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"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
from langchain.callbacks.base import BaseCallbackManager
from langchain.schema import BaseLanguageModel, Generation, LLMResult, PromptValue
def _get_verbosity() -> bool:
return langchain.verbose
def get_prompts(
params: Dict[str, Any], prompts: List[str]
) -> Tuple[Dict[int, List], str, List[int], List[str]]:
"""Get prompts that are already cached."""
llm_string = str(sorted([(k, v) for k, v in params.items()]))
missing_prompts = []
missing_prompt_idxs = []
existing_prompts = {}
for i, prompt in enumerate(prompts):
if langchain.llm_cache is not None:
cache_val = langchain.llm_cache.lookup(prompt, llm_string)
if isinstance(cache_val, list):
existing_prompts[i] = cache_val
else:
missing_prompts.append(prompt)
missing_prompt_idxs.append(i)
return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts
def update_cache(
existing_prompts: Dict[int, List],
llm_string: str,
missing_prompt_idxs: List[int],
new_results: LLMResult,
prompts: List[str],
) -> Optional[dict]:
"""Update the cache and get the LLM output."""
for i, result in enumerate(new_results.generations):
existing_prompts[missing_prompt_idxs[i]] = result
prompt = prompts[missing_prompt_idxs[i]]
if langchain.llm_cache is not None:
langchain.llm_cache.update(prompt, llm_string, result)
llm_output = new_results.llm_output
return llm_output
class BaseLLM(BaseLanguageModel, BaseModel, ABC):
"""LLM wrapper should take in a prompt and return a string."""
cache: Optional[bool] = None
verbose: bool = Field(default_factory=_get_verbosity)
"""Whether to print out response text."""
callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@validator("callback_manager", pre=True, always=True)
def set_callback_manager(
cls, callback_manager: Optional[BaseCallbackManager]
) -> BaseCallbackManager:
"""If callback manager is None, set it.
This allows users to pass in None as callback manager, which is a nice UX.
"""
return callback_manager or get_callback_manager()
@validator("verbose", pre=True, always=True)
def set_verbose(cls, verbose: Optional[bool]) -> bool:
"""If verbose is None, set it.
This allows users to pass in None as verbose to access the global setting.
"""
if verbose is None:
return _get_verbosity()
else:
return verbose
@abstractmethod
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompts."""
@abstractmethod
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompts."""
def generate_prompt(
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
) -> LLMResult:
prompt_strings = [p.to_string() for p in prompts]
return self.generate(prompt_strings, stop=stop)
async def agenerate_prompt(
self, prompts: List[PromptValue], stop: Optional[List[str]] = None
) -> LLMResult:
prompt_strings = [p.to_string() for p in prompts]
return await self.agenerate(prompt_strings, stop=stop)
def generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
# If string is passed in directly no errors will be raised but outputs will
# not make sense.
if not isinstance(prompts, list):
raise ValueError(
"Argument 'prompts' is expected to be of type List[str], received"
f" argument of type {type(prompts)}."
)
disregard_cache = self.cache is not None and not self.cache
if langchain.llm_cache is None or disregard_cache:
# This happens when langchain.cache is None, but self.cache is True
if self.cache is not None and self.cache:
raise ValueError(
"Asked to cache, but no cache found at `langchain.cache`."
)
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompts, verbose=self.verbose
)
try:
output = self._generate(prompts, stop=stop)
except (KeyboardInterrupt, Exception) as e:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
self.callback_manager.on_llm_end(output, verbose=self.verbose)
return output
params = self.dict()
params["stop"] = stop
(
existing_prompts,
llm_string,
missing_prompt_idxs,
missing_prompts,
) = get_prompts(params, prompts)
if len(missing_prompts) > 0:
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, missing_prompts, verbose=self.verbose
)
try:
new_results = self._generate(missing_prompts, stop=stop)
except (KeyboardInterrupt, Exception) as e:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
self.callback_manager.on_llm_end(new_results, verbose=self.verbose)
llm_output = update_cache(
existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
)
else:
llm_output = {}
generations = [existing_prompts[i] for i in range(len(prompts))]
return LLMResult(generations=generations, llm_output=llm_output)
async def agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
disregard_cache = self.cache is not None and not self.cache
if langchain.llm_cache is None or disregard_cache:
# This happens when langchain.cache is None, but self.cache is True
if self.cache is not None and self.cache:
raise ValueError(
"Asked to cache, but no cache found at `langchain.cache`."
)
if self.callback_manager.is_async:
await self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompts, verbose=self.verbose
)
else:
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__}, prompts, verbose=self.verbose
)
try:
output = await self._agenerate(prompts, stop=stop)
except (KeyboardInterrupt, Exception) as e:
if self.callback_manager.is_async:
await self.callback_manager.on_llm_error(e, verbose=self.verbose)
else:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
if self.callback_manager.is_async:
await self.callback_manager.on_llm_end(output, verbose=self.verbose)
else:
self.callback_manager.on_llm_end(output, verbose=self.verbose)
return output
params = self.dict()
params["stop"] = stop
(
existing_prompts,
llm_string,
missing_prompt_idxs,
missing_prompts,
) = get_prompts(params, prompts)
if len(missing_prompts) > 0:
if self.callback_manager.is_async:
await self.callback_manager.on_llm_start(
{"name": self.__class__.__name__},
missing_prompts,
verbose=self.verbose,
)
else:
self.callback_manager.on_llm_start(
{"name": self.__class__.__name__},
missing_prompts,
verbose=self.verbose,
)
try:
new_results = await self._agenerate(missing_prompts, stop=stop)
except (KeyboardInterrupt, Exception) as e:
if self.callback_manager.is_async:
await self.callback_manager.on_llm_error(e, verbose=self.verbose)
else:
self.callback_manager.on_llm_error(e, verbose=self.verbose)
raise e
if self.callback_manager.is_async:
await self.callback_manager.on_llm_end(
new_results, verbose=self.verbose
)
else:
self.callback_manager.on_llm_end(new_results, verbose=self.verbose)
llm_output = update_cache(
existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts
)
else:
llm_output = {}
generations = [existing_prompts[i] for i in range(len(prompts))]
return LLMResult(generations=generations, llm_output=llm_output)
def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Check Cache and run the LLM on the given prompt and input."""
return self.generate([prompt], stop=stop).generations[0][0].text
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {}
def __str__(self) -> str:
"""Get a string representation of the object for printing."""
cls_name = f"\033[1m{self.__class__.__name__}\033[0m"
return f"{cls_name}\nParams: {self._identifying_params}"
@property
@abstractmethod
def _llm_type(self) -> str:
"""Return type of llm."""
def dict(self, **kwargs: Any) -> Dict:
"""Return a dictionary of the LLM."""
starter_dict = dict(self._identifying_params)
starter_dict["_type"] = self._llm_type
return starter_dict
def save(self, file_path: Union[Path, str]) -> None:
"""Save the LLM.
Args:
file_path: Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path="path/llm.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(prompt_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(file_path, "w") as f:
yaml.dump(prompt_dict, f, default_flow_style=False)
else:
raise ValueError(f"{save_path} must be json or yaml")
class LLM(BaseLLM):
"""LLM class that expect subclasses to implement a simpler call method.
The purpose of this class is to expose a simpler interface for working
with LLMs, rather than expect the user to implement the full _generate method.
"""
@abstractmethod
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Run the LLM on the given prompt and input."""
async def _acall(self, prompt: str, stop: Optional[List[str]] = None) -> str:
"""Run the LLM on the given prompt and input."""
raise NotImplementedError("Async generation not implemented for this LLM.")
def _generate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
# TODO: add caching here.
generations = []
for prompt in prompts:
text = self._call(prompt, stop=stop)
generations.append([Generation(text=text)])
return LLMResult(generations=generations)
async def _agenerate(
self, prompts: List[str], stop: Optional[List[str]] = None
) -> LLMResult:
"""Run the LLM on the given prompt and input."""
generations = []
for prompt in prompts:
text = await self._acall(prompt, stop=stop)
generations.append([Generation(text=text)])
return LLMResult(generations=generations)
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