code: """Base interface for large language models to expose."""
  import inspect
  import json
  import warnings
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
  
  import yaml
  from pydantic import Extra, Field, root_validator, validator
  
  import langchain
  from langchain.base_language import BaseLanguageModel
  from langchain.callbacks.base import BaseCallbackManager
  from langchain.callbacks.manager import (
      AsyncCallbackManager,
      AsyncCallbackManagerForLLMRun,
      CallbackManager,
      CallbackManagerForLLMRun,
      Callbacks,
  )
  from langchain.schema import (
      AIMessage,
      BaseMessage,
      Generation,
      LLMResult,
      PromptValue,
      get_buffer_string,
  )
  
  
  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, 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."""
      callbacks: Callbacks = Field(default=None, exclude=True)
      callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
  
      class Config:
          """Configuration for this pydantic object."""
  
          extra = Extra.forbid
          arbitrary_types_allowed = True
  
      @root_validator()
      def raise_deprecation(cls, values: Dict) -> Dict:
          """Raise deprecation warning if callback_manager is used."""
          if values.get("callback_manager") is not None:
              warnings.warn(
                  "callback_manager is deprecated. Please use callbacks instead.",
                  DeprecationWarning,
              )
              values["callbacks"] = values.pop("callback_manager", None)
          return values
  
      @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,
          run_manager: Optional[CallbackManagerForLLMRun] = None,
      ) -> LLMResult:
          """Run the LLM on the given prompts."""
  
      @abstractmethod
      async def _agenerate(
          self,
          prompts: List[str],
          stop: Optional[List[str]] = None,
          run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
      ) -> LLMResult:
          """Run the LLM on the given prompts."""
  
      def generate_prompt(
          self,
          prompts: List[PromptValue],
          stop: Optional[List[str]] = None,
          callbacks: Callbacks = None,
      ) -> LLMResult:
          prompt_strings = [p.to_string() for p in prompts]
          return self.generate(prompt_strings, stop=stop, callbacks=callbacks)
  
      async def agenerate_prompt(
          self,
          prompts: List[PromptValue],
          stop: Optional[List[str]] = None,
          callbacks: Callbacks = None,
      ) -> LLMResult:
          prompt_strings = [p.to_string() for p in prompts]
          return await self.agenerate(prompt_strings, stop=stop, callbacks=callbacks)
  
      def generate(
          self,
          prompts: List[str],
          stop: Optional[List[str]] = None,
          callbacks: Callbacks = 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)}."
              )
          params = self.dict()
          params["stop"] = stop
          (
              existing_prompts,
              llm_string,
              missing_prompt_idxs,
              missing_prompts,
          ) = get_prompts(params, prompts)
          disregard_cache = self.cache is not None and not self.cache
          callback_manager = CallbackManager.configure(
              callbacks, self.callbacks, self.verbose
          )
          new_arg_supported = inspect.signature(self._generate).parameters.get(
              "run_manager"
          )
          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`."
                  )
              run_manager = callback_manager.on_llm_start(
                  {"name": self.__class__.__name__}, prompts, invocation_params=params
              )
              try:
                  output = (
                      self._generate(prompts, stop=stop, run_manager=run_manager)
                      if new_arg_supported
                      else self._generate(prompts, stop=stop)
                  )
              except (KeyboardInterrupt, Exception) as e:
                  run_manager.on_llm_error(e)
                  raise e
              run_manager.on_llm_end(output)
              return output
          if len(missing_prompts) > 0:
              run_manager = callback_manager.on_llm_start(
                  {"name": self.__class__.__name__},
                  missing_prompts,
                  invocation_params=params,
              )
              try:
                  new_results = (
                      self._generate(missing_prompts, stop=stop, run_manager=run_manager)
                      if new_arg_supported
                      else self._generate(missing_prompts, stop=stop)
                  )
              except (KeyboardInterrupt, Exception) as e:
                  run_manager.on_llm_error(e)
                  raise e
              run_manager.on_llm_end(new_results)
              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,
          callbacks: Callbacks = None,
      ) -> LLMResult:
          """Run the LLM on the given prompt and input."""
          params = self.dict()
          params["stop"] = stop
          (
              existing_prompts,
              llm_string,
              missing_prompt_idxs,
              missing_prompts,
          ) = get_prompts(params, prompts)
          disregard_cache = self.cache is not None and not self.cache
          callback_manager = AsyncCallbackManager.configure(
              callbacks, self.callbacks, self.verbose
          )
          new_arg_supported = inspect.signature(self._agenerate).parameters.get(
              "run_manager"
          )
          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`."
                  )
              run_manager = await callback_manager.on_llm_start(
                  {"name": self.__class__.__name__}, prompts, invocation_params=params
              )
              try:
                  output = (
                      await self._agenerate(prompts, stop=stop, run_manager=run_manager)
                      if new_arg_supported
                      else await self._agenerate(prompts, stop=stop)
                  )
              except (KeyboardInterrupt, Exception) as e:
                  await run_manager.on_llm_error(e, verbose=self.verbose)
                  raise e
              await run_manager.on_llm_end(output, verbose=self.verbose)
              return output
          if len(missing_prompts) > 0:
              run_manager = await callback_manager.on_llm_start(
                  {"name": self.__class__.__name__},
                  missing_prompts,
                  invocation_params=params,
              )
              try:
                  new_results = (
                      await self._agenerate(
                          missing_prompts, stop=stop, run_manager=run_manager
                      )
                      if new_arg_supported
                      else await self._agenerate(missing_prompts, stop=stop)
                  )
              except (KeyboardInterrupt, Exception) as e:
                  await run_manager.on_llm_error(e)
                  raise e
              await run_manager.on_llm_end(new_results)
              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, callbacks: Callbacks = None
      ) -> str:
          """Check Cache and run the LLM on the given prompt and input."""
          if not isinstance(prompt, str):
              raise ValueError(
                  "Argument `prompt` is expected to be a string. Instead found "
                  f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
                  "`generate` instead."
              )
          return (
              self.generate([prompt], stop=stop, callbacks=callbacks)
              .generations[0][0]
              .text
          )
  
      async def _call_async(
          self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None
      ) -> str:
          """Check Cache and run the LLM on the given prompt and input."""
          result = await self.agenerate([prompt], stop=stop, callbacks=callbacks)
          return result.generations[0][0].text
  
      def predict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
          if stop is None:
              _stop = None
          else:
              _stop = list(stop)
          return self(text, stop=_stop)
  
      def predict_messages(
          self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
      ) -> BaseMessage:
          text = get_buffer_string(messages)
          if stop is None:
              _stop = None
          else:
              _stop = list(stop)
          content = self(text, stop=_stop)
          return AIMessage(content=content)
  
      async def apredict(self, text: str, *, stop: Optional[Sequence[str]] = None) -> str:
          if stop is None:
              _stop = None
          else:
              _stop = list(stop)
          return await self._call_async(text, stop=_stop)
  
      async def apredict_messages(
          self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None
      ) -> BaseMessage:
          text = get_buffer_string(messages)
          if stop is None:
              _stop = None
          else:
              _stop = list(stop)
          content = await self._call_async(text, stop=_stop)
          return AIMessage(content=content)
  
      @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,
          run_manager: Optional[CallbackManagerForLLMRun] = None,
      ) -> str:
          """Run the LLM on the given prompt and input."""
  
      async def _acall(
          self,
          prompt: str,
          stop: Optional[List[str]] = None,
          run_manager: Optional[AsyncCallbackManagerForLLMRun] = 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,
          run_manager: Optional[CallbackManagerForLLMRun] = None,
      ) -> LLMResult:
          """Run the LLM on the given prompt and input."""
          # TODO: add caching here.
          generations = []
          new_arg_supported = inspect.signature(self._call).parameters.get("run_manager")
          for prompt in prompts:
              text = (
                  self._call(prompt, stop=stop, run_manager=run_manager)
                  if new_arg_supported
                  else 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,
          run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
      ) -> LLMResult:
          """Run the LLM on the given prompt and input."""
          generations = []
          new_arg_supported = inspect.signature(self._acall).parameters.get("run_manager")
          for prompt in prompts:
              text = (
                  await self._acall(prompt, stop=stop, run_manager=run_manager)
                  if new_arg_supported
                  else await self._acall(prompt, stop=stop)
              )
              generations.append([Generation(text=text)])
          return LLMResult(generations=generations)
  
apis: langchain.llm_cache.lookup langchain.schema.Generation langchain.schema.AIMessage langchain.llm_cache.update langchain.callbacks.manager.AsyncCallbackManager.configure langchain.callbacks.manager.CallbackManager.configure langchain.schema.LLMResult langchain.schema.get_buffer_string
extract_api: [((2302, 2339), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2307, 2339), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2413, 2446), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (2418, 2446), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2501, 2534), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (2506, 2534), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2683, 2699), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (2697, 2699), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((3135, 3178), 'pydantic.validator', 'validator', (['"""verbose"""'], {'pre': '(True)', 'always': '(True)'}), "('verbose', pre=True, always=True)\n", (3144, 3178), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((5507, 5573), 'langchain.callbacks.manager.CallbackManager.configure', 'CallbackManager.configure', (['callbacks', 'self.callbacks', 'self.verbose'], {}), '(callbacks, self.callbacks, self.verbose)\n', (5532, 5573), False, 'from langchain.callbacks.manager import AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks\n'), ((7567, 7624), 'langchain.schema.LLMResult', 'LLMResult', ([], {'generations': 'generations', 'llm_output': 'llm_output'}), '(generations=generations, llm_output=llm_output)\n', (7576, 7624), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((8170, 8241), 'langchain.callbacks.manager.AsyncCallbackManager.configure', 'AsyncCallbackManager.configure', (['callbacks', 'self.callbacks', 'self.verbose'], {}), '(callbacks, self.callbacks, self.verbose)\n', (8200, 8241), False, 'from langchain.callbacks.manager import AsyncCallbackManager, AsyncCallbackManagerForLLMRun, CallbackManager, CallbackManagerForLLMRun, Callbacks\n'), ((10390, 10447), 'langchain.schema.LLMResult', 'LLMResult', ([], {'generations': 'generations', 'llm_output': 'llm_output'}), '(generations=generations, llm_output=llm_output)\n', (10399, 10447), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((11768, 11795), 'langchain.schema.get_buffer_string', 'get_buffer_string', (['messages'], {}), '(messages)\n', (11785, 11795), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((11947, 11973), 'langchain.schema.AIMessage', 'AIMessage', ([], {'content': 'content'}), '(content=content)\n', (11956, 11973), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((12369, 12396), 'langchain.schema.get_buffer_string', 'get_buffer_string', (['messages'], {}), '(messages)\n', (12386, 12396), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((12566, 12592), 'langchain.schema.AIMessage', 'AIMessage', ([], {'content': 'content'}), '(content=content)\n', (12575, 12592), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((15773, 15807), 'langchain.schema.LLMResult', 'LLMResult', ([], {'generations': 'generations'}), '(generations=generations)\n', (15782, 15807), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((16489, 16523), 'langchain.schema.LLMResult', 'LLMResult', ([], {'generations': 'generations'}), '(generations=generations)\n', (16498, 16523), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((1235, 1281), 'langchain.llm_cache.lookup', 'langchain.llm_cache.lookup', (['prompt', 'llm_string'], {}), '(prompt, llm_string)\n', (1261, 1281), False, 'import langchain\n'), ((2023, 2077), 'langchain.llm_cache.update', 'langchain.llm_cache.update', (['prompt', 'llm_string', 'result'], {}), '(prompt, llm_string, result)\n', (2049, 2077), False, 'import langchain\n'), ((2890, 2992), 'warnings.warn', 'warnings.warn', (['"""callback_manager is deprecated. Please use callbacks instead."""', 'DeprecationWarning'], {}), "('callback_manager is deprecated. Please use callbacks instead.',\n    DeprecationWarning)\n", (2903, 2992), False, 'import warnings\n'), ((13642, 13657), 'pathlib.Path', 'Path', (['file_path'], {}), '(file_path)\n', (13646, 13657), False, 'from pathlib import Path\n'), ((13978, 14013), 'json.dump', 'json.dump', (['prompt_dict', 'f'], {'indent': '(4)'}), '(prompt_dict, f, indent=4)\n', (13987, 14013), False, 'import json\n'), ((5624, 5657), 'inspect.signature', 'inspect.signature', (['self._generate'], {}), '(self._generate)\n', (5641, 5657), False, 'import inspect\n'), ((8292, 8326), 'inspect.signature', 'inspect.signature', (['self._agenerate'], {}), '(self._agenerate)\n', (8309, 8326), False, 'import inspect\n'), ((14116, 14167), 'yaml.dump', 'yaml.dump', (['prompt_dict', 'f'], {'default_flow_style': '(False)'}), '(prompt_dict, f, default_flow_style=False)\n', (14125, 14167), False, 'import yaml\n'), ((15417, 15446), 'inspect.signature', 'inspect.signature', (['self._call'], {}), '(self._call)\n', (15434, 15446), False, 'import inspect\n'), ((15734, 15755), 'langchain.schema.Generation', 'Generation', ([], {'text': 'text'}), '(text=text)\n', (15744, 15755), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n'), ((16118, 16148), 'inspect.signature', 'inspect.signature', (['self._acall'], {}), '(self._acall)\n', (16135, 16148), False, 'import inspect\n'), ((16450, 16471), 'langchain.schema.Generation', 'Generation', ([], {'text': 'text'}), '(text=text)\n', (16460, 16471), False, 'from langchain.schema import AIMessage, BaseMessage, Generation, LLMResult, PromptValue, get_buffer_string\n')]