Have you ever encountered the frustrating situation where you can’t seem to get a token count from the API for your Python/Flask Langchain RetrievalQA chatbot? It’s a common issue that many developers face, but fear not! In this article, we will delve into troubleshooting this problem and find a solution that works for you.
Bạn đang xem: Can’t Get a Token Count: Troubleshooting Python/Flask Langchain RetrievalQA Chatbot
The Challenge: Counting Tokens
The challenge at hand is that the API doesn’t provide a token count, preventing you from deducting credits from your user’s accounts. While your deduct_credits
function works fine, you need a way to reliably obtain the token count for each API call. Let’s explore some possible solutions.
Testing Callback Code
Xem thêm : How to Create Recovery Image in Chromebook and Use It To Fix Problems
One approach is to test the callback code just after initializing your agent. By running the code snippet below, you can obtain the RetrievalQA chains token count in your terminal:
agent = initialize_agent(agent="conversational-react-description", llm=llm, tools=tools, verbose=True, max_iterations=1, early_stopping_method='generate', handle_parsing_errors=True, memory=memory, persona=persona)
with get_openai_callback() as cb:
response = agent.run("Who is Olivia Wilde’s boyfriend? What is his current age raised to the 0.23 power?")
print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost (USD): ${cb.total_cost}")
This method provides a convenient way to track the token count, but it doesn’t fulfill the requirement of monitoring user interactions with the chatbot. To achieve that, we need to integrate the callback into the /chat
endpoint.
Integrating the Callback
To integrate the callback into the /chat
endpoint, you can follow the code example below:
@app.route('/chat', methods=['POST'])
def chat():
try:
data = request.get_json()
user_message = data.get('message')
if not user_message:
app.logger.error("No message provided")
return jsonify({"error": "No message provided"}), 400
# Embed the user message
user_message_embedding = Embeddings.embed_query(user_message)
if user_message_embedding is None:
app.logger.info("Embedding for user message is None")
# Generate response using the agent
agent_response = agent.run(user_message)
original_response = agent_response['output']
# Embed the original agent response
original_response_embedding = Embeddings.embed_query(original_response)
if original_response_embedding is None:
app.logger.info("Embedding for original agent response is None")
# Post-process the response to ensure a professional tone
professional_response = post_process_with_professional_tone(original_response)
# Embed the post-processed response
professional_response_embedding = Embeddings.embed_query(professional_response)
if professional_response_embedding is None:
app.logger.info("Embedding for post-processed response is None")
# Store the embeddings in the Chroma DB
memory.append({
'role': 'user',
'content': user_message
})
memory.append({
'role': 'assistant',
'content': professional_response
})
vectorstore.add([user_message_embedding, original_response_embedding, professional_response_embedding])
return jsonify({"user_message": user_message, "chatbot_response": professional_response})
except Exception as e:
traceback.print_exc()
return jsonify({"error": str(e)}), 500
Xem thêm : Persona 5 Guide: Conquering Kaneshiro’s Bank Palace
By integrating the callback into the /chat
endpoint, you can effectively track the user’s chatbot interactions while obtaining the token count for each API call. This ensures a seamless deduction of credits from your user’s account.
Conclusion
In this article, we have explored the challenge of obtaining a token count from the API for your Python/Flask Langchain RetrievalQA chatbot. Through testing the callback code and integrating it into the /chat
endpoint, you can reliably track the token usage for each API call and deduct credits accordingly. Remember to modify the code provided to fit your specific chatbot implementation.
Happy coding, and may your chatbot endeavors be successful!
Nguồn: https://eireview.org
Danh mục: Technology