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import gradio as gr
import os
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
def create_chains(openai_key):
os.environ["OPENAI_API_KEY"] = openai_key
# Create the classifier chain
classifier_prompt = PromptTemplate.from_template(
"""Given the user question below, classify it as either being about `LangChain`, `OpenAI`, or `Other`.
Do not respond with more than one word.
<question>
{question}
</question>
Classification:"""
)
classifier_chain = classifier_prompt | ChatOpenAI(model="gpt-4") | StrOutputParser()
# Create specialized chains
langchain_chain = (
PromptTemplate.from_template(
"""You are an expert in LangChain.
Always answer questions starting with "As a LangChain expert".
Question: {question}
Answer:"""
) | ChatOpenAI(model="gpt-4")
)
openai_chain = (
PromptTemplate.from_template(
"""You are an expert in OpenAI.
Always answer questions starting with "As an OpenAI expert".
Question: {question}
Answer:"""
) | ChatOpenAI(model="gpt-4")
)
general_chain = (
PromptTemplate.from_template(
"""Respond to the following question:
Question: {question}
Answer:"""
) | ChatOpenAI(model="gpt-4")
)
return classifier_chain, langchain_chain, openai_chain, general_chain
def route_question(question, openai_key):
try:
classifier_chain, langchain_chain, openai_chain, general_chain = create_chains(openai_key)
# Classify the question
classification = classifier_chain.invoke({"question": question})
# Route to appropriate chain
if "langchain" in classification.lower():
response = langchain_chain.invoke({"question": question})
elif "openai" in classification.lower():
response = openai_chain.invoke({"question": question})
else:
response = general_chain.invoke({"question": question})
return f"Classification: {classification}\nResponse: {response.content}"
except Exception as e:
return f"Error: {str(e)}"
# Example questions for each category
example_questions = [
["What is LangChain and how does it work?"],
["How do I use OpenAI's GPT models?"],
["What is the capital of France?"],
["Explain LangChain's routing capabilities"],
["Tell me about OpenAI's latest developments"]
]
# Create Gradio interface
demo = gr.Interface(
fn=route_question,
inputs=[
gr.Textbox(label="Enter your question", placeholder="Type your question here..."),
gr.Textbox(label="OpenAI API Key", type="password", placeholder="Enter your OpenAI API key")
],
outputs=gr.Textbox(label="Response"),
title="LangChain Router Demo",
description="""This demo shows how routing works in LangChain. Ask questions about LangChain, OpenAI, or any other topic.
""",
examples=example_questions,
cache_examples=False
)
if __name__ == "__main__":
demo.launch()
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