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285 lines
10 KiB
Python
285 lines
10 KiB
Python
"""Chain that just formats a prompt and calls an LLM."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
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from pydantic import Extra
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import (
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AsyncCallbackManager,
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AsyncCallbackManagerForChainRun,
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CallbackManager,
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CallbackManagerForChainRun,
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Callbacks,
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)
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from langchain.chains.base import Chain
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from langchain.input import get_colored_text
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import LLMResult, PromptValue
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class LLMChain(Chain):
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"""Chain to run queries against LLMs.
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Example:
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.. code-block:: python
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from langchain import LLMChain, OpenAI, PromptTemplate
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prompt_template = "Tell me a {adjective} joke"
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prompt = PromptTemplate(
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input_variables=["adjective"], template=prompt_template
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)
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llm = LLMChain(llm=OpenAI(), prompt=prompt)
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"""
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prompt: BasePromptTemplate
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"""Prompt object to use."""
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llm: BaseLanguageModel
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output_key: str = "text" #: :meta private:
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Will be whatever keys the prompt expects.
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:meta private:
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"""
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return self.prompt.input_variables
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@property
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def output_keys(self) -> List[str]:
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"""Will always return text key.
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:meta private:
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"""
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return [self.output_key]
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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response = self.generate([inputs], run_manager=run_manager)
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return self.create_outputs(response)[0]
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def generate(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = self.prep_prompts(input_list, run_manager=run_manager)
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return self.llm.generate_prompt(
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prompts, stop, callbacks=run_manager.get_child() if run_manager else None
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)
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async def agenerate(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> LLMResult:
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"""Generate LLM result from inputs."""
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prompts, stop = await self.aprep_prompts(input_list)
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return await self.llm.agenerate_prompt(
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prompts, stop, callbacks=run_manager.get_child() if run_manager else None
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)
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def prep_prompts(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Tuple[List[PromptValue], Optional[List[str]]]:
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"""Prepare prompts from inputs."""
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stop = None
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if "stop" in input_list[0]:
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stop = input_list[0]["stop"]
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prompts = []
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for inputs in input_list:
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
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prompt = self.prompt.format_prompt(**selected_inputs)
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_colored_text = get_colored_text(prompt.to_string(), "green")
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_text = "Prompt after formatting:\n" + _colored_text
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if run_manager:
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run_manager.on_text(_text, end="\n", verbose=self.verbose)
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if "stop" in inputs and inputs["stop"] != stop:
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raise ValueError(
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"If `stop` is present in any inputs, should be present in all."
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)
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prompts.append(prompt)
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return prompts, stop
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async def aprep_prompts(
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self,
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input_list: List[Dict[str, Any]],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Tuple[List[PromptValue], Optional[List[str]]]:
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"""Prepare prompts from inputs."""
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stop = None
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if "stop" in input_list[0]:
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stop = input_list[0]["stop"]
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prompts = []
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for inputs in input_list:
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selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
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prompt = self.prompt.format_prompt(**selected_inputs)
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_colored_text = get_colored_text(prompt.to_string(), "green")
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_text = "Prompt after formatting:\n" + _colored_text
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if run_manager:
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await run_manager.on_text(_text, end="\n", verbose=self.verbose)
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if "stop" in inputs and inputs["stop"] != stop:
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raise ValueError(
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"If `stop` is present in any inputs, should be present in all."
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)
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prompts.append(prompt)
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return prompts, stop
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def apply(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> List[Dict[str, str]]:
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"""Utilize the LLM generate method for speed gains."""
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callback_manager = CallbackManager.configure(
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callbacks, self.callbacks, self.verbose
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)
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run_manager = callback_manager.on_chain_start(
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{"name": self.__class__.__name__},
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{"input_list": input_list},
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)
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try:
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response = self.generate(input_list, run_manager=run_manager)
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except (KeyboardInterrupt, Exception) as e:
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run_manager.on_chain_error(e)
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raise e
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outputs = self.create_outputs(response)
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run_manager.on_chain_end({"outputs": outputs})
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return outputs
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async def aapply(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> List[Dict[str, str]]:
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"""Utilize the LLM generate method for speed gains."""
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callback_manager = AsyncCallbackManager.configure(
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callbacks, self.callbacks, self.verbose
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)
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run_manager = await callback_manager.on_chain_start(
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{"name": self.__class__.__name__},
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{"input_list": input_list},
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)
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try:
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response = await self.agenerate(input_list, run_manager=run_manager)
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except (KeyboardInterrupt, Exception) as e:
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await run_manager.on_chain_error(e)
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raise e
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outputs = self.create_outputs(response)
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await run_manager.on_chain_end({"outputs": outputs})
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return outputs
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def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
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"""Create outputs from response."""
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return [
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# Get the text of the top generated string.
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{self.output_key: generation[0].text}
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for generation in response.generations
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]
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async def _acall(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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response = await self.agenerate([inputs], run_manager=run_manager)
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return self.create_outputs(response)[0]
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def predict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
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"""Format prompt with kwargs and pass to LLM.
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Args:
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callbacks: Callbacks to pass to LLMChain
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**kwargs: Keys to pass to prompt template.
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Returns:
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Completion from LLM.
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Example:
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.. code-block:: python
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completion = llm.predict(adjective="funny")
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"""
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return self(kwargs, callbacks=callbacks)[self.output_key]
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async def apredict(self, callbacks: Callbacks = None, **kwargs: Any) -> str:
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"""Format prompt with kwargs and pass to LLM.
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Args:
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callbacks: Callbacks to pass to LLMChain
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**kwargs: Keys to pass to prompt template.
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Returns:
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Completion from LLM.
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Example:
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.. code-block:: python
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completion = llm.predict(adjective="funny")
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"""
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return (await self.acall(kwargs, callbacks=callbacks))[self.output_key]
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def predict_and_parse(
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self, callbacks: Callbacks = None, **kwargs: Any
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) -> Union[str, List[str], Dict[str, str]]:
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"""Call predict and then parse the results."""
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result = self.predict(callbacks=callbacks, **kwargs)
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if self.prompt.output_parser is not None:
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return self.prompt.output_parser.parse(result)
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else:
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return result
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async def apredict_and_parse(
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self, callbacks: Callbacks = None, **kwargs: Any
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) -> Union[str, List[str], Dict[str, str]]:
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"""Call apredict and then parse the results."""
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result = await self.apredict(callbacks=callbacks, **kwargs)
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if self.prompt.output_parser is not None:
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return self.prompt.output_parser.parse(result)
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else:
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return result
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def apply_and_parse(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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"""Call apply and then parse the results."""
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result = self.apply(input_list, callbacks=callbacks)
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return self._parse_result(result)
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def _parse_result(
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self, result: List[Dict[str, str]]
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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if self.prompt.output_parser is not None:
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return [
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self.prompt.output_parser.parse(res[self.output_key]) for res in result
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]
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else:
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return result
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async def aapply_and_parse(
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self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
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) -> Sequence[Union[str, List[str], Dict[str, str]]]:
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"""Call apply and then parse the results."""
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result = await self.aapply(input_list, callbacks=callbacks)
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return self._parse_result(result)
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@property
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def _chain_type(self) -> str:
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return "llm_chain"
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@classmethod
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def from_string(cls, llm: BaseLanguageModel, template: str) -> Chain:
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"""Create LLMChain from LLM and template."""
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prompt_template = PromptTemplate.from_template(template)
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return cls(llm=llm, prompt=prompt_template)
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