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chore:improve thinking display for llm from xinference and ollama pro… (#13318)

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呆萌闷油瓶 2025-02-07 14:29:29 +08:00 committed by GitHub
parent 1caa578771
commit 3f42fabff8
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2 changed files with 3 additions and 23 deletions
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10
api/core/model_runtime/model_providers/ollama/llm/llm.py
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@ -314,7 +314,6 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
""" """
full_text = "" full_text = ""
chunk_index = 0 chunk_index = 0
is_reasoning_started = False
def create_final_llm_result_chunk( def create_final_llm_result_chunk(
index: int, message: AssistantPromptMessage, finish_reason: str index: int, message: AssistantPromptMessage, finish_reason: str
@ -368,14 +367,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
# transform assistant message to prompt message # transform assistant message to prompt message
text = chunk_json["response"] text = chunk_json["response"]
if "<think>" in text: text = self._wrap_thinking_by_tag(text)
is_reasoning_started = True
text = text.replace("<think>", "> 💭 ")
elif "</think>" in text:
is_reasoning_started = False
text = text.replace("</think>", "") + "\n\n"
elif is_reasoning_started:
text = text.replace("\n", "\n> ")
assistant_prompt_message = AssistantPromptMessage(content=text) assistant_prompt_message = AssistantPromptMessage(content=text)

16
api/core/model_runtime/model_providers/xinference/llm/llm.py
View File

@ -1,4 +1,3 @@
import re
from collections.abc import Generator, Iterator from collections.abc import Generator, Iterator
from typing import Optional, cast from typing import Optional, cast
@ -636,16 +635,13 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
handle stream chat generate response handle stream chat generate response
""" """
full_response = "" full_response = ""
is_reasoning_started_tag = False
for chunk in resp: for chunk in resp:
if len(chunk.choices) == 0: if len(chunk.choices) == 0:
continue continue
delta = chunk.choices[0] delta = chunk.choices[0]
if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""): if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
continue continue
delta_content = delta.delta.content delta_content = delta.delta.content or ""
if not delta_content:
delta_content = ""
# check if there is a tool call in the response # check if there is a tool call in the response
function_call = None function_call = None
tool_calls = [] tool_calls = []
@ -658,15 +654,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
if function_call: if function_call:
assistant_message_tool_calls += [self._extract_response_function_call(function_call)] assistant_message_tool_calls += [self._extract_response_function_call(function_call)]
if not is_reasoning_started_tag and "<think>" in delta_content: delta_content = self._wrap_thinking_by_tag(delta_content)
is_reasoning_started_tag = True
delta_content = "> 💭 " + delta_content.replace("<think>", "")
elif is_reasoning_started_tag and "</think>" in delta_content:
delta_content = delta_content.replace("</think>", "") + "\n\n"
is_reasoning_started_tag = False
elif is_reasoning_started_tag:
if "\n" in delta_content:
delta_content = re.sub(r"\n(?!(>|\n))", "\n> ", delta_content)
# transform assistant message to prompt message # transform assistant message to prompt message
assistant_prompt_message = AssistantPromptMessage( assistant_prompt_message = AssistantPromptMessage(
content=delta_content or "", tool_calls=assistant_message_tool_calls content=delta_content or "", tool_calls=assistant_message_tool_calls