Blittech/contexta_be
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fixed bugs

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This commit is contained in:
belviskhoremk
2026-02-23 15:43:07 +00:00
parent 2976c04eb1
commit e151c42e81
4 changed files with 77 additions and 31 deletions
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51
app/routers/chat.py
View File

@@ -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,
message: ChatMessage,
user=Depends(get_optional_user),
chatbot_id: str,
message: ChatMessage,
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,
session_id: str,
user=Depends(get_optional_user),
chatbot_id: str,
session_id: str,
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,
session_id: str,
user_id: Optional[str],
language: str,
supabase,
chatbot_id: str,
session_id: str,
user_id: Optional[str],
language: str,
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,
role: str,
content: str,
supabase,
sources: Optional[list] = None,
model: str = "",
conversation_id: str,
role: str,
content: str,
supabase,
sources: Optional[list] = None,
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()

41
app/services/rag.py
View File

@@ -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
2. If information is not in the context, say "I don't have information about that in my knowledge base"
1. Answer based on the provided context below
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)
for msg in conversation_history[-10:]:
# FIX: Conversation history must be in CHRONOLOGICAL order (oldest first).
# 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()