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feat: add agent thinking content display of deepseek R1 (#12949)
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@ -1,10 +1,13 @@
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import json
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from collections.abc import Generator
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from typing import Optional, Union
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import requests
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from yarl import URL
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from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
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from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
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from core.model_runtime.entities.message_entities import (
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AssistantPromptMessage,
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PromptMessage,
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PromptMessageTool,
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)
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@ -36,3 +39,208 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
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credentials["mode"] = LLMMode.CHAT.value
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credentials["function_calling_type"] = "tool_call"
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credentials["stream_function_calling"] = "support"
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def _handle_generate_stream_response(
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self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
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) -> Generator:
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"""
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Handle llm stream response
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:param model: model name
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:param credentials: model credentials
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:param response: streamed response
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:param prompt_messages: prompt messages
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:return: llm response chunk generator
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"""
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full_assistant_content = ""
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chunk_index = 0
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is_reasoning_started = False # Add flag to track reasoning state
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def create_final_llm_result_chunk(
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id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
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) -> LLMResultChunk:
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# calculate num tokens
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prompt_tokens = usage and usage.get("prompt_tokens")
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if prompt_tokens is None:
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prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
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completion_tokens = usage and usage.get("completion_tokens")
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if completion_tokens is None:
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completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
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# transform usage
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usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
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return LLMResultChunk(
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id=id,
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model=model,
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prompt_messages=prompt_messages,
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delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
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)
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# delimiter for stream response, need unicode_escape
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import codecs
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delimiter = credentials.get("stream_mode_delimiter", "\n\n")
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delimiter = codecs.decode(delimiter, "unicode_escape")
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tools_calls: list[AssistantPromptMessage.ToolCall] = []
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def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
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def get_tool_call(tool_call_id: str):
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if not tool_call_id:
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return tools_calls[-1]
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tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
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if tool_call is None:
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tool_call = AssistantPromptMessage.ToolCall(
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id=tool_call_id,
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type="function",
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function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
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)
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tools_calls.append(tool_call)
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return tool_call
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for new_tool_call in new_tool_calls:
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# get tool call
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tool_call = get_tool_call(new_tool_call.function.name)
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# update tool call
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if new_tool_call.id:
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tool_call.id = new_tool_call.id
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if new_tool_call.type:
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tool_call.type = new_tool_call.type
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if new_tool_call.function.name:
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tool_call.function.name = new_tool_call.function.name
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if new_tool_call.function.arguments:
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tool_call.function.arguments += new_tool_call.function.arguments
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finish_reason = None # The default value of finish_reason is None
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message_id, usage = None, None
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for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
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chunk = chunk.strip()
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if chunk:
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# ignore sse comments
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if chunk.startswith(":"):
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continue
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decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
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if decoded_chunk == "[DONE]": # Some provider returns "data: [DONE]"
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continue
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try:
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chunk_json: dict = json.loads(decoded_chunk)
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# stream ended
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except json.JSONDecodeError as e:
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yield create_final_llm_result_chunk(
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id=message_id,
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index=chunk_index + 1,
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message=AssistantPromptMessage(content=""),
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finish_reason="Non-JSON encountered.",
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usage=usage,
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)
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break
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# handle the error here. for issue #11629
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if chunk_json.get("error") and chunk_json.get("choices") is None:
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raise ValueError(chunk_json.get("error"))
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if chunk_json:
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if u := chunk_json.get("usage"):
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usage = u
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if not chunk_json or len(chunk_json["choices"]) == 0:
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continue
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choice = chunk_json["choices"][0]
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finish_reason = chunk_json["choices"][0].get("finish_reason")
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message_id = chunk_json.get("id")
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chunk_index += 1
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if "delta" in choice:
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delta = choice["delta"]
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is_reasoning = delta.get("reasoning_content")
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delta_content = delta.get("content") or delta.get("reasoning_content")
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assistant_message_tool_calls = None
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if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
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assistant_message_tool_calls = delta.get("tool_calls", None)
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elif (
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"function_call" in delta
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and credentials.get("function_calling_type", "no_call") == "function_call"
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):
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assistant_message_tool_calls = [
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{"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
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]
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# assistant_message_function_call = delta.delta.function_call
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# extract tool calls from response
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if assistant_message_tool_calls:
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tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
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increase_tool_call(tool_calls)
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if delta_content is None or delta_content == "":
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continue
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# Add markdown quote markers for reasoning content
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if is_reasoning:
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if not is_reasoning_started:
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delta_content = "> 💭 " + delta_content
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is_reasoning_started = True
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elif "\n\n" in delta_content:
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delta_content = delta_content.replace("\n\n", "\n> ")
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elif "\n" in delta_content:
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delta_content = delta_content.replace("\n", "\n> ")
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elif is_reasoning_started:
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# If we were in reasoning mode but now getting regular content,
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# add \n\n to close the reasoning block
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delta_content = "\n\n" + delta_content
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is_reasoning_started = False
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# transform assistant message to prompt message
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assistant_prompt_message = AssistantPromptMessage(
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content=delta_content,
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)
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# reset tool calls
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tool_calls = []
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full_assistant_content += delta_content
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elif "text" in choice:
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choice_text = choice.get("text", "")
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if choice_text == "":
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continue
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# transform assistant message to prompt message
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assistant_prompt_message = AssistantPromptMessage(content=choice_text)
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full_assistant_content += choice_text
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else:
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continue
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yield LLMResultChunk(
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id=message_id,
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model=model,
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prompt_messages=prompt_messages,
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delta=LLMResultChunkDelta(
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index=chunk_index,
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message=assistant_prompt_message,
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),
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)
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chunk_index += 1
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if tools_calls:
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yield LLMResultChunk(
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id=message_id,
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model=model,
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prompt_messages=prompt_messages,
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delta=LLMResultChunkDelta(
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index=chunk_index,
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message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
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),
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)
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yield create_final_llm_result_chunk(
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id=message_id,
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index=chunk_index,
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message=AssistantPromptMessage(content=""),
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finish_reason=finish_reason,
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usage=usage,
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)
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