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mistralai[patch]: Added Json Mode for ChatMistralAI (#22213)

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- **Description:** Powered
[ChatMistralAI.with_structured_output](fbfed65fb1/libs/partners/mistralai/langchain_mistralai/chat_models.py (L609))
via json mode
 

-  **Issue:** #22081

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Mohammad Mohtashim 2024-05-28 02:16:52 +05:00 committed by GitHub
parent 25c270b5a5
commit 577ed68b59
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2 changed files with 84 additions and 11 deletions
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1
docs/scripts/model_feat_table.py
View File

@ -24,6 +24,7 @@ CHAT_MODEL_FEAT_TABLE = {
"ChatMistralAI": {
"tool_calling": True,
"structured_output": True,
"json_model": True,
"package": "langchain-mistralai",
"link": "/docs/integrations/chat/mistralai/",
},

76
libs/partners/mistralai/langchain_mistralai/chat_models.py
View File

@ -12,6 +12,7 @@ from typing import (
Dict,
Iterator,
List,
Literal,
Optional,
Sequence,
Tuple,
@ -49,6 +50,10 @@ from langchain_core.messages import (
ToolCall,
ToolMessage,
)
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,
@ -608,8 +613,9 @@ class ChatMistralAI(BaseChatModel):
def with_structured_output(
self,
schema: Union[Dict, Type[BaseModel]],
schema: Optional[Union[Dict, Type[BaseModel]]] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
@ -622,6 +628,12 @@ class ChatMistralAI(BaseChatModel):
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.
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
@ -709,10 +721,64 @@ class ChatMistralAI(BaseChatModel):
# '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: JSON mode, Pydantic schema (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: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
.. code-block::
from langchain_mistralai import ChatMistralAI
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 issubclass(schema, BaseModel)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is 'function_calling'. "
"Received None."
)
llm = self.bind_tools([schema], tool_choice="any")
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
@ -723,7 +789,13 @@ class ChatMistralAI(BaseChatModel):
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)
if is_pydantic_schema
else JsonOutputParser()
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None