Snapshot: full project state

llm.py

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+# Procyon — LLM client (Ollama) with graceful keyword fallback.
+# NEVER runs inference on the CT: it calls the 24/7 LAN Ollama host over HTTP.
+# Under saturation, a circuit breaker fast-fails so the pipeline never blocks.
+
+import json
+import time
+import urllib.request
+
+import db
+
+SYSTEM = ("You are an expert technical recruiter and resume writer. You help a candidate "
+          "present their REAL experience in the strongest honest light. HARD RULE: never "
+          "invent, exaggerate, or fabricate any employer, job title, date, skill, credential, "
+          "or metric that is not present in the source resume. You may reorder, re-emphasize, "
+          "and rephrase only.")
+
+# circuit breaker: if the last LLM call failed, skip LLM for COOLDOWN seconds
+_last_failure = 0.0
+COOLDOWN = 90
+
+
+def _post(url, payload, timeout):
+    req = urllib.request.Request(
+        url,
+        data=json.dumps(payload).encode('utf-8'),
+        headers={'Content-Type': 'application/json'},
+    )
+    with urllib.request.urlopen(req, timeout=timeout) as r:
+        return json.loads(r.read().decode('utf-8'))
+
+
+def _timeout():
+    return int(db.get_setting('llm_timeout', '45'))
+
+
+def llm_available():
+    """Quick preflight: is the model host answering at all?"""
+    global _last_failure
+    if time.time() - _last_failure < COOLDOWN:
+        return False
+    base = db.get_setting('llm_base_url', 'http://10.30.20.29:11434').rstrip('/')
+    try:
+        _post(f'{base}/api/tags', {}, 4)
+        return True
+    except Exception:
+        _last_failure = time.time()
+        return False
+
+
+def llm_chat(prompt, system=SYSTEM, model=None):
+    """Return text, or None if the model is unavailable/saturated."""
+    global _last_failure
+    if time.time() - _last_failure < COOLDOWN:
+        return None  # circuit open — fall back immediately
+    base = db.get_setting('llm_base_url', 'http://10.30.20.29:11434').rstrip('/')
+    model = model or db.get_setting('llm_model', 'qwen3.8fast:latest')
+    full = f"{system}\n\n{prompt}" if system else prompt
+    try:
+        data = _post(f'{base}/api/generate', {
+            'model': model,
+            'prompt': full,
+            'stream': False,
+            'think': False,
+        }, _timeout())
+        txt = (data.get('response') or '').strip()
+        if not txt:
+            raise ValueError('empty response')
+        _last_failure = 0.0
+        return txt
+    except Exception:
+        _last_failure = time.time()
+        return None
+
+
+def llm_json(prompt, model=None):
+    txt = llm_chat(prompt + '\n\nRespond with ONLY valid JSON, no markdown fences.', model=model)
+    if not txt:
+        return None
+    txt = txt.strip().lstrip('```json').lstrip('```').rstrip('```').strip()
+    try:
+        return json.loads(txt)
+    except Exception:
+        import re
+        m = re.search(r'\{.*\}', txt, re.DOTALL)
+        if m:
+            try:
+                return json.loads(m.group(0))
+            except Exception:
+                return None
+        return None
+
+
+# ---- keyword fallback (deterministic, always works) ----
+
+def _skill_keywords():
+    return [
+        'python', 'flask', 'sqlite', 'postgres', 'mysql', 'docker', 'proxmox', 'lxc',
+        'linux', 'bash', 'nginx', 'systemd', 'networking', 'tcp/ip', 'dns', 'vpn',
+        'automation', 'ci/cd', 'git', 'gitea', 'rest api', 'api', 'json', 'llm',
+        'ollama', 'gpu', 'machine learning', 'ai', 'agent', 'mcp', 'javascript',
+        'html', 'css', 'react', 'node', 'full-stack', 'devops', 'sysadmin',
+        'monitoring', 'grafana', 'prometheus', 'backup', 'security', 'homelab',
+        'esp32', 'sensor', 'hardware', 'raspberry pi', 'cryptocurrency', 'bitcoin',
+        'btcpay', 'payments', 'cloudflare', 'tailscale', 'virtualization', 'kvm',
+    ]
+
+
+def keyword_score(description):
+    """Deterministic fit score 0..1 based on skill overlap."""
+    if not description:
+        return 0.0, 'No description available.'
+    d = description.lower()
+    hits = [k for k in _skill_keywords() if k in d]
+    score = min(1.0, len(hits) / 14.0)
+    matched = ', '.join(hits[:8]) or 'no direct keyword matches'
+    return round(score, 3), f'Keyword overlap: {len(hits)} skills ({matched}).'