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Use model in Azure LLM integration

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Cyber MacGeddon 2026-03-04 12:05:40 +00:00
parent e19ea8667d
commit ae27eccbed
1 changed files with 16 additions and 5 deletions
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21
trustgraph-flow/trustgraph/model/text_completion/azure/llm.py
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@ -55,11 +55,13 @@ class Processor(LlmService):
self.max_output = max_output self.max_output = max_output
self.default_model = model self.default_model = model
def build_prompt(self, system, content, temperature=None, stream=False): def build_prompt(self, system, content, temperature=None, stream=False, model=None):
# Use provided temperature or fall back to default # Use provided temperature or fall back to default
effective_temperature = temperature if temperature is not None else self.temperature effective_temperature = temperature if temperature is not None else self.temperature
model_name = model or self.default_model
data = { data = {
"model": model_name,
"messages": [ "messages": [
{ {
"role": "system", "content": system "role": "system", "content": system
@ -100,7 +102,8 @@ class Processor(LlmService):
raise TooManyRequests() raise TooManyRequests()
if resp.status_code != 200: if resp.status_code != 200:
raise RuntimeError("LLM failure") logger.error(f"Azure API error: status={resp.status_code}, body={resp.text}")
raise RuntimeError(f"LLM failure: HTTP {resp.status_code}")
result = resp.json() result = resp.json()
@ -121,7 +124,8 @@ class Processor(LlmService):
prompt = self.build_prompt( prompt = self.build_prompt(
system, system,
prompt, prompt,
effective_temperature effective_temperature,
model=model_name
) )
response = self.call_llm(prompt) response = self.call_llm(prompt)
@ -174,7 +178,7 @@ class Processor(LlmService):
logger.debug(f"Using temperature: {effective_temperature}") logger.debug(f"Using temperature: {effective_temperature}")
try: try:
body = self.build_prompt(system, prompt, effective_temperature, stream=True) body = self.build_prompt(system, prompt, effective_temperature, stream=True, model=model_name)
url = self.endpoint url = self.endpoint
api_key = self.token api_key = self.token
@ -190,7 +194,8 @@ class Processor(LlmService):
raise TooManyRequests() raise TooManyRequests()
if response.status_code != 200: if response.status_code != 200:
raise RuntimeError("LLM failure") logger.error(f"Azure API error: status={response.status_code}, body={response.text}")
raise RuntimeError(f"LLM failure: HTTP {response.status_code}")
total_input_tokens = 0 total_input_tokens = 0
total_output_tokens = 0 total_output_tokens = 0
@ -279,6 +284,12 @@ class Processor(LlmService):
help=f'LLM max output tokens (default: {default_max_output})' help=f'LLM max output tokens (default: {default_max_output})'
) )
parser.add_argument(
'-m', '--model',
default=default_model,
help=f'LLM model name (default: {default_model})'
)
def run(): def run():
Processor.launch(default_ident, __doc__) Processor.launch(default_ident, __doc__)