sequentialchain.py

from dotenv import load_dotenv
load_dotenv()

from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Simple sequential execution function
def run_sequential_chain(topic):
    """Run the chains sequentially and return results"""

    # Generate explanation from topic
    explanation = explanation_chain.invoke({'topic': topic})
    
    # Generate summary from explanation
    summary = summary_chain.invoke({'explanation': explanation})
    
    return {
        "explanation": explanation,
        "summary": summary
    }

llm = ChatOpenAI(
    model = "gpt-4o-mini",
    temperature = 0.7
)

explanation_prompt = PromptTemplate.from_template("Explain {topic} in a detailed paragraph suitable for a beginner.")

summary_prompt = PromptTemplate.from_template("Summarize the following paragraph in 2 short sentences:\n {explanation}")


explanation_chain = explanation_prompt | llm | StrOutputParser()

summary_chain = summary_prompt | llm | StrOutputParser()


# Get user input
topic = input("Enter a topic: ")

# Run the sequential chain
print("\n\033[1m> Running sequential chain...\033[0m")

result = run_sequential_chain(topic)

print("\033[1m> Finished!\033[0m")

# Display results
print("\nFull Explanation:\n", result["explanation"])
print("\nSummary:\n", result["summary"])