433 lines
15 KiB
Python
433 lines
15 KiB
Python
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"""Chain that just formats a prompt and calls an LLM."""
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from __future__ import annotations
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import warnings
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from collections.abc import Sequence
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from typing import Any, cast
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from langchain_core._api import deprecated
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from langchain_core.callbacks 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_core.language_models import (
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BaseLanguageModel,
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LanguageModelInput,
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)
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from langchain_core.messages import BaseMessage
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from langchain_core.output_parsers import BaseLLMOutputParser, StrOutputParser
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from langchain_core.outputs import ChatGeneration, Generation, LLMResult
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from langchain_core.prompt_values import PromptValue
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from langchain_core.prompts import BasePromptTemplate, PromptTemplate
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from langchain_core.runnables import (
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Runnable,
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RunnableBinding,
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RunnableBranch,
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RunnableWithFallbacks,
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)
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from langchain_core.runnables.configurable import DynamicRunnable
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from langchain_core.utils.input import get_colored_text
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from pydantic import ConfigDict, Field
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from typing_extensions import override
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from langchain_classic.chains.base import Chain
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@deprecated(
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since="0.1.17",
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alternative="RunnableSequence, e.g., `prompt | llm`",
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removal="1.0",
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)
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class LLMChain(Chain):
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"""Chain to run queries against LLMs.
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This class is deprecated. See below for an example implementation using
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LangChain runnables:
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```python
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import PromptTemplate
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from langchain_openai import OpenAI
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prompt_template = "Tell me a {adjective} joke"
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prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
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model = OpenAI()
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chain = prompt | model | StrOutputParser()
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chain.invoke("your adjective here")
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```
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Example:
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```python
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from langchain_classic.chains import LLMChain
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from langchain_openai import OpenAI
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from langchain_core.prompts import PromptTemplate
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prompt_template = "Tell me a {adjective} joke"
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prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
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model = LLMChain(llm=OpenAI(), prompt=prompt)
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```
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"""
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@classmethod
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@override
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def is_lc_serializable(cls) -> bool:
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return True
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prompt: BasePromptTemplate
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"""Prompt object to use."""
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llm: Runnable[LanguageModelInput, str] | Runnable[LanguageModelInput, BaseMessage]
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"""Language model to call."""
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output_key: str = "text"
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output_parser: BaseLLMOutputParser = Field(default_factory=StrOutputParser)
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"""Output parser to use.
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Defaults to one that takes the most likely string but does not change it
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otherwise."""
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return_final_only: bool = True
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"""Whether to return only the final parsed result.
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If `False`, will return a bunch of extra information about the generation."""
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llm_kwargs: dict = Field(default_factory=dict)
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model_config = ConfigDict(
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arbitrary_types_allowed=True,
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extra="forbid",
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)
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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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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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if self.return_final_only:
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return [self.output_key]
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return [self.output_key, "full_generation"]
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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: CallbackManagerForChainRun | None = 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: CallbackManagerForChainRun | None = 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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callbacks = run_manager.get_child() if run_manager else None
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if isinstance(self.llm, BaseLanguageModel):
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return self.llm.generate_prompt(
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prompts,
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stop,
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callbacks=callbacks,
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**self.llm_kwargs,
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)
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results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
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cast("list", prompts),
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{"callbacks": callbacks},
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)
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generations: list[list[Generation]] = []
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for res in results:
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if isinstance(res, BaseMessage):
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generations.append([ChatGeneration(message=res)])
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else:
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generations.append([Generation(text=res)])
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return LLMResult(generations=generations)
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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: AsyncCallbackManagerForChainRun | None = 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, run_manager=run_manager)
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callbacks = run_manager.get_child() if run_manager else None
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if isinstance(self.llm, BaseLanguageModel):
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return await self.llm.agenerate_prompt(
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prompts,
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stop,
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callbacks=callbacks,
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**self.llm_kwargs,
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)
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results = await self.llm.bind(stop=stop, **self.llm_kwargs).abatch(
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cast("list", prompts),
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{"callbacks": callbacks},
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)
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generations: list[list[Generation]] = []
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for res in results:
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if isinstance(res, BaseMessage):
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generations.append([ChatGeneration(message=res)])
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else:
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generations.append([Generation(text=res)])
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return LLMResult(generations=generations)
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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: CallbackManagerForChainRun | None = None,
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) -> tuple[list[PromptValue], list[str] | None]:
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"""Prepare prompts from inputs."""
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stop = None
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if len(input_list) == 0:
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return [], stop
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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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msg = "If `stop` is present in any inputs, should be present in all."
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raise ValueError(msg)
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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: AsyncCallbackManagerForChainRun | None = None,
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) -> tuple[list[PromptValue], list[str] | None]:
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"""Prepare prompts from inputs."""
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stop = None
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if len(input_list) == 0:
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return [], stop
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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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msg = "If `stop` is present in any inputs, should be present in all."
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raise ValueError(msg)
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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,
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input_list: list[dict[str, Any]],
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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,
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self.callbacks,
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self.verbose,
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)
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run_manager = callback_manager.on_chain_start(
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None,
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{"input_list": input_list},
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name=self.get_name(),
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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 BaseException as e:
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run_manager.on_chain_error(e)
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raise
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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,
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input_list: list[dict[str, Any]],
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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,
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self.callbacks,
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self.verbose,
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)
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run_manager = await callback_manager.on_chain_start(
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None,
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{"input_list": input_list},
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name=self.get_name(),
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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 BaseException as e:
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await run_manager.on_chain_error(e)
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raise
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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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@property
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def _run_output_key(self) -> str:
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return self.output_key
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def create_outputs(self, llm_result: LLMResult) -> list[dict[str, Any]]:
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"""Create outputs from response."""
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result = [
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# Get the text of the top generated string.
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{
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self.output_key: self.output_parser.parse_result(generation),
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"full_generation": generation,
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}
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for generation in llm_result.generations
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]
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if self.return_final_only:
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result = [{self.output_key: r[self.output_key]} for r in result]
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return result
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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: AsyncCallbackManagerForChainRun | None = 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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```python
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completion = llm.predict(adjective="funny")
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```
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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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```python
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completion = llm.predict(adjective="funny")
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```
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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,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> str | list[str] | dict[str, Any]:
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"""Call predict and then parse the results."""
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warnings.warn(
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"The predict_and_parse method is deprecated, "
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"instead pass an output parser directly to LLMChain.",
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stacklevel=2,
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)
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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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return result
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async def apredict_and_parse(
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self,
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> str | list[str] | dict[str, str]:
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"""Call apredict and then parse the results."""
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warnings.warn(
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"The apredict_and_parse method is deprecated, "
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"instead pass an output parser directly to LLMChain.",
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stacklevel=2,
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)
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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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return result
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def apply_and_parse(
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self,
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input_list: list[dict[str, Any]],
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callbacks: Callbacks = None,
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) -> Sequence[str | list[str] | dict[str, str]]:
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"""Call apply and then parse the results."""
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warnings.warn(
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"The apply_and_parse method is deprecated, "
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"instead pass an output parser directly to LLMChain.",
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stacklevel=2,
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)
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result = self.apply(input_list, callbacks=callbacks)
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return self._parse_generation(result)
|
||
|
|
|
||
|
|
def _parse_generation(
|
||
|
|
self,
|
||
|
|
generation: list[dict[str, str]],
|
||
|
|
) -> Sequence[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
|
||
|
|
]
|
||
|
|
return generation
|
||
|
|
|
||
|
|
async def aapply_and_parse(
|
||
|
|
self,
|
||
|
|
input_list: list[dict[str, Any]],
|
||
|
|
callbacks: Callbacks = None,
|
||
|
|
) -> Sequence[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.",
|
||
|
|
stacklevel=2,
|
||
|
|
)
|
||
|
|
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
|
||
|
|
if isinstance(llm_like, RunnableBinding):
|
||
|
|
return _get_language_model(llm_like.bound)
|
||
|
|
if isinstance(llm_like, RunnableWithFallbacks):
|
||
|
|
return _get_language_model(llm_like.runnable)
|
||
|
|
if isinstance(llm_like, (RunnableBranch, DynamicRunnable)):
|
||
|
|
return _get_language_model(llm_like.default)
|
||
|
|
msg = (
|
||
|
|
f"Unable to extract BaseLanguageModel from llm_like object of type "
|
||
|
|
f"{type(llm_like)}"
|
||
|
|
)
|
||
|
|
raise ValueError(msg)
|