LangChain Chatbot

pip install gradio langchain-openai traceai-langchain

# export FI_API_KEY="xxxasxas"
# export FI_SECRET_KEY="hasdaxxasa21"
# export OPENAI_API_KEY="jasfapsd"

import os
import gradio as gr
from langchain_openai import ChatOpenAI
from fi_instrumentation import register
from fi_instrumentation.fi_types import (
    EvalName,
    EvalSpanKind,
    EvalTag,
    EvalTagType,
    ProjectType
)

# Initialize tracing
trace_provider = register(
project_type=ProjectType.OBSERVE,
    project_name="Your-Project-Name"
)

import os
import gradio as gr
from langchain_openai import ChatOpenAI
from fi_instrumentation import register
from traceai_langchain import LangChainInstrumentor
from fi_instrumentation.fi_types import (
    EvalName,
    EvalSpanKind,
    EvalTag,
    EvalTagType,
    ProjectType
)

# Set up tracing with FutureAGI
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="Simple-Chat-App"
)

LangChainInstrumentor().instrument(tracer_provider=trace_provider)

# Set up the LLM
llm = ChatOpenAI(temperature=0, model="gpt-4o-mini")

def process_message(message, history):
    """Process user message and generate response with observability"""
    try:
        # Generate response using LLM
        response = llm.invoke(message)

        # Return formatted response
        return history + [(message, response.content)]
    except Exception as e:
        error_message = f"Sorry, I encountered an error: {str(e)}"
        return history + [(message, error_message)]

def main():
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        # Create chat interface
        chatbot = gr.Chatbot(
            label="Simple Chat Assistant",
            height=400,
            value=[],
            type="chat",
            autoscroll=True
        )

        with gr.Row():
            msg = gr.Textbox(
                label="Message",
                placeholder="Type your message here.",
                scale=4,
                container=False,
                autofocus=True,
                show_label=False
            )
            submit_button = gr.Button(
                "Send",
                variant="primary",
                scale=1,
                size="sm"
            )

        # Example queries
        gr.Examples(
            examples=[
                "What is artificial intelligence?",
                "Describe quantum computing in everyday language",
                "What are the advantages of observability?",
            ],
            inputs=msg
        )

        # Handle message submission
        msg.submit(
            fn=process_message,
            inputs=[msg, chatbot],
            outputs=[chatbot],
            queue=False
        ).then(
            lambda: "",
            None,
            msg,
            queue=False
        )

        # Also trigger on button click
        submit_button.click(
            fn=process_message,
            inputs=[msg, chatbot],
            outputs=[chatbot],
            queue=False
        ).then(
            lambda: "",
            None,
            msg,
            queue=False
        )

    # Launch the demo
    demo.launch(
        share=True,
        show_error=True
    )

if __name__ == "__main__":
    main()