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# Source code for langchain.chains.llm

    """Chain that just formats a prompt and calls an LLM."""
    from __future__ import annotations

    import warnings
    from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast

    from langchain_core._api import deprecated
    from langchain_core.callbacks import (
        AsyncCallbackManager,
        AsyncCallbackManagerForChainRun,
        CallbackManager,
        CallbackManagerForChainRun,
        Callbacks,
    )
    from langchain_core.language_models import (
        BaseLanguageModel,
        LanguageModelInput,
    )
    from langchain_core.load.dump import dumpd
    from langchain_core.messages import BaseMessage
    from langchain_core.output_parsers import BaseLLMOutputParser, StrOutputParser
    from langchain_core.outputs import ChatGeneration, Generation, LLMResult
    from langchain_core.prompt_values import PromptValue
    from langchain_core.prompts import BasePromptTemplate, PromptTemplate
    from langchain_core.pydantic_v1 import Extra, Field
    from langchain_core.runnables import (
        Runnable,
        RunnableBinding,
        RunnableBranch,
        RunnableWithFallbacks,
    )
    from langchain_core.runnables.configurable import DynamicRunnable
    from langchain_core.utils.input import get_colored_text

    from langchain.chains.base import Chain

    @deprecated(
        since="0.1.17",
        alternative="RunnableSequence, e.g., `prompt | llm`",
        removal="0.3.0",
    )
    class LLMChain(Chain):
        """Chain to run queries against LLMs.

        This class is deprecated. See below for an example implementation using
        LangChain runnables:

            .. code-block:: python

                from langchain_core.prompts import PromptTemplate
                from langchain_openai import OpenAI

                prompt_template = "Tell me a {adjective} joke"
                prompt = PromptTemplate(
                    input_variables=["adjective"], template=prompt_template
                )
                llm = OpenAI()
                chain = prompt | llm

                chain.invoke("your adjective here")

        Example:
            .. code-block:: python

                from langchain.chains import LLMChain
                from langchain_community.llms import OpenAI
                from langchain_core.prompts import PromptTemplate
                prompt_template = "Tell me a {adjective} joke"
                prompt = PromptTemplate(
                    input_variables=["adjective"], template=prompt_template
                )
                llm = LLMChain(llm=OpenAI(), prompt=prompt)
        """

        @classmethod
        def is_lc_serializable(self) -> bool:
            return True

        prompt: BasePromptTemplate
        """Prompt object to use."""
        llm: Union[
            Runnable[LanguageModelInput, str], Runnable[LanguageModelInput, BaseMessage]
        ]
        """Language model to call."""
        output_key: str = "text"  #: :meta private:
        output_parser: BaseLLMOutputParser = Field(default_factory=StrOutputParser)
        """Output parser to use.
        Defaults to one that takes the most likely string but does not change it
        otherwise."""
        return_final_only: bool = True
        """Whether to return only the final parsed result. Defaults to True.
        If false, will return a bunch of extra information about the generation."""
        llm_kwargs: dict = Field(default_factory=dict)

        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:
            """
            if self.return_final_only:
                return [self.output_key]
            else:
                return [self.output_key, "full_generation"]

        def _call(
            self,
            inputs: Dict[str, Any],
            run_manager: Optional[CallbackManagerForChainRun] = None,
        ) -> Dict[str, str]:
            response = self.generate([inputs], run_manager=run_manager)
            return self.create_outputs(response)[0]

        def generate(
            self,
            input_list: List[Dict[str, Any]],
            run_manager: Optional[CallbackManagerForChainRun] = None,
        ) -> LLMResult:
            """Generate LLM result from inputs."""
            prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
            callbacks = run_manager.get_child() if run_manager else None
            if isinstance(self.llm, BaseLanguageModel):
                return self.llm.generate_prompt(
                    prompts,
                    stop,
                    callbacks=callbacks,
                    **self.llm_kwargs,
                )
            else:
                results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
                    cast(List, prompts), {"callbacks": callbacks}
                )
                generations: List[List[Generation]] = []
                for res in results:
                    if isinstance(res, BaseMessage):
                        generations.append([ChatGeneration(message=res)])
                    else:
                        generations.append([Generation(text=res)])
                return LLMResult(generations=generations)

        async def agenerate(
            self,
            input_list: List[Dict[str, Any]],
            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
        ) -> LLMResult:
            """Generate LLM result from inputs."""
            prompts, stop = await self.aprep_prompts(input_list, run_manager=run_manager)
            callbacks = run_manager.get_child() if run_manager else None
            if isinstance(self.llm, BaseLanguageModel):
                return await self.llm.agenerate_prompt(
                    prompts,
                    stop,
                    callbacks=callbacks,
                    **self.llm_kwargs,
                )
            else:
                results = await self.llm.bind(stop=stop, **self.llm_kwargs).abatch(
                    cast(List, prompts), {"callbacks": callbacks}
                )
                generations: List[List[Generation]] = []
                for res in results:
                    if isinstance(res, BaseMessage):
                        generations.append([ChatGeneration(message=res)])
                    else:
                        generations.append([Generation(text=res)])
                return LLMResult(generations=generations)

        def prep_prompts(
            self,
            input_list: List[Dict[str, Any]],
            run_manager: Optional[CallbackManagerForChainRun] = None,
        ) -> Tuple[List[PromptValue], Optional[List[str]]]:
            """Prepare prompts from inputs."""
            stop = None
            if len(input_list) == 0:
                return [], stop
            if "stop" in input_list[0]:
                stop = input_list[0]["stop"]
            prompts = []
            for inputs in input_list:
                selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
                prompt = self.prompt.format_prompt(**selected_inputs)
                _colored_text = get_colored_text(prompt.to_string(), "green")
                _text = "Prompt after formatting:\n" + _colored_text
                if run_manager:
                    run_manager.on_text(_text, end="\n", verbose=self.verbose)
                if "stop" in inputs and inputs["stop"] != stop:
                    raise ValueError(
                        "If `stop` is present in any inputs, should be present in all."
                    )
                prompts.append(prompt)
            return prompts, stop

        async def aprep_prompts(
            self,
            input_list: List[Dict[str, Any]],
            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
        ) -> Tuple[List[PromptValue], Optional[List[str]]]:
            """Prepare prompts from inputs."""
            stop = None
            if len(input_list) == 0:
                return [], stop
            if "stop" in input_list[0]:
                stop = input_list[0]["stop"]
            prompts = []
            for inputs in input_list:
                selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
                prompt = self.prompt.format_prompt(**selected_inputs)
                _colored_text = get_colored_text(prompt.to_string(), "green")
                _text = "Prompt after formatting:\n" + _colored_text
                if run_manager:
                    await run_manager.on_text(_text, end="\n", verbose=self.verbose)
                if "stop" in inputs and inputs["stop"] != stop:
                    raise ValueError(
                        "If `stop` is present in any inputs, should be present in all."
                    )
                prompts.append(prompt)
            return prompts, stop

        def apply(
            self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
        ) -> List[Dict[str, str]]:
            """Utilize the LLM generate method for speed gains."""
            callback_manager = CallbackManager.configure(
                callbacks, self.callbacks, self.verbose
            )
            run_manager = callback_manager.on_chain_start(
                dumpd(self),
                {"input_list": input_list},
            )
            try:
                response = self.generate(input_list, run_manager=run_manager)
            except BaseException as e:
                run_manager.on_chain_error(e)
                raise e
            outputs = self.create_outputs(response)
            run_manager.on_chain_end({"outputs": outputs})
            return outputs

        async def aapply(
            self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
        ) -> List[Dict[str, str]]:
            """Utilize the LLM generate method for speed gains."""
            callback_manager = AsyncCallbackManager.configure(
                callbacks, self.callbacks, self.verbose
            )
            run_manager = await callback_manager.on_chain_start(
                dumpd(self),
                {"input_list": input_list},
            )
            try:
                response = await self.agenerate(input_list, run_manager=run_manager)
            except BaseException as e:
                await run_manager.on_chain_error(e)
                raise e
            outputs = self.create_outputs(response)
            await run_manager.on_chain_end({"outputs": outputs})
            return outputs

        @property
        def _run_output_key(self) -> str:
            return self.output_key

        def create_outputs(self, llm_result: LLMResult) -> List[Dict[str, Any]]:
            """Create outputs from response."""
            result = [
                # Get the text of the top generated string.
                {
                    self.output_key: self.output_parser.parse_result(generation),
                    "full_generation": generation,
                }
                for generation in llm_result.generations
            ]
            if self.return_final_only:
                result = [{self.output_key: r[self.output_key]} for r in result]
            return result

        async def _acall(
            self,
            inputs: Dict[str, Any],
            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
        ) -> Dict[str, str]:
            response = await self.agenerate([inputs], run_manager=run_manager)
            return self.create_outputs(response)[0]

        def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
            """Format prompt with kwargs and pass to LLM.

            Args:
                callbacks: Callbacks to pass to LLMChain
                **kwargs: Keys to pass to prompt template.

            Returns:
                Completion from LLM.

            Example:
                .. code-block:: python

                    completion = llm.predict(adjective="funny")
            """
            return self(kwargs, callbacks=callbacks)[self.output_key]

        async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
            """Format prompt with kwargs and pass to LLM.

            Args:
                callbacks: Callbacks to pass to LLMChain
                **kwargs: Keys to pass to prompt template.

            Returns:
                Completion from LLM.

            Example:
                .. code-block:: python

                    completion = llm.predict(adjective="funny")
            """
            return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]

        def predict_and_parse(
            self, callbacks: Callbacks = None, **kwargs: Any
        ) -> Union[str, List[str], Dict[str, Any]]:
            """Call predict and then parse the results."""
            warnings.warn(
                "The predict_and_parse method is deprecated, "
                "instead pass an output parser directly to LLMChain."
            )
            result = self.predict(callbacks=callbacks, **kwargs)
            if self.prompt.output_parser is not None:
                return self.prompt.output_parser.parse(result)
            else:
                return result

        async def apredict_and_parse(
            self, callbacks: Callbacks = None, **kwargs: Any
        ) -> Union[str, List[str], Dict[str, str]]:
            """Call apredict and then parse the results."""
            warnings.warn(
                "The apredict_and_parse method is deprecated, "
                "instead pass an output parser directly to LLMChain."
            )
            result = await self.apredict(callbacks=callbacks, **kwargs)
            if self.prompt.output_parser is not None:
                return self.prompt.output_parser.parse(result)
            else:
                return result

        def apply_and_parse(
            self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
        ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
            """Call apply and then parse the results."""
            warnings.warn(
                "The apply_and_parse method is deprecated, "
                "instead pass an output parser directly to LLMChain."
            )
            result = self.apply(input_list, callbacks=callbacks)
            return self._parse_generation(result)

        def _parse_generation(
            self, generation: List[Dict[str, str]]
        ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
            if self.prompt.output_parser is not None:
                return [
                    self.prompt.output_parser.parse(res[self.output_key])
                    for res in generation
                ]
            else:
                return generation

        async def aapply_and_parse(
            self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
        ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
            """Call apply and then parse the results."""
            warnings.warn(
                "The aapply_and_parse method is deprecated, "
                "instead pass an output parser directly to LLMChain."
            )
            result = await self.aapply(input_list, callbacks=callbacks)
            return self._parse_generation(result)

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

        @classmethod
        def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain:
            """Create LLMChain from LLM and template."""
            prompt_template = PromptTemplate.from_template(template)
            return cls(llm=llm, prompt=prompt_template)

        def _get_num_tokens(self, text: str) -> int:
            return _get_language_model(self.llm).get_num_tokens(text)

    def _get_language_model(llm_like: Runnable) -> BaseLanguageModel:
        if isinstance(llm_like, BaseLanguageModel):
            return llm_like
        elif isinstance(llm_like, RunnableBinding):
            return _get_language_model(llm_like.bound)
        elif isinstance(llm_like, RunnableWithFallbacks):
            return _get_language_model(llm_like.runnable)
        elif isinstance(llm_like, (RunnableBranch, DynamicRunnable)):
            return _get_language_model(llm_like.default)
        else:
            raise ValueError(
                f"Unable to extract BaseLanguageModel from llm_like object of type "
                f"{type(llm_like)}"
            )