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https://github.com/hwchase17/langchain.git
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63673b765b
**partners: Enable max_retries in ChatMistralAI** **Description** - This pull request reactivates the retry logic in the completion_with_retry method of the ChatMistralAI class, restoring the intended functionality of the previously ineffective max_retries parameter. New unit test that mocks failed/successful retry calls and an integration test to confirm end-to-end functionality. **Issue** - Closes #30362 **Dependencies** - No additional dependencies required Co-authored-by: andrasfe <andrasf94@gmail.com>
1108 lines
44 KiB
Python
1108 lines
44 KiB
Python
from __future__ import annotations
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import hashlib
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import json
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import logging
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import os
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import re
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import ssl
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import uuid
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from operator import itemgetter
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from typing import (
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Any,
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AsyncContextManager,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Literal,
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Optional,
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Sequence,
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Tuple,
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Type,
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Union,
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cast,
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)
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import certifi
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import httpx
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from httpx_sse import EventSource, aconnect_sse, connect_sse
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models import LanguageModelInput
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from langchain_core.language_models.chat_models import (
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BaseChatModel,
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LangSmithParams,
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agenerate_from_stream,
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generate_from_stream,
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)
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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InvalidToolCall,
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SystemMessage,
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SystemMessageChunk,
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ToolCall,
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ToolMessage,
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)
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from langchain_core.messages.tool import tool_call_chunk
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from langchain_core.output_parsers import (
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JsonOutputParser,
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PydanticOutputParser,
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)
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from langchain_core.output_parsers.base import OutputParserLike
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from langchain_core.output_parsers.openai_tools import (
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JsonOutputKeyToolsParser,
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PydanticToolsParser,
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make_invalid_tool_call,
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parse_tool_call,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
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from langchain_core.tools import BaseTool
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from langchain_core.utils import get_pydantic_field_names, secret_from_env
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from langchain_core.utils.function_calling import convert_to_openai_tool
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from langchain_core.utils.pydantic import is_basemodel_subclass
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from langchain_core.utils.utils import _build_model_kwargs
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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SecretStr,
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model_validator,
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)
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from typing_extensions import Self
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logger = logging.getLogger(__name__)
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# Mistral enforces a specific pattern for tool call IDs
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TOOL_CALL_ID_PATTERN = re.compile(r"^[a-zA-Z0-9]{9}$")
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# This SSL context is equivelent to the default `verify=True`.
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# https://www.python-httpx.org/advanced/ssl/#configuring-client-instances
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global_ssl_context = ssl.create_default_context(cafile=certifi.where())
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def _create_retry_decorator(
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llm: ChatMistralAI,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Returns a tenacity retry decorator, preconfigured to handle exceptions"""
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errors = [httpx.RequestError, httpx.StreamError]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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def _is_valid_mistral_tool_call_id(tool_call_id: str) -> bool:
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"""Check if tool call ID is nine character string consisting of a-z, A-Z, 0-9"""
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return bool(TOOL_CALL_ID_PATTERN.match(tool_call_id))
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def _base62_encode(num: int) -> str:
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"""Encodes a number in base62 and ensures result is of a specified length."""
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base62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"
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if num == 0:
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return base62[0]
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arr = []
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base = len(base62)
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while num:
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num, rem = divmod(num, base)
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arr.append(base62[rem])
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arr.reverse()
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return "".join(arr)
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def _convert_tool_call_id_to_mistral_compatible(tool_call_id: str) -> str:
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"""Convert a tool call ID to a Mistral-compatible format"""
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if _is_valid_mistral_tool_call_id(tool_call_id):
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return tool_call_id
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else:
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hash_bytes = hashlib.sha256(tool_call_id.encode()).digest()
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hash_int = int.from_bytes(hash_bytes, byteorder="big")
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base62_str = _base62_encode(hash_int)
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if len(base62_str) >= 9:
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return base62_str[:9]
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else:
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return base62_str.rjust(9, "0")
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def _convert_mistral_chat_message_to_message(
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_message: Dict,
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) -> BaseMessage:
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role = _message["role"]
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assert role == "assistant", f"Expected role to be 'assistant', got {role}"
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content = cast(str, _message["content"])
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additional_kwargs: Dict = {}
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tool_calls = []
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invalid_tool_calls = []
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if raw_tool_calls := _message.get("tool_calls"):
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additional_kwargs["tool_calls"] = raw_tool_calls
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for raw_tool_call in raw_tool_calls:
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try:
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parsed: dict = cast(
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dict, parse_tool_call(raw_tool_call, return_id=True)
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)
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if not parsed["id"]:
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parsed["id"] = uuid.uuid4().hex[:]
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tool_calls.append(parsed)
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except Exception as e:
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invalid_tool_calls.append(make_invalid_tool_call(raw_tool_call, str(e)))
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return AIMessage(
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content=content,
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additional_kwargs=additional_kwargs,
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tool_calls=tool_calls,
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invalid_tool_calls=invalid_tool_calls,
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)
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def _raise_on_error(response: httpx.Response) -> None:
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"""Raise an error if the response is an error."""
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if httpx.codes.is_error(response.status_code):
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error_message = response.read().decode("utf-8")
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raise httpx.HTTPStatusError(
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f"Error response {response.status_code} "
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f"while fetching {response.url}: {error_message}",
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request=response.request,
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response=response,
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)
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async def _araise_on_error(response: httpx.Response) -> None:
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"""Raise an error if the response is an error."""
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if httpx.codes.is_error(response.status_code):
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error_message = (await response.aread()).decode("utf-8")
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raise httpx.HTTPStatusError(
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f"Error response {response.status_code} "
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f"while fetching {response.url}: {error_message}",
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request=response.request,
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response=response,
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)
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async def _aiter_sse(
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event_source_mgr: AsyncContextManager[EventSource],
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) -> AsyncIterator[Dict]:
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"""Iterate over the server-sent events."""
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async with event_source_mgr as event_source:
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await _araise_on_error(event_source.response)
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async for event in event_source.aiter_sse():
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if event.data == "[DONE]":
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return
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yield event.json()
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async def acompletion_with_retry(
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llm: ChatMistralAI,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@retry_decorator
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async def _completion_with_retry(**kwargs: Any) -> Any:
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if "stream" not in kwargs:
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kwargs["stream"] = False
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stream = kwargs["stream"]
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if stream:
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event_source = aconnect_sse(
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llm.async_client, "POST", "/chat/completions", json=kwargs
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)
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return _aiter_sse(event_source)
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else:
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response = await llm.async_client.post(url="/chat/completions", json=kwargs)
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await _araise_on_error(response)
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return response.json()
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return await _completion_with_retry(**kwargs)
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def _convert_chunk_to_message_chunk(
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chunk: Dict, default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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_choice = chunk["choices"][0]
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_delta = _choice["delta"]
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role = _delta.get("role")
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content = _delta.get("content") or ""
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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additional_kwargs: Dict = {}
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response_metadata = {}
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if raw_tool_calls := _delta.get("tool_calls"):
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additional_kwargs["tool_calls"] = raw_tool_calls
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try:
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tool_call_chunks = []
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for raw_tool_call in raw_tool_calls:
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if not raw_tool_call.get("index") and not raw_tool_call.get("id"):
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tool_call_id = uuid.uuid4().hex[:]
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else:
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tool_call_id = raw_tool_call.get("id")
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tool_call_chunks.append(
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tool_call_chunk(
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name=raw_tool_call["function"].get("name"),
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args=raw_tool_call["function"].get("arguments"),
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id=tool_call_id,
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index=raw_tool_call.get("index"),
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)
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)
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except KeyError:
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pass
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else:
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tool_call_chunks = []
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if token_usage := chunk.get("usage"):
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usage_metadata = {
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"input_tokens": token_usage.get("prompt_tokens", 0),
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"output_tokens": token_usage.get("completion_tokens", 0),
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"total_tokens": token_usage.get("total_tokens", 0),
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}
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else:
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usage_metadata = None
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if _choice.get("finish_reason") is not None and isinstance(
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chunk.get("model"), str
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):
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response_metadata["model_name"] = chunk.get("model")
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return AIMessageChunk(
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content=content,
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additional_kwargs=additional_kwargs,
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tool_call_chunks=tool_call_chunks, # type: ignore[arg-type]
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usage_metadata=usage_metadata, # type: ignore[arg-type]
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response_metadata=response_metadata,
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)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content) # type: ignore[call-arg]
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def _format_tool_call_for_mistral(tool_call: ToolCall) -> dict:
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"""Format Langchain ToolCall to dict expected by Mistral."""
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result: Dict[str, Any] = {
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"function": {
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"name": tool_call["name"],
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"arguments": json.dumps(tool_call["args"]),
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}
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}
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if _id := tool_call.get("id"):
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result["id"] = _convert_tool_call_id_to_mistral_compatible(_id)
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return result
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def _format_invalid_tool_call_for_mistral(invalid_tool_call: InvalidToolCall) -> dict:
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"""Format Langchain InvalidToolCall to dict expected by Mistral."""
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result: Dict[str, Any] = {
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"function": {
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"name": invalid_tool_call["name"],
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"arguments": invalid_tool_call["args"],
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}
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}
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if _id := invalid_tool_call.get("id"):
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result["id"] = _convert_tool_call_id_to_mistral_compatible(_id)
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return result
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def _convert_message_to_mistral_chat_message(
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message: BaseMessage,
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) -> Dict:
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if isinstance(message, ChatMessage):
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return dict(role=message.role, content=message.content)
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elif isinstance(message, HumanMessage):
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return dict(role="user", content=message.content)
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elif isinstance(message, AIMessage):
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message_dict: Dict[str, Any] = {"role": "assistant"}
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tool_calls = []
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if message.tool_calls or message.invalid_tool_calls:
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for tool_call in message.tool_calls:
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tool_calls.append(_format_tool_call_for_mistral(tool_call))
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for invalid_tool_call in message.invalid_tool_calls:
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tool_calls.append(
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_format_invalid_tool_call_for_mistral(invalid_tool_call)
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)
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elif "tool_calls" in message.additional_kwargs:
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for tc in message.additional_kwargs["tool_calls"]:
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chunk = {
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"function": {
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"],
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}
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}
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if _id := tc.get("id"):
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chunk["id"] = _id
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tool_calls.append(chunk)
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else:
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pass
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if tool_calls: # do not populate empty list tool_calls
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message_dict["tool_calls"] = tool_calls
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if tool_calls and message.content:
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# Assistant message must have either content or tool_calls, but not both.
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# Some providers may not support tool_calls in the same message as content.
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# This is done to ensure compatibility with messages from other providers.
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message_dict["content"] = ""
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else:
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message_dict["content"] = message.content
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if "prefix" in message.additional_kwargs:
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message_dict["prefix"] = message.additional_kwargs["prefix"]
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return message_dict
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elif isinstance(message, SystemMessage):
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return dict(role="system", content=message.content)
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elif isinstance(message, ToolMessage):
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return {
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"role": "tool",
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"content": message.content,
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"name": message.name,
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"tool_call_id": _convert_tool_call_id_to_mistral_compatible(
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message.tool_call_id
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),
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}
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else:
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raise ValueError(f"Got unknown type {message}")
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class ChatMistralAI(BaseChatModel):
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"""A chat model that uses the MistralAI API."""
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# The type for client and async_client is ignored because the type is not
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# an Optional after the model is initialized and the model_validator
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# is run.
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client: httpx.Client = Field( # type: ignore # : meta private:
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default=None, exclude=True
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)
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async_client: httpx.AsyncClient = Field( # type: ignore # : meta private:
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default=None, exclude=True
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) #: :meta private:
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mistral_api_key: Optional[SecretStr] = Field(
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alias="api_key",
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default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
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)
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endpoint: Optional[str] = Field(default=None, alias="base_url")
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max_retries: int = 5
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timeout: int = 120
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max_concurrent_requests: int = 64
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model: str = Field(default="mistral-small", alias="model_name")
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temperature: float = 0.7
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max_tokens: Optional[int] = None
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top_p: float = 1
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"""Decode using nucleus sampling: consider the smallest set of tokens whose
|
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probability sum is at least top_p. Must be in the closed interval [0.0, 1.0]."""
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random_seed: Optional[int] = None
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safe_mode: Optional[bool] = None
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streaming: bool = False
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any invocation parameters not explicitly specified."""
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model_config = ConfigDict(
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populate_by_name=True,
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arbitrary_types_allowed=True,
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)
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|
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@model_validator(mode="before")
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@classmethod
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def build_extra(cls, values: Dict[str, Any]) -> Any:
|
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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values = _build_model_kwargs(values, all_required_field_names)
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return values
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|
|
@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling the API."""
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defaults = {
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"model": self.model,
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"temperature": self.temperature,
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"max_tokens": self.max_tokens,
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"top_p": self.top_p,
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"random_seed": self.random_seed,
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"safe_prompt": self.safe_mode,
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**self.model_kwargs,
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}
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filtered = {k: v for k, v in defaults.items() if v is not None}
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return filtered
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|
|
def _get_ls_params(
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self, stop: Optional[List[str]] = None, **kwargs: Any
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) -> LangSmithParams:
|
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"""Get standard params for tracing."""
|
|
params = self._get_invocation_params(stop=stop, **kwargs)
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ls_params = LangSmithParams(
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ls_provider="mistral",
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ls_model_name=self.model,
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ls_model_type="chat",
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ls_temperature=params.get("temperature", self.temperature),
|
|
)
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if ls_max_tokens := params.get("max_tokens", self.max_tokens):
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ls_params["ls_max_tokens"] = ls_max_tokens
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if ls_stop := stop or params.get("stop", None):
|
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ls_params["ls_stop"] = ls_stop
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return ls_params
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|
|
@property
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def _client_params(self) -> Dict[str, Any]:
|
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"""Get the parameters used for the client."""
|
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return self._default_params
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|
|
def completion_with_retry(
|
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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:
|
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if "stream" not in kwargs:
|
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kwargs["stream"] = False
|
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stream = kwargs["stream"]
|
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if stream:
|
|
|
|
def iter_sse() -> Iterator[Dict]:
|
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with connect_sse(
|
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self.client, "POST", "/chat/completions", json=kwargs
|
|
) as event_source:
|
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_raise_on_error(event_source.response)
|
|
for event in event_source.iter_sse():
|
|
if event.data == "[DONE]":
|
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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
|
|
|
|
@model_validator(mode="after")
|
|
def validate_environment(self) -> Self:
|
|
"""Validate api key, python package exists, temperature, and top_p."""
|
|
if isinstance(self.mistral_api_key, SecretStr):
|
|
api_key_str: Optional[str] = self.mistral_api_key.get_secret_value()
|
|
else:
|
|
api_key_str = self.mistral_api_key
|
|
|
|
# todo: handle retries
|
|
base_url_str = (
|
|
self.endpoint
|
|
or os.environ.get("MISTRAL_BASE_URL")
|
|
or "https://api.mistral.ai/v1"
|
|
)
|
|
self.endpoint = base_url_str
|
|
if not self.client:
|
|
self.client = httpx.Client(
|
|
base_url=base_url_str,
|
|
headers={
|
|
"Content-Type": "application/json",
|
|
"Accept": "application/json",
|
|
"Authorization": f"Bearer {api_key_str}",
|
|
},
|
|
timeout=self.timeout,
|
|
verify=global_ssl_context,
|
|
)
|
|
# todo: handle retries and max_concurrency
|
|
if not self.async_client:
|
|
self.async_client = httpx.AsyncClient(
|
|
base_url=base_url_str,
|
|
headers={
|
|
"Content-Type": "application/json",
|
|
"Accept": "application/json",
|
|
"Authorization": f"Bearer {api_key_str}",
|
|
},
|
|
timeout=self.timeout,
|
|
verify=global_ssl_context,
|
|
)
|
|
|
|
if self.temperature is not None and not 0 <= self.temperature <= 1:
|
|
raise ValueError("temperature must be in the range [0.0, 1.0]")
|
|
|
|
if self.top_p is not None and not 0 <= self.top_p <= 1:
|
|
raise ValueError("top_p must be in the range [0.0, 1.0]")
|
|
|
|
return self
|
|
|
|
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_name": self.model,
|
|
"model": self.model, # Backwards compatability
|
|
}
|
|
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.get("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.get("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)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
|
|
tool_choice: Optional[Union[dict, str, Literal["auto", "any"]]] = None,
|
|
**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.
|
|
Supports any tool definition handled by
|
|
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
|
|
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 are passed directly to
|
|
``self.bind(**kwargs)``.
|
|
"""
|
|
|
|
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
|
if tool_choice:
|
|
tool_names = []
|
|
for tool in formatted_tools:
|
|
if "function" in tool and (name := tool["function"].get("name")):
|
|
tool_names.append(name)
|
|
elif name := tool.get("name"):
|
|
tool_names.append(name)
|
|
else:
|
|
pass
|
|
if tool_choice in tool_names:
|
|
kwargs["tool_choice"] = {
|
|
"type": "function",
|
|
"function": {"name": tool_choice},
|
|
}
|
|
else:
|
|
kwargs["tool_choice"] = tool_choice
|
|
return super().bind(tools=formatted_tools, **kwargs)
|
|
|
|
def with_structured_output(
|
|
self,
|
|
schema: Optional[Union[Dict, Type]] = None,
|
|
*,
|
|
method: Literal[
|
|
"function_calling", "json_mode", "json_schema"
|
|
] = "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, one of:
|
|
|
|
- "function_calling":
|
|
Uses Mistral's
|
|
`function-calling feature <https://docs.mistral.ai/capabilities/function_calling/>`_.
|
|
- "json_schema":
|
|
Uses Mistral's
|
|
`structured output feature <https://docs.mistral.ai/capabilities/structured-output/custom_structured_output/>`_.
|
|
- "json_mode":
|
|
Uses Mistral's
|
|
`JSON mode <https://docs.mistral.ai/capabilities/structured-output/json_mode/>`_.
|
|
Note that if using JSON mode then you
|
|
must include instructions for formatting the output into the
|
|
desired schema into the model call.
|
|
|
|
.. versionchanged:: 0.2.5
|
|
|
|
Added method="json_schema"
|
|
|
|
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 pydantic 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 pydantic 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 pydantic 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
|
|
_ = kwargs.pop("strict", None)
|
|
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",
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": "function_calling"},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
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"},
|
|
ls_structured_output_format={
|
|
"kwargs": {
|
|
# this is correct - name difference with mistral api
|
|
"method": "json_mode"
|
|
},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
|
|
if is_pydantic_schema
|
|
else JsonOutputParser()
|
|
)
|
|
elif method == "json_schema":
|
|
if schema is None:
|
|
raise ValueError(
|
|
"schema must be specified when method is 'json_schema'. "
|
|
"Received None."
|
|
)
|
|
response_format = _convert_to_openai_response_format(schema, strict=True)
|
|
llm = self.bind(
|
|
response_format=response_format,
|
|
ls_structured_output_format={
|
|
"kwargs": {"method": "json_schema"},
|
|
"schema": schema,
|
|
},
|
|
)
|
|
|
|
output_parser = (
|
|
PydanticOutputParser(pydantic_object=schema) # type: ignore[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"]
|
|
|
|
|
|
def _convert_to_openai_response_format(
|
|
schema: Union[Dict[str, Any], Type], *, strict: Optional[bool] = None
|
|
) -> Dict:
|
|
"""Same as in ChatOpenAI, but don't pass through Pydantic BaseModels."""
|
|
if (
|
|
isinstance(schema, dict)
|
|
and "json_schema" in schema
|
|
and schema.get("type") == "json_schema"
|
|
):
|
|
response_format = schema
|
|
elif isinstance(schema, dict) and "name" in schema and "schema" in schema:
|
|
response_format = {"type": "json_schema", "json_schema": schema}
|
|
else:
|
|
if strict is None:
|
|
if isinstance(schema, dict) and isinstance(schema.get("strict"), bool):
|
|
strict = schema["strict"]
|
|
else:
|
|
strict = False
|
|
function = convert_to_openai_tool(schema, strict=strict)["function"]
|
|
function["schema"] = function.pop("parameters")
|
|
response_format = {"type": "json_schema", "json_schema": function}
|
|
|
|
if strict is not None and strict is not response_format["json_schema"].get(
|
|
"strict"
|
|
):
|
|
msg = (
|
|
f"Output schema already has 'strict' value set to "
|
|
f"{schema['json_schema']['strict']} but 'strict' also passed in to "
|
|
f"with_structured_output as {strict}. Please make sure that "
|
|
f"'strict' is only specified in one place."
|
|
)
|
|
raise ValueError(msg)
|
|
return response_format
|