from __future__ import annotations
import hashlib
import json
import logging
import os
import re
import uuid
from operator import itemgetter
from typing import (
Any,
AsyncContextManager,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import httpx
from httpx_sse import EventSource, aconnect_sse, connect_sse
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
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,
InvalidToolCall,
SystemMessage,
SystemMessageChunk,
ToolCall,
ToolMessage,
)
from langchain_core.messages.tool import tool_call_chunk
from langchain_core.output_parsers import (
JsonOutputParser,
PydanticOutputParser,
)
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 secret_from_env
from langchain_core.utils.function_calling import convert_to_openai_tool
from langchain_core.utils.pydantic import is_basemodel_subclass
logger = logging.getLogger(__name__)
# Mistral enforces a specific pattern for tool call IDs
TOOL_CALL_ID_PATTERN = re.compile(r"^[a-zA-Z0-9]{9}$")
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 _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool:
"""Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9"""
return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id))
def _base62_encode(num: int) -> str:
"""Encodes a number in base62 and ensures result is of a specified length."""
base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
if num == 0:
return base62[0]
arr = []
base = len(base62)
while num:
num, rem = divmod(num, base)
arr.append(base62[rem])
arr.reverse()
return "".join(arr)
def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str:
"""Convert a tool call ID to a Mistral-compatible format"""
if _is_valid_mistral_tool_call_id(tool_call_id):
return tool_call_id
else:
hash_bytes = hashlib.sha256(tool_call_id.encode()).digest()
hash_int = int.from_bytes(hash_bytes, byteorder="big")
base62_str = _base62_encode(hash_int)
if len(base62_str) >= 9:
return base62_str[:9]
else:
return base62_str.rjust(9, "0")
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=True)
)
if not parsed["id"]:
parsed["id"] = uuid.uuid4().hex[:]
tool_calls.append(parsed)
except Exception as e:
invalid_tool_calls.append(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,
)
def _raise_on_error(response: httpx.Response) -> None:
"""Raise an error if the response is an error."""
if httpx.codes.is_error(response.status_code):
error_message = response.read().decode("utf-8")
raise httpx.HTTPStatusError(
f"Error response {response.status_code} "
f"while fetching {response.url}: {error_message}",
request=response.request,
response=response,
)
async def _araise_on_error(response: httpx.Response) -> None:
"""Raise an error if the response is an error."""
if httpx.codes.is_error(response.status_code):
error_message = (await response.aread()).decode("utf-8")
raise httpx.HTTPStatusError(
f"Error response {response.status_code} "
f"while fetching {response.url}: {error_message}",
request=response.request,
response=response,
)
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:
await _araise_on_error(event_source.response)
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)
await _araise_on_error(response)
return response.json()
return await _completion_with_retry(**kwargs)
def _convert_chunk_to_message_chunk(
chunk: Dict, default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
_delta = chunk["choices"][0]["delta"]
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 = []
for raw_tool_call in raw_tool_calls:
if not raw_tool_call.get("index") and not raw_tool_call.get("id"):
tool_call_id = uuid.uuid4().hex[:]
else:
tool_call_id = raw_tool_call.get("id")
tool_call_chunks.append(
tool_call_chunk(
name=raw_tool_call["function"].get("name"),
args=raw_tool_call["function"].get("arguments"),
id=tool_call_id,
index=raw_tool_call.get("index"),
)
)
except KeyError:
pass
else:
tool_call_chunks = []
if token_usage := chunk.get("usage"):
usage_metadata = {
"input_tokens": token_usage.get("prompt_tokens", 0),
"output_tokens": token_usage.get("completion_tokens", 0),
"total_tokens": token_usage.get("total_tokens", 0),
}
else:
usage_metadata = None
return AIMessageChunk(
content=content,
additional_kwargs=additional_kwargs,
tool_call_chunks=tool_call_chunks, # type: ignore[arg-type]
usage_metadata=usage_metadata, # type: ignore[arg-type]
)
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) # type: ignore[call-arg]
def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict:
"""Format Langchain ToolCall to dict expected by Mistral."""
result: Dict[str, Any] = {
"function": {
"name": tool_call["name"],
"arguments": json.dumps(tool_call["args"]),
}
}
if _id := tool_call.get("id"):
result["id"] = _convert_tool_call_id_to_mistral_compatible(_id)
return result
def _format_invalid_tool_call_for_mistral(invalid_tool_call: InvalidToolCall) -> dict:
"""Format Langchain InvalidToolCall to dict expected by Mistral."""
result: Dict[str, Any] = {
"function": {
"name": invalid_tool_call["name"],
"arguments": invalid_tool_call["args"],
}
}
if _id := invalid_tool_call.get("id"):
result["id"] = _convert_tool_call_id_to_mistral_compatible(_id)
return result
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):
message_dict: Dict[str, Any] = {"role": "assistant"}
tool_calls = []
if message.tool_calls or message.invalid_tool_calls:
for tool_call in message.tool_calls:
tool_calls.append(_format_tool_call_for_mistral(tool_call))
for invalid_tool_call in message.invalid_tool_calls:
tool_calls.append(
_format_invalid_tool_call_for_mistral(invalid_tool_call)
)
elif "tool_calls" in message.additional_kwargs:
for tc in message.additional_kwargs["tool_calls"]:
chunk = {
"function": {
"name": tc["function"]["name"],
"arguments": tc["function"]["arguments"],
}
}
if _id := tc.get("id"):
chunk["id"] = _id
tool_calls.append(chunk)
else:
pass
if tool_calls: # do not populate empty list tool_calls
message_dict["tool_calls"] = tool_calls
if tool_calls and message.content:
# Assistant message must have either content or tool_calls, but not both.
# Some providers may not support tool_calls in the same message as content.
# This is done to ensure compatibility with messages from other providers.
message_dict["content"] = ""
else:
message_dict["content"] = message.content
return message_dict
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}")
[docs]class ChatMistralAI(BaseChatModel):
"""A chat model that uses the MistralAI API."""
client: httpx.Client = Field(default=None, exclude=True) #: :meta private:
async_client: httpx.AsyncClient = Field(
default=None, exclude=True
) #: :meta private:
mistral_api_key: Optional[SecretStr] = Field(
alias="api_key",
default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
)
endpoint: Optional[str] = Field(default=None, alias="base_url")
max_retries: int = 5
timeout: int = 120
max_concurrent_requests: int = 64
model: str = Field(default="mistral-small", alias="model_name")
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
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="mistral",
ls_model_name=self.model,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None):
ls_params["ls_stop"] = ls_stop
return ls_params
@property
def _client_params(self) -> Dict[str, Any]:
"""Get the parameters used for the client."""
return self._default_params
[docs] 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:
_raise_on_error(event_source.response)
for event in event_source.iter_sse():
if event.data == "[DONE]":
return
yield event.json()
return iter_sse()
else:
response = self.client.post(url="/chat/completions", json=kwargs)
_raise_on_error(response)
return response.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(pre=False, skip_on_failure=True)
def validate_environment(cls, values: Dict) -> Dict:
"""Validate api key, python package exists, temperature, and top_p."""
api_key_str = values["mistral_api_key"].get_secret_value()
# todo: handle retries
base_url_str = (
values.get("endpoint")
or os.environ.get("MISTRAL_BASE_URL")
or "https://api.mistral.ai/v1"
)
values["endpoint"] = base_url_str
if not values.get("client"):
values["client"] = httpx.Client(
base_url=base_url_str,
headers={
"Content-Type": "application/json",
"Accept": "application/json",
"Authorization": f"Bearer {api_key_str}",
},
timeout=values["timeout"],
)
# todo: handle retries and max_concurrency
if not values.get("async_client"):
values["async_client"] = httpx.AsyncClient(
base_url=base_url_str,
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 = []
token_usage = response.get("usage", {})
for res in response["choices"]:
finish_reason = res.get("finish_reason")
message = _convert_mistral_chat_message_to_message(res["message"])
if token_usage and isinstance(message, AIMessage):
message.usage_metadata = {
"input_tokens": token_usage.get("prompt_tokens", 0),
"output_tokens": token_usage.get("completion_tokens", 0),
"total_tokens": token_usage.get("total_tokens", 0),
}
gen = ChatGeneration(
message=message,
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
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
new_chunk = _convert_chunk_to_message_chunk(chunk, 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
new_chunk = _convert_chunk_to_message_chunk(chunk, 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 self.streaming
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)
[docs] def with_structured_output(
self,
schema: Optional[Union[Dict, Type]] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
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 0.1.12),
- 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:: 0.1.12
Added support for TypedDict class.
method:
The method for steering model generation, either "function_calling"
or "json_mode". If "function_calling" then the schema will be converted
to an OpenAI function and the returned model will make use of the
function-calling API. If "json_mode" then OpenAI's JSON mode will be
used. Note that if using "json_mode" then you must include instructions
for formatting the output into the desired schema into the model call.
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: schema=Pydantic class, method="function_calling", include_raw=False:
.. code-block:: python
from typing import Optional
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: Optional[str] = Field(
default=None, description="A justification for the answer."
)
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: schema=Pydantic class, 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: schema=TypedDict class, method="function_calling", include_raw=False:
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_mistralai import ChatMistralAI
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
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"
)
# -> {
# '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.'
# }
Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
.. code-block:: python
from langchain_mistralai import ChatMistralAI
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(oai_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.'
# }
Example: schema=Pydantic class, method="json_mode", include_raw=True:
.. code-block::
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
method="json_mode",
include_raw=True
)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
# 'parsing_error': None
# }
Example: schema=None, method="json_mode", include_raw=True:
.. code-block::
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
# 'parsed': {
# 'answer': 'They are both the same weight.',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
# },
# 'parsing_error': None
# }
""" # noqa: E501
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
is_pydantic_schema = isinstance(schema, type) and is_basemodel_subclass(schema)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is 'function_calling'. "
"Received None."
)
# TODO: Update to pass in tool name as tool_choice if/when Mistral supports
# specifying a tool.
llm = self.bind_tools([schema], tool_choice="any")
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
)
else:
key_name = convert_to_openai_tool(schema)["function"]["name"]
output_parser = JsonOutputKeyToolsParser(
key_name=key_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
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"]
