Source code for langchain_core.language_models.llms

"""Base interface for large language models to expose."""

from __future__ import annotations

import asyncio
import functools
import inspect
import json
import logging
import uuid
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
)

import yaml
from tenacity import (
    RetryCallState,
    before_sleep_log,
    retry,
    retry_base,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential,
)

from langchain_core._api import deprecated
from langchain_core.caches import BaseCache
from langchain_core.callbacks import (
    AsyncCallbackManager,
    AsyncCallbackManagerForLLMRun,
    BaseCallbackManager,
    CallbackManager,
    CallbackManagerForLLMRun,
    Callbacks,
)
from langchain_core.globals import get_llm_cache
from langchain_core.language_models.base import BaseLanguageModel, LanguageModelInput
from langchain_core.load import dumpd
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    convert_to_messages,
    get_buffer_string,
)
from langchain_core.outputs import Generation, GenerationChunk, LLMResult, RunInfo
from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.runnables import RunnableConfig, ensure_config, get_config_list
from langchain_core.runnables.config import run_in_executor

logger = logging.getLogger(__name__)


@functools.lru_cache
def _log_error_once(msg: str) -> None:
    """Log an error once."""
    logger.error(msg)


[docs]def create_base_retry_decorator( error_types: List[Type[BaseException]], max_retries: int = 1, run_manager: Optional[ Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] ] = None, ) -> Callable[[Any], Any]: """Create a retry decorator for a given LLM and provided list of error types.""" _logging = before_sleep_log(logger, logging.WARNING) def _before_sleep(retry_state: RetryCallState) -> None: _logging(retry_state) if run_manager: if isinstance(run_manager, AsyncCallbackManagerForLLMRun): coro = run_manager.on_retry(retry_state) try: loop = asyncio.get_event_loop() if loop.is_running(): loop.create_task(coro) else: asyncio.run(coro) except Exception as e: _log_error_once(f"Error in on_retry: {e}") else: run_manager.on_retry(retry_state) return None min_seconds = 4 max_seconds = 10 # Wait 2^x * 1 second between each retry starting with # 4 seconds, then up to 10 seconds, then 10 seconds afterwards retry_instance: "retry_base" = retry_if_exception_type(error_types[0]) for error in error_types[1:]: retry_instance = retry_instance | retry_if_exception_type(error) return retry( reraise=True, stop=stop_after_attempt(max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), retry=retry_instance, before_sleep=_before_sleep, )
def _resolve_cache(cache: Union[BaseCache, bool, None]) -> Optional[BaseCache]: """Resolve the cache.""" if isinstance(cache, BaseCache): llm_cache = cache elif cache is None: llm_cache = get_llm_cache() elif cache is True: llm_cache = get_llm_cache() if llm_cache is None: raise ValueError( "No global cache was configured. Use `set_llm_cache`." "to set a global cache if you want to use a global cache." "Otherwise either pass a cache object or set cache to False/None" ) elif cache is False: llm_cache = None else: raise ValueError(f"Unsupported cache value {cache}") return llm_cache
[docs]def get_prompts( params: Dict[str, Any], prompts: List[str], cache: Optional[Union[BaseCache, bool, None]] = None, ) -> 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 = {} llm_cache = _resolve_cache(cache) for i, prompt in enumerate(prompts): if llm_cache: cache_val = 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
[docs]async def aget_prompts( params: Dict[str, Any], prompts: List[str], cache: Optional[Union[BaseCache, bool, None]] = None, ) -> Tuple[Dict[int, List], str, List[int], List[str]]: """Get prompts that are already cached. Async version.""" llm_string = str(sorted([(k, v) for k, v in params.items()])) missing_prompts = [] missing_prompt_idxs = [] existing_prompts = {} llm_cache = _resolve_cache(cache) for i, prompt in enumerate(prompts): if llm_cache: cache_val = await llm_cache.alookup(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
[docs]def update_cache( cache: Union[BaseCache, bool, None], 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.""" llm_cache = _resolve_cache(cache) for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if llm_cache is not None: llm_cache.update(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output
[docs]async def aupdate_cache( cache: Union[BaseCache, bool, None], 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. Async version""" llm_cache = _resolve_cache(cache) for i, result in enumerate(new_results.generations): existing_prompts[missing_prompt_idxs[i]] = result prompt = prompts[missing_prompt_idxs[i]] if llm_cache: await llm_cache.aupdate(prompt, llm_string, result) llm_output = new_results.llm_output return llm_output
[docs]class BaseLLM(BaseLanguageModel[str], ABC): """Base LLM abstract interface. It should take in a prompt and return a string.""" callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """[DEPRECATED]""" class Config: """Configuration for this pydantic object.""" 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 # --- Runnable methods --- @property def OutputType(self) -> Type[str]: """Get the input type for this runnable.""" return str def _convert_input(self, input: LanguageModelInput) -> PromptValue: if isinstance(input, PromptValue): return input elif isinstance(input, str): return StringPromptValue(text=input) elif isinstance(input, Sequence): return ChatPromptValue(messages=convert_to_messages(input)) else: raise ValueError( f"Invalid input type {type(input)}. " "Must be a PromptValue, str, or list of BaseMessages." )
[docs] def invoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any, ) -> str: config = ensure_config(config) return ( self.generate_prompt( [self._convert_input(input)], stop=stop, callbacks=config.get("callbacks"), tags=config.get("tags"), metadata=config.get("metadata"), run_name=config.get("run_name"), run_id=config.pop("run_id", None), **kwargs, ) .generations[0][0] .text )
[docs] async def ainvoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any, ) -> str: config = ensure_config(config) llm_result = await self.agenerate_prompt( [self._convert_input(input)], stop=stop, callbacks=config.get("callbacks"), tags=config.get("tags"), metadata=config.get("metadata"), run_name=config.get("run_name"), run_id=config.pop("run_id", None), **kwargs, ) return llm_result.generations[0][0].text
[docs] def batch( self, inputs: List[LanguageModelInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any, ) -> List[str]: if not inputs: return [] config = get_config_list(config, len(inputs)) max_concurrency = config[0].get("max_concurrency") if max_concurrency is None: try: llm_result = self.generate_prompt( [self._convert_input(input) for input in inputs], callbacks=[c.get("callbacks") for c in config], tags=[c.get("tags") for c in config], metadata=[c.get("metadata") for c in config], run_name=[c.get("run_name") for c in config], **kwargs, ) return [g[0].text for g in llm_result.generations] except Exception as e: if return_exceptions: return cast(List[str], [e for _ in inputs]) else: raise e else: batches = [ inputs[i : i + max_concurrency] for i in range(0, len(inputs), max_concurrency) ] config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc] return [ output for i, batch in enumerate(batches) for output in self.batch( batch, config=config[i * max_concurrency : (i + 1) * max_concurrency], return_exceptions=return_exceptions, **kwargs, ) ]
[docs] async def abatch( self, inputs: List[LanguageModelInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any, ) -> List[str]: if not inputs: return [] config = get_config_list(config, len(inputs)) max_concurrency = config[0].get("max_concurrency") if max_concurrency is None: try: llm_result = await self.agenerate_prompt( [self._convert_input(input) for input in inputs], callbacks=[c.get("callbacks") for c in config], tags=[c.get("tags") for c in config], metadata=[c.get("metadata") for c in config], run_name=[c.get("run_name") for c in config], **kwargs, ) return [g[0].text for g in llm_result.generations] except Exception as e: if return_exceptions: return cast(List[str], [e for _ in inputs]) else: raise e else: batches = [ inputs[i : i + max_concurrency] for i in range(0, len(inputs), max_concurrency) ] config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc] return [ output for i, batch in enumerate(batches) for output in await self.abatch( batch, config=config[i * max_concurrency : (i + 1) * max_concurrency], return_exceptions=return_exceptions, **kwargs, ) ]
[docs] def stream( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any, ) -> Iterator[str]: if type(self)._stream == BaseLLM._stream: # model doesn't implement streaming, so use default implementation yield self.invoke(input, config=config, stop=stop, **kwargs) else: prompt = self._convert_input(input).to_string() config = ensure_config(config) params = self.dict() params["stop"] = stop params = {**params, **kwargs} options = {"stop": stop} callback_manager = CallbackManager.configure( config.get("callbacks"), self.callbacks, self.verbose, config.get("tags"), self.tags, config.get("metadata"), self.metadata, ) (run_manager,) = callback_manager.on_llm_start( dumpd(self), [prompt], invocation_params=params, options=options, name=config.get("run_name"), run_id=config.pop("run_id", None), batch_size=1, ) generation: Optional[GenerationChunk] = None try: for chunk in self._stream( prompt, stop=stop, run_manager=run_manager, **kwargs ): yield chunk.text if generation is None: generation = chunk else: generation += chunk assert generation is not None except BaseException as e: run_manager.on_llm_error( e, response=LLMResult( generations=[[generation]] if generation else [] ), ) raise e else: run_manager.on_llm_end(LLMResult(generations=[[generation]]))
[docs] async def astream( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any, ) -> AsyncIterator[str]: if ( type(self)._astream is BaseLLM._astream and type(self)._stream is BaseLLM._stream ): yield await self.ainvoke(input, config=config, stop=stop, **kwargs) return prompt = self._convert_input(input).to_string() config = ensure_config(config) params = self.dict() params["stop"] = stop params = {**params, **kwargs} options = {"stop": stop} callback_manager = AsyncCallbackManager.configure( config.get("callbacks"), self.callbacks, self.verbose, config.get("tags"), self.tags, config.get("metadata"), self.metadata, ) (run_manager,) = await callback_manager.on_llm_start( dumpd(self), [prompt], invocation_params=params, options=options, name=config.get("run_name"), run_id=config.pop("run_id", None), batch_size=1, ) generation: Optional[GenerationChunk] = None try: async for chunk in self._astream( prompt, stop=stop, run_manager=run_manager, **kwargs, ): yield chunk.text if generation is None: generation = chunk else: generation += chunk assert generation is not None except BaseException as e: await run_manager.on_llm_error( e, response=LLMResult(generations=[[generation]] if generation else []), ) raise e else: await run_manager.on_llm_end(LLMResult(generations=[[generation]]))
# --- Custom methods --- @abstractmethod def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Run the LLM on the given prompts.""" return await run_in_executor( None, self._generate, prompts, stop, run_manager.get_sync() if run_manager else None, **kwargs, ) def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: """Stream the LLM on the given prompt. This method should be overridden by subclasses that support streaming. If not implemented, the default behavior of calls to stream will be to fallback to the non-streaming version of the model and return the output as a single chunk. Args: prompt: The prompt to generate from. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. run_manager: Callback manager for the run. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: An iterator of GenerationChunks. """ raise NotImplementedError() async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: """An async version of the _stream method. The default implementation uses the synchronous _stream method and wraps it in an async iterator. Subclasses that need to provide a true async implementation should override this method. Args: prompt: The prompt to generate from. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. run_manager: Callback manager for the run. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: An async iterator of GenerationChunks. """ iterator = await run_in_executor( None, self._stream, prompt, stop, run_manager.get_sync() if run_manager else None, **kwargs, ) done = object() while True: item = await run_in_executor( None, next, iterator, done, # type: ignore[call-arg, arg-type] ) if item is done: break yield item # type: ignore[misc]
[docs] def generate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
[docs] async def agenerate_prompt( self, prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None, **kwargs: Any, ) -> LLMResult: prompt_strings = [p.to_string() for p in prompts] return await self.agenerate( prompt_strings, stop=stop, callbacks=callbacks, **kwargs )
def _generate_helper( self, prompts: List[str], stop: Optional[List[str]], run_managers: List[CallbackManagerForLLMRun], new_arg_supported: bool, **kwargs: Any, ) -> LLMResult: try: output = ( self._generate( prompts, stop=stop, # TODO: support multiple run managers run_manager=run_managers[0] if run_managers else None, **kwargs, ) if new_arg_supported else self._generate(prompts, stop=stop) ) except BaseException as e: for run_manager in run_managers: run_manager.on_llm_error(e, response=LLMResult(generations=[])) raise e flattened_outputs = output.flatten() for manager, flattened_output in zip(run_managers, flattened_outputs): manager.on_llm_end(flattened_output) if run_managers: output.run = [ RunInfo(run_id=run_manager.run_id) for run_manager in run_managers ] return output
[docs] def generate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[uuid.UUID, List[Optional[uuid.UUID]]]] = None, **kwargs: Any, ) -> LLMResult: """Pass a sequence of prompts to a model and return generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: 1. take advantage of batched calls, 2. need more output from the model than just the top generated value, 3. are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models). Args: prompts: List of string prompts. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks: Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output. """ if not isinstance(prompts, list): raise ValueError( "Argument 'prompts' is expected to be of type List[str], received" f" argument of type {type(prompts)}." ) # Create callback managers if ( isinstance(callbacks, list) and callbacks and ( isinstance(callbacks[0], (list, BaseCallbackManager)) or callbacks[0] is None ) ): # We've received a list of callbacks args to apply to each input assert len(callbacks) == len(prompts) assert tags is None or ( isinstance(tags, list) and len(tags) == len(prompts) ) assert metadata is None or ( isinstance(metadata, list) and len(metadata) == len(prompts) ) assert run_name is None or ( isinstance(run_name, list) and len(run_name) == len(prompts) ) callbacks = cast(List[Callbacks], callbacks) tags_list = cast(List[Optional[List[str]]], tags or ([None] * len(prompts))) metadata_list = cast( List[Optional[Dict[str, Any]]], metadata or ([{}] * len(prompts)) ) run_name_list = run_name or cast( List[Optional[str]], ([None] * len(prompts)) ) callback_managers = [ CallbackManager.configure( callback, self.callbacks, self.verbose, tag, self.tags, meta, self.metadata, ) for callback, tag, meta in zip(callbacks, tags_list, metadata_list) ] else: # We've received a single callbacks arg to apply to all inputs callback_managers = [ CallbackManager.configure( cast(Callbacks, callbacks), self.callbacks, self.verbose, cast(List[str], tags), self.tags, cast(Dict[str, Any], metadata), self.metadata, ) ] * len(prompts) run_name_list = [cast(Optional[str], run_name)] * len(prompts) run_ids_list = self._get_run_ids_list(run_id, prompts) params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = get_prompts(params, prompts, self.cache) new_arg_supported = inspect.signature(self._generate).parameters.get( "run_manager" ) if (self.cache is None and get_llm_cache() is None) or self.cache is False: run_managers = [ callback_manager.on_llm_start( dumpd(self), [prompt], invocation_params=params, options=options, name=run_name, batch_size=len(prompts), run_id=run_id_, )[0] for callback_manager, prompt, run_name, run_id_ in zip( callback_managers, prompts, run_name_list, run_ids_list ) ] output = self._generate_helper( prompts, stop, run_managers, bool(new_arg_supported), **kwargs ) return output if len(missing_prompts) > 0: run_managers = [ callback_managers[idx].on_llm_start( dumpd(self), [prompts[idx]], invocation_params=params, options=options, name=run_name_list[idx], batch_size=len(missing_prompts), )[0] for idx in missing_prompt_idxs ] new_results = self._generate_helper( missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs ) llm_output = update_cache( self.cache, existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts, ) run_info = ( [RunInfo(run_id=run_manager.run_id) for run_manager in run_managers] if run_managers else None ) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
@staticmethod def _get_run_ids_list( run_id: Optional[Union[uuid.UUID, List[Optional[uuid.UUID]]]], prompts: list ) -> list: if run_id is None: return [None] * len(prompts) if isinstance(run_id, list): if len(run_id) != len(prompts): raise ValueError( "Number of manually provided run_id's does not match batch length." f" {len(run_id)} != {len(prompts)}" ) return run_id return [run_id] + [None] * (len(prompts) - 1) async def _agenerate_helper( self, prompts: List[str], stop: Optional[List[str]], run_managers: List[AsyncCallbackManagerForLLMRun], new_arg_supported: bool, **kwargs: Any, ) -> LLMResult: try: output = ( await self._agenerate( prompts, stop=stop, run_manager=run_managers[0] if run_managers else None, **kwargs, ) if new_arg_supported else await self._agenerate(prompts, stop=stop) ) except BaseException as e: await asyncio.gather( *[ run_manager.on_llm_error(e, response=LLMResult(generations=[])) for run_manager in run_managers ] ) raise e flattened_outputs = output.flatten() await asyncio.gather( *[ run_manager.on_llm_end(flattened_output) for run_manager, flattened_output in zip( run_managers, flattened_outputs ) ] ) if run_managers: output.run = [ RunInfo(run_id=run_manager.run_id) for run_manager in run_managers ] return output
[docs] async def agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[Callbacks, List[Callbacks]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, run_name: Optional[Union[str, List[str]]] = None, run_id: Optional[Union[uuid.UUID, List[Optional[uuid.UUID]]]] = None, **kwargs: Any, ) -> LLMResult: """Asynchronously pass a sequence of prompts to a model and return generations. This method should make use of batched calls for models that expose a batched API. Use this method when you want to: 1. take advantage of batched calls, 2. need more output from the model than just the top generated value, 3. are building chains that are agnostic to the underlying language model type (e.g., pure text completion models vs chat models). Args: prompts: List of string prompts. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings. callbacks: Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: An LLMResult, which contains a list of candidate Generations for each input prompt and additional model provider-specific output. """ # Create callback managers if isinstance(callbacks, list) and ( isinstance(callbacks[0], (list, BaseCallbackManager)) or callbacks[0] is None ): # We've received a list of callbacks args to apply to each input assert len(callbacks) == len(prompts) assert tags is None or ( isinstance(tags, list) and len(tags) == len(prompts) ) assert metadata is None or ( isinstance(metadata, list) and len(metadata) == len(prompts) ) assert run_name is None or ( isinstance(run_name, list) and len(run_name) == len(prompts) ) callbacks = cast(List[Callbacks], callbacks) tags_list = cast(List[Optional[List[str]]], tags or ([None] * len(prompts))) metadata_list = cast( List[Optional[Dict[str, Any]]], metadata or ([{}] * len(prompts)) ) run_name_list = run_name or cast( List[Optional[str]], ([None] * len(prompts)) ) callback_managers = [ AsyncCallbackManager.configure( callback, self.callbacks, self.verbose, tag, self.tags, meta, self.metadata, ) for callback, tag, meta in zip(callbacks, tags_list, metadata_list) ] else: # We've received a single callbacks arg to apply to all inputs callback_managers = [ AsyncCallbackManager.configure( cast(Callbacks, callbacks), self.callbacks, self.verbose, cast(List[str], tags), self.tags, cast(Dict[str, Any], metadata), self.metadata, ) ] * len(prompts) run_name_list = [cast(Optional[str], run_name)] * len(prompts) run_ids_list = self._get_run_ids_list(run_id, prompts) params = self.dict() params["stop"] = stop options = {"stop": stop} ( existing_prompts, llm_string, missing_prompt_idxs, missing_prompts, ) = await aget_prompts(params, prompts, self.cache) # Verify whether the cache is set, and if the cache is set, # verify whether the cache is available. new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" ) if (self.cache is None and get_llm_cache() is None) or self.cache is False: run_managers = await asyncio.gather( *[ callback_manager.on_llm_start( dumpd(self), [prompt], invocation_params=params, options=options, name=run_name, batch_size=len(prompts), run_id=run_id_, ) for callback_manager, prompt, run_name, run_id_ in zip( callback_managers, prompts, run_name_list, run_ids_list ) ] ) run_managers = [r[0] for r in run_managers] # type: ignore[misc] output = await self._agenerate_helper( prompts, stop, run_managers, # type: ignore[arg-type] bool(new_arg_supported), **kwargs, # type: ignore[arg-type] ) return output if len(missing_prompts) > 0: run_managers = await asyncio.gather( *[ callback_managers[idx].on_llm_start( dumpd(self), [prompts[idx]], invocation_params=params, options=options, name=run_name_list[idx], batch_size=len(missing_prompts), ) for idx in missing_prompt_idxs ] ) run_managers = [r[0] for r in run_managers] # type: ignore[misc] new_results = await self._agenerate_helper( missing_prompts, stop, run_managers, # type: ignore[arg-type] bool(new_arg_supported), **kwargs, # type: ignore[arg-type] ) llm_output = await aupdate_cache( self.cache, existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts, ) run_info = ( [RunInfo(run_id=run_manager.run_id) for run_manager in run_managers] # type: ignore[attr-defined] if run_managers else None ) else: llm_output = {} run_info = None generations = [existing_prompts[i] for i in range(len(prompts))] return LLMResult(generations=generations, llm_output=llm_output, run=run_info)
[docs] @deprecated("0.1.7", alternative="invoke", removal="0.2.0") def __call__( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> 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, tags=tags, metadata=metadata, **kwargs, ) .generations[0][0] .text )
async def _call_async( self, prompt: str, stop: Optional[List[str]] = None, callbacks: Callbacks = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> str: """Check Cache and run the LLM on the given prompt and input.""" result = await self.agenerate( [prompt], stop=stop, callbacks=callbacks, tags=tags, metadata=metadata, **kwargs, ) return result.generations[0][0].text
[docs] @deprecated("0.1.7", alternative="invoke", removal="0.2.0") def predict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return self(text, stop=_stop, **kwargs)
[docs] @deprecated("0.1.7", alternative="invoke", removal="0.2.0") def predict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = self(text, stop=_stop, **kwargs) return AIMessage(content=content)
[docs] @deprecated("0.1.7", alternative="ainvoke", removal="0.2.0") async def apredict( self, text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any ) -> str: if stop is None: _stop = None else: _stop = list(stop) return await self._call_async(text, stop=_stop, **kwargs)
[docs] @deprecated("0.1.7", alternative="ainvoke", removal="0.2.0") async def apredict_messages( self, messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any, ) -> BaseMessage: text = get_buffer_string(messages) if stop is None: _stop = None else: _stop = list(stop) content = await self._call_async(text, stop=_stop, **kwargs) return AIMessage(content=content)
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."""
[docs] 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
[docs] 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.endswith((".yaml", ".yml")): 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")
[docs]class LLM(BaseLLM): """This class exposes a simple interface for implementing a custom LLM. You should subclass this class and implement the following: - `_call` method: Run the LLM on the given prompt and input (used by `invoke`). - `_identifying_params` property: Return a dictionary of the identifying parameters This is critical for caching and tracing purposes. Identifying parameters is a dict that identifies the LLM. It should mostly include a `model_name`. Optional: Override the following methods to provide more optimizations: - `_acall`: Provide a native async version of the `_call` method. If not provided, will delegate to the synchronous version using `run_in_executor`. (Used by `ainvoke`). - `_stream`: Stream the LLM on the given prompt and input. `stream` will use `_stream` if provided, otherwise it use `_call` and output will arrive in one chunk. - `_astream`: Override to provide a native async version of the `_stream` method. `astream` will use `_astream` if provided, otherwise it will implement a fallback behavior that will use `_stream` if `_stream` is implemented, and use `_acall` if `_stream` is not implemented. """ @abstractmethod def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Run the LLM on the given input. Override this method to implement the LLM logic. Args: prompt: The prompt to generate from. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of the stop substrings. If stop tokens are not supported consider raising NotImplementedError. run_manager: Callback manager for the run. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: The model output as a string. SHOULD NOT include the prompt. """ async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Async version of the _call method. The default implementation delegates to the synchronous _call method using `run_in_executor`. Subclasses that need to provide a true async implementation should override this method to reduce the overhead of using `run_in_executor`. Args: prompt: The prompt to generate from. stop: Stop words to use when generating. Model output is cut off at the first occurrence of any of the stop substrings. If stop tokens are not supported consider raising NotImplementedError. run_manager: Callback manager for the run. **kwargs: Arbitrary additional keyword arguments. These are usually passed to the model provider API call. Returns: The model output as a string. SHOULD NOT include the prompt. """ return await run_in_executor( None, self._call, prompt, stop, run_manager.get_sync() if run_manager else None, **kwargs, ) def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> 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, **kwargs) if new_arg_supported else self._call(prompt, stop=stop, **kwargs) ) 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, **kwargs: Any, ) -> 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, **kwargs) if new_arg_supported else await self._acall(prompt, stop=stop, **kwargs) ) generations.append([Generation(text=text)]) return LLMResult(generations=generations)