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from openai import OpenAI
import pdb
from langchain_openai import ChatOpenAI
from langchain_core.globals import get_llm_cache
from langchain_core.language_models.base import (
BaseLanguageModel,
LangSmithParams,
LanguageModelInput,
)
from langchain_core.load import dumpd, dumps
from langchain_core.messages import (
AIMessage,
SystemMessage,
AnyMessage,
BaseMessage,
BaseMessageChunk,
HumanMessage,
convert_to_messages,
message_chunk_to_message,
)
from langchain_core.outputs import (
ChatGeneration,
ChatGenerationChunk,
ChatResult,
LLMResult,
RunInfo,
)
from langchain_ollama import ChatOllama
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.runnables import Runnable, RunnableConfig
from langchain_core.tools import BaseTool
from typing import (
TYPE_CHECKING,
Any,
Callable,
Literal,
Optional,
Union,
cast, List,
)
class DeepSeekR1ChatOpenAI(ChatOpenAI):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.client = OpenAI(
base_url=kwargs.get("base_url"),
api_key=kwargs.get("api_key")
)
async def ainvoke(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> AIMessage:
message_history = []
for input_ in input:
if isinstance(input_, SystemMessage):
message_history.append({"role": "system", "content": input_.content})
elif isinstance(input_, AIMessage):
message_history.append({"role": "assistant", "content": input_.content})
else:
message_history.append({"role": "user", "content": input_.content})
response = self.client.chat.completions.create(
model=self.model_name,
messages=message_history
)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
return AIMessage(content=content, reasoning_content=reasoning_content)
def invoke(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> AIMessage:
message_history = []
for input_ in input:
if isinstance(input_, SystemMessage):
message_history.append({"role": "system", "content": input_.content})
elif isinstance(input_, AIMessage):
message_history.append({"role": "assistant", "content": input_.content})
else:
message_history.append({"role": "user", "content": input_.content})
response = self.client.chat.completions.create(
model=self.model_name,
messages=message_history
)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
return AIMessage(content=content, reasoning_content=reasoning_content)
class DeepSeekR1ChatOllama(ChatOllama):
async def ainvoke(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> AIMessage:
org_ai_message = await super().ainvoke(input=input)
org_content = org_ai_message.content
reasoning_content = org_content.split("</think>")[0].replace("<think>", "")
content = org_content.split("</think>")[1]
if "**JSON Response:**" in content:
content = content.split("**JSON Response:**")[-1]
return AIMessage(content=content, reasoning_content=reasoning_content)
def invoke(
self,
input: LanguageModelInput,
config: Optional[RunnableConfig] = None,
*,
stop: Optional[list[str]] = None,
**kwargs: Any,
) -> AIMessage:
org_ai_message = super().invoke(input=input)
org_content = org_ai_message.content
reasoning_content = org_content.split("</think>")[0].replace("<think>", "")
content = org_content.split("</think>")[1]
if "**JSON Response:**" in content:
content = content.split("**JSON Response:**")[-1]
return AIMessage(content=content, reasoning_content=reasoning_content)
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