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Merge remote-tracking branch 'origin/main' into feat/queue-based-graph-engine

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-LAN-
2025-09-03 01:33:17 +08:00
parent 1770b93e5b d33dfee8a3
commit 77a9a73d0d
115 changed files with 5875 additions and 268 deletions
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78
api/core/llm_generator/llm_generator.py
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@ -57,11 +57,8 @@ 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)
@ -114,13 +111,10 @@ 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()
@ -163,11 +157,8 @@ 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)
@ -213,11 +204,8 @@ 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)
@ -249,11 +237,8 @@ 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:
@ -261,11 +246,8 @@ 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:
@ -308,11 +290,8 @@ 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)
@ -339,13 +318,10 @@ 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)
@ -368,11 +344,8 @@ 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
@ -556,11 +529,8 @@ 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)