from __future__ import annotations
import asyncio
import inspect
import json
import uuid
import warnings
from abc import ABC, abstractmethod
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Type,
Union,
cast,
)
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,
LangSmithParams,
LanguageModelInput,
)
from langchain_core.load import dumpd, dumps
from langchain_core.messages import (
AIMessage,
AnyMessage,
BaseMessage,
BaseMessageChunk,
HumanMessage,
convert_to_messages,
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 (
BaseModel,
Field,
root_validator,
)
from langchain_core.rate_limiters import BaseRateLimiter
from langchain_core.runnables import RunnableMap, RunnablePassthrough
from langchain_core.runnables.config import ensure_config, run_in_executor
from langchain_core.tracers._streaming import _StreamingCallbackHandler
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
if TYPE_CHECKING:
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.runnables import Runnable, RunnableConfig
from langchain_core.tools import BaseTool
[docs]def generate_from_stream(stream: Iterator[ChatGenerationChunk]) -> ChatResult:
"""Generate from a stream.
Args:
stream: Iterator of ChatGenerationChunk.
Returns:
ChatResult: Chat result.
"""
generation = next(stream, None)
if generation:
generation += list(stream)
if generation is None:
raise ValueError("No generations found in stream.")
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.
Args:
stream: Iterator of ChatGenerationChunk.
Returns:
ChatResult: Chat result.
"""
chunks = [chunk async for chunk in stream]
return await run_in_executor(None, generate_from_stream, iter(chunks))
[docs]class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
"""Base class for chat models.
Key imperative methods:
Methods that actually call the underlying model.
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| Method | Input | Output | Description |
+===========================+================================================================+=====================================================================+==================================================================================================+
| `invoke` | str | List[dict | tuple | BaseMessage] | PromptValue | BaseMessage | A single chat model call. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `ainvoke` | ''' | BaseMessage | Defaults to running invoke in an async executor. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `stream` | ''' | Iterator[BaseMessageChunk] | Defaults to yielding output of invoke. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `astream` | ''' | AsyncIterator[BaseMessageChunk] | Defaults to yielding output of ainvoke. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `astream_events` | ''' | AsyncIterator[StreamEvent] | Event types: 'on_chat_model_start', 'on_chat_model_stream', 'on_chat_model_end'. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `batch` | List['''] | List[BaseMessage] | Defaults to running invoke in concurrent threads. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `abatch` | List['''] | List[BaseMessage] | Defaults to running ainvoke in concurrent threads. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `batch_as_completed` | List['''] | Iterator[Tuple[int, Union[BaseMessage, Exception]]] | Defaults to running invoke in concurrent threads. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
| `abatch_as_completed` | List['''] | AsyncIterator[Tuple[int, Union[BaseMessage, Exception]]] | Defaults to running ainvoke in concurrent threads. |
+---------------------------+----------------------------------------------------------------+---------------------------------------------------------------------+--------------------------------------------------------------------------------------------------+
This table provides a brief overview of the main imperative methods. Please see the base Runnable reference for full documentation.
Key declarative methods:
Methods for creating another Runnable using the ChatModel.
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| Method | Description |
+==================================+===========================================================================================================+
| `bind_tools` | Create ChatModel that can call tools. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| `with_structured_output` | Create wrapper that structures model output using schema. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| `with_retry` | Create wrapper that retries model calls on failure. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| `with_fallbacks` | Create wrapper that falls back to other models on failure. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| `configurable_fields` | Specify init args of the model that can be configured at runtime via the RunnableConfig. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
| `configurable_alternatives` | Specify alternative models which can be swapped in at runtime via the RunnableConfig. |
+----------------------------------+-----------------------------------------------------------------------------------------------------------+
This table provides a brief overview of the main declarative methods. Please see the reference for each method for full documentation.
Creating custom chat model:
Custom chat model implementations should inherit from this class.
Please reference the table below for information about which
methods and properties are required or optional for implementations.
+----------------------------------+--------------------------------------------------------------------+-------------------+
| Method/Property | Description | Required/Optional |
+==================================+====================================================================+===================+
| `_generate` | Use to generate a chat result from a prompt | Required |
+----------------------------------+--------------------------------------------------------------------+-------------------+
| `_llm_type` (property) | Used to uniquely identify the type of the model. Used for logging. | Required |
+----------------------------------+--------------------------------------------------------------------+-------------------+
| `_identifying_params` (property) | Represent model parameterization for tracing purposes. | Optional |
+----------------------------------+--------------------------------------------------------------------+-------------------+
| `_stream` | Use to implement streaming | Optional |
+----------------------------------+--------------------------------------------------------------------+-------------------+
| `_agenerate` | Use to implement a native async method | Optional |
+----------------------------------+--------------------------------------------------------------------+-------------------+
| `_astream` | Use to implement async version of `_stream` | Optional |
+----------------------------------+--------------------------------------------------------------------+-------------------+
Follow the guide for more information on how to implement a custom Chat Model:
[Guide](https://python.langchain.com/v0.2/docs/how_to/custom_chat_model/).
""" # noqa: E501
callback_manager: Optional[BaseCallbackManager] = deprecated(
name="callback_manager", since="0.1.7", removal="1.0", alternative="callbacks"
)(
Field(
default=None,
exclude=True,
description="Callback manager to add to the run trace.",
)
)
rate_limiter: Optional[BaseRateLimiter] = Field(default=None, exclude=True)
"An optional rate limiter to use for limiting the number of requests."
disable_streaming: Union[bool, Literal["tool_calling"]] = False
"""Whether to disable streaming for this model.
If streaming is bypassed, then ``stream()/astream()`` will defer to
``invoke()/ainvoke()``.
- If True, will always bypass streaming case.
- If "tool_calling", will bypass streaming case only when the model is called
with a ``tools`` keyword argument.
- If False (default), will always use streaming case if available.
"""
@root_validator(pre=True)
def raise_deprecation(cls, values: Dict) -> Dict:
"""Raise deprecation warning if callback_manager is used.
Args:
values (Dict): Values to validate.
Returns:
Dict: Validated values.
Raises:
DeprecationWarning: If callback_manager is used.
"""
if values.get("callback_manager") is not None:
warnings.warn(
"callback_manager is deprecated. Please use callbacks instead.",
DeprecationWarning,
stacklevel=5,
)
values["callbacks"] = values.pop("callback_manager", None)
return values
class Config:
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=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,
) -> 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"),
run_id=config.pop("run_id", None),
**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"),
run_id=config.pop("run_id", None),
**kwargs,
)
return cast(ChatGeneration, llm_result.generations[0][0]).message
def _should_stream(
self,
*,
async_api: bool,
run_manager: Optional[
Union[CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun]
] = None,
**kwargs: Any,
) -> bool:
"""Determine if a given model call should hit the streaming API."""
sync_not_implemented = type(self)._stream == BaseChatModel._stream
async_not_implemented = type(self)._astream == BaseChatModel._astream
# Check if streaming is implemented.
if (not async_api) and sync_not_implemented:
return False
# Note, since async falls back to sync we check both here.
if async_api and async_not_implemented and sync_not_implemented:
return False
# Check if streaming has been disabled on this instance.
if self.disable_streaming is True:
return False
# We assume tools are passed in via "tools" kwarg in all models.
if self.disable_streaming == "tool_calling" and kwargs.get("tools"):
return False
# Check if a runtime streaming flag has been passed in.
if "stream" in kwargs:
return kwargs["stream"]
# Check if any streaming callback handlers have been passed in.
handlers = run_manager.handlers if run_manager else []
return any(isinstance(h, _StreamingCallbackHandler) for h in handlers)
[docs] def stream(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> Iterator[BaseMessageChunk]:
if not self._should_stream(async_api=False, **{**kwargs, **{"stream": True}}):
# 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}
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(stop=stop, **kwargs),
}
callback_manager = CallbackManager.configure(
config.get("callbacks"),
self.callbacks,
self.verbose,
config.get("tags"),
self.tags,
inheritable_metadata,
self.metadata,
)
(run_manager,) = callback_manager.on_chat_model_start(
dumpd(self),
[messages],
invocation_params=params,
options=options,
name=config.get("run_name"),
run_id=config.pop("run_id", None),
batch_size=1,
)
generation: Optional[ChatGenerationChunk] = None
if self.rate_limiter:
self.rate_limiter.acquire(blocking=True)
try:
for chunk in self._stream(messages, stop=stop, **kwargs):
if chunk.message.id is None:
chunk.message.id = f"run-{run_manager.run_id}"
chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
run_manager.on_llm_new_token(
cast(str, chunk.message.content), chunk=chunk
)
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 not self._should_stream(async_api=True, **{**kwargs, **{"stream": True}}):
# No async or sync stream is implemented, so fall back to ainvoke
yield cast(
BaseMessageChunk,
await self.ainvoke(input, config=config, stop=stop, **kwargs),
)
return
config = ensure_config(config)
messages = self._convert_input(input).to_messages()
params = self._get_invocation_params(stop=stop, **kwargs)
options = {"stop": stop, **kwargs}
inheritable_metadata = {
**(config.get("metadata") or {}),
**self._get_ls_params(stop=stop, **kwargs),
}
callback_manager = AsyncCallbackManager.configure(
config.get("callbacks"),
self.callbacks,
self.verbose,
config.get("tags"),
self.tags,
inheritable_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"),
run_id=config.pop("run_id", None),
batch_size=1,
)
if self.rate_limiter:
self.rate_limiter.acquire(blocking=True)
generation: Optional[ChatGenerationChunk] = None
try:
async for chunk in self._astream(
messages,
stop=stop,
**kwargs,
):
if chunk.message.id is None:
chunk.message.id = f"run-{run_manager.run_id}"
chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
await run_manager.on_llm_new_token(
cast(str, chunk.message.content), chunk=chunk
)
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_ls_params(
self,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> LangSmithParams:
"""Get standard params for tracing."""
# get default provider from class name
default_provider = self.__class__.__name__
if default_provider.startswith("Chat"):
default_provider = default_provider[4:].lower()
elif default_provider.endswith("Chat"):
default_provider = default_provider[:-4]
default_provider = default_provider.lower()
ls_params = LangSmithParams(ls_provider=default_provider, ls_model_type="chat")
if stop:
ls_params["ls_stop"] = stop
# model
if hasattr(self, "model") and isinstance(self.model, str):
ls_params["ls_model_name"] = self.model
elif hasattr(self, "model_name") and isinstance(self.model_name, str):
ls_params["ls_model_name"] = self.model_name
# temperature
if "temperature" in kwargs and isinstance(kwargs["temperature"], float):
ls_params["ls_temperature"] = kwargs["temperature"]
elif hasattr(self, "temperature") and isinstance(self.temperature, float):
ls_params["ls_temperature"] = self.temperature
# max_tokens
if "max_tokens" in kwargs and isinstance(kwargs["max_tokens"], int):
ls_params["ls_max_tokens"] = kwargs["max_tokens"]
elif hasattr(self, "max_tokens") and isinstance(self.max_tokens, int):
ls_params["ls_max_tokens"] = self.max_tokens
return ls_params
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()]))
# This code is not super efficient as it goes back and forth between
# json and dict.
serialized_repr = dumpd(self)
_cleanup_llm_representation(serialized_repr, 1)
llm_string = json.dumps(serialized_repr, sort_keys=True)
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,
run_id: Optional[uuid.UUID] = 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}
inheritable_metadata = {
**(metadata or {}),
**self._get_ls_params(stop=stop, **kwargs),
}
callback_manager = CallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
tags,
self.tags,
inheritable_metadata,
self.metadata,
)
run_managers = callback_manager.on_chat_model_start(
dumpd(self),
messages,
invocation_params=params,
options=options,
name=run_name,
run_id=run_id,
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) # type: ignore[list-item]
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) # type: ignore[arg-type]
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,
run_id: 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:
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}
inheritable_metadata = {
**(metadata or {}),
**self._get_ls_params(stop=stop, **kwargs),
}
callback_manager = AsyncCallbackManager.configure(
callbacks,
self.callbacks,
self.verbose,
tags,
self.tags,
inheritable_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),
run_id=run_id,
)
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], # type: ignore[list-item, union-attr]
llm_output=res.llm_output, # type: ignore[list-item, union-attr]
)
)
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) # type: ignore[list-item, union-attr]
for res in results
]
llm_output = self._combine_llm_outputs([res.llm_output for res in results]) # type: ignore[union-attr]
generations = [res.generations for res in results] # type: ignore[union-attr]
output = LLMResult(generations=generations, llm_output=llm_output) # type: ignore[arg-type]
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:
if isinstance(self.cache, BaseCache):
llm_cache = self.cache
else:
llm_cache = get_llm_cache()
# We should check the cache unless it's explicitly set to False
# A None cache means we should use the default global cache
# if it's configured.
check_cache = self.cache or self.cache is None
if check_cache:
if llm_cache:
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)
elif self.cache is None:
pass
else:
raise ValueError(
"Asked to cache, but no cache found at `langchain.cache`."
)
# Apply the rate limiter after checking the cache, since
# we usually don't want to rate limit cache lookups, but
# we do want to rate limit API requests.
if self.rate_limiter:
self.rate_limiter.acquire(blocking=True)
# If stream is not explicitly set, check if implicitly requested by
# astream_events() or astream_log(). Bail out if _stream not implemented
if self._should_stream(
async_api=False,
run_manager=run_manager,
**kwargs,
):
chunks: List[ChatGenerationChunk] = []
for chunk in self._stream(messages, stop=stop, **kwargs):
chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
if run_manager:
if chunk.message.id is None:
chunk.message.id = f"run-{run_manager.run_id}"
run_manager.on_llm_new_token(
cast(str, chunk.message.content), chunk=chunk
)
chunks.append(chunk)
result = generate_from_stream(iter(chunks))
else:
if inspect.signature(self._generate).parameters.get("run_manager"):
result = self._generate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
result = self._generate(messages, stop=stop, **kwargs)
# Add response metadata to each generation
for idx, generation in enumerate(result.generations):
if run_manager and generation.message.id is None:
generation.message.id = f"run-{run_manager.run_id}-{idx}"
generation.message.response_metadata = _gen_info_and_msg_metadata(
generation
)
if len(result.generations) == 1 and result.llm_output is not None:
result.generations[0].message.response_metadata = {
**result.llm_output,
**result.generations[0].message.response_metadata,
}
if check_cache and llm_cache:
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:
if isinstance(self.cache, BaseCache):
llm_cache = self.cache
else:
llm_cache = get_llm_cache()
# We should check the cache unless it's explicitly set to False
# A None cache means we should use the default global cache
# if it's configured.
check_cache = self.cache or self.cache is None
if check_cache:
if llm_cache:
llm_string = self._get_llm_string(stop=stop, **kwargs)
prompt = dumps(messages)
cache_val = await llm_cache.alookup(prompt, llm_string)
if isinstance(cache_val, list):
return ChatResult(generations=cache_val)
elif self.cache is None:
pass
else:
raise ValueError(
"Asked to cache, but no cache found at `langchain.cache`."
)
# Apply the rate limiter after checking the cache, since
# we usually don't want to rate limit cache lookups, but
# we do want to rate limit API requests.
if self.rate_limiter:
self.rate_limiter.acquire(blocking=True)
# If stream is not explicitly set, check if implicitly requested by
# astream_events() or astream_log(). Bail out if _astream not implemented
if self._should_stream(
async_api=True,
run_manager=run_manager,
**kwargs,
):
chunks: List[ChatGenerationChunk] = []
async for chunk in self._astream(messages, stop=stop, **kwargs):
chunk.message.response_metadata = _gen_info_and_msg_metadata(chunk)
if run_manager:
if chunk.message.id is None:
chunk.message.id = f"run-{run_manager.run_id}"
await run_manager.on_llm_new_token(
cast(str, chunk.message.content), chunk=chunk
)
chunks.append(chunk)
result = generate_from_stream(iter(chunks))
else:
if inspect.signature(self._agenerate).parameters.get("run_manager"):
result = await self._agenerate(
messages, stop=stop, run_manager=run_manager, **kwargs
)
else:
result = await self._agenerate(messages, stop=stop, **kwargs)
# Add response metadata to each generation
for idx, generation in enumerate(result.generations):
if run_manager and generation.message.id is None:
generation.message.id = f"run-{run_manager.run_id}-{idx}"
generation.message.response_metadata = _gen_info_and_msg_metadata(
generation
)
if len(result.generations) == 1 and result.llm_output is not None:
result.generations[0].message.response_metadata = {
**result.llm_output,
**result.generations[0].message.response_metadata,
}
if check_cache and llm_cache:
await llm_cache.aupdate(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()
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
iterator = await run_in_executor(
None,
self._stream,
messages,
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] @deprecated("0.1.7", alternative="invoke", removal="1.0")
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="1.0")
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="1.0")
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="1.0")
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="1.0")
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="1.0")
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
@abstractmethod
def _llm_type(self) -> str:
"""Return type of chat model."""
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 with_structured_output(
self,
schema: Union[Dict, Type],
*,
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
The output schema. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class (support added in 0.2.26),
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
.. versionchanged:: 0.2.26
Added support for TypedDict class.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: Pydantic schema (include_raw=False):
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Pydantic schema (include_raw=True):
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: Dict schema (include_raw=False):
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.utils.function_calling import convert_to_openai_tool
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
dict_schema = convert_to_openai_tool(AnswerWithJustification)
llm = ChatModel(model="model-name", temperature=0)
structured_llm = llm.with_structured_output(dict_schema)
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
""" # noqa: E501
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
)
if self.bind_tools is BaseChatModel.bind_tools:
raise NotImplementedError(
"with_structured_output is not implemented for this model."
)
llm = self.bind_tools([schema], tool_choice="any")
if isinstance(schema, type) and is_basemodel_subclass(schema):
output_parser: OutputParserLike = PydanticToolsParser(
tools=[cast(TypeBaseModel, schema)], first_tool_only=True
)
else:
key_name = convert_to_openai_tool(schema)["function"]["name"]
output_parser = JsonOutputKeyToolsParser(
key_name=key_name, first_tool_only=True
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
[docs]class SimpleChatModel(BaseChatModel):
"""Simplified implementation for a chat model to inherit from.
**Note** This implementation is primarily here for backwards compatibility.
For new implementations, please use `BaseChatModel` directly.
"""
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,
)
def _gen_info_and_msg_metadata(
generation: Union[ChatGeneration, ChatGenerationChunk],
) -> dict:
return {
**(generation.generation_info or {}),
**generation.message.response_metadata,
}
def _cleanup_llm_representation(serialized: Any, depth: int) -> None:
"""Remove non-serializable objects from a serialized object."""
if depth > 100: # Don't cooperate for pathological cases
return
if not isinstance(serialized, dict):
return
if "type" in serialized and serialized["type"] == "not_implemented":
if "repr" in serialized:
del serialized["repr"]
if "graph" in serialized:
del serialized["graph"]
if "kwargs" in serialized:
kwargs = serialized["kwargs"]
for value in kwargs.values():
_cleanup_llm_representation(value, depth + 1)
