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from langchain_groq import ChatGroq
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from dotenv import load_dotenv
# Load environment variables (like GROQ_API_KEY) from .env file
load_dotenv()
# 1. Initialize the Chat Model
# Groq Chat model with a specific version and settings
model = ChatGroq(
model="llama-3.1-8b-instant",
temperature=0.0,
max_retries=2,
)
# 2. Define the Prompt Template
# This defines the structure of the input, including the system role.
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful, ethical, and thoughtful agent. Always provide a balanced and nuanced answer, especially for philosophical questions like the trolley problem."),
("human", "{question}"),
])
# 3. Create the Chain (Simple Agent)
# This chain sequences the prompt and the model.
# RunnablePassthrough allows the input to flow through to the prompt.
chain = prompt | model
def run_agent_query(question: str):
print(f"--- Agent Query ---")
print(f"Question: {question}\n")
print(f"--- Agent Response (Streaming) ---")
# 4. Stream the response from the chain
# Pass the 'question' as the input to the chain.
for chunk in chain.stream({"question": question}):
# chunk.content is used for the output from a Chat Model in a chain
print(chunk.content, end="", flush=True)
print("\n---------------------------------")
def main():
# The agent's query
query = "what is your answer to the trolley problem? Discuss the difference between a utilitarian and a deontological perspective."
run_agent_query(query)
if __name__ == "__main__":
main()
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