File size: 2,802 Bytes
c14624d d05fec7 c14624d 3e65911 c14624d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import gradio as gr
import pickle
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_community.chat_message_histories import SQLChatMessageHistory
from langchain_groq import ChatGroq
from langchain_core.runnables import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from langchain_core.runnables.history import RunnableWithMessageHistory
from operator import itemgetter
# Load the vector store
with open("qdrant_vectorstore.pkl", "rb") as f:
qdrant_vectorstore = pickle.load(f)
# Updated function definition
def echo_user_input(*args):
user_input = args[0] # Extract user_input from args
# Set up retriever
qdrant_retriever = qdrant_vectorstore.as_retriever()
found_docs = qdrant_vectorstore.similarity_search(user_input)
context_str = ""
for context_data in found_docs:
context_str += context_data.page_content + '\n\n'
# Define prompt template
prompt = ChatPromptTemplate.from_messages([
("system", "Act as a helpful AI Assistant. Here is some {context}"),
MessagesPlaceholder(variable_name="history"),
("human", "{human_input}")
])
# Set up session history
def get_session_history(session_id):
return SQLChatMessageHistory(session_id, "sqlite:///memory.db")
# Initialize the LLM with Groq
groq_api_key = "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"
llm = ChatGroq(groq_api_key=groq_api_key, model_name="Gemma2-9b-It")
# Chain context with retriever
context = itemgetter("human_input") | qdrant_retriever
first_step = RunnablePassthrough.assign(context=context)
llm_chain = first_step | prompt | llm | StrOutputParser()
conv_chain = RunnableWithMessageHistory(llm_chain, get_session_history, input_messages_key="human_input", history_messages_key="history")
# Define a session ID for the conversation
session_id = 'bond007'
# return conv_chain.invoke(({"human_input": user_input}), {'configurable': {'session_id': session_id}})
llm_response = conv_chain.invoke(({"human_input": user_input}), {'configurable': {'session_id': session_id}})
# Combine context with the LLM response for Gradio output
# combined_output = f"**Retrieved Context:**\n{context_str}\n\n**Response:**\n{llm_response}"
combined_output = f"**Summarized Response:**\n{llm_response}**Retrieved Context:**\n{context_str}\n\n"
return combined_output
# Define the Gradio chat interface
interface = gr.ChatInterface(
fn=echo_user_input,
title="MCG Demo",
description="Type your question and press enter to see a conversational response. 🤖",
)
# Launch the interface with share=True for a public link
if __name__ == "__main__":
interface.launch(share=True)
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