mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-08 20:41:52 +00:00
mistral[minor]: Function calling and with_structured_output (#18150)
This commit is contained in:
Bagatur
GitHub
parent
c53aa5cd37
commit
b3f4de38ae
@ -2,6 +2,7 @@ from __future__ import annotations
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import importlib.util
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import logging
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from operator import itemgetter
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from typing import (
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Any,
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AsyncIterator,
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@ -10,15 +11,19 @@ from typing import (
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Iterator,
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List,
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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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from langchain_core._api import beta
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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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agenerate_from_stream,
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@ -36,19 +41,22 @@ from langchain_core.messages import (
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessage,
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)
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from langchain_core.outputs import (
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ChatGeneration,
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ChatGenerationChunk,
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ChatResult,
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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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)
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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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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from mistralai.async_client import MistralAsyncClient
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from mistralai.client import MistralClient
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from mistralai.constants import (
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ENDPOINT as DEFAULT_MISTRAL_ENDPOINT,
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)
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from mistralai.constants import ENDPOINT as DEFAULT_MISTRAL_ENDPOINT
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from mistralai.exceptions import (
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MistralAPIException,
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MistralConnectionException,
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@ -57,12 +65,8 @@ from mistralai.exceptions import (
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from mistralai.models.chat_completion import (
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ChatCompletionResponse as MistralChatCompletionResponse,
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)
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from mistralai.models.chat_completion import (
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ChatMessage as MistralChatMessage,
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)
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from mistralai.models.chat_completion import (
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DeltaMessage as MistralDeltaMessage,
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)
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from mistralai.models.chat_completion import ChatMessage as MistralChatMessage
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from mistralai.models.chat_completion import DeltaMessage as MistralDeltaMessage
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logger = logging.getLogger(__name__)
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@ -89,14 +93,22 @@ def _convert_mistral_chat_message_to_message(
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_message: MistralChatMessage,
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) -> BaseMessage:
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role = _message.role
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content = cast(Union[str, List], _message.content)
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if role == "user":
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return HumanMessage(content=_message.content)
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return HumanMessage(content=content)
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elif role == "assistant":
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return AIMessage(content=_message.content)
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additional_kwargs: Dict = {}
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if hasattr(_message, "tool_calls") and getattr(_message, "tool_calls"):
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additional_kwargs["tool_calls"] = [
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tc.model_dump() for tc in getattr(_message, "tool_calls")
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]
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=_message.content)
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return SystemMessage(content=content)
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elif role == "tool":
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return ToolMessage(content=content, name=_message.name) # type: ignore[attr-defined]
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else:
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return ChatMessage(content=_message.content, role=role)
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return ChatMessage(content=content, role=role)
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async def acompletion_with_retry(
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@ -119,14 +131,19 @@ async def acompletion_with_retry(
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def _convert_delta_to_message_chunk(
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_obj: MistralDeltaMessage, default_class: Type[BaseMessageChunk]
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_delta: MistralDeltaMessage, default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = getattr(_obj, "role")
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content = getattr(_obj, "content", "")
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role = getattr(_delta, "role")
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content = getattr(_delta, "content", "")
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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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return AIMessageChunk(content=content)
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additional_kwargs: Dict = {}
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if hasattr(_delta, "tool_calls") and getattr(_delta, "tool_calls"):
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additional_kwargs["tool_calls"] = [
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tc.model_dump() for tc in getattr(_delta, "tool_calls")
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]
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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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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@ -143,9 +160,26 @@ def _convert_message_to_mistral_chat_message(
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elif isinstance(message, HumanMessage):
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mistral_message = MistralChatMessage(role="user", content=message.content)
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elif isinstance(message, AIMessage):
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mistral_message = MistralChatMessage(role="assistant", content=message.content)
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if "tool_calls" in message.additional_kwargs:
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from mistralai.models.chat_completion import ( # type: ignore[attr-defined]
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ToolCall as MistralToolCall,
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)
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tool_calls = [
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MistralToolCall.model_validate(tc)
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for tc in message.additional_kwargs["tool_calls"]
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]
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else:
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tool_calls = None
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mistral_message = MistralChatMessage(
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role="assistant", content=message.content, tool_calls=tool_calls
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)
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elif isinstance(message, SystemMessage):
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mistral_message = MistralChatMessage(role="system", content=message.content)
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elif isinstance(message, ToolMessage):
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mistral_message = MistralChatMessage(
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role="tool", content=message.content, 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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return mistral_message
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@ -368,6 +402,163 @@ class ChatMistralAI(BaseChatModel):
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)
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return self._create_chat_result(response)
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def bind_tools(
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self,
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tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, BaseMessage]:
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"""Bind tool-like objects to this chat model.
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Assumes model is compatible with OpenAI tool-calling API.
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Args:
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tools: A list of tool definitions to bind to this chat model.
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Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic
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models, callables, and BaseTools will be automatically converted to
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their schema dictionary representation.
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tool_choice: Which tool to require the model to call.
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Must be the name of the single provided function or
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"auto" to automatically determine which function to call
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(if any), or a dict of the form:
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{"type": "function", "function": {"name": <<tool_name>>}}.
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**kwargs: Any additional parameters to pass to the
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:class:`~langchain.runnable.Runnable` constructor.
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"""
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formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
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return super().bind(tools=formatted_tools, **kwargs)
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@beta()
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def with_structured_output(
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self,
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schema: Union[Dict, Type[BaseModel]],
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*,
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include_raw: bool = False,
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**kwargs: Any,
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) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
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"""Model wrapper that returns outputs formatted to match the given schema.
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Args:
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schema: The output schema as a dict or a Pydantic class. If a Pydantic class
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then the model output will be an object of that class. If a dict then
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the model output will be a dict. With a Pydantic class the returned
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attributes will be validated, whereas with a dict they will not be. If
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`method` is "function_calling" and `schema` is a dict, then the dict
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must match the OpenAI function-calling spec.
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include_raw: If False then only the parsed structured output is returned. If
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an error occurs during model output parsing it will be raised. If True
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then both the raw model response (a BaseMessage) and the parsed model
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response will be returned. If an error occurs during output parsing it
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will be caught and returned as well. The final output is always a dict
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with keys "raw", "parsed", and "parsing_error".
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Returns:
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A Runnable that takes any ChatModel input and returns as output:
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If include_raw is True then a dict with keys:
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raw: BaseMessage
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parsed: Optional[_DictOrPydantic]
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parsing_error: Optional[BaseException]
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If include_raw is False then just _DictOrPydantic is returned,
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where _DictOrPydantic depends on the schema:
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If schema is a Pydantic class then _DictOrPydantic is the Pydantic
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class.
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If schema is a dict then _DictOrPydantic is a dict.
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Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
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.. code-block:: python
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from langchain_mistralai import ChatMistralAI
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> AnswerWithJustification(
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# answer='They weigh the same',
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# 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.'
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# )
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Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
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.. code-block:: python
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from langchain_mistralai import ChatMistralAI
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from langchain_core.pydantic_v1 import BaseModel
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
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structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> {
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# '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'}]}),
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# '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.'),
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# 'parsing_error': None
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# }
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Example: Function-calling, dict schema (method="function_calling", include_raw=False):
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.. code-block:: python
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from langchain_mistralai import ChatMistralAI
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.utils.function_calling import convert_to_openai_tool
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class AnswerWithJustification(BaseModel):
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'''An answer to the user question along with justification for the answer.'''
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answer: str
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justification: str
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dict_schema = convert_to_openai_tool(AnswerWithJustification)
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llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
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structured_llm = llm.with_structured_output(dict_schema)
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structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
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# -> {
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# 'answer': 'They weigh the same',
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# '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.'
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# }
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""" # noqa: E501
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if kwargs:
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raise ValueError(f"Received unsupported arguments {kwargs}")
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is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
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llm = self.bind_tools([schema], tool_choice="any")
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if is_pydantic_schema:
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output_parser: OutputParserLike = PydanticToolsParser(
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tools=[schema], first_tool_only=True
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)
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else:
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key_name = convert_to_openai_tool(schema)["function"]["name"]
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output_parser = JsonOutputKeyToolsParser(
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key_name=key_name, first_tool_only=True
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)
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if include_raw:
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parser_assign = RunnablePassthrough.assign(
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parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
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)
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parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
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parser_with_fallback = parser_assign.with_fallbacks(
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[parser_none], exception_key="parsing_error"
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)
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return RunnableMap(raw=llm) | parser_with_fallback
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else:
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return llm | output_parser
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@property
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def _identifying_params(self) -> Dict[str, Any]:
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"""Get the identifying parameters."""
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546
libs/partners/mistralai/poetry.lock
generated
546
libs/partners/mistralai/poetry.lock
generated
@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""}
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[[package]]
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name = "anyio"
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version = "4.2.0"
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version = "4.3.0"
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description = "High level compatibility layer for multiple asynchronous event loop implementations"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"},
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{file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"},
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{file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
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{file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
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]
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[package.dependencies]
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@ -252,13 +252,13 @@ files = [
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[[package]]
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name = "httpcore"
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version = "1.0.2"
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version = "1.0.4"
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description = "A minimal low-level HTTP client."
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"},
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{file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"},
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{file = "httpcore-1.0.4-py3-none-any.whl", hash = "sha256:ac418c1db41bade2ad53ae2f3834a3a0f5ae76b56cf5aa497d2d033384fc7d73"},
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{file = "httpcore-1.0.4.tar.gz", hash = "sha256:cb2839ccfcba0d2d3c1131d3c3e26dfc327326fbe7a5dc0dbfe9f6c9151bb022"},
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]
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[package.dependencies]
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@ -269,7 +269,7 @@ h11 = ">=0.13,<0.15"
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asyncio = ["anyio (>=4.0,<5.0)"]
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http2 = ["h2 (>=3,<5)"]
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socks = ["socksio (==1.*)"]
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trio = ["trio (>=0.22.0,<0.23.0)"]
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trio = ["trio (>=0.22.0,<0.25.0)"]
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[[package]]
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name = "httpx"
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@ -376,7 +376,7 @@ files = [
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[[package]]
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name = "langchain-core"
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version = "0.1.23"
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version = "0.1.26"
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description = "Building applications with LLMs through composability"
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optional = false
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python-versions = ">=3.8.1,<4.0"
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@ -386,7 +386,7 @@ develop = true
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[package.dependencies]
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anyio = ">=3,<5"
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jsonpatch = "^1.33"
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langsmith = "^0.0.87"
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langsmith = "^0.1.0"
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packaging = "^23.2"
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pydantic = ">=1,<3"
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PyYAML = ">=5.3"
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@ -402,16 +402,17 @@ url = "../../core"
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[[package]]
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name = "langsmith"
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version = "0.0.87"
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version = "0.1.8"
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description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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optional = false
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python-versions = ">=3.8.1,<4.0"
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files = [
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{file = "langsmith-0.0.87-py3-none-any.whl", hash = "sha256:8903d3811b9fc89eb18f5961c8e6935fbd2d0f119884fbf30dc70b8f8f4121fc"},
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{file = "langsmith-0.0.87.tar.gz", hash = "sha256:36c4cc47e5b54be57d038036a30fb19ce6e4c73048cd7a464b8f25b459694d34"},
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{file = "langsmith-0.1.8-py3-none-any.whl", hash = "sha256:f4320fd80ec9d311a648e7d4c44e0814e6e5454772c5026f40db0307bc07e287"},
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{file = "langsmith-0.1.8.tar.gz", hash = "sha256:ab5f1cdfb7d418109ea506d41928fb8708547db2f6c7f7da7cfe997f3c55767b"},
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]
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[package.dependencies]
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orjson = ">=3.9.14,<4.0.0"
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pydantic = ">=1,<3"
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requests = ">=2,<3"
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||||
|
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{file = "pydantic_core-2.16.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:b29eeb887aa931c2fcef5aa515d9d176d25006794610c264ddc114c053bf96fe"},
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||||
{file = "pydantic_core-2.16.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75f76ee558751746d6a38f89d60b6228fa174e5172d143886af0f85aa306fd89"},
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||||
{file = "pydantic_core-2.16.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:00ee1c97b5364b84cb0bd82e9bbf645d5e2871fb8c58059d158412fee2d33d8a"},
|
||||
{file = "pydantic_core-2.16.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:287073c66748f624be4cef893ef9174e3eb88fe0b8a78dc22e88eca4bc357ca6"},
|
||||
{file = "pydantic_core-2.16.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:ed25e1835c00a332cb10c683cd39da96a719ab1dfc08427d476bce41b92531fc"},
|
||||
{file = "pydantic_core-2.16.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:86b3d0033580bd6bbe07590152007275bd7af95f98eaa5bd36f3da219dcd93da"},
|
||||
{file = "pydantic_core-2.16.3.tar.gz", hash = "sha256:1cac689f80a3abab2d3c0048b29eea5751114054f032a941a32de4c852c59cad"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -727,6 +912,31 @@ pytest = ">=7.0.0"
|
||||
docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"]
|
||||
testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "python-dateutil"
|
||||
version = "2.8.2"
|
||||
description = "Extensions to the standard Python datetime module"
|
||||
optional = false
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
|
||||
files = [
|
||||
{file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
|
||||
{file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
six = ">=1.5"
|
||||
|
||||
[[package]]
|
||||
name = "pytz"
|
||||
version = "2024.1"
|
||||
description = "World timezone definitions, modern and historical"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"},
|
||||
{file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pyyaml"
|
||||
version = "6.0.1"
|
||||
@ -834,15 +1044,26 @@ files = [
|
||||
{file = "ruff-0.1.15.tar.gz", hash = "sha256:f6dfa8c1b21c913c326919056c390966648b680966febcb796cc9d1aaab8564e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.16.0"
|
||||
description = "Python 2 and 3 compatibility utilities"
|
||||
optional = false
|
||||
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
files = [
|
||||
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
|
||||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sniffio"
|
||||
version = "1.3.0"
|
||||
version = "1.3.1"
|
||||
description = "Sniff out which async library your code is running under"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "sniffio-1.3.0-py3-none-any.whl", hash = "sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384"},
|
||||
{file = "sniffio-1.3.0.tar.gz", hash = "sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101"},
|
||||
{file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
|
||||
{file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1019,24 +1240,35 @@ telegram = ["requests"]
|
||||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.9.0"
|
||||
version = "4.10.0"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
|
||||
{file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
|
||||
{file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
|
||||
{file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.1"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"},
|
||||
{file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.2.0"
|
||||
version = "2.2.1"
|
||||
description = "HTTP library with thread-safe connection pooling, file post, and more."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"},
|
||||
{file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"},
|
||||
{file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
|
||||
{file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -1048,4 +1280,4 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "80ff38d21357b7d1d0dded7726d1d61b342307280838454ded0a7ed1b1220b40"
|
||||
content-hash = "1c452f2c7766fd2ea8327da8d028edc517354f8fbc62a3efa3dcb852c461d058"
|
||||
|
||||
@ -13,7 +13,7 @@ license = "MIT"
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.8.1,<4.0"
|
||||
langchain-core = "^0.1"
|
||||
mistralai = ">=0.0.11,<0.1"
|
||||
mistralai = [{version = "^0.1", python = "^3.9"}, {version = ">=0.0.11,<0.2", python="3.8"}]
|
||||
tokenizers = "^0.15.1"
|
||||
|
||||
[tool.poetry.group.test]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user