This is an interactive notebook. You can run it locally or use the links below:

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It is often hard to automatically evaluate a generated LLM response so, depending on your risk tolerance, you can gather direct user feedback to find areas to improve. In this tutorial, we’ll use a custom chatbot as an example app from which to collect user feedback. We’ll use Streamlit to build the interface and we’ll capture the LLM interactions and feedback in Weave.

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Setup

!pip install weave openai streamlit wandb
!pip install set-env-colab-kaggle-dotenv -q # for env var
python
# Add a .env file with your OpenAI and WandB API keys
from set_env import set_env

_ = set_env("OPENAI_API_KEY")
_ = set_env("WANDB_API_KEY")
Next, create a file called chatbot.py with the following contents: 
# chatbot.py

import openai
import streamlit as st
import wandb
from set_env import set_env

import weave

_ = set_env("OPENAI_API_KEY")
_ = set_env("WANDB_API_KEY")

# highlight-next-line
wandb.login()

# highlight-next-line
weave_client = weave.init("feedback-example")
oai_client = openai.OpenAI()

def init_states():
    """Set up session_state keys if they don't exist yet."""
    if "messages" not in st.session_state:
        st.session_state["messages"] = []
    if "calls" not in st.session_state:
        st.session_state["calls"] = []
    if "session_id" not in st.session_state:
        st.session_state["session_id"] = "123abc"

# highlight-next-line
@weave.op
def chat_response(full_history):
    """
    Calls the OpenAI API in streaming mode given the entire conversation history so far.
    full_history is a list of dicts: [{"role":"user"|"assistant","content":...}, ...]
    """
    stream = oai_client.chat.completions.create(
        model="gpt-4", messages=full_history, stream=True
    )
    response_text = st.write_stream(stream)
    return {"response": response_text}

def render_feedback_buttons(call_idx):
    """Renders thumbs up/down and text feedback for the call."""
    col1, col2, col3 = st.columns([1, 1, 4])

    # Thumbs up button
    with col1:
        if st.button("👍", key=f"thumbs_up_{call_idx}"):
            st.session_state.calls[call_idx].feedback.add_reaction("👍")
            st.success("Thanks for the feedback!")

    # Thumbs down button
    with col2:
        if st.button("👎", key=f"thumbs_down_{call_idx}"):
            st.session_state.calls[call_idx].feedback.add_reaction("👎")
            st.success("Thanks for the feedback!")

    # Text feedback
    with col3:
        feedback_text = st.text_input("Feedback", key=f"feedback_input_{call_idx}")
        if (
            st.button("Submit Feedback", key=f"submit_feedback_{call_idx}")
            and feedback_text
        ):
            st.session_state.calls[call_idx].feedback.add_note(feedback_text)
            st.success("Feedback submitted!")

def display_old_messages():
    """Displays the conversation stored in st.session_state.messages with feedback buttons"""
    for idx, message in enumerate(st.session_state.messages):
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

            # If it's an assistant message, show feedback form
            if message["role"] == "assistant":
                # Figure out index of this assistant message in st.session_state.calls
                assistant_idx = (
                    len(
                        [
                            m
                            for m in st.session_state.messages[: idx + 1]
                            if m["role"] == "assistant"
                        ]
                    )
                    - 1
                )
                # Render thumbs up/down & text feedback
                if assistant_idx < len(st.session_state.calls):
                    render_feedback_buttons(assistant_idx)

def display_chat_prompt():
    """Displays the chat prompt input box."""
    if prompt := st.chat_input("Ask me anything!"):
        # Immediately render new user message
        with st.chat_message("user"):
            st.markdown(prompt)

        # Save user message in session
        st.session_state.messages.append({"role": "user", "content": prompt})

        # Prepare chat history for the API
        full_history = [
            {"role": msg["role"], "content": msg["content"]}
            for msg in st.session_state.messages
        ]

        with st.chat_message("assistant"):
            # Attach Weave attributes for tracking of conversation instances
            with weave.attributes(
                {"session": st.session_state["session_id"], "env": "prod"}
            ):
                # Call the OpenAI API (stream)
                result, call = chat_response.call(full_history)

                # Store the assistant message
                st.session_state.messages.append(
                    {"role": "assistant", "content": result["response"]}
                )

                # Store the weave call object to link feedback to the specific response
                st.session_state.calls.append(call)

                # Render feedback buttons for the new message
                new_assistant_idx = (
                    len(
                        [
                            m
                            for m in st.session_state.messages
                            if m["role"] == "assistant"
                        ]
                    )
                    - 1
                )

                # Render feedback buttons
                if new_assistant_idx < len(st.session_state.calls):
                    render_feedback_buttons(new_assistant_idx)

def main():
    st.title("Chatbot with immediate feedback forms")
    init_states()
    display_old_messages()
    display_chat_prompt()

if __name__ == "__main__":
    main()
You can run this with streamlit run chatbot.py. Now, you can interact with this application and click the feedback buttons after each response. Visit the Weave UI to see the attached feedback.

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Explanation

If we consider our decorated prediction function as: 
import weave

weave.init("feedback-example")

# highlight-next-line
@weave.op
def predict(input_data):
    # Your prediction logic here
    some_result = "hello world"
    return some_result
We can use it as usual to deliver some model response to the user: 
with weave.attributes(
    {"session": "123abc", "env": "prod"}
):  # attach arbitrary attributes to the call alongside inputs & outputs
    result = predict(input_data="your data here")  # user question through the App UI
To attach feedback, you need the call object, which is obtained by using the .call() method instead of calling the function as normal: 
result, call = predict.call(input_data="your data here")
This call object is needed for attaching feedback to the specific response. After making the call, the output of the operation is available using result above. 
call.feedback.add_reaction("👍")  # user reaction through the App UI

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Conclusion

In this tutorial, we built a chat UI with Streamlit which had inputs & outputs captured in Weave, alongside 👍👎 buttons to capture user feedback.