from __future__ import annotations
import asyncio
import inspect
import warnings
from abc import ABC, abstractmethod
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Dict,
Iterator,
List,
Optional,
Sequence,
cast,
)
from langchain_core._api import deprecated
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, dumps
from langchain_core.messages import (
AIMessage,
AnyMessage,
BaseMessage,
BaseMessageChunk,
HumanMessage,
message_chunk_to_message,
)
from langchain_core.outputs import (
ChatGeneration,
ChatGenerationChunk,
ChatResult,
LLMResult,
RunInfo,
)
from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.runnables.config import ensure_config, run_in_executor
if TYPE_CHECKING:
from langchain_core.runnables import RunnableConfig
def _get_verbosity() -> bool:
from langchain_core.globals import get_verbose
return get_verbose()
[docs]def generate_from_stream(stream: Iterator[ChatGenerationChunk]) -> ChatResult:
"""Generate from a stream."""
generation: Optional[ChatGenerationChunk] = None
for chunk in stream:
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
return ChatResult(
generations=[
ChatGeneration(
message=message_chunk_to_message(generation.message),
generation_info=generation.generation_info,
)
]
)
[docs]async def agenerate_from_stream(
stream: AsyncIterator[ChatGenerationChunk],
) -> ChatResult:
"""Async generate from a stream."""
generation: Optional[ChatGenerationChunk] = None
async for chunk in stream:
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
return ChatResult(
generations=[
ChatGeneration(
message=message_chunk_to_message(generation.message),
generation_info=generation.generation_info,
)
]
)
[docs]class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
"""Base class for Chat models."""
cache: Optional[bool] = None
"""Whether to cache the response."""
verbose: bool = Field(default_factory=_get_verbosity)
"""Whether to print out response text."""
callbacks: Callbacks = Field(default=None, exclude=True)
"""Callbacks to add to the run trace."""
callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
"""[DEPRECATED] Callback manager to add to the run trace."""
tags: Optional[List[str]] = Field(default=None, exclude=True)
"""Tags to add to the run trace."""
metadata: Optional[Dict[str, Any]] = Field(default=None, exclude=True)
"""Metadata to add to the run trace."""
@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
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
# --- Runnable methods ---
@property
def OutputType(self) -> Any:
"""Get the output type for this runnable."""
return AnyMessage
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=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,
) -> BaseMessage:
config = ensure_config(config)
return cast(
ChatGeneration,
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"),
**kwargs,
).generations[0][0],
).message
[docs] async def ainvoke(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> BaseMessage:
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"),
**kwargs,
)
return cast(ChatGeneration, llm_result.generations[0][0]).message
[docs] def stream(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[BaseMessageChunk]:
if type(self)._stream == BaseChatModel._stream:
# model doesn't implement streaming, so use default implementation
yield cast(
BaseMessageChunk, self.invoke(input, config=config, stop=stop, **kwargs)
)
else:
config = ensure_config(config)
messages = self._convert_input(input).to_messages()
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
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_chat_model_start(
dumpd(self),
[messages],
invocation_params=params,
options=options,
name=config.get("run_name"),
batch_size=1,
)
generation: Optional[ChatGenerationChunk] = None
try:
for chunk in self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
):
yield chunk.message
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[BaseMessageChunk]:
if type(self)._astream == BaseChatModel._astream:
# model doesn't implement streaming, so use default implementation
yield cast(
BaseMessageChunk,
await self.ainvoke(input, config=config, stop=stop, **kwargs),
)
else:
config = ensure_config(config)
messages = self._convert_input(input).to_messages()
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
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_chat_model_start(
dumpd(self),
[messages],
invocation_params=params,
options=options,
name=config.get("run_name"),
batch_size=1,
)
generation: Optional[ChatGenerationChunk] = None
try:
async for chunk in self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
):
yield chunk.message
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 ---
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
return {}
def _get_invocation_params(
self,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> dict:
params = self.dict()
params["stop"] = stop
return {**params, **kwargs}
def _get_llm_string(self, stop: Optional[List[str]] = None, **kwargs: Any) -> str:
if self.is_lc_serializable():
params = {**kwargs, **{"stop": stop}}
param_string = str(sorted([(k, v) for k, v in params.items()]))
llm_string = dumps(self)
return llm_string + "---" + param_string
else:
params = self._get_invocation_params(stop=stop, **kwargs)
params = {**params, **kwargs}
return str(sorted([(k, v) for k, v in params.items()]))
[docs] def generate(
self,
messages: List[List[BaseMessage]],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = None,
**kwargs: Any,
) -> LLMResult:
"""Pass a sequence of prompts to the model and return model 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:
messages: List of list of messages.
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.
"""
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
callback_manager = CallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
tags,
self.tags,
metadata,
self.metadata,
)
run_managers = callback_manager.on_chat_model_start(
dumpd(self),
messages,
invocation_params=params,
options=options,
name=run_name,
batch_size=len(messages),
)
results = []
for i, m in enumerate(messages):
try:
results.append(
self._generate_with_cache(
m,
stop=stop,
run_manager=run_managers[i] if run_managers else None,
**kwargs,
)
)
except BaseException as e:
if run_managers:
run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
raise e
flattened_outputs = [
LLMResult(generations=[res.generations], llm_output=res.llm_output)
for res in results
]
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
generations = [res.generations for res in results]
output = LLMResult(generations=generations, llm_output=llm_output)
if run_managers:
run_infos = []
for manager, flattened_output in zip(run_managers, flattened_outputs):
manager.on_llm_end(flattened_output)
run_infos.append(RunInfo(run_id=manager.run_id))
output.run = run_infos
return output
[docs] async def agenerate(
self,
messages: List[List[BaseMessage]],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
*,
tags: Optional[List[str]] = None,
metadata: Optional[Dict[str, Any]] = None,
run_name: Optional[str] = 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:
messages: List of list of messages.
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.
"""
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop}
callback_manager = AsyncCallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
tags,
self.tags,
metadata,
self.metadata,
)
run_managers = await callback_manager.on_chat_model_start(
dumpd(self),
messages,
invocation_params=params,
options=options,
name=run_name,
batch_size=len(messages),
)
results = await asyncio.gather(
*[
self._agenerate_with_cache(
m,
stop=stop,
run_manager=run_managers[i] if run_managers else None,
**kwargs,
)
for i, m in enumerate(messages)
],
return_exceptions=True,
)
exceptions = []
for i, res in enumerate(results):
if isinstance(res, BaseException):
if run_managers:
await run_managers[i].on_llm_error(
res, response=LLMResult(generations=[])
)
exceptions.append(res)
if exceptions:
if run_managers:
await asyncio.gather(
*[
run_manager.on_llm_end(
LLMResult(
generations=[res.generations], llm_output=res.llm_output
)
)
for run_manager, res in zip(run_managers, results)
if not isinstance(res, Exception)
]
)
raise exceptions[0]
flattened_outputs = [
LLMResult(generations=[res.generations], llm_output=res.llm_output)
for res in results
]
llm_output = self._combine_llm_outputs([res.llm_output for res in results])
generations = [res.generations for res in results]
output = LLMResult(generations=generations, llm_output=llm_output)
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] def generate_prompt(
self,
prompts: List[PromptValue],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
prompt_messages = [p.to_messages() for p in prompts]
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
[docs] async def agenerate_prompt(
self,
prompts: List[PromptValue],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> LLMResult:
prompt_messages = [p.to_messages() for p in prompts]
return await self.agenerate(
prompt_messages, stop=stop, callbacks=callbacks, **kwargs
)
def _generate_with_cache(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
new_arg_supported = inspect.signature(self._generate).parameters.get(
"run_manager"
)
disregard_cache = self.cache is not None and not self.cache
llm_cache = get_llm_cache()
if 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 new_arg_supported:
return self._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
return self._generate(messages, stop=stop, **kwargs)
else:
llm_string = self._get_llm_string(stop=stop, **kwargs)
prompt = dumps(messages)
cache_val = llm_cache.lookup(prompt, llm_string)
if isinstance(cache_val, list):
return ChatResult(generations=cache_val)
else:
if new_arg_supported:
result = self._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
result = self._generate(messages, stop=stop, **kwargs)
llm_cache.update(prompt, llm_string, result.generations)
return result
async def _agenerate_with_cache(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
new_arg_supported = inspect.signature(self._agenerate).parameters.get(
"run_manager"
)
disregard_cache = self.cache is not None and not self.cache
llm_cache = get_llm_cache()
if 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 new_arg_supported:
return await self._agenerate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
return await self._agenerate(messages, stop=stop, **kwargs)
else:
llm_string = self._get_llm_string(stop=stop, **kwargs)
prompt = dumps(messages)
cache_val = llm_cache.lookup(prompt, llm_string)
if isinstance(cache_val, list):
return ChatResult(generations=cache_val)
else:
if new_arg_supported:
result = await self._agenerate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
result = await self._agenerate(messages, stop=stop, **kwargs)
llm_cache.update(prompt, llm_string, result.generations)
return result
@abstractmethod
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Top Level call"""
return await run_in_executor(
None,
self._generate,
messages,
stop,
run_manager.get_sync() if run_manager else None,
**kwargs,
)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
raise NotImplementedError()
def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
raise NotImplementedError()
[docs] @deprecated("0.1.7", alternative="invoke", removal="0.2.0")
def __call__(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> BaseMessage:
generation = self.generate(
[messages], stop=stop, callbacks=callbacks, **kwargs
).generations[0][0]
if isinstance(generation, ChatGeneration):
return generation.message
else:
raise ValueError("Unexpected generation type")
async def _call_async(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
callbacks: Callbacks = None,
**kwargs: Any,
) -> BaseMessage:
result = await self.agenerate(
[messages], stop=stop, callbacks=callbacks, **kwargs
)
generation = result.generations[0][0]
if isinstance(generation, ChatGeneration):
return generation.message
else:
raise ValueError("Unexpected generation type")
[docs] @deprecated("0.1.7", alternative="invoke", removal="0.2.0")
def call_as_llm(
self, message: str, stop: Optional[List[str]] = None, **kwargs: Any
) -> str:
return self.predict(message, stop=stop, **kwargs)
[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)
result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
if isinstance(result.content, str):
return result.content
else:
raise ValueError("Cannot use predict when output is not a string.")
[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:
if stop is None:
_stop = None
else:
_stop = list(stop)
return self(messages, stop=_stop, **kwargs)
[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)
result = await self._call_async(
[HumanMessage(content=text)], stop=_stop, **kwargs
)
if isinstance(result.content, str):
return result.content
else:
raise ValueError("Cannot use predict when output is not a string.")
[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:
if stop is None:
_stop = None
else:
_stop = list(stop)
return await self._call_async(messages, stop=_stop, **kwargs)
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {}
@property
@abstractmethod
def _llm_type(self) -> str:
"""Return type of chat model."""
[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]class SimpleChatModel(BaseChatModel):
"""Simple Chat Model."""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
output_str = self._call(messages, stop=stop, run_manager=run_manager, **kwargs)
message = AIMessage(content=output_str)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
@abstractmethod
def _call(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Simpler interface."""
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
return await run_in_executor(
None,
self._generate,
messages,
stop=stop,
run_manager=run_manager.get_sync() if run_manager else None,
**kwargs,
)