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from typing import Any
import gradio as gr
from langchain_core.messages import AIMessage
from create_rag_agent import create_rag_agent
def gradio_main():
rag_agent = create_rag_agent()
def rag_agent_response(message: str, history: list[dict[str, Any]]):
"""
The function integrated with Gradio, calling your LangChain rag_agent.
It now passes the full conversation history for conversational context.
The type hint for history is now the built-in generic: list[dict].
"""
full_messages = history + [{"role": "user", "content": message}]
agent_input = {
"messages": full_messages
}
stream = rag_agent.stream(agent_input)
current_response=""
# Iterate over the stream of chunks
for chunk in stream:
model_in_chunk = chunk.get("model", [])
if model_in_chunk:
messages_in_chunk = model_in_chunk.get("messages", [])
if messages_in_chunk:
# The final item in the messages list contains the generated text chunk
message_chunk = messages_in_chunk[-1]
# We use getattr to safely get the content from a message object/chunk
content_chunk = getattr(message_chunk, "text", None)
if content_chunk:
# Accumulate and yield the running response
current_response += content_chunk
yield current_response
gr_interface = gr.ChatInterface(
fn=rag_agent_response,
type="messages",
chatbot=gr.Chatbot(
height=500,
label="User Manual Chatbot",
type="messages"
),
textbox=gr.Textbox(placeholder="Enter your query here...", container=False, scale=7),
title="User Manual Chatbot",
description="Ask any technical question you wish",
theme="soft"
)
return gr_interface
if __name__ == "__main__":
gradio_main().queue().launch() # pyright: ignore[reportUnusedCallResult] |