wehub-resource-sync
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
import os
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from groq import Groq
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from langchain.chains import ConversationChain, LLMChain
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from langchain_core.prompts import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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)
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from langchain_core.messages import SystemMessage
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from langchain.chains.conversation.memory import ConversationBufferWindowMemory
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from langchain_groq import ChatGroq
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from langchain.prompts import PromptTemplate
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def main():
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"""
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This function is the main entry point of the application. It sets up the Groq client, the Streamlit interface, and handles the chat interaction.
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"""
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# Get Groq API key
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groq_api_key = os.environ['GROQ_API_KEY']
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model = 'llama3-8b-8192'
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# Initialize Groq Langchain chat object and conversation
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groq_chat = ChatGroq(
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groq_api_key=groq_api_key,
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model_name=model
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)
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print("Hello! I'm your friendly Groq chatbot. I can help answer your questions, provide information, or just chat. I'm also super fast! Let's start our conversation!")
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system_prompt = 'You are a friendly conversational chatbot'
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conversational_memory_length = 5 # number of previous messages the chatbot will remember during the conversation
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memory = ConversationBufferWindowMemory(k=conversational_memory_length, memory_key="chat_history", return_messages=True)
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#chat_history = []
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while True:
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user_question = input("Ask a question: ")
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# If the user has asked a question,
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if user_question:
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# Construct a chat prompt template using various components
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prompt = ChatPromptTemplate.from_messages(
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[
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SystemMessage(
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content=system_prompt
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), # This is the persistent system prompt that is always included at the start of the chat.
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MessagesPlaceholder(
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variable_name="chat_history"
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), # This placeholder will be replaced by the actual chat history during the conversation. It helps in maintaining context.
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HumanMessagePromptTemplate.from_template(
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"{human_input}"
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), # This template is where the user's current input will be injected into the prompt.
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]
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)
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# Create a conversation chain using the LangChain LLM (Language Learning Model)
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conversation = LLMChain(
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llm=groq_chat, # The Groq LangChain chat object initialized earlier.
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prompt=prompt, # The constructed prompt template.
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verbose=False, # TRUE Enables verbose output, which can be useful for debugging.
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memory=memory, # The conversational memory object that stores and manages the conversation history.
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)
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# The chatbot's answer is generated by sending the full prompt to the Groq API.
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response = conversation.predict(human_input=user_question)
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print("Chatbot:", response)
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if __name__ == "__main__":
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main() |