854e0fa93b5d8021d0594b639cc8f6453c3c78a1d/strictyaml-1.7.3-py3-none-any.whl", hash = "sha256:fb5c8a4edb43bebb765959e420f9b3978d7f1af88c80606c03fb420888f5d1c7", size = 123917, upload-time = "2023-03-10T12:50:17.242Z" },
 ]
 
+[[package]]
+name = "stripe"
+version = "14.3.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "requests" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/49/67/8a38222a57fc2ba359c4dcb66528d94c00d803c7fde8f8d8470ad6bdccbb/stripe-14.3.0.tar.gz", hash = "sha256:4c76137d741bd43e8bb433a596c198ca20f4cdf17a8fe04604faf37c74b01978", size = 1463618, upload-time = "2026-01-28T21:20:29.856Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/4b/0b7d5920f2be5e42d72bdfc44a9fae57b422668bfc8dacdf2f74886f6daa/stripe-14.3.0-py3-none-any.whl", hash = "sha256:3e36b68b256c8970e99b703e195d947e2a2919095758788c7074ac4485ac255e", size = 2106980, upload-time = "2026-01-28T21:20:27.566Z" },
+]
+
 [[package]]
 name = "supabase"
 version = "2.28.0"