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Add LLM request/response logging to database

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Log every LLM call (analysis, chat, image, raw_analyze) to a new
LlmLog table with request type, model, token counts, duration,
success/failure, and truncated request/response payloads. Enables
debugging prompt issues and tracking usage.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
AJ Isaacs
2026-02-22 22:55:19 -05:00
parent fd798ce027
commit b04d3da2bf
3 changed files with 159 additions and 16 deletions
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99
utils/llm_client.py
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@@ -2,6 +2,7 @@ import asyncio
import base64
import json
import logging
import time
from pathlib import Path
from openai import AsyncOpenAI
@@ -97,9 +98,10 @@ ANALYSIS_TOOL = {
class LLMClient:
def __init__(self, base_url: str, model: str, api_key: str = "not-needed"):
def __init__(self, base_url: str, model: str, api_key: str = "not-needed", db=None):
self.model = model
self.host = base_url.rstrip("/")
self._db = db
self._client = AsyncOpenAI(
base_url=f"{self.host}/v1",
api_key=api_key,
@@ -107,6 +109,24 @@ class LLMClient:
)
self._semaphore = asyncio.Semaphore(1) # serialize requests to avoid overloading
def _log_llm(self, request_type: str, duration_ms: int, success: bool,
request: str, response: str | None = None, error: str | None = None,
input_tokens: int | None = None, output_tokens: int | None = None):
"""Fire-and-forget LLM log entry to the database."""
if not self._db:
return
asyncio.create_task(self._db.save_llm_log(
request_type=request_type,
model=self.model,
duration_ms=duration_ms,
success=success,
request=request,
response=response,
error=error,
input_tokens=input_tokens,
output_tokens=output_tokens,
))
async def close(self):
await self._client.close()
@@ -119,7 +139,13 @@ class LLMClient:
user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
if channel_context:
user_content += f"=== CHANNEL INFO ===\n{channel_context}\n\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}\n/no_think"
req_json = json.dumps([
{"role": "system", "content": SYSTEM_PROMPT[:500]},
{"role": "user", "content": user_content},
], default=str)
t0 = time.monotonic()
async with self._semaphore:
try:
@@ -132,26 +158,38 @@ class LLMClient:
tools=[ANALYSIS_TOOL],
tool_choice={"type": "function", "function": {"name": "report_analysis"}},
temperature=0.1,
max_tokens=1024,
max_tokens=2048,
)
elapsed = int((time.monotonic() - t0) * 1000)
choice = response.choices[0]
usage = response.usage
# Extract tool call arguments
if choice.message.tool_calls:
tool_call = choice.message.tool_calls[0]
args = json.loads(tool_call.function.arguments)
resp_text = tool_call.function.arguments
args = json.loads(resp_text)
self._log_llm("analysis", elapsed, True, req_json, resp_text,
input_tokens=usage.prompt_tokens if usage else None,
output_tokens=usage.completion_tokens if usage else None)
return self._validate_result(args)
# Fallback: try parsing the message content as JSON
if choice.message.content:
self._log_llm("analysis", elapsed, True, req_json, choice.message.content,
input_tokens=usage.prompt_tokens if usage else None,
output_tokens=usage.completion_tokens if usage else None)
return self._parse_content_fallback(choice.message.content)
logger.warning("No tool call or content in LLM response.")
self._log_llm("analysis", elapsed, False, req_json, error="Empty response")
return None
except Exception as e:
elapsed = int((time.monotonic() - t0) * 1000)
logger.error("LLM analysis error: %s", e)
self._log_llm("analysis", elapsed, False, req_json, error=str(e))
return None
def _validate_result(self, result: dict) -> dict:
@@ -219,16 +257,29 @@ class LLMClient:
first content token arrives (useful for triggering the typing indicator
only after the model starts generating).
"""
# Append /no_think to the last user message to disable thinking on Qwen3
patched = []
for m in messages:
patched.append(m)
if patched and patched[-1].get("role") == "user":
patched[-1] = {**patched[-1], "content": patched[-1]["content"] + "\n/no_think"}
req_json = json.dumps([
{"role": "system", "content": system_prompt[:500]},
*[{"role": m["role"], "content": str(m.get("content", ""))[:200]} for m in patched],
], default=str)
t0 = time.monotonic()
async with self._semaphore:
try:
stream = await self._client.chat.completions.create(
model=self.model,
messages=[
{"role": "system", "content": system_prompt},
*messages,
*patched,
],
temperature=0.8,
max_tokens=300,
max_tokens=2048,
stream=True,
)
@@ -243,9 +294,13 @@ class LLMClient:
chunks.append(delta.content)
content = "".join(chunks).strip()
elapsed = int((time.monotonic() - t0) * 1000)
self._log_llm("chat", elapsed, bool(content), req_json, content or None)
return content if content else None
except Exception as e:
elapsed = int((time.monotonic() - t0) * 1000)
logger.error("LLM chat error: %s", e)
self._log_llm("chat", elapsed, False, req_json, error=str(e))
return None
async def analyze_image(
@@ -265,8 +320,13 @@ class LLMClient:
user_content: list[dict] = [
{"type": "image_url", "image_url": {"url": data_url}},
]
if user_text:
user_content.append({"type": "text", "text": user_text})
user_content.append({"type": "text", "text": (user_text or "") + "\n/no_think"})
req_json = json.dumps([
{"role": "system", "content": system_prompt[:500]},
{"role": "user", "content": f"[image {len(image_bytes)} bytes] {user_text or ''}"},
], default=str)
t0 = time.monotonic()
async with self._semaphore:
try:
@@ -277,7 +337,7 @@ class LLMClient:
{"role": "user", "content": user_content},
],
temperature=0.8,
max_tokens=500,
max_tokens=2048,
stream=True,
)
@@ -292,9 +352,13 @@ class LLMClient:
chunks.append(delta.content)
content = "".join(chunks).strip()
elapsed = int((time.monotonic() - t0) * 1000)
self._log_llm("image", elapsed, bool(content), req_json, content or None)
return content if content else None
except Exception as e:
elapsed = int((time.monotonic() - t0) * 1000)
logger.error("LLM image analysis error: %s", e)
self._log_llm("image", elapsed, False, req_json, error=str(e))
return None
async def raw_analyze(
@@ -307,7 +371,13 @@ class LLMClient:
user_content += f"=== NOTES ABOUT THIS USER (from prior analysis) ===\n{user_notes}\n\n"
if channel_context:
user_content += f"=== CHANNEL INFO ===\n{channel_context}\n\n"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}"
user_content += f"=== TARGET MESSAGE (analyze THIS message only) ===\n{message}\n/no_think"
req_json = json.dumps([
{"role": "system", "content": SYSTEM_PROMPT[:500]},
{"role": "user", "content": user_content},
], default=str)
t0 = time.monotonic()
async with self._semaphore:
try:
@@ -320,10 +390,12 @@ class LLMClient:
tools=[ANALYSIS_TOOL],
tool_choice={"type": "function", "function": {"name": "report_analysis"}},
temperature=0.1,
max_tokens=1024,
max_tokens=2048,
)
elapsed = int((time.monotonic() - t0) * 1000)
choice = response.choices[0]
usage = response.usage
parts = []
parsed = None
@@ -342,7 +414,12 @@ class LLMClient:
parsed = self._parse_content_fallback(choice.message.content)
raw = "\n".join(parts) or "(empty response)"
self._log_llm("raw_analyze", elapsed, parsed is not None, req_json, raw,
input_tokens=usage.prompt_tokens if usage else None,
output_tokens=usage.completion_tokens if usage else None)
return raw, parsed
except Exception as e:
elapsed = int((time.monotonic() - t0) * 1000)
self._log_llm("raw_analyze", elapsed, False, req_json, error=str(e))
return f"Error: {e}", None