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849 lines
34 KiB
Python
849 lines
34 KiB
Python
from __future__ import annotations
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import json
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import logging
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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 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.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.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
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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 convert_to_secret_str, get_from_dict_or_env
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from langchain_core.utils.function_calling import convert_to_openai_tool
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logger = logging.getLogger(__name__)
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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 _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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tool_call_id = uuid.uuid4().hex[:]
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tool_calls.append(
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{
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**parsed,
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**{"id": tool_call_id},
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},
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)
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else:
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tool_calls.append(parsed)
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except Exception as e:
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invalid_tool_calls.append(
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dict(make_invalid_tool_call(raw_tool_call, str(e)))
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)
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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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_delta = chunk["choices"][0]["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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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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{
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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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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,
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usage_metadata=usage_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)
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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"] = _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"] = _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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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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}
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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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client: httpx.Client = Field(default=None) #: :meta private:
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async_client: httpx.AsyncClient = Field(default=None) #: :meta private:
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mistral_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
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endpoint: str = "https://api.mistral.ai/v1"
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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: bool = False
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streaming: bool = False
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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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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}
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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."""
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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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)
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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
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) -> Any:
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"""Use tenacity to retry the completion call."""
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# retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
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# @retry_decorator
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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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def iter_sse() -> Iterator[Dict]:
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with connect_sse(
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self.client, "POST", "/chat/completions", json=kwargs
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) as event_source:
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_raise_on_error(event_source.response)
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for event in event_source.iter_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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return iter_sse()
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else:
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response = self.client.post(url="/chat/completions", json=kwargs)
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_raise_on_error(response)
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return response.json()
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rtn = _completion_with_retry(**kwargs)
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return rtn
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|
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
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overall_token_usage: dict = {}
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for output in llm_outputs:
|
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if output is None:
|
|
# Happens in streaming
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continue
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token_usage = output["token_usage"]
|
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if token_usage is not None:
|
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for k, v in token_usage.items():
|
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if k in overall_token_usage:
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overall_token_usage[k] += v
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else:
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overall_token_usage[k] = v
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combined = {"token_usage": overall_token_usage, "model_name": self.model}
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return combined
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|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
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"""Validate api key, python package exists, temperature, and top_p."""
|
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|
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values["mistral_api_key"] = convert_to_secret_str(
|
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get_from_dict_or_env(
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values, "mistral_api_key", "MISTRAL_API_KEY", default=""
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)
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|
)
|
|
api_key_str = values["mistral_api_key"].get_secret_value()
|
|
# todo: handle retries
|
|
if not values.get("client"):
|
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values["client"] = httpx.Client(
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base_url=values["endpoint"],
|
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headers={
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"Content-Type": "application/json",
|
|
"Accept": "application/json",
|
|
"Authorization": f"Bearer {api_key_str}",
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},
|
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timeout=values["timeout"],
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)
|
|
# todo: handle retries and max_concurrency
|
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if not values.get("async_client"):
|
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values["async_client"] = httpx.AsyncClient(
|
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base_url=values["endpoint"],
|
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headers={
|
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"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:
|
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raise ValueError("temperature must be in the range [0.0, 1.0]")
|
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|
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if values["top_p"] is not None and not 0 <= values["top_p"] <= 1:
|
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raise ValueError("top_p must be in the range [0.0, 1.0]")
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|
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return values
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|
|
def _generate(
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self,
|
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messages: List[BaseMessage],
|
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stop: Optional[List[str]] = None,
|
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run_manager: Optional[CallbackManagerForLLMRun] = None,
|
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stream: Optional[bool] = None,
|
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**kwargs: Any,
|
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) -> ChatResult:
|
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should_stream = stream if stream is not None else self.streaming
|
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if should_stream:
|
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stream_iter = self._stream(
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messages, stop=stop, run_manager=run_manager, **kwargs
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)
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return generate_from_stream(stream_iter)
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|
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs}
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response = self.completion_with_retry(
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messages=message_dicts, run_manager=run_manager, **params
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)
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return self._create_chat_result(response)
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|
|
def _create_chat_result(self, response: Dict) -> ChatResult:
|
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generations = []
|
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token_usage = response.get("usage", {})
|
|
for res in response["choices"]:
|
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finish_reason = res.get("finish_reason")
|
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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 False
|
|
if should_stream:
|
|
stream_iter = self._astream(
|
|
messages=messages, stop=stop, run_manager=run_manager, **kwargs
|
|
)
|
|
return await agenerate_from_stream(stream_iter)
|
|
|
|
message_dicts, params = self._create_message_dicts(messages, stop)
|
|
params = {**params, **kwargs}
|
|
response = await acompletion_with_retry(
|
|
self, messages=message_dicts, run_manager=run_manager, **params
|
|
)
|
|
return self._create_chat_result(response)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
Assumes model is compatible with OpenAI tool-calling API.
|
|
|
|
Args:
|
|
tools: A list of tool definitions to bind to this chat model.
|
|
Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
|
|
models, callables, and BaseTools will be automatically converted to
|
|
their schema dictionary representation.
|
|
tool_choice: Which tool to require the model to call.
|
|
Must be the name of the single provided function or
|
|
"auto" to automatically determine which function to call
|
|
(if any), or a dict of the form:
|
|
{"type": "function", "function": {"name": <<tool_name>>}}.
|
|
**kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
|
|
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
|
|
return super().bind(tools=formatted_tools, **kwargs)
|
|
|
|
def with_structured_output(
|
|
self,
|
|
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]]:
|
|
"""Model wrapper that returns outputs formatted to match the given schema.
|
|
|
|
Args:
|
|
schema: The output schema as a dict or a Pydantic class. If a Pydantic class
|
|
then the model output will be an object of that class. If a dict then
|
|
the model output will be a dict. With a Pydantic class the returned
|
|
attributes will be validated, whereas with a dict they will not be. If
|
|
`method` is "function_calling" and `schema` is a dict, then the dict
|
|
must match the OpenAI function-calling spec.
|
|
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 any ChatModel input and returns as output:
|
|
|
|
If include_raw is True then a dict with keys:
|
|
raw: BaseMessage
|
|
parsed: Optional[_DictOrPydantic]
|
|
parsing_error: Optional[BaseException]
|
|
|
|
If include_raw is False then just _DictOrPydantic is returned,
|
|
where _DictOrPydantic depends on the schema:
|
|
|
|
If schema is a Pydantic class then _DictOrPydantic is the Pydantic
|
|
class.
|
|
|
|
If schema is a dict then _DictOrPydantic is a dict.
|
|
|
|
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
|
|
.. code-block:: python
|
|
|
|
from langchain_mistralai import ChatMistralAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
|
|
# -> AnswerWithJustification(
|
|
# answer='They weigh the same',
|
|
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
|
|
# )
|
|
|
|
Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
|
|
.. code-block:: python
|
|
|
|
from langchain_mistralai import ChatMistralAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
|
|
structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
# -> {
|
|
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
|
|
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
|
|
# 'parsing_error': None
|
|
# }
|
|
|
|
Example: Function-calling, dict schema (method="function_calling", include_raw=False):
|
|
.. code-block:: python
|
|
|
|
from langchain_mistralai import ChatMistralAI
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
from langchain_core.utils.function_calling import convert_to_openai_tool
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
'''An answer to the user question along with justification for the answer.'''
|
|
answer: str
|
|
justification: str
|
|
|
|
dict_schema = convert_to_openai_tool(AnswerWithJustification)
|
|
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
|
|
structured_llm = llm.with_structured_output(dict_schema)
|
|
|
|
structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
|
|
# -> {
|
|
# 'answer': 'They weigh the same',
|
|
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
|
|
# }
|
|
|
|
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(
|
|
tools=[schema], first_tool_only=True
|
|
)
|
|
else:
|
|
key_name = convert_to_openai_tool(schema)["function"]["name"]
|
|
output_parser = JsonOutputKeyToolsParser(
|
|
key_name=key_name, first_tool_only=True
|
|
)
|
|
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
|
|
)
|
|
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"]
|