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chore: apply ty checks on api code with script and ci action (#24653)

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This commit is contained in:
Bowen Liang
2025-09-02 16:05:13 +08:00
committed by GitHub
parent c373b734bc
commit 7b379e2a61
48 changed files with 188 additions and 142 deletions
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78
api/core/llm_generator/llm_generator.py
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@@ -56,11 +56,8 @@ class LLMGenerator:
prompts = [UserPromptMessage(content=prompt)]
with measure_time() as timer:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompts), model_parameters={"max_tokens": 500, "temperature": 1}, stream=False
),
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)
cleaned_answer = re.sub(r"^.*(\{.*\}).*$", r"\1", answer, flags=re.DOTALL)
@@ -113,13 +110,10 @@ class LLMGenerator:
prompt_messages = [UserPromptMessage(content=prompt)]
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages),
model_parameters={"max_tokens": 256, "temperature": 0},
stream=False,
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages),
model_parameters={"max_tokens": 256, "temperature": 0},
stream=False,
)
text_content = response.message.get_text_content()
@@ -162,11 +156,8 @@ class LLMGenerator:
)
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
rule_config["prompt"] = cast(str, response.message.content)
@@ -212,11 +203,8 @@ class LLMGenerator:
try:
try:
# the first step to generate the task prompt
prompt_content = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
prompt_content: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
except InvokeError as e:
error = str(e)
@@ -248,11 +236,8 @@ class LLMGenerator:
statement_messages = [UserPromptMessage(content=statement_generate_prompt)]
try:
parameter_content = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(parameter_messages), model_parameters=model_parameters, stream=False
),
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))
except InvokeError as e:
@@ -260,11 +245,8 @@ class LLMGenerator:
error_step = "generate variables"
try:
statement_content = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(statement_messages), model_parameters=model_parameters, stream=False
),
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)
except InvokeError as e:
@@ -307,11 +289,8 @@ class LLMGenerator:
prompt_messages = [UserPromptMessage(content=prompt)]
model_parameters = model_config.get("completion_params", {})
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
generated_code = cast(str, response.message.content)
@@ -338,13 +317,10 @@ class LLMGenerator:
prompt_messages = [SystemPromptMessage(content=prompt), UserPromptMessage(content=query)]
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={"temperature": 0.01, "max_tokens": 2000},
stream=False,
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={"temperature": 0.01, "max_tokens": 2000},
stream=False,
)
answer = cast(str, response.message.content)
@@ -367,11 +343,8 @@ class LLMGenerator:
model_parameters = model_config.get("model_parameters", {})
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
raw_content = response.message.content
@@ -555,11 +528,8 @@ class LLMGenerator:
model_parameters = {"temperature": 0.4}
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
response: LLMResult = model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
)
generated_raw = cast(str, response.message.content)