apunkt/trustgraph
1
0
Fork 0
mirror of https://github.com/trustgraph-ai/trustgraph.git synced 2026-07-08 21:02:12 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Implement logging strategy (#444)

Browse source 
* Logging strategy and convert all prints() to logging invocations
This commit is contained in:
cybermaggedon 2025-07-30 23:18:38 +01:00 • committed by GitHub
parent 3e0651222b
commit dd70aade11
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
117 changed files with 1216 additions and 667 deletions
Download patch file Download diff file Expand all files Collapse all files

18
trustgraph-flow/trustgraph/model/text_completion/azure/llm.py
View file

@ -8,10 +8,14 @@ import requests
import json
from prometheus_client import Histogram
import os
import logging
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
# Module logger
logger = logging.getLogger(__name__)
default_ident = "text-completion"
default_temperature = 0.0
@ -111,11 +115,11 @@ class Processor(LlmService):
inputtokens = response['usage']['prompt_tokens']
outputtokens = response['usage']['completion_tokens']
print(resp, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
print("Send response...", flush=True)
logger.debug("Sending response...")
resp = LlmResult(
text = resp,
@ -128,7 +132,7 @@ class Processor(LlmService):
except TooManyRequests:
print("Rate limit...")
logger.warning("Rate limit exceeded")
# Leave rate limit retries to the base handler
raise TooManyRequests()
@ -137,10 +141,10 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {e}")
logger.error(f"Azure LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
print("Done.", flush=True)
logger.debug("Azure LLM processing complete")
@staticmethod
def add_args(parser):

18
trustgraph-flow/trustgraph/model/text_completion/azure_openai/llm.py
View file

@ -8,6 +8,10 @@ import json
from prometheus_client import Histogram
from openai import AzureOpenAI, RateLimitError
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -84,10 +88,10 @@ class Processor(LlmService):
inputtokens = resp.usage.prompt_tokens
outputtokens = resp.usage.completion_tokens
print(resp.choices[0].message.content, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
print("Send response...", flush=True)
logger.debug(f"LLM response: {resp.choices[0].message.content}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
logger.debug("Sending response...")
r = LlmResult(
text = resp.choices[0].message.content,
@ -100,7 +104,7 @@ class Processor(LlmService):
except RateLimitError:
print("Send rate limit response...", flush=True)
logger.warning("Rate limit exceeded")
# Leave rate limit retries to the base handler
raise TooManyRequests()
@ -108,10 +112,10 @@ class Processor(LlmService):
except Exception as e:
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {e}")
logger.error(f"Azure OpenAI LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
print("Done.", flush=True)
logger.debug("Azure OpenAI LLM processing complete")
@staticmethod
def add_args(parser):

14
trustgraph-flow/trustgraph/model/text_completion/claude/llm.py
View file

@ -6,10 +6,14 @@ Input is prompt, output is response.
import anthropic
import os
import logging
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
# Module logger
logger = logging.getLogger(__name__)
default_ident = "text-completion"
default_model = 'claude-3-5-sonnet-20240620'
@ -42,7 +46,7 @@ class Processor(LlmService):
self.temperature = temperature
self.max_output = max_output
print("Initialised", flush=True)
logger.info("Claude LLM service initialized")
async def generate_content(self, system, prompt):
@ -69,9 +73,9 @@ class Processor(LlmService):
resp = response.content[0].text
inputtokens = response.usage.input_tokens
outputtokens = response.usage.output_tokens
print(resp, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp,
@ -91,7 +95,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {e}")
logger.error(f"Claude LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

14
trustgraph-flow/trustgraph/model/text_completion/cohere/llm.py
View file

@ -7,6 +7,10 @@ Input is prompt, output is response.
import cohere
from prometheus_client import Histogram
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -39,7 +43,7 @@ class Processor(LlmService):
self.temperature = temperature
self.cohere = cohere.Client(api_key=api_key)
print("Initialised", flush=True)
logger.info("Cohere LLM service initialized")
async def generate_content(self, system, prompt):
@ -59,9 +63,9 @@ class Processor(LlmService):
inputtokens = int(output.meta.billed_units.input_tokens)
outputtokens = int(output.meta.billed_units.output_tokens)
print(resp, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp,
@ -83,7 +87,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {e}")
logger.error(f"Cohere LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

17
trustgraph-flow/trustgraph/model/text_completion/googleaistudio/llm.py
View file

@ -17,6 +17,10 @@ from google.genai import types
from google.genai.types import HarmCategory, HarmBlockThreshold
from google.api_core.exceptions import ResourceExhausted
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -77,7 +81,7 @@ class Processor(LlmService):
# HarmCategory.HARM_CATEGORY_CIVIC_INTEGRITY: block_level,
]
print("Initialised", flush=True)
logger.info("GoogleAIStudio LLM service initialized")
async def generate_content(self, system, prompt):
@ -102,9 +106,9 @@ class Processor(LlmService):
resp = response.text
inputtokens = int(response.usage_metadata.prompt_token_count)
outputtokens = int(response.usage_metadata.candidates_token_count)
print(resp, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp,
@ -117,7 +121,7 @@ class Processor(LlmService):
except ResourceExhausted as e:
print("Hit rate limit:", e, flush=True)
logger.warning("Rate limit exceeded")
# Leave rate limit retries to the default handler
raise TooManyRequests()
@ -126,8 +130,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(type(e), flush=True)
print(f"Exception: {e}", flush=True)
logger.error(f"GoogleAIStudio LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

14
trustgraph-flow/trustgraph/model/text_completion/llamafile/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
from openai import OpenAI
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -44,7 +48,7 @@ class Processor(LlmService):
api_key = "sk-no-key-required",
)
print("Initialised", flush=True)
logger.info("Llamafile LLM service initialized")
async def generate_content(self, system, prompt):
@ -70,9 +74,9 @@ class Processor(LlmService):
inputtokens = resp.usage.prompt_tokens
outputtokens = resp.usage.completion_tokens
print(resp.choices[0].message.content, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp.choices[0].message.content}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp.choices[0].message.content,
@ -87,7 +91,7 @@ class Processor(LlmService):
except Exception as e:
print(f"Exception: {e}")
logger.error(f"Llamafile LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

18
trustgraph-flow/trustgraph/model/text_completion/lmstudio/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
from openai import OpenAI
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -44,7 +48,7 @@ class Processor(LlmService):
api_key = "sk-no-key-required",
)
print("Initialised", flush=True)
logger.info("LMStudio LLM service initialized")
async def generate_content(self, system, prompt):
@ -52,7 +56,7 @@ class Processor(LlmService):
try:
print(prompt)
logger.debug(f"Prompt: {prompt}")
resp = self.openai.chat.completions.create(
model=self.model,
@ -69,14 +73,14 @@ class Processor(LlmService):
#}
)
print(resp)
logger.debug(f"Full response: {resp}")
inputtokens = resp.usage.prompt_tokens
outputtokens = resp.usage.completion_tokens
print(resp.choices[0].message.content, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp.choices[0].message.content}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp.choices[0].message.content,
@ -91,7 +95,7 @@ class Processor(LlmService):
except Exception as e:
print(f"Exception: {e}")
logger.error(f"LMStudio LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

14
trustgraph-flow/trustgraph/model/text_completion/mistral/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
from mistralai import Mistral
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -42,7 +46,7 @@ class Processor(LlmService):
self.max_output = max_output
self.mistral = Mistral(api_key=api_key)
print("Initialised", flush=True)
logger.info("Mistral LLM service initialized")
async def generate_content(self, system, prompt):
@ -75,9 +79,9 @@ class Processor(LlmService):
inputtokens = resp.usage.prompt_tokens
outputtokens = resp.usage.completion_tokens
print(resp.choices[0].message.content, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp.choices[0].message.content}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp.choices[0].message.content,
@ -105,7 +109,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {e}")
logger.error(f"Mistral LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

10
trustgraph-flow/trustgraph/model/text_completion/ollama/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
from ollama import Client
import os
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -41,8 +45,8 @@ class Processor(LlmService):
response = self.llm.generate(self.model, prompt)
response_text = response['response']
print("Send response...", flush=True)
print(response_text, flush=True)
logger.debug("Sending response...")
logger.debug(f"LLM response: {response_text}")
inputtokens = int(response['prompt_eval_count'])
outputtokens = int(response['eval_count'])
@ -60,7 +64,7 @@ class Processor(LlmService):
except Exception as e:
print(f"Exception: {e}")
logger.error(f"Ollama LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

14
trustgraph-flow/trustgraph/model/text_completion/openai/llm.py
View file

@ -6,10 +6,14 @@ Input is prompt, output is response.
from openai import OpenAI, RateLimitError
import os
import logging
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
# Module logger
logger = logging.getLogger(__name__)
default_ident = "text-completion"
default_model = 'gpt-3.5-turbo'
@ -52,7 +56,7 @@ class Processor(LlmService):
else:
self.openai = OpenAI(api_key=api_key)
print("Initialised", flush=True)
logger.info("OpenAI LLM service initialized")
async def generate_content(self, system, prompt):
@ -85,9 +89,9 @@ class Processor(LlmService):
inputtokens = resp.usage.prompt_tokens
outputtokens = resp.usage.completion_tokens
print(resp.choices[0].message.content, flush=True)
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
logger.debug(f"LLM response: {resp.choices[0].message.content}")
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
resp = LlmResult(
text = resp.choices[0].message.content,
@ -109,7 +113,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {type(e)} {e}")
logger.error(f"OpenAI LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

17
trustgraph-flow/trustgraph/model/text_completion/tgi/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
import os
import aiohttp
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -41,9 +45,8 @@ class Processor(LlmService):
self.session = aiohttp.ClientSession()
print("Using TGI service at", base_url)
print("Initialised", flush=True)
logger.info(f"Using TGI service at {base_url}")
logger.info("TGI LLM service initialized")
async def generate_content(self, system, prompt):
@ -85,9 +88,9 @@ class Processor(LlmService):
inputtokens = resp["usage"]["prompt_tokens"]
outputtokens = resp["usage"]["completion_tokens"]
ans = resp["choices"][0]["message"]["content"]
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
print(ans, flush=True)
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
logger.debug(f"LLM response: {ans}")
resp = LlmResult(
text = ans,
@ -104,7 +107,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {type(e)} {e}")
logger.error(f"TGI LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod

17
trustgraph-flow/trustgraph/model/text_completion/vllm/llm.py
View file

@ -6,6 +6,10 @@ Input is prompt, output is response.
import os
import aiohttp
import logging
# Module logger
logger = logging.getLogger(__name__)
from .... exceptions import TooManyRequests
from .... base import LlmService, LlmResult
@ -45,9 +49,8 @@ class Processor(LlmService):
self.session = aiohttp.ClientSession()
print("Using vLLM service at", base_url)
print("Initialised", flush=True)
logger.info(f"Using vLLM service at {base_url}")
logger.info("vLLM LLM service initialized")
async def generate_content(self, system, prompt):
@ -80,9 +83,9 @@ class Processor(LlmService):
inputtokens = resp["usage"]["prompt_tokens"]
outputtokens = resp["usage"]["completion_tokens"]
ans = resp["choices"][0]["text"]
print(f"Input Tokens: {inputtokens}", flush=True)
print(f"Output Tokens: {outputtokens}", flush=True)
print(ans, flush=True)
logger.info(f"Input Tokens: {inputtokens}")
logger.info(f"Output Tokens: {outputtokens}")
logger.debug(f"LLM response: {ans}")
resp = LlmResult(
text = ans,
@ -99,7 +102,7 @@ class Processor(LlmService):
# Apart from rate limits, treat all exceptions as unrecoverable
print(f"Exception: {type(e)} {e}")
logger.error(f"vLLM LLM exception ({type(e).__name__}): {e}", exc_info=True)
raise e
@staticmethod