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fix: call `get_text_content()` instead of casting to `str` (#31121) 

Signed-off-by: Stream <Stream_2@qq.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Stream 2026-01-16 17:41:00 +08:00 committed by GitHub
parent 6903c31b84
commit de610cbf39
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GPG Key ID: B5690EEEBB952194
1 changed files with 11 additions and 16 deletions
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27
api/core/llm_generator/llm_generator.py
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@ -71,8 +71,8 @@ class LLMGenerator:
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompts), model_parameters={"max_tokens": 500, "temperature": 1}, stream=False
)
answer = cast(str, response.message.content)
if answer is None:
answer = response.message.get_text_content()
if answer == "":
return ""
try:
result_dict = json.loads(answer)
@ -184,7 +184,7 @@ class LLMGenerator:
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
rule_config["prompt"] = cast(str, response.message.content)
rule_config["prompt"] = response.message.get_text_content()
except InvokeError as e:
error = str(e)
@ -237,13 +237,11 @@ class LLMGenerator:
return rule_config
rule_config["prompt"] = cast(str, prompt_content.message.content)
rule_config["prompt"] = prompt_content.message.get_text_content()
if not isinstance(prompt_content.message.content, str):
raise NotImplementedError("prompt content is not a string")
parameter_generate_prompt = parameter_template.format(
inputs={
"INPUT_TEXT": prompt_content.message.content,
"INPUT_TEXT": prompt_content.message.get_text_content(),
},
remove_template_variables=False,
)
@ -253,7 +251,7 @@ class LLMGenerator:
statement_generate_prompt = statement_template.format(
inputs={
"TASK_DESCRIPTION": instruction,
"INPUT_TEXT": prompt_content.message.content,
"INPUT_TEXT": prompt_content.message.get_text_content(),
},
remove_template_variables=False,
)
@ -263,7 +261,7 @@ class LLMGenerator:
parameter_content: LLMResult = model_instance.invoke_llm(
prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
)
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', cast(str, parameter_content.message.content))
rule_config["variables"] = re.findall(r'"\s*([^"]+)\s*"', parameter_content.message.get_text_content())
except InvokeError as e:
error = str(e)
error_step = "generate variables"
@ -272,7 +270,7 @@ class LLMGenerator:
statement_content: LLMResult = model_instance.invoke_llm(
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
)
rule_config["opening_statement"] = cast(str, statement_content.message.content)
rule_config["opening_statement"] = statement_content.message.get_text_content()
except InvokeError as e:
error = str(e)
error_step = "generate conversation opener"
@ -315,7 +313,7 @@ class LLMGenerator:
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
generated_code = cast(str, response.message.content)
generated_code = response.message.get_text_content()
return {"code": generated_code, "language": code_language, "error": ""}
except InvokeError as e:
@ -351,7 +349,7 @@ class LLMGenerator:
raise TypeError("Expected LLMResult when stream=False")
response = result
answer = cast(str, response.message.content)
answer = response.message.get_text_content()
return answer.strip()
@classmethod
@ -375,10 +373,7 @@ class LLMGenerator:
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
raw_content = response.message.content
if not isinstance(raw_content, str):
raise ValueError(f"LLM response content must be a string, got: {type(raw_content)}")
raw_content = response.message.get_text_content()
try:
parsed_content = json.loads(raw_content)