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langchain/libs/partners/mistralai/langchain_mistralai/chat_models.py
Bagatur 9514bc4d67
core[minor], ...: add tool calls message (#18947) 
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]

```python
class ToolCall(TypedDict):
    name: str
    args: Dict[str, Any]
    id: Optional[str]

class InvalidToolCall(TypedDict):
    name: Optional[str]
    args: Optional[str]
    id: Optional[str]
    error: Optional[str]

class ToolCallChunk(TypedDict):
    name: Optional[str]
    args: Optional[str]
    id: Optional[str]
    index: Optional[int]


class AIMessage(BaseMessage):
    ...
    tool_calls: List[ToolCall] = []
    invalid_tool_calls: List[InvalidToolCall] = []
    ...


class AIMessageChunk(AIMessage, BaseMessageChunk):
    ...
    tool_call_chunks: Optional[List[ToolCallChunk]] = None
    ...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
  - additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).

Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2024-04-09 18:41:42 -05:00

661 lines
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from __future__ import annotations
import logging
from operator import itemgetter
from typing import (
Any,
AsyncContextManager,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import httpx
from httpx_sse import EventSource, aconnect_sse, connect_sse
from langchain_core._api import beta
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.language_models.llms import create_base_retry_decorator
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
ToolMessage,
)
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
make_invalid_tool_call,
parse_tool_call,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from langchain_core.utils.function_calling import convert_to_openai_tool
logger = logging.getLogger(__name__)
def _create_retry_decorator(
llm: ChatMistralAI,
run_manager: Optional[
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
] = None,
) -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to handle exceptions"""
errors = [httpx.RequestError, httpx.StreamError]
return create_base_retry_decorator(
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
)
def _convert_mistral_chat_message_to_message(
_message: Dict,
) -> BaseMessage:
role = _message["role"]
assert role == "assistant", f"Expected role to be 'assistant', got {role}"
content = cast(str, _message["content"])
additional_kwargs: Dict = {}
tool_calls = []
invalid_tool_calls = []
if raw_tool_calls := _message.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for raw_tool_call in raw_tool_calls:
try:
parsed: dict = cast(
dict, parse_tool_call(raw_tool_call, return_id=False)
)
tool_calls.append(
{
**parsed,
**{"id": None},
},
)
except Exception as e:
invalid_tool_calls.append(
dict(make_invalid_tool_call(raw_tool_call, str(e)))
)
return AIMessage(
content=content,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
async def _aiter_sse(
event_source_mgr: AsyncContextManager[EventSource],
) -> AsyncIterator[Dict]:
"""Iterate over the server-sent events."""
async with event_source_mgr as event_source:
async for event in event_source.aiter_sse():
if event.data == "[DONE]":
return
yield event.json()
async def acompletion_with_retry(
llm: ChatMistralAI,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Any:
"""Use tenacity to retry the async completion call."""
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
@retry_decorator
async def _completion_with_retry(**kwargs: Any) -> Any:
if "stream" not in kwargs:
kwargs["stream"] = False
stream = kwargs["stream"]
if stream:
event_source = aconnect_sse(
llm.async_client, "POST", "/chat/completions", json=kwargs
)
return _aiter_sse(event_source)
else:
response = await llm.async_client.post(url="/chat/completions", json=kwargs)
return response.json()
return await _completion_with_retry(**kwargs)
def _convert_delta_to_message_chunk(
_delta: Dict, default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _delta.get("role")
content = _delta.get("content") or ""
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
additional_kwargs: Dict = {}
if raw_tool_calls := _delta.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
try:
tool_call_chunks = [
{
"name": rtc["function"].get("name"),
"args": rtc["function"].get("arguments"),
"id": rtc.get("id"),
"index": rtc.get("index"),
}
for rtc in raw_tool_calls
]
except KeyError:
pass
else:
tool_call_chunks = []
return AIMessageChunk(
content=content,
additional_kwargs=additional_kwargs,
tool_call_chunks=tool_call_chunks,
)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content)
def _convert_message_to_mistral_chat_message(
message: BaseMessage,
) -> Dict:
if isinstance(message, ChatMessage):
return dict(role=message.role, content=message.content)
elif isinstance(message, HumanMessage):
return dict(role="user", content=message.content)
elif isinstance(message, AIMessage):
if "tool_calls" in message.additional_kwargs:
tool_calls = [
{
"function": {
"name": tc["function"]["name"],
"arguments": tc["function"]["arguments"],
}
}
for tc in message.additional_kwargs["tool_calls"]
]
else:
tool_calls = []
return {
"role": "assistant",
"content": message.content,
"tool_calls": tool_calls,
}
elif isinstance(message, SystemMessage):
return dict(role="system", content=message.content)
elif isinstance(message, ToolMessage):
return {
"role": "tool",
"content": message.content,
"name": message.name,
}
else:
raise ValueError(f"Got unknown type {message}")
class ChatMistralAI(BaseChatModel):
"""A chat model that uses the MistralAI API."""
client: httpx.Client = Field(default=None) #: :meta private:
async_client: httpx.AsyncClient = Field(default=None) #: :meta private:
mistral_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
endpoint: str = "https://api.mistral.ai/v1"
max_retries: int = 5
timeout: int = 120
max_concurrent_requests: int = 64
model: str = "mistral-small"
temperature: float = 0.7
max_tokens: Optional[int] = None
top_p: float = 1
"""Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
random_seed: Optional[int] = None
safe_mode: bool = False
streaming: bool = False
class Config:
"""Configuration for this pydantic object."""
allow_population_by_field_name = True
arbitrary_types_allowed = True
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling the API."""
defaults = {
"model": self.model,
"temperature": self.temperature,
"max_tokens": self.max_tokens,
"top_p": self.top_p,
"random_seed": self.random_seed,
"safe_prompt": self.safe_mode,
}
filtered = {k: v for k, v in defaults.items() if v is not None}
return filtered
@property
def _client_params(self) -> Dict[str, Any]:
"""Get the parameters used for the client."""
return self._default_params
def completion_with_retry(
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
) -> Any:
"""Use tenacity to retry the completion call."""
# retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
# @retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
if "stream" not in kwargs:
kwargs["stream"] = False
stream = kwargs["stream"]
if stream:
def iter_sse() -> Iterator[Dict]:
with connect_sse(
self.client, "POST", "/chat/completions", json=kwargs
) as event_source:
for event in event_source.iter_sse():
if event.data == "[DONE]":
return
yield event.json()
return iter_sse()
else:
return self.client.post(url="/chat/completions", json=kwargs).json()
rtn = _completion_with_retry(**kwargs)
return rtn
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output["token_usage"]
if token_usage is not None:
for k, v in token_usage.items():
if k in overall_token_usage:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
combined = {"token_usage": overall_token_usage, "model_name": self.model}
return combined
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists, temperature, and top_p."""
values["mistral_api_key"] = convert_to_secret_str(
get_from_dict_or_env(
values, "mistral_api_key", "MISTRAL_API_KEY", default=""
)
)
api_key_str = values["mistral_api_key"].get_secret_value()
# todo: handle retries
values["client"] = httpx.Client(
base_url=values["endpoint"],
headers={
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"Bearer {api_key_str}",
},
timeout=values["timeout"],
)
# todo: handle retries and max_concurrency
values["async_client"] = httpx.AsyncClient(
base_url=values["endpoint"],
headers={
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"Bearer {api_key_str}",
},
timeout=values["timeout"],
)
if values["temperature"] is not None and not 0 <= values["temperature"] <= 1:
raise ValueError("temperature must be in the range [0.0, 1.0]")
if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
raise ValueError("top_p must be in the range [0.0, 1.0]")
return values
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else self.streaming
if should_stream:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
)
return self._create_chat_result(response)
def _create_chat_result(self, response: Dict) -> ChatResult:
generations = []
for res in response["choices"]:
finish_reason = res.get("finish_reason")
gen = ChatGeneration(
message=_convert_mistral_chat_message_to_message(res["message"]),
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
token_usage = response.get("usage", {})
llm_output = {"token_usage": token_usage, "model": self.model}
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict], Dict[str, Any]]:
params = self._client_params
if stop is not None or "stop" in params:
if "stop" in params:
params.pop("stop")
logger.warning(
"Parameter `stop` not yet supported (https://docs.mistral.ai/api)"
)
message_dicts = [_convert_message_to_mistral_chat_message(m) for m in messages]
return message_dicts, params
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
for chunk in self.completion_with_retry(
messages=message_dicts, run_manager=run_manager, **params
):
if len(chunk["choices"]) == 0:
continue
delta = chunk["choices"][0]["delta"]
new_chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
# make future chunks same type as first chunk
default_chunk_class = new_chunk.__class__
gen_chunk = ChatGenerationChunk(message=new_chunk)
if run_manager:
run_manager.on_llm_new_token(
token=cast(str, new_chunk.content), chunk=gen_chunk
)
yield gen_chunk
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
async for chunk in await acompletion_with_retry(
self, messages=message_dicts, run_manager=run_manager, **params
):
if len(chunk["choices"]) == 0:
continue
delta = chunk["choices"][0]["delta"]
new_chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
# make future chunks same type as first chunk
default_chunk_class = new_chunk.__class__
gen_chunk = ChatGenerationChunk(message=new_chunk)
if run_manager:
await run_manager.on_llm_new_token(
token=cast(str, new_chunk.content), chunk=gen_chunk
)
yield gen_chunk
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
stream: Optional[bool] = None,
**kwargs: Any,
) -> ChatResult:
should_stream = stream if stream is not None else False
if should_stream:
stream_iter = self._astream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs}
response = await acompletion_with_retry(
self, messages=message_dicts, run_manager=run_manager, **params
)
return self._create_chat_result(response)
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.
Assumes model is compatible with OpenAI tool-calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
models, callables, and BaseTools will be automatically converted to
their schema dictionary representation.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
return super().bind(tools=formatted_tools, **kwargs)
@beta()
def with_structured_output(
self,
schema: Union[Dict, Type[BaseModel]],
*,
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 as a dict or a Pydantic class. If a Pydantic class
then the model output will be an object of that class. If a dict then
the model output will be a dict. With a Pydantic class the returned
attributes will be validated, whereas with a dict they will not be. If
`method` is "function_calling" and `schema` is a dict, then the dict
must match the OpenAI function-calling spec.
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 any ChatModel input and returns as output:
If include_raw is True then a dict with keys:
raw: BaseMessage
parsed: Optional[_DictOrPydantic]
parsing_error: Optional[BaseException]
If include_raw is False then just _DictOrPydantic is returned,
where _DictOrPydantic depends on the schema:
If schema is a Pydantic class then _DictOrPydantic is the Pydantic
class.
If schema is a dict then _DictOrPydantic is a dict.
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
.. code-block:: python
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
.. code-block:: python
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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: Function-calling, dict schema (method="function_calling", include_raw=False):
.. code-block:: python
from langchain_mistralai import ChatMistralAI
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 = ChatMistralAI(model="mistral-large-latest", 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}")
is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
llm = self.bind_tools([schema], tool_choice="any")
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[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
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return self._default_params
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "mistralai-chat"
@property
def lc_secrets(self) -> Dict[str, str]:
return {"mistral_api_key": "MISTRAL_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "mistralai"]
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