"""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
[docs]@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)
"""
[docs] @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]
[docs] 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)
[docs] 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)
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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]
[docs] 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]
[docs] 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]
[docs] 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
[docs] 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
[docs] 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
[docs] 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"
[docs] @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)}"
)