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https://github.com/trustgraph-ai/trustgraph.git
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Logging strategy updates
This commit is contained in:
Cyber MacGeddon
parent
c28ba58591
commit
905e17ac36
3 changed files with 41 additions and 29 deletions
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@ -4,10 +4,14 @@ Simple token counter for each LLM response.
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from prometheus_client import Counter
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import json
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import logging
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from .. schema import TextCompletionResponse, Error
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from .. base import FlowProcessor, ConsumerSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "metering"
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class Processor(FlowProcessor):
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@ -59,10 +63,10 @@ class Processor(FlowProcessor):
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# Load token costs from the config service
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async def on_cost_config(self, config, version):
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print("Loading configuration version", version)
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logger.info(f"Loading metering configuration version {version}")
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if self.config_key not in config:
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print(f"No key {self.config_key} in config", flush=True)
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logger.warning(f"No key {self.config_key} in config")
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return
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config = config[self.config_key]
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@ -102,9 +106,9 @@ class Processor(FlowProcessor):
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__class__.input_cost_metric.inc(cost_in)
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__class__.output_cost_metric.inc(cost_out)
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print(f"Input Tokens: {num_in}", flush=True)
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print(f"Output Tokens: {num_out}", flush=True)
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print(f"Cost for call: ${cost_per_call}", flush=True)
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logger.info(f"Input Tokens: {num_in}")
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logger.info(f"Output Tokens: {num_out}")
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logger.info(f"Cost for call: ${cost_per_call}")
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@staticmethod
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def add_args(parser):
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@ -6,10 +6,14 @@ Input is prompt, output is response.
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from openai import OpenAI, RateLimitError
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import os
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import logging
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from .... exceptions import TooManyRequests
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from .... base import LlmService, LlmResult
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "text-completion"
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default_model = 'gpt-3.5-turbo'
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@ -52,7 +56,7 @@ class Processor(LlmService):
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else:
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self.openai = OpenAI(api_key=api_key)
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print("Initialised", flush=True)
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logger.info("OpenAI LLM service initialized")
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async def generate_content(self, system, prompt):
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@ -85,9 +89,9 @@ class Processor(LlmService):
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inputtokens = resp.usage.prompt_tokens
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outputtokens = resp.usage.completion_tokens
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print(resp.choices[0].message.content, flush=True)
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print(f"Input Tokens: {inputtokens}", flush=True)
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print(f"Output Tokens: {outputtokens}", flush=True)
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logger.debug(f"LLM response: {resp.choices[0].message.content}")
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logger.info(f"Input Tokens: {inputtokens}")
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logger.info(f"Output Tokens: {outputtokens}")
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resp = LlmResult(
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text = resp.choices[0].message.content,
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@ -109,7 +113,7 @@ class Processor(LlmService):
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# Apart from rate limits, treat all exceptions as unrecoverable
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print(f"Exception: {type(e)} {e}")
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logger.error(f"OpenAI LLM exception ({type(e).__name__}): {e}", exc_info=True)
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raise e
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@staticmethod
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@ -6,6 +6,7 @@ Language service abstracts prompt engineering from LLM.
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import asyncio
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import json
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import re
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import logging
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from ...schema import Definition, Relationship, Triple
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from ...schema import Topic
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@ -17,6 +18,9 @@ from ...base import ProducerSpec, ConsumerSpec, TextCompletionClientSpec
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from ...template import PromptManager
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "prompt"
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default_concurrency = 1
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@ -68,10 +72,10 @@ class Processor(FlowProcessor):
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async def on_prompt_config(self, config, version):
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print("Loading configuration version", version)
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logger.info(f"Loading prompt configuration version {version}")
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if self.config_key not in config:
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print(f"No key {self.config_key} in config", flush=True)
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logger.warning(f"No key {self.config_key} in config")
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return
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config = config[self.config_key]
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@ -80,12 +84,12 @@ class Processor(FlowProcessor):
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self.manager.load_config(config)
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print("Prompt configuration reloaded.", flush=True)
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logger.info("Prompt configuration reloaded")
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except Exception as e:
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print("Exception:", e, flush=True)
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print("Configuration reload failed", flush=True)
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logger.error(f"Prompt configuration exception: {e}", exc_info=True)
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logger.error("Configuration reload failed")
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async def on_request(self, msg, consumer, flow):
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@ -99,19 +103,19 @@ class Processor(FlowProcessor):
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try:
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print(v.terms, flush=True)
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logger.debug(f"Prompt terms: {v.terms}")
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input = {
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k: json.loads(v)
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for k, v in v.terms.items()
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}
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print(f"Handling kind {kind}...", flush=True)
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logger.debug(f"Handling prompt kind {kind}...")
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async def llm(system, prompt):
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print(system, flush=True)
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print(prompt, flush=True)
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logger.debug(f"System prompt: {system}")
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logger.debug(f"User prompt: {prompt}")
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resp = await flow("text-completion-request").text_completion(
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system = system, prompt = prompt,
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@ -120,20 +124,20 @@ class Processor(FlowProcessor):
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try:
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return resp
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except Exception as e:
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print("LLM Exception:", e, flush=True)
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logger.error(f"LLM Exception: {e}", exc_info=True)
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return None
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try:
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resp = await self.manager.invoke(kind, input, llm)
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except Exception as e:
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print("Invocation exception:", e, flush=True)
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logger.error(f"Prompt invocation exception: {e}", exc_info=True)
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raise e
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print(resp, flush=True)
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logger.debug(f"Prompt response: {resp}")
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if isinstance(resp, str):
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print("Send text response...", flush=True)
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logger.debug("Sending text response...")
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r = PromptResponse(
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text=resp,
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@ -147,8 +151,8 @@ class Processor(FlowProcessor):
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else:
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print("Send object response...", flush=True)
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print(json.dumps(resp, indent=4), flush=True)
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logger.debug("Sending object response...")
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logger.debug(f"Response object: {json.dumps(resp, indent=4)}")
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r = PromptResponse(
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text=None,
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@ -162,9 +166,9 @@ class Processor(FlowProcessor):
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except Exception as e:
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print(f"Exception: {e}", flush=True)
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logger.error(f"Prompt service exception: {e}", exc_info=True)
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print("Send error response...", flush=True)
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logger.debug("Sending error response...")
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r = PromptResponse(
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error=Error(
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@ -178,9 +182,9 @@ class Processor(FlowProcessor):
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except Exception as e:
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print(f"Exception: {e}", flush=True)
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logger.error(f"Prompt service exception: {e}", exc_info=True)
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print("Send error response...", flush=True)
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logger.debug("Sending error response...")
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r = PromptResponse(
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error=Error(
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