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882 lines
36 KiB
Python
882 lines
36 KiB
Python
"""Fireworks chat wrapper."""
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from __future__ import annotations
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import logging
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import os
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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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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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Mapping,
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Optional,
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Sequence,
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Tuple,
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Type,
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TypedDict,
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Union,
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cast,
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)
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from fireworks.client import AsyncFireworks, Fireworks # type: ignore
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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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generate_from_stream,
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)
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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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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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ToolMessage,
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ToolMessageChunk,
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)
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from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
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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 (
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convert_to_secret_str,
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get_from_dict_or_env,
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get_pydantic_field_names,
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)
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from langchain_core.utils.function_calling import (
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convert_to_openai_function,
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convert_to_openai_tool,
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)
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from langchain_core.utils.utils import build_extra_kwargs
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logger = logging.getLogger(__name__)
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def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
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"""Convert a dictionary to a LangChain message.
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Args:
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_dict: The dictionary.
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Returns:
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The LangChain message.
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"""
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role = _dict.get("role")
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if role == "user":
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return HumanMessage(content=_dict.get("content", ""))
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elif role == "assistant":
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# Fix for azure
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# Also Fireworks returns None for tool invocations
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content = _dict.get("content", "") or ""
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additional_kwargs: Dict = {}
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if function_call := _dict.get("function_call"):
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additional_kwargs["function_call"] = dict(function_call)
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tool_calls = []
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invalid_tool_calls = []
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if raw_tool_calls := _dict.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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tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
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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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elif role == "system":
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return SystemMessage(content=_dict.get("content", ""))
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elif role == "function":
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return FunctionMessage(
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content=_dict.get("content", ""), name=_dict.get("name", "")
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)
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elif role == "tool":
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additional_kwargs = {}
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if "name" in _dict:
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additional_kwargs["name"] = _dict["name"]
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return ToolMessage(
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content=_dict.get("content", ""),
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tool_call_id=_dict.get("tool_call_id", ""),
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additional_kwargs=additional_kwargs,
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)
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else:
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return ChatMessage(content=_dict.get("content", ""), role=role or "")
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a LangChain message to a dictionary.
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Args:
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message: The LangChain message.
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Returns:
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The dictionary.
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"""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["function_call"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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if "tool_calls" in message.additional_kwargs:
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message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
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# If tool calls only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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elif isinstance(message, SystemMessage):
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message_dict = {"role": "system", "content": message.content}
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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elif isinstance(message, ToolMessage):
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message_dict = {
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"role": "tool",
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"content": message.content,
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"tool_call_id": message.tool_call_id,
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}
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else:
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raise TypeError(f"Got unknown type {message}")
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if "name" in message.additional_kwargs:
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message_dict["name"] = message.additional_kwargs["name"]
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return message_dict
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def _convert_delta_to_message_chunk(
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_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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role = cast(str, _dict.get("role"))
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content = cast(str, _dict.get("content") or "")
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additional_kwargs: Dict = {}
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if _dict.get("function_call"):
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function_call = dict(_dict["function_call"])
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if "name" in function_call and function_call["name"] is None:
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function_call["name"] = ""
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additional_kwargs["function_call"] = function_call
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if raw_tool_calls := _dict.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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{
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"name": rtc["function"].get("name"),
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"args": rtc["function"].get("arguments"),
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"id": rtc.get("id"),
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"index": rtc["index"],
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}
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for rtc in raw_tool_calls
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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 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(
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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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)
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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 == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict["name"])
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elif role == "tool" or default_class == ToolMessageChunk:
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return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content) # type: ignore
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class _FunctionCall(TypedDict):
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name: str
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# This is basically a copy and replace for ChatFireworks, except
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# - I needed to gut out tiktoken and some of the token estimation logic
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# (not sure how important it is)
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# - Environment variable is different
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# we should refactor into some OpenAI-like class in the future
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class ChatFireworks(BaseChatModel):
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"""`Fireworks` Chat large language models API.
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To use, you should have the
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environment variable ``FIREWORKS_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the fireworks.create call
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can be passed in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from langchain_fireworks.chat_models import ChatFireworks
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fireworks = ChatFireworks(
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model_name="accounts/fireworks/models/mixtral-8x7b-instruct")
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"""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"fireworks_api_key": "FIREWORKS_API_KEY"}
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "fireworks"]
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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attributes: Dict[str, Any] = {}
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if self.fireworks_api_base:
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attributes["fireworks_api_base"] = self.fireworks_api_base
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return attributes
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@classmethod
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def is_lc_serializable(cls) -> bool:
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"""Return whether this model can be serialized by Langchain."""
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return True
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(
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default="accounts/fireworks/models/mixtral-8x7b-instruct", alias="model"
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)
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"""Model name to use."""
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temperature: float = 0.0
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"""What sampling temperature to use."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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fireworks_api_key: SecretStr = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `FIREWORKS_API_KEY` if not provided."""
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fireworks_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to Fireworks completion API. Can be float, httpx.Timeout or
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None."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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n: int = 1
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"""Number of chat completions to generate for each prompt."""
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max_tokens: Optional[int] = None
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"""Maximum number of tokens to generate."""
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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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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["n"] > 1 and values["streaming"]:
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raise ValueError("n must be 1 when streaming.")
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values["fireworks_api_key"] = convert_to_secret_str(
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get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
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)
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values["fireworks_api_base"] = values["fireworks_api_base"] or os.getenv(
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"FIREWORKS_API_BASE"
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)
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client_params = {
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"api_key": (
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values["fireworks_api_key"].get_secret_value()
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if values["fireworks_api_key"]
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else None
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),
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"base_url": values["fireworks_api_base"],
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"timeout": values["request_timeout"],
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}
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if not values.get("client"):
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values["client"] = Fireworks(**client_params).chat.completions
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if not values.get("async_client"):
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values["async_client"] = AsyncFireworks(**client_params).chat.completions
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling Fireworks API."""
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params = {
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"model": self.model_name,
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"stream": self.streaming,
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"n": self.n,
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"temperature": self.temperature,
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**self.model_kwargs,
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}
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if self.max_tokens is not None:
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params["max_tokens"] = self.max_tokens
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return params
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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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system_fingerprint = None
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for output in llm_outputs:
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if output is None:
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# 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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if system_fingerprint is None:
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system_fingerprint = output.get("system_fingerprint")
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combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
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if system_fingerprint:
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combined["system_fingerprint"] = system_fingerprint
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return combined
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def _stream(
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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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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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for chunk in self.client.create(messages=message_dicts, **params):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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generation_info = {}
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if finish_reason := choice.get("finish_reason"):
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generation_info["finish_reason"] = finish_reason
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logprobs = choice.get("logprobs")
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if logprobs:
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generation_info["logprobs"] = logprobs
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(
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message=chunk, generation_info=generation_info or None
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)
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
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yield chunk
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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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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {
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**params,
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**({"stream": stream} if stream is not None else {}),
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**kwargs,
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}
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response = self.client.create(messages=message_dicts, **params)
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return self._create_chat_result(response)
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def _create_message_dicts(
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self, messages: List[BaseMessage], stop: Optional[List[str]]
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) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
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params = self._default_params
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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message_dicts = [_convert_message_to_dict(m) for m in messages]
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return message_dicts, params
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def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
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generations = []
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if not isinstance(response, dict):
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response = response.dict()
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for res in response["choices"]:
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message = _convert_dict_to_message(res["message"])
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generation_info = dict(finish_reason=res.get("finish_reason"))
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if "logprobs" in res:
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generation_info["logprobs"] = res["logprobs"]
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gen = ChatGeneration(
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message=message,
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generation_info=generation_info,
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)
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generations.append(gen)
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token_usage = response.get("usage", {})
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llm_output = {
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"token_usage": token_usage,
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"model_name": self.model_name,
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"system_fingerprint": response.get("system_fingerprint", ""),
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}
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return ChatResult(generations=generations, llm_output=llm_output)
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async def _astream(
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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[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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message_dicts, params = self._create_message_dicts(messages, stop)
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params = {**params, **kwargs, "stream": True}
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default_chunk_class = AIMessageChunk
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async for chunk in self.async_client.acreate(messages=message_dicts, **params):
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if not isinstance(chunk, dict):
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chunk = chunk.dict()
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if len(chunk["choices"]) == 0:
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continue
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choice = chunk["choices"][0]
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chunk = _convert_delta_to_message_chunk(
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choice["delta"], default_chunk_class
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)
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generation_info = {}
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if finish_reason := choice.get("finish_reason"):
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generation_info["finish_reason"] = finish_reason
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logprobs = choice.get("logprobs")
|
|
if logprobs:
|
|
generation_info["logprobs"] = logprobs
|
|
default_chunk_class = chunk.__class__
|
|
chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info or None
|
|
)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(
|
|
token=chunk.text, chunk=chunk, logprobs=logprobs
|
|
)
|
|
yield chunk
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
stream: Optional[bool] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
should_stream = stream if stream is not None else self.streaming
|
|
if should_stream:
|
|
stream_iter = self._astream(
|
|
messages, 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,
|
|
**({"stream": stream} if stream is not None else {}),
|
|
**kwargs,
|
|
}
|
|
response = await self.async_client.acreate(messages=message_dicts, **params)
|
|
return self._create_chat_result(response)
|
|
|
|
@property
|
|
def _identifying_params(self) -> Dict[str, Any]:
|
|
"""Get the identifying parameters."""
|
|
return {"model_name": self.model_name, **self._default_params}
|
|
|
|
def _get_invocation_params(
|
|
self, stop: Optional[List[str]] = None, **kwargs: Any
|
|
) -> Dict[str, Any]:
|
|
"""Get the parameters used to invoke the model."""
|
|
return {
|
|
"model": self.model_name,
|
|
**super()._get_invocation_params(stop=stop),
|
|
**self._default_params,
|
|
**kwargs,
|
|
}
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of chat model."""
|
|
return "fireworks-chat"
|
|
|
|
def bind_functions(
|
|
self,
|
|
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
function_call: Optional[
|
|
Union[_FunctionCall, str, Literal["auto", "none"]]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind functions (and other objects) to this chat model.
|
|
|
|
Assumes model is compatible with Fireworks function-calling API.
|
|
|
|
NOTE: Using bind_tools is recommended instead, as the `functions` and
|
|
`function_call` request parameters are officially marked as deprecated by
|
|
Fireworks.
|
|
|
|
Args:
|
|
functions: A list of function definitions to bind to this chat model.
|
|
Can be a dictionary, pydantic model, or callable. Pydantic
|
|
models and callables will be automatically converted to
|
|
their schema dictionary representation.
|
|
function_call: Which function 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).
|
|
**kwargs: Any additional parameters to pass to the
|
|
:class:`~langchain.runnable.Runnable` constructor.
|
|
"""
|
|
|
|
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
|
|
if function_call is not None:
|
|
function_call = (
|
|
{"name": function_call}
|
|
if isinstance(function_call, str)
|
|
and function_call not in ("auto", "none")
|
|
else function_call
|
|
)
|
|
if isinstance(function_call, dict) and len(formatted_functions) != 1:
|
|
raise ValueError(
|
|
"When specifying `function_call`, you must provide exactly one "
|
|
"function."
|
|
)
|
|
if (
|
|
isinstance(function_call, dict)
|
|
and formatted_functions[0]["name"] != function_call["name"]
|
|
):
|
|
raise ValueError(
|
|
f"Function call {function_call} was specified, but the only "
|
|
f"provided function was {formatted_functions[0]['name']}."
|
|
)
|
|
kwargs = {**kwargs, "function_call": function_call}
|
|
return super().bind(
|
|
functions=formatted_functions,
|
|
**kwargs,
|
|
)
|
|
|
|
def bind_tools(
|
|
self,
|
|
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
|
|
*,
|
|
tool_choice: Optional[
|
|
Union[dict, str, Literal["auto", "any", "none"], bool]
|
|
] = None,
|
|
**kwargs: Any,
|
|
) -> Runnable[LanguageModelInput, BaseMessage]:
|
|
"""Bind tool-like objects to this chat model.
|
|
|
|
Assumes model is compatible with Fireworks 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,
|
|
"auto" to automatically determine which function to call
|
|
with the option to not call any function, "any" to enforce that some
|
|
function is called, 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]
|
|
if tool_choice is not None and tool_choice:
|
|
if isinstance(tool_choice, str) and (
|
|
tool_choice not in ("auto", "any", "none")
|
|
):
|
|
tool_choice = {"type": "function", "function": {"name": tool_choice}}
|
|
if isinstance(tool_choice, dict) and (len(formatted_tools) != 1):
|
|
raise ValueError(
|
|
"When specifying `tool_choice`, you must provide exactly one "
|
|
f"tool. Received {len(formatted_tools)} tools."
|
|
)
|
|
if isinstance(tool_choice, dict) and (
|
|
formatted_tools[0]["function"]["name"]
|
|
!= tool_choice["function"]["name"]
|
|
):
|
|
raise ValueError(
|
|
f"Tool choice {tool_choice} was specified, but the only "
|
|
f"provided tool was {formatted_tools[0]['function']['name']}."
|
|
)
|
|
if isinstance(tool_choice, bool):
|
|
if len(tools) > 1:
|
|
raise ValueError(
|
|
"tool_choice can only be True when there is one tool. Received "
|
|
f"{len(tools)} tools."
|
|
)
|
|
tool_name = formatted_tools[0]["function"]["name"]
|
|
tool_choice = {
|
|
"type": "function",
|
|
"function": {"name": tool_name},
|
|
}
|
|
|
|
kwargs["tool_choice"] = tool_choice
|
|
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 Fireworks 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 a Fireworks function and the returned model will make use of the
|
|
function-calling API. If "json_mode" then Fireworks'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_fireworks import ChatFireworks
|
|
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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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_fireworks import ChatFireworks
|
|
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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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_fireworks import ChatFireworks
|
|
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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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_fireworks import ChatFireworks
|
|
from langchain_core.pydantic_v1 import BaseModel
|
|
|
|
class AnswerWithJustification(BaseModel):
|
|
answer: str
|
|
justification: str
|
|
|
|
llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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_fireworks import ChatFireworks
|
|
|
|
llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)
|
|
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 = _is_pydantic_class(schema)
|
|
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=True)
|
|
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()
|
|
)
|
|
else:
|
|
raise ValueError(
|
|
f"Unrecognized method argument. Expected one of 'function_calling' or "
|
|
f"'json_format'. Received: '{method}'"
|
|
)
|
|
|
|
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
|
|
|
|
|
|
def _is_pydantic_class(obj: Any) -> bool:
|
|
return isinstance(obj, type) and issubclass(obj, BaseModel)
|