forked from Archives/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
"""Chain that just formats a prompt and calls an LLM."""
|
|
from typing import Any, Dict, List, Union
|
|
|
|
from pydantic import BaseModel, Extra
|
|
|
|
from langchain.chains.base import Chain
|
|
from langchain.input import print_text
|
|
from langchain.llms.base import LLM
|
|
from langchain.prompts.base import BasePromptTemplate
|
|
|
|
|
|
class LLMChain(Chain, BaseModel):
|
|
"""Chain to run queries against LLMs.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain import LLMChain, OpenAI, PromptTemplate
|
|
prompt_template = "Tell me a {adjective} joke"
|
|
prompt = PromptTemplate(
|
|
input_variables=["adjective"], template=prompt_template
|
|
)
|
|
llm = LLMChain(llm=OpenAI(), prompt=prompt)
|
|
"""
|
|
|
|
prompt: BasePromptTemplate
|
|
"""Prompt object to use."""
|
|
llm: LLM
|
|
"""LLM wrapper to use."""
|
|
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]) -> Dict[str, str]:
|
|
selected_inputs = {k: inputs[k] for k in self.prompt.input_variables}
|
|
prompt = self.prompt.format(**selected_inputs)
|
|
if self.verbose:
|
|
print("Prompt after formatting:")
|
|
print_text(prompt, color="green", end="\n")
|
|
kwargs = {}
|
|
if "stop" in inputs:
|
|
kwargs["stop"] = inputs["stop"]
|
|
response = self.llm(prompt, **kwargs)
|
|
return {self.output_key: response}
|
|
|
|
def predict(self, **kwargs: Any) -> str:
|
|
"""Format prompt with kwargs and pass to LLM.
|
|
|
|
Args:
|
|
**kwargs: Keys to pass to prompt template.
|
|
|
|
Returns:
|
|
Completion from LLM.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
completion = llm.predict(adjective="funny")
|
|
"""
|
|
return self(kwargs)[self.output_key]
|
|
|
|
def predict_and_parse(self, **kwargs: Any) -> Union[str, List[str], Dict[str, str]]:
|
|
"""Call predict and then parse the results."""
|
|
result = self.predict(**kwargs)
|
|
if self.prompt.output_parser is not None:
|
|
return self.prompt.output_parser.parse(result)
|
|
else:
|
|
return result
|