Custom Chains in langchain

from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain, SimpleSequentialChain
from langchain_google_genai import ChatGoogleGenerativeAI
import os
os.environ["GOOGLE_API_KEY"] = "API Key"
llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.7)

prompt1 = PromptTemplate(
    input_variables=["topic"],
    template="Write a short paragraph about {topic}."
)
prompt2 = PromptTemplate(
    input_variables=["text"],
    template="Summarize the following paragraph in one line:\n{text}"
)
chain1 = LLMChain(llm=llm, prompt=prompt1)
chain2 = LLMChain(llm=llm, prompt=prompt2)

class CustomChain:
    def __init__(self, chain1, chain2):
        self.chain1 = chain1
        self.chain2 = chain2
        self.pipeline = SimpleSequentialChain(chains=[chain1, chain2])

    def run(self, topic):
        """Run the full custom chain."""
        return self.pipeline.run(topic)


if __name__ == "__main__":
    custom_chain = CustomChain(chain1, chain2)
    result = custom_chain.run("Artificial Intelligence")
    print("Final Output:\n", result)

import os
from langchain.chains.base import Chain
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI

os.environ["GOOGLE_API_KEY"] = "API KEY"
llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.7)

summary_prompt = PromptTemplate(
    input_variables=["text"],
    template="Summarize the following paragraph in one line:\n{text}"
)
translate_prompt = PromptTemplate(
    input_variables=["summary"],
    template="Translate the following English sentence into Hindi:\n{summary}"
)

class SummarizeTranslateChain(Chain):
    summarize_chain: LLMChain
    translate_chain: LLMChain

    @property
    def input_keys(self):
        return ["text"]

    @property
    def output_keys(self):
        return ["translated_text"]

    def _call(self, inputs):
        """Custom logic: summarize → translate"""
        text = inputs["text"]
        summary = self.summarize_chain.run(text)
        translated = self.translate_chain.run(summary)
        return {"translated_text": translated}

if __name__ == "__main__":
    summarize_chain = LLMChain(llm=llm, prompt=summary_prompt)
    translate_chain = LLMChain(llm=llm, prompt=translate_prompt)

    custom_chain = SummarizeTranslateChain(
        summarize_chain=summarize_chain,
        translate_chain=translate_chain
    )

    result = custom_chain.run(
        "Artificial Intelligence helps machines learn from data and make intelligent decisions, enabling them to perform tasks like understanding language, recognizing images, and predicting outcomes. It continues to evolve, making technology smarter and more adaptive to human needs."
    )

    print("Final Output:\n", result)

from langchain_core.prompts import PromptTemplate
from langchain.schema.runnable import RunnableSequence
from langchain_google_genai import ChatGoogleGenerativeAI
import random, re

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.8)

guess_prompt = PromptTemplate(
    input_variables=["hint"],
    template="Guess a number close to {hint} between 1 and 10."
)
response_prompt = PromptTemplate(
    input_variables=["guess"],
    template="Player guessed {guess}. Reveal the actual number."
)

guess_chain = guess_prompt | llm
response_chain = response_prompt | llm
custom_chain = RunnableSequence(first=guess_chain, last=response_chain)

number = random.randint(1, 10)
print(f"The actual number: {number}")

guess = guess_chain.invoke({"hint": number}).content
 
print("\nFinal Output:\n", guess)

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain

class SimpleDynamicQuiz:
    def __init__(self, llm, max_levels=4):
        self.llm = llm
        self.level = 1
        self.max_levels = max_levels

    def create_check_chain(self, question):
        prompt = PromptTemplate(
            input_variables=["answer"],
            template=(
                f"You are a quiz checker at Level {self.level}.\n"
                f"Question: {question}\n"
                f"Player's answer: {{answer}}\n"
                "Respond only with 'Correct' or 'Incorrect'."
            )
        )
        return LLMChain(llm=self.llm, prompt=prompt)

    def create_next_question_chain(self):
        prompt = PromptTemplate(
            input_variables=["level"],
            template=(
                "You are a quiz generator AI.\n"
                "Generate one short and simple question for Level {level}.\n"
                "Make it a basic math question (like addition, subtraction, multiplication, or division).\n"
                "Do not include any story or description.\n"
                "Respond only with the question text."
            )
        )
        return LLMChain(llm=self.llm, prompt=prompt)

    def play(self, first_question):
        current_question = first_question
        print("Welcome to the 4-Level Dynamic Math Quiz!\n")

        while self.level <= self.max_levels:
            print(f"Level {self.level}: {current_question}")
            user_answer = input("Your answer: ").strip()

           
            check_chain = self.create_check_chain(current_question)
            result = check_chain.run({"answer": user_answer}).strip()
            print(f"Quiz Master: {result}")

            if "incorrect" in result.lower():
                print(" Game over. Try again next time!")
                break

            if self.level == self.max_levels:
                print(" Congratulations! You completed all 4 levels!")
                break
            next_q_chain = self.create_next_question_chain()
            next_question = next_q_chain.run({"level": str(self.level + 1)}).strip()

            self.level += 1
            current_question = next_question

if __name__ == "__main__":
    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.5)
    game = SimpleDynamicQuiz(llm, max_levels=4)
    game.play("What is 5 + 3?")