从零开始认识langchain（四）组件-链(chains)

class Chain(BaseModel, ABC):
    """Base interface that all chains should implement."""

    memory: BaseMemory
    callbacks: Callbacks

    def __call__(
        self,
        inputs: Any,
        return_only_outputs: bool = False,
        callbacks: Callbacks = None,
    ) -> Dict[str, Any]:
        ...

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain only specifying the input variable.
print(chain.run("colorful socks"))

    Colorful Toes Co.

prompt = PromptTemplate(
    input_variables=["company", "product"],
    template="What is a good name for {company} that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run({
    'company': "ABC Startup",
    'product': "colorful socks"
    }))

    Socktopia Colourful Creations.

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
)
human_message_prompt = HumanMessagePromptTemplate(
        prompt=PromptTemplate(
            template="What is a good name for a company that makes {product}?",
            input_variables=["product"],
        )
    )
chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt])
chat = ChatOpenAI(temperature=0.9)
chain = LLMChain(llm=chat, prompt=chat_prompt_template)
print(chain.run("colorful socks"))

    Rainbow Socks Co.

import asyncio
import time

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain


def generate_serially():
    llm = OpenAI(temperature=0.9)
    prompt = PromptTemplate(
        input_variables=["product"],
        template="What is a good name for a company that makes {product}?",
    )
    chain = LLMChain(llm=llm, prompt=prompt)
    for _ in range(5):
        resp = chain.run(product="toothpaste")
        print(resp)


async def async_generate(chain):
    resp = await chain.arun(product="toothpaste")
    print(resp)


async def generate_concurrently():
    llm = OpenAI(temperature=0.9)
    prompt = PromptTemplate(
        input_variables=["product"],
        template="What is a good name for a company that makes {product}?",
    )
    chain = LLMChain(llm=llm, prompt=prompt)
    tasks = [async_generate(chain) for _ in range(5)]
    await asyncio.gather(*tasks)


s = time.perf_counter()
# If running this outside of Jupyter, use asyncio.run(generate_concurrently())
await generate_concurrently()
elapsed = time.perf_counter() - s
print("\033[1m" + f"Concurrent executed in {elapsed:0.2f} seconds." + "\033[0m")

s = time.perf_counter()
generate_serially()
elapsed = time.perf_counter() - s
print("\033[1m" + f"Serial executed in {elapsed:0.2f} seconds." + "\033[0m")

chat = ChatOpenAI(temperature=0)
prompt_template = "Tell me a {adjective} joke"
llm_chain = LLMChain(llm=chat, prompt=PromptTemplate.from_template(prompt_template))

llm_chain(inputs={"adjective": "corny"})

    {'adjective': 'corny',
     'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}

llm_chain("corny", return_only_outputs=True)

    {'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}
  

# llm_chain only has one output key, so we can use run
llm_chain.output_keys

    ['text']

llm_chain.run({"adjective": "corny"})

    'Why did the tomato turn red? Because it saw the salad dressing!'

# These two are equivalent
llm_chain.run({"adjective": "corny"})
llm_chain.run("corny")

# These two are also equivalent
llm_chain("corny")
llm_chain({"adjective": "corny"})

    {'adjective': 'corny',
     'text': 'Why did the tomato turn red? Because it saw the salad dressing!'}

from __future__ import annotations

from typing import Any, Dict, List, Optional

from pydantic import Extra

from langchain.base_language import BaseLanguageModel
from langchain.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain.chains.base import Chain
from langchain.prompts.base import BasePromptTemplate


class MyCustomChain(Chain):
    """
    An example of a custom chain.
    """

    prompt: BasePromptTemplate
    """Prompt object to use."""
    llm: BaseLanguageModel
    output_key: str = "text"  #: :meta private:

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the prompt expects.

        :meta private:
        """
        return self.prompt.input_variables

    @property
    def output_keys(self) -> List[str]:
        """Will always return text key.

        :meta private:
        """
        return [self.output_key]

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        # Your custom chain logic goes here
        # This is just an example that mimics LLMChain
        prompt_value = self.prompt.format_prompt(**inputs)

        # Whenever you call a language model, or another chain, you should pass
        # a callback manager to it. This allows the inner run to be tracked by
        # any callbacks that are registered on the outer run.
        # You can always obtain a callback manager for this by calling
        # `run_manager.get_child()` as shown below.
        response = self.llm.generate_prompt(
            [prompt_value], callbacks=run_manager.get_child() if run_manager else None
        )

        # If you want to log something about this run, you can do so by calling
        # methods on the `run_manager`, as shown below. This will trigger any
        # callbacks that are registered for that event.
        if run_manager:
            run_manager.on_text("Log something about this run")

        return {self.output_key: response.generations[0][0].text}

    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, str]:
        # Your custom chain logic goes here
        # This is just an example that mimics LLMChain
        prompt_value = self.prompt.format_prompt(**inputs)

        # Whenever you call a language model, or another chain, you should pass
        # a callback manager to it. This allows the inner run to be tracked by
        # any callbacks that are registered on the outer run.
        # You can always obtain a callback manager for this by calling
        # `run_manager.get_child()` as shown below.
        response = await self.llm.agenerate_prompt(
            [prompt_value], callbacks=run_manager.get_child() if run_manager else None
        )

        # If you want to log something about this run, you can do so by calling
        # methods on the `run_manager`, as shown below. This will trigger any
        # callbacks that are registered for that event.
        if run_manager:
            await run_manager.on_text("Log something about this run")

        return {self.output_key: response.generations[0][0].text}

    @property
    def _chain_type(self) -> str:
        return "my_custom_chain"

from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.chat_models.openai import ChatOpenAI
from langchain.prompts.prompt import PromptTemplate


chain = MyCustomChain(
    prompt=PromptTemplate.from_template("tell us a joke about {topic}"),
    llm=ChatOpenAI(),
)

chain.run({"topic": "callbacks"}, callbacks=[StdOutCallbackHandler()])

    
    
    > Entering new MyCustomChain chain...
    Log something about this run
    > Finished chain.





    'Why did the callback function feel lonely? Because it was always waiting for someone to call it back!'

conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory(),
    verbose=True
)
conversation.run("What is ChatGPT?")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:

    Human: What is ChatGPT?
    AI:

    > Finished chain.

    'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.'

from langchain.chains import load_chain

chain = load_chain("lc://chains/llm-math/chain.json")

chain.run("whats 2 raised to .12")

    
    
    > Entering new LLMMathChain chain...
    whats 2 raised to .12
    Answer: 1.0791812460476249
    > Finished chain.





    'Answer: 1.0791812460476249'

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain import OpenAI, VectorDBQA

from langchain.document_loaders import TextLoader

loader = TextLoader("../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

    Running Chroma using direct local API.
    Using DuckDB in-memory for database. Data will be transient.

chain = load_chain("lc://chains/vector-db-qa/stuff/chain.json", vectorstore=vectorstore)


query = "What did the president say about Ketanji Brown Jackson"
chain.run(query)

    " The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence."

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

conversation = ConversationChain(
    llm=chat,
    memory=ConversationBufferMemory()
)

conversation.run("Answer briefly. What are the first 3 colors of a rainbow?")
# -> The first three colors of a rainbow are red, orange, and yellow.
conversation.run("And the next 4?")
# -> The next four colors of a rainbow are green, blue, indigo, and violet.

    'The next four colors of a rainbow are green, blue, indigo, and violet.'

from langchain import PromptTemplate, OpenAI, LLMChain

template = """Question: {question}

Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)

llm_chain.save("llm_chain.json")

cat llm_chain.json

    {
        "memory": null,
        "verbose": true,
        "prompt": {
            "input_variables": [
                "question"
            ],
            "output_parser": null,
            "template": "Question: {question}\n\nAnswer: Let's think step by step.",
            "template_format": "f-string"
        },
        "llm": {
            "model_name": "text-davinci-003",
            "temperature": 0.0,
            "max_tokens": 256,
            "top_p": 1,
            "frequency_penalty": 0,
            "presence_penalty": 0,
            "n": 1,
            "best_of": 1,
            "request_timeout": null,
            "logit_bias": {},
            "_type": "openai"
        },
        "output_key": "text",
        "_type": "llm_chain"
    }

from langchain.chains import load_chain

chain = load_chain("llm_chain.json")

chain.run("whats 2 + 2")

    
    
    > Entering new LLMChain chain...
    Prompt after formatting:
    Question: whats 2 + 2
    
    Answer: Let's think step by step.
    
    > Finished chain.





    ' 2 + 2 = 4'

llm_chain.prompt.save("prompt.json")

cat prompt.json

    {
        "input_variables": [
            "question"
        ],
        "output_parser": null,
        "template": "Question: {question}\n\nAnswer: Let's think step by step.",
        "template_format": "f-string"
    }

llm_chain.llm.save("llm.json")

cat llm.json

    {
        "model_name": "text-davinci-003",
        "temperature": 0.0,
        "max_tokens": 256,
        "top_p": 1,
        "frequency_penalty": 0,
        "presence_penalty": 0,
        "n": 1,
        "best_of": 1,
        "request_timeout": null,
        "logit_bias": {},
        "_type": "openai"
    }

config = {
    "memory": None,
    "verbose": True,
    "prompt_path": "prompt.json",
    "llm_path": "llm.json",
    "output_key": "text",
    "_type": "llm_chain",
}
import json

with open("llm_chain_separate.json", "w") as f:
    json.dump(config, f, indent=2)

cat llm_chain_separate.json

    {
      "memory": null,
      "verbose": true,
      "prompt_path": "prompt.json",
      "llm_path": "llm.json",
      "output_key": "text",
      "_type": "llm_chain"
    }
    
chain = load_chain("llm_chain_separate.json")

chain.run("whats 2 + 2")

    
    
    > Entering new LLMChain chain...
    Prompt after formatting:
    Question: whats 2 + 2
    
    Answer: Let's think step by step.
    
    > Finished chain.





    ' 2 + 2 = 4'

from langchain import PromptTemplate, OpenAI, LLMChain

prompt_template = "What is a good name for a company that makes {product}?"

llm = OpenAI(temperature=0)
llm_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate.from_template(prompt_template)
)
llm_chain("colorful socks")

    {'product': 'colorful socks', 'text': '\n\nSocktastic!'}

input_list = [
    {"product": "socks"},
    {"product": "computer"},
    {"product": "shoes"}
]

llm_chain.apply(input_list)

    [{'text': '\n\nSocktastic!'},
     {'text': '\n\nTechCore Solutions.'},
     {'text': '\n\nFootwear Factory.'}]

llm_chain.generate(input_list)

    LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'})

# Single input example
llm_chain.predict(product="colorful socks")

    '\n\nSocktastic!'

# Multiple inputs example

template = """Tell me a {adjective} joke about {subject}."""
prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"])
llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))

llm_chain.predict(adjective="sad", subject="ducks")

    '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'

from langchain.output_parsers import CommaSeparatedListOutputParser

output_parser = CommaSeparatedListOutputParser()
template = """List all the colors in a rainbow"""
prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)
llm_chain = LLMChain(prompt=prompt, llm=llm)

llm_chain.predict()


    '\n\nRed, orange, yellow, green, blue, indigo, violet'

llm_chain.predict_and_parse()

    ['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']

template = """Tell me a {adjective} joke about {subject}."""
llm_chain = LLMChain.from_string(llm=llm, template=template)

llm_chain.predict(adjective="sad", subject="ducks")

    '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.'

from langchain.chains.router import MultiPromptChain
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate

physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise and easy to understand manner. \
When you don't know the answer to a question you admit that you don't know.

Here is a question:
{input}"""


math_template = """You are a very good mathematician. You are great at answering math questions. \
You are so good because you are able to break down hard problems into their component parts, \
answer the component parts, and then put them together to answer the broader question.

Here is a question:
{input}"""

prompt_infos = [
    {
        "name": "physics",
        "description": "Good for answering questions about physics",
        "prompt_template": physics_template,
    },
    {
        "name": "math",
        "description": "Good for answering math questions",
        "prompt_template": math_template,
    },
]

llm = OpenAI()

destination_chains = {}
for p_info in prompt_infos:
    name = p_info["name"]
    prompt_template = p_info["prompt_template"]
    prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
    chain = LLMChain(llm=llm, prompt=prompt)
    destination_chains[name] = chain
default_chain = ConversationChain(llm=llm, output_key="text")

from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE

destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(destinations=destinations_str)
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)

chain = MultiPromptChain(
    router_chain=router_chain,
    destination_chains=destination_chains,
    default_chain=default_chain,
    verbose=True,
)

print(chain.run("What is black body radiation?"))

    
    
    > Entering new MultiPromptChain chain...
    physics: {'input': 'What is black body radiation?'}
    > Finished chain.
    
    
    Black body radiation is the term used to describe the electromagnetic radiation emitted by a “black body”—an object that absorbs all radiation incident upon it. A black body is an idealized physical body that absorbs all incident electromagnetic radiation, regardless of frequency or angle of incidence. It does not reflect, emit or transmit energy. This type of radiation is the result of the thermal motion of the body's atoms and molecules, and it is emitted at all wavelengths. The spectrum of radiation emitted is described by Planck's law and is known as the black body spectrum.


print(
    chain.run(
        "What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"
    )
)


    
    
    > Entering new MultiPromptChain chain...
    math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
    > Finished chain.
    ?
    
    The answer is 43. One plus 43 is 44 which is divisible by 3.


print(chain.run("What is the name of the type of cloud that rins"))

    
    
    > Entering new MultiPromptChain chain...
    None: {'input': 'What is the name of the type of cloud that rains?'}
    > Finished chain.
     The type of cloud that rains is called a cumulonimbus cloud. It is a tall and dense cloud that is often accompanied by thunder and lightning.

from langchain.chains.router.embedding_router import EmbeddingRouterChain
from langchain.embeddings import CohereEmbeddings
from langchain.vectorstores import Chroma

names_and_descriptions = [
    ("physics", ["for questions about physics"]),
    ("math", ["for questions about math"]),
]

router_chain = EmbeddingRouterChain.from_names_and_descriptions(
    names_and_descriptions, Chroma, CohereEmbeddings(), routing_keys=["input"]
)

    Using embedded DuckDB without persistence: data will be transient

chain = MultiPromptChain(
    router_chain=router_chain,
    destination_chains=destination_chains,
    default_chain=default_chain,
    verbose=True,
)

print(chain.run("What is black body radiation?"))

    
    
    > Entering new MultiPromptChain chain...
    physics: {'input': 'What is black body radiation?'}
    > Finished chain.
    
    
    Black body radiation is the emission of energy from an idealized physical body (known as a black body) that is in thermal equilibrium with its environment. It is emitted in a characteristic pattern of frequencies known as a black-body spectrum, which depends only on the temperature of the body. The study of black body radiation is an important part of astrophysics and atmospheric physics, as the thermal radiation emitted by stars and planets can often be approximated as black body radiation.


print(
    chain.run(
        "What is the first prime number greater than 40 such that one plus the prime number is divisible by 3"
    )
)


    
    
    > Entering new MultiPromptChain chain...
    math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3'}
    > Finished chain.
    ?
    
    Answer: The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43.

from langchain.llms import OpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# This is an LLMChain to write a synopsis given a title of a play.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.

Title: {title}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)


# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template)


# This is the overall chain where we run these two chains in sequence.
from langchain.chains import SimpleSequentialChain
overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)


review = overall_chain.run("Tragedy at sunset on the beach")

    
    
    > Entering new SimpleSequentialChain chain...
    
    
    Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future. 
    
    The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever.
    
    The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset.
    
    
    Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. 
    
    The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. 
    
    The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.
    
    > Finished chain.


print(review)

    
    
    Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. 
    
    The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. 
    
    The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful.

# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.
llm = OpenAI(temperature=.7)
template = """You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.

Title: {title}
Era: {era}
Playwright: This is a synopsis for the above play:"""
prompt_template = PromptTemplate(input_variables=["title", "era"], template=template)
synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="synopsis")


# This is an LLMChain to write a review of a play given a synopsis.
llm = OpenAI(temperature=.7)
template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:"""
prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)
review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review")


# This is the overall chain where we run these two chains in sequence.
from langchain.chains import SequentialChain
overall_chain = SequentialChain(
    chains=[synopsis_chain, review_chain],
    input_variables=["era", "title"],
    # Here we return multiple variables
    output_variables=["synopsis", "review"],
    verbose=True)

overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"})

    
    
    > Entering new SequentialChain chain...
    
    > Finished chain.





    {'title': 'Tragedy at sunset on the beach',
     'era': 'Victorian England',
     'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn their journey, they make their way to a beach at sunset, where they plan to exchange their vows of love. Unbeknownst to them, their plans are overheard by John's father, who has been tracking them. He follows them to the beach and, in a fit of rage, confronts them. \n\nA physical altercation ensues, and in the struggle, John's father accidentally stabs Mary in the chest with his sword. The two are left in shock and disbelief as Mary dies in John's arms, her last words being a declaration of her love for him.\n\nThe tragedy of the play comes to a head when John, broken and with no hope of a future, chooses to take his own life by jumping off the cliffs into the sea below. \n\nThe play is a powerful story of love, hope, and loss set against the backdrop of 19th century England.",
     'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and start a new life together, and the audience is taken on a journey of hope and optimism for the future.\n\nUnfortunately, their dreams are cut short when John's father discovers them and in a fit of rage, fatally stabs Mary. The tragedy of the play is further compounded when John, broken and without hope, takes his own life. The storyline is not only realistic, but also emotionally compelling, drawing the audience in from start to finish.\n\nThe acting was also commendable, with the actors delivering believable and nuanced performances. The playwright and director have successfully crafted a timeless tale of love and loss that will resonate with audiences for years to come. Highly recommended."}

from langchain.chains import SequentialChain
from langchain.memory import SimpleMemory

llm = OpenAI(temperature=.7)
template = """You are a social media manager for a theater company.  Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.

Here is some context about the time and location of the play:
Date and Time: {time}
Location: {location}

Play Synopsis:
{synopsis}
Review from a New York Times play critic of the above play:
{review}

Social Media Post:
"""
prompt_template = PromptTemplate(input_variables=["synopsis", "review", "time", "location"], template=template)
social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="social_post_text")

overall_chain = SequentialChain(
    memory=SimpleMemory(memories={"time": "December 25th, 8pm PST", "location": "Theater in the Park"}),
    chains=[synopsis_chain, review_chain, social_chain],
    input_variables=["era", "title"],
    # Here we return multiple variables
    output_variables=["social_post_text"],
    verbose=True)

overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"})


    
    
    > Entering new SequentialChain chain...
    
    > Finished chain.





    {'title': 'Tragedy at sunset on the beach',
     'era': 'Victorian England',
     'time': 'December 25th, 8pm PST',
     'location': 'Theater in the Park',
     'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland"}

from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()

def transform_func(inputs: dict) -> dict:
    text = inputs["text"]
    shortened_text = "\n\n".join(text.split("\n\n")[:3])
    return {"output_text": shortened_text}


transform_chain = TransformChain(
    input_variables=["text"], output_variables=["output_text"], transform=transform_func
)


template = """Summarize this text:

{output_text}

Summary:"""
prompt = PromptTemplate(input_variables=["output_text"], template=template)
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt)

sequential_chain = SimpleSequentialChain(chains=[transform_chain, llm_chain])

sequential_chain.run(state_of_the_union)

    ' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'

from langchain.chains.api import open_meteo_docs
chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)


chain_new.run('What is the weather like right now in Munich, Germany in degrees Fahrenheit?')




    > Entering new APIChain chain...
    https://api.open-meteo.com/v1/forecast?latitude=48.1351&longitude=11.5820&temperature_unit=fahrenheit&current_weather=true
    {"latitude":48.14,"longitude":11.58,"generationtime_ms":0.33104419708251953,"utc_offset_seconds":0,"timezone":"GMT","timezone_abbreviation":"GMT","elevation":521.0,"current_weather":{"temperature":33.4,"windspeed":6.8,"winddirection":198.0,"weathercode":2,"time":"2023-01-16T01:00"}}

    > Finished chain.





    ' The current temperature in Munich, Germany is 33.4 degrees Fahrenheit with a windspeed of 6.8 km/h and a wind direction of 198 degrees. The weathercode is 2.'

from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma

loader = TextLoader("../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)

qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())


query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

    " The president said that she is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support, from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="map_reduce", retriever=docsearch.as_retriever())


query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

    " The president said that Judge Ketanji Brown Jackson is one of our nation's top legal minds, a former top litigator in private practice and a former federal public defender, from a family of public school educators and police officers, a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

from langchain.chains.question_answering import load_qa_chain
qa_chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
qa = RetrievalQA(combine_documents_chain=qa_chain, retriever=docsearch.as_retriever())

query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

from langchain.prompts import PromptTemplate
prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.

{context}

Question: {question}
Answer in Italian:"""
PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)


chain_type_kwargs = {"prompt": PROMPT}
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)


query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)

    " Il presidente ha detto che Ketanji Brown Jackson è una delle menti legali più importanti del paese, che continuerà l'eccellenza di Justice Breyer e che ha ricevuto un ampio sostegno, da Fraternal Order of Police a ex giudici nominati da democratici e repubblicani."

qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever(), return_source_documents=True)


query = "What did the president say about Ketanji Brown Jackson"
result = qa({"query": query})

result["result"]

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice and a former federal public defender from a family of public school educators and police officers, and that she has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."


result["source_documents"]

    [Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
     Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \n\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.  \n\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.  \n\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \n\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
     Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \n\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \n\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.  \n\nFirst, beat the opioid epidemic.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0),
     Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \n\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up.  \n\nThat ends on my watch. \n\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \n\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \n\nLet’s pass the Paycheck Fairness Act and paid leave.  \n\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \n\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', lookup_str='', metadata={'source': '../../state_of_the_union.txt'}, lookup_index=0)]

docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))])


from langchain.chains import RetrievalQAWithSourcesChain
from langchain import OpenAI

chain = RetrievalQAWithSourcesChain.from_chain_type(OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever())


chain({"question": "What did the president say about Justice Breyer"}, return_only_outputs=True)


    {'answer': ' The president honored Justice Breyer for his service and mentioned his legacy of excellence.\n',
     'sources': '31-pl'}

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain

from langchain.document_loaders import TextLoader
loader = TextLoader("../../state_of_the_union.txt")
documents = loader.load()

# loaders = [....]
# docs = []
# for loader in loaders:
#     docs.extend(loader.load())

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)

    Using embedded DuckDB without persistence: data will be transient
    
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)

query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query})

 " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."


query = "Did he mention who she suceeded"
result = qa({"question": query})

result['answer']

    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result["answer"]

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

result['answer']

    ' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'

from langchain.chat_models import ChatOpenAI

qa = ConversationalRetrievalChain.from_llm(
    ChatOpenAI(temperature=0, model="gpt-4"),
    vectorstore.as_retriever(),
    condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)


chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result['source_documents'][0]

    Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})

vectordbkwargs = {"search_distance": 0.9}

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})

from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT

llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")

chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})

result['answer']

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

from langchain.chains.qa_with_sources import load_qa_with_sources_chain

llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")

chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})

result['answer']

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \nSOURCES: ../../state_of_the_union.txt"

from langchain.chains.llm import LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
from langchain.chains.question_answering import load_qa_chain

# Construct a ConversationalRetrievalChain with a streaming llm for combine docs
# and a separate, non-streaming llm for question generation
llm = OpenAI(temperature=0)
streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)

question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)

qa = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)


chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

     The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.


chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

     Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.

def get_chat_history(inputs) -> str:
    res = []
    for human, ai in inputs:
        res.append(f"Human:{human}\nAI:{ai}")
    return "\n".join(res)
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)


chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result['answer']

    " The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

from langchain import OpenAI, PromptTemplate, LLMChain
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains.mapreduce import MapReduceChain
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0)

text_splitter = CharacterTextSplitter()

with open("../../state_of_the_union.txt") as f:
    state_of_the_union = f.read()
texts = text_splitter.split_text(state_of_the_union)

from langchain.docstore.document import Document

docs = [Document(page_content=t) for t in texts[:3]]

from langchain.chains.summarize import load_summarize_chain

chain = load_summarize_chain(llm, chain_type="map_reduce")
chain.run(docs)

    ' In response to Russian aggression in Ukraine, the United States and its allies are taking action to hold Putin accountable, including economic sanctions, asset seizures, and military assistance. The US is also providing economic and humanitarian aid to Ukraine, and has passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and create jobs. The US remains unified and determined to protect Ukraine and the free world.'

chain = load_summarize_chain(llm, chain_type="stuff")

chain.run(docs)

    ' In his speech, President Biden addressed the crisis in Ukraine, the American Rescue Plan, and the Bipartisan Infrastructure Law. He discussed the need to invest in America, educate Americans, and build the economy from the bottom up. He also announced the release of 60 million barrels of oil from reserves around the world, and the creation of a dedicated task force to go after the crimes of Russian oligarchs. He concluded by emphasizing the need to Buy American and use taxpayer dollars to rebuild America.'

prompt_template = """Write a concise summary of the following:


{text}


CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(llm, chain_type="stuff", prompt=PROMPT)
chain.run(docs)

    "\n\nIn questa serata, il Presidente degli Stati Uniti ha annunciato una serie di misure per affrontare la crisi in Ucraina, causata dall'aggressione di Putin. Ha anche annunciato l'invio di aiuti economici, militari e umanitari all'Ucraina. Ha anche annunciato che gli Stati Uniti e i loro alleati stanno imponendo sanzioni economiche a Putin e stanno rilasciando 60 milioni di barili di petrolio dalle riserve di tutto il mondo. Inoltre, ha annunciato che il Dipartimento di Giustizia degli Stati Uniti sta creando una task force dedicata ai crimini degli oligarchi russi. Il Presidente ha anche annunciato l'approvazione della legge bipartitica sull'infrastruttura, che prevede investimenti per la ricostruzione dell'America. Questo porterà a creare posti"

chain = load_summarize_chain(llm, chain_type="map_reduce")

chain.run(docs)

    " In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and releasing oil from its Strategic Petroleum Reserve. President Biden and Vice President Harris have passed legislation to help struggling families and rebuild America's infrastructure."

chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True)


chain({"input_documents": docs}, return_only_outputs=True)

    {'map_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
      ' The United States and its European allies are taking action to punish Russia for its invasion of Ukraine, including seizing assets, closing off airspace, and providing economic and military assistance to Ukraine. The US is also mobilizing forces to protect NATO countries and has released 30 million barrels of oil from its Strategic Petroleum Reserve to help blunt gas prices. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens.',
      " President Biden and Vice President Harris ran for office with a new economic vision for America, and have since passed the American Rescue Plan and the Bipartisan Infrastructure Law to help struggling families and rebuild America's infrastructure. This includes creating jobs, modernizing roads, airports, ports, and waterways, replacing lead pipes, providing affordable high-speed internet, and investing in American products to support American jobs."],
     'output_text': " In response to Russia's aggression in Ukraine, the United States and its allies have imposed economic sanctions and are taking other measures to hold Putin accountable. The US is also providing economic and military assistance to Ukraine, protecting NATO countries, and passing legislation to help struggling families and rebuild America's infrastructure. The world is uniting in support of Ukraine and democracy, and the US stands with its Ukrainian-American citizens."}

prompt_template = """Write a concise summary of the following:


{text}


CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
chain = load_summarize_chain(OpenAI(temperature=0), chain_type="map_reduce", return_intermediate_steps=True, map_prompt=PROMPT, combine_prompt=PROMPT)
chain({"input_documents": docs}, return_only_outputs=True)


    {'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Gli Stati Uniti e i loro alleati stanno ora imponendo sanzioni economiche a Putin e stanno tagliando l'accesso della Russia alla tecnologia. Il Dipartimento di Giustizia degli Stati Uniti sta anche creando una task force dedicata per andare dopo i crimini degli oligarchi russi.",
      "\n\nStiamo unendo le nostre forze con quelle dei nostri alleati europei per sequestrare yacht, appartamenti di lusso e jet privati di Putin. Abbiamo chiuso lo spazio aereo americano ai voli russi e stiamo fornendo più di un miliardo di dollari in assistenza all'Ucraina. Abbiamo anche mobilitato le nostre forze terrestri, aeree e navali per proteggere i paesi della NATO. Abbiamo anche rilasciato 60 milioni di barili di petrolio dalle riserve di tutto il mondo, di cui 30 milioni dalla nostra riserva strategica di petrolio. Stiamo affrontando una prova reale e ci vorrà del tempo, ma alla fine Putin non riuscirà a spegnere l'amore dei popoli per la libertà.",
      "\n\nIl Presidente Biden ha lottato per passare l'American Rescue Plan per aiutare le persone che soffrivano a causa della pandemia. Il piano ha fornito sollievo economico immediato a milioni di americani, ha aiutato a mettere cibo sulla loro tavola, a mantenere un tetto sopra le loro teste e a ridurre il costo dell'assicurazione sanitaria. Il piano ha anche creato più di 6,5 milioni di nuovi posti di lavoro, il più alto numero di posti di lavoro creati in un anno nella storia degli Stati Uniti. Il Presidente Biden ha anche firmato la legge bipartitica sull'infrastruttura, la più ampia iniziativa di ricostruzione della storia degli Stati Uniti. Il piano prevede di modernizzare le strade, gli aeroporti, i porti e le vie navigabili in"],
     'output_text': "\n\nIl Presidente Biden sta lavorando per aiutare le persone che soffrono a causa della pandemia attraverso l'American Rescue Plan e la legge bipartitica sull'infrastruttura. Gli Stati Uniti e i loro alleati stanno anche imponendo sanzioni economiche a Putin e tagliando l'accesso della Russia alla tecnologia. Stanno anche sequestrando yacht, appartamenti di lusso e jet privati di Putin e fornendo più di un miliardo di dollari in assistenza all'Ucraina. Alla fine, Putin non riuscirà a spegnere l'amore dei popoli per la libertà."}

from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain

map_template_string = """Give the following python code information, generate a description that explains what the code does and also mention the time complexity.
Code:
{code}

Return the the description in the following format:
name of the function: description of the function
"""


reduce_template_string = """Given the following python function names and descriptions, answer the following question
{code_description}
Question: {question}
Answer:
"""

MAP_PROMPT = PromptTemplate(input_variables=["code"], template=map_template_string)
REDUCE_PROMPT = PromptTemplate(input_variables=["code_description", "question"], template=reduce_template_string)

llm = OpenAI()

map_llm_chain = LLMChain(llm=llm, prompt=MAP_PROMPT)
reduce_llm_chain = LLMChain(llm=llm, prompt=REDUCE_PROMPT)

generative_result_reduce_chain = StuffDocumentsChain(
    llm_chain=reduce_llm_chain,
    document_variable_name="code_description",
)

combine_documents = MapReduceDocumentsChain(
    llm_chain=map_llm_chain,
    combine_document_chain=generative_result_reduce_chain,
    document_variable_name="code",
)

map_reduce = MapReduceChain(
    combine_documents_chain=combine_documents,
    text_splitter=CharacterTextSplitter(separator="\n##\n", chunk_size=100, chunk_overlap=0),
)

code = """
def bubblesort(list):
   for iter_num in range(len(list)-1,0,-1):
      for idx in range(iter_num):
         if list[idx]>list[idx+1]:
            temp = list[idx]
            list[idx] = list[idx+1]
            list[idx+1] = temp
    return list
##
def insertion_sort(InputList):
   for i in range(1, len(InputList)):
      j = i-1
      nxt_element = InputList[i]
   while (InputList[j] > nxt_element) and (j >= 0):
      InputList[j+1] = InputList[j]
      j=j-1
   InputList[j+1] = nxt_element
   return InputList
##
def shellSort(input_list):
   gap = len(input_list) // 2
   while gap > 0:
      for i in range(gap, len(input_list)):
         temp = input_list[i]
         j = i
   while j >= gap and input_list[j - gap] > temp:
      input_list[j] = input_list[j - gap]
      j = j-gap
      input_list[j] = temp
   gap = gap//2
   return input_list

"""

map_reduce.run(input_text=code, question="Which function has a better time complexity?")


    Created a chunk of size 247, which is longer than the specified 100
    Created a chunk of size 267, which is longer than the specified 100





    'shellSort has a better time complexity than both bubblesort and insertion_sort, as it has a time complexity of O(n^2), while the other two have a time complexity of O(n^2).'

chain = load_summarize_chain(llm, chain_type="refine")

chain.run(docs)

    "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This investment will"

chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True)

chain({"input_documents": docs}, return_only_outputs=True)


    {'refine_steps': [" In response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains.",
      "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. Putin's war on Ukraine has left Russia weaker and the rest of the world stronger, with the world uniting in support of democracy and peace.",
      "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"],
     'output_text': "\n\nIn response to Russia's aggression in Ukraine, the United States has united with other freedom-loving nations to impose economic sanctions and hold Putin accountable. The U.S. Department of Justice is also assembling a task force to go after the crimes of Russian oligarchs and seize their ill-gotten gains. We are joining with our European allies to find and seize the assets of Russian oligarchs, including yachts, luxury apartments, and private jets. The U.S. is also closing off American airspace to all Russian flights, further isolating Russia and adding an additional squeeze on their economy. The U.S. and its allies are providing support to the Ukrainians in their fight for freedom, including military, economic, and humanitarian assistance. The U.S. is also mobilizing ground forces, air squadrons, and ship deployments to protect NATO countries. The U.S. and its allies are also releasing 60 million barrels of oil from reserves around the world, with the U.S. contributing 30 million barrels from its own Strategic Petroleum Reserve. In addition, the U.S. has passed the American Rescue Plan to provide immediate economic relief for tens of millions of Americans, and the Bipartisan Infrastructure Law to rebuild America and create jobs. This includes investing"}

prompt_template = """Write a concise summary of the following:


{text}


CONCISE SUMMARY IN ITALIAN:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
refine_template = (
    "Your job is to produce a final summary\n"
    "We have provided an existing summary up to a certain point: {existing_answer}\n"
    "We have the opportunity to refine the existing summary"
    "(only if needed) with some more context below.\n"
    "------------\n"
    "{text}\n"
    "------------\n"
    "Given the new context, refine the original summary in Italian"
    "If the context isn't useful, return the original summary."
)
refine_prompt = PromptTemplate(
    input_variables=["existing_answer", "text"],
    template=refine_template,
)
chain = load_summarize_chain(OpenAI(temperature=0), chain_type="refine", return_intermediate_steps=True, question_prompt=PROMPT, refine_prompt=refine_prompt)
chain({"input_documents": docs}, return_only_outputs=True)

    {'intermediate_steps': ["\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia e bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi.",
      "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare,",
      "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."],
     'output_text': "\n\nQuesta sera, ci incontriamo come democratici, repubblicani e indipendenti, ma soprattutto come americani. La Russia di Putin ha cercato di scuotere le fondamenta del mondo libero, ma ha sottovalutato la forza della gente ucraina. Insieme ai nostri alleati, stiamo imponendo sanzioni economiche, tagliando l'accesso della Russia alla tecnologia, bloccando i suoi più grandi istituti bancari dal sistema finanziario internazionale e chiudendo lo spazio aereo americano a tutti i voli russi. Il Dipartimento di Giustizia degli Stati Uniti sta anche assemblando una task force dedicata per andare dopo i crimini degli oligarchi russi. Stiamo fornendo più di un miliardo di dollari in assistenza diretta all'Ucraina e fornendo assistenza militare."}