Cadenas secuenciales y sencillas

import os
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# IMPORTANT: Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
# For local testing, ensure it's set before running.

def main():
    # 1. Define the LLM
    llm = ChatOpenAI(temperature=0.7)

    # 2. Define the Prompt Template
    prompt_template = PromptTemplate(
        input_variables=["product_type"],
        template="Suggest a catchy name for a new {product_type}."
    )

    # 3. Create the LLMChain
    name_chain = LLMChain(llm=llm, prompt=prompt_template)

    # 4. Run the chain
    result = name_chain.invoke({"product_type": "AI assistant"})
    print(f"Suggested Name: {result['text']}")

if __name__ == "__main__":
    main()

import os
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# IMPORTANT: Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"

def main():
    llm = ChatOpenAI(temperature=0.7)

    # Chain 1: Generate a product name
    prompt_name = PromptTemplate(
        input_variables=["product_type"],
        template="Suggest a catchy, short name for a new {product_type}. Response should only be the name."
    )
    chain_name = LLMChain(llm=llm, prompt=prompt_name, output_key="product_name")

    # This chain will output a dictionary like {'product_type': '...', 'product_name': '...'}
    # The 'product_name' will be passed to the next chain.
    result = chain_name.invoke({"product_type": "smartwatch"})
    print(f"Input: {result['product_type']}")
    print(f"Output (product_name): {result['product_name']}")

if __name__ == "__main__":
    main()

import os
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# IMPORTANT: Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"

def main():
    llm = ChatOpenAI(temperature=0.7)

    # Chain 2: Generate a slogan based on the product name
    prompt_slogan = PromptTemplate(
        input_variables=["product_name"],
        template="Write a creative slogan for a product named {product_name}. Response should only be the slogan."
    )
    chain_slogan = LLMChain(llm=llm, prompt=prompt_slogan, output_key="slogan")

    # Example of running this chain independently
    result = chain_slogan.invoke({"product_name": "ChronoMind"})
    print(f"Input: {result['product_name']}")
    print(f"Output (slogan): {result['slogan']}")

if __name__ == "__main__":
    main()

import os
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# IMPORTANT: Set your OpenAI API key as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"

def main():
    llm = ChatOpenAI(temperature=0.7)

    # Chain 1: Generate a product name
    prompt_name = PromptTemplate(
        input_variables=["product_type"],
        template="Suggest a catchy, short name for a new {product_type}. Response should only be the name."
    )
    chain_name = LLMChain(llm=llm, prompt=prompt_name, output_key="product_name")

    # Chain 2: Generate a slogan
    prompt_slogan = PromptTemplate(
        input_variables=["product_name"],
        template="Write a creative slogan for a product named {product_name}. Response should only be the slogan."
    )
    chain_slogan = LLMChain(llm=llm, prompt=prompt_slogan, output_key="slogan")

    # Combine into a SimpleSequentialChain
    overall_chain = SimpleSequentialChain(
        chains=[chain_name, chain_slogan],
        verbose=True # Set to True to see intermediate steps
    )

    # Run the overall chain with the initial input
    final_result = overall_chain.invoke({"product_type": "AI-powered coffee maker"})
    print("\n--- Final Result ---")
    print(f"Product Slogan: {final_result['slogan']}")

if __name__ == "__main__":
    main()