Apprentice Capture

I have integrated the ApprenticeCallback into your LangChain workflow and configured the environment variables for capture.

Integrated Trace Capture

I updated chains/retrieval_chain.py to include the Apprentice tracker. This implementation uses the ApprenticeCallbackHandler which automatically captures the input question, retrieved context, and the final model response.

import os
from langchain_openai import ChatOpenAI
from runapprentice.integrations.langchain import ApprenticeCallbackHandler

# Initialize Apprentice handler
apprentice_handler = ApprenticeCallbackHandler(
    task="document-qa-bot",
    api_key=os.environ.get("APPRENTICE_API_KEY")
)

# Add handler to your chain execution
chain = rag_pipeline | llm
response = chain.invoke(
    {"question": user_input},
    config={"callbacks": [apprentice_handler]}
)

# The trace_id is available in the run context if feedback is needed

I also added APPRENTICE_API_KEY to your .env.example.

Next steps

1. Run your chain a few times to populate the dataset.
2. Verify at least 20 rows at https://runapprentice.com/tasks/document-qa-bot/review to reach the optimization threshold.
3. Run apprentice optimize document-qa-bot once the rows land as gold.