Langfuse를 활용한 자가 개선형 AI 고객 지원 에이전트 구축 가이드

def semantic_search(query):
    # Create a vector representation of the query
    query_embedding = genai.embed_content(
        model="models/text-embedding-004",
        content=query
    )["embedding"]

    # Using math, find the nearest meanings to the query
    similarities = cosine_similarity([query_embedding], product_vectors)
    return get_top_matches(similarities)

@langfuse.observe(as_type="generation")
def classify_user_intent(user_input):
    prompt = f"""
    Use the following user input to classify the user's intent into one of the three categories:
    1. PRODUCT_SEARCH
    2. ORDER_STATUS
    3. GENERAL_CHAT
    Input: {user_input}
    """
    # Call Gemini model here...
    intent = "PRODUCT_SEARCH" # Placeholder
    return intent

@langfuse.observe() # Root Trace
def handle_customer_user_input(user_input, session_id):
    # Tag the session
    langfuse.update_current_trace(session_id=session_id)
    # Think
    intent = get_classified_intent(user_input)
    # Act based on classified intent
    if intent == "PRODUCT_SEARCH":
        context = use_semantic_search(user_input)
    # ... (중략)
    response = generate_ai_response(context, intent)
    return response

if col1.button("👍"):
    lf_client.score(trace_id=trace_id, name="user-satisfaction", value=1)
if col2.button("👎"):
    lf_client.score(trace_id=trace_id, name="user-satisfaction", value=0)