pyr0ball
1c0a747c46
Truncation fix: call_llm() in _llm_client.py now accepts max_tokens (default 2048) and passes it in both the cf-orch task payload and the OpenAI-compat fallback body. Hypothesizer uses max_tokens=1024 (JSON array output); synthesizer and legacy summarize use 2048 (structured 5-section narrative). Without this, backends use their own default (often 512 tokens), causing mid-sentence truncation of the diagnosis output. UI fix: reasoning card changed from bg-accent/5 border-accent/30 (opacity modifiers on CSS variables don't compose reliably across themes) to the callout pattern: bg-surface-raised with a solid border-l-4 border-accent. Header label changed from text-text-dim to text-accent for visual anchoring. Text remains text-text-primary for guaranteed contrast on both light and dark themes. Tracks: #56 (technical-level post-processor, filed as follow-on feature)
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
import logging
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import httpx
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from app.services.search import SearchResult
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logger = logging.getLogger(__name__)
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_SEVERITY_RANK = {"CRITICAL": 0, "ERROR": 1, "WARN": 2, "WARNING": 2}
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_PROMPT_TEMPLATE = """\
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You are a homelab diagnostic assistant. A user described a symptom and the system retrieved relevant log entries.
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Analyze the log entries below and write a 2-4 sentence plain-language diagnosis. Focus on errors and their likely root cause. Be specific and concise — name the services involved, not generic platitudes.
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User query: {query}
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{context_section}
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Log entries ({n} shown, highest severity first):
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{log_block}
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Diagnosis:"""
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def _build_context(entries: list[SearchResult], max_entries: int = 25) -> str:
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ranked = sorted(
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entries,
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key=lambda e: (_SEVERITY_RANK.get(e.severity or "", 3), e.timestamp_iso or ""),
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)[:max_entries]
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return "\n".join(
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f"[{e.timestamp_iso or '?'}] [{e.severity or 'INFO'}] {e.text[:200]}"
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for e in ranked
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)
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def _extract_content(resp_json: dict) -> str | None:
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"""Pull text content from an OpenAI-compat chat completion response."""
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choices = resp_json.get("choices") or []
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if not choices:
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return None
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return (choices[0].get("message", {}).get("content") or "").strip() or None
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def summarize(
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query: str,
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entries: list[SearchResult],
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llm_url: str,
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llm_model: str,
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api_key: str | None = None,
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timeout: float = 120.0,
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context_block: str | None = None,
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) -> str | None:
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if not entries:
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return None
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log_block = _build_context(entries)
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context_section = (
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f"\nEnvironment context:\n{context_block}\n" if context_block else ""
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)
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prompt = _PROMPT_TEMPLATE.format(
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query=query,
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n=min(len(entries), 25),
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log_block=log_block,
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context_section=context_section,
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)
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headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
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messages = [{"role": "user", "content": prompt}]
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# Try cf-orch task-based endpoint first (routes to the security reasoning model
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# assigned to turnstone.log_analysis without needing an explicit model name).
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task_url = f"{llm_url.rstrip('/')}/api/inference/task"
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try:
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resp = httpx.post(
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task_url,
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json={
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"product": "turnstone",
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"task": "log_analysis",
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"payload": {"messages": messages, "stream": False, "max_tokens": 1024},
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},
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headers=headers,
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timeout=timeout,
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)
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if resp.status_code == 200:
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return _extract_content(resp.json())
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if resp.status_code != 404:
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resp.raise_for_status()
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# 404 means no assignment configured — fall through to direct model call
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logger.debug("No task assignment for turnstone.log_analysis — falling back to direct model")
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except Exception as exc:
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logger.debug("Task endpoint unavailable (%s) — falling back to direct model", exc)
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# Fallback: OpenAI-compat endpoint with explicit model name (local instances,
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# xanderland, or any cf-orch that doesn't have task assignments loaded).
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try:
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resp = httpx.post(
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f"{llm_url.rstrip('/')}/v1/chat/completions",
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json={"model": llm_model, "messages": messages, "stream": False, "max_tokens": 1024},
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headers=headers,
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timeout=timeout,
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)
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resp.raise_for_status()
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return _extract_content(resp.json())
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except Exception as exc:
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logger.warning("LLM summarization failed (%s): %s", type(exc).__name__, exc)
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return None
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