Upload monitor.py

monitor.py

@@ -0,0 +1,136 @@
+"""
+monitor.py
+Lightweight monitoring logger for DocMind AI.
+Logs every query, retrieval, LLM response, latency, and errors
+to a local JSONL file readable from HuggingFace Logs tab.
+"""
+
+import json
+import time
+import os
+from datetime import datetime, timezone
+
+
+LOG_FILE = "/tmp/docmind_logs.jsonl"
+
+
+def log_query(
+    question: str,
+    answer: str,
+    sources: list,
+    latency_ms: float,
+    model_used: str = "",
+    chunk_count: int = 0,
+    error: str = "",
+):
+    """Log a single query event."""
+    entry = {
+        "timestamp":   datetime.now(timezone.utc).isoformat(),
+        "event":       "query",
+        "question":    question[:200],          # truncate for log size
+        "answer_len":  len(answer),
+        "sources":     sources,
+        "latency_ms":  round(latency_ms, 1),
+        "model":       model_used,
+        "chunk_count": chunk_count,
+        "error":       error,
+        "success":     not bool(error),
+    }
+    _write(entry)
+    _print_summary(entry)
+
+
+def log_ingestion(filename: str, chunk_count: int, latency_ms: float, error: str = ""):
+    """Log a document ingestion event."""
+    entry = {
+        "timestamp":   datetime.now(timezone.utc).isoformat(),
+        "event":       "ingestion",
+        "filename":    filename,
+        "chunk_count": chunk_count,
+        "latency_ms":  round(latency_ms, 1),
+        "error":       error,
+        "success":     not bool(error),
+    }
+    _write(entry)
+    _print_summary(entry)
+
+
+def log_startup():
+    """Log app startup."""
+    entry = {
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "event":     "startup",
+        "message":   "DocMind AI started",
+    }
+    _write(entry)
+    print("[DocMind] App started at {}".format(entry["timestamp"]))
+
+
+def get_stats() -> dict:
+    """Read log file and compute summary stats."""
+    if not os.path.exists(LOG_FILE):
+        return {
+            "total_queries":    0,
+            "success_rate":     0.0,
+            "avg_latency_ms":   0.0,
+            "total_ingestions": 0,
+            "errors":           [],
+        }
+
+    queries    = []
+    ingestions = []
+    errors     = []
+
+    with open(LOG_FILE, "r") as f:
+        for line in f:
+            try:
+                entry = json.loads(line.strip())
+                if entry["event"] == "query":
+                    queries.append(entry)
+                    if entry.get("error"):
+                        errors.append(entry)
+                elif entry["event"] == "ingestion":
+                    ingestions.append(entry)
+            except Exception:
+                continue
+
+    success_count = sum(1 for q in queries if q.get("success"))
+    latencies     = [q["latency_ms"] for q in queries if q.get("latency_ms")]
+
+    return {
+        "total_queries":    len(queries),
+        "success_rate":     round(success_count / len(queries) * 100, 1) if queries else 0.0,
+        "avg_latency_ms":   round(sum(latencies) / len(latencies), 1) if latencies else 0.0,
+        "min_latency_ms":   round(min(latencies), 1) if latencies else 0.0,
+        "max_latency_ms":   round(max(latencies), 1) if latencies else 0.0,
+        "total_ingestions": len(ingestions),
+        "total_errors":     len(errors),
+        "recent_errors":    [e.get("error", "") for e in errors[-3:]],
+    }
+
+
+def _write(entry: dict):
+    try:
+        with open(LOG_FILE, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+    except Exception as e:
+        print("[DocMind Monitor] Failed to write log: {}".format(e))
+
+
+def _print_summary(entry: dict):
+    """Print to stdout so it shows in HuggingFace Logs tab."""
+    if entry["event"] == "query":
+        status = "OK" if entry["success"] else "ERROR"
+        print("[DocMind][{}] Q: '{}...' | Latency: {}ms | Model: {} | AnswerLen: {}".format(
+            status,
+            entry["question"][:50],
+            entry["latency_ms"],
+            entry.get("model", "unknown"),
+            entry["answer_len"],
+        ))
+    elif entry["event"] == "ingestion":
+        print("[DocMind][INGEST] File: {} | Chunks: {} | Latency: {}ms".format(
+            entry["filename"],
+            entry["chunk_count"],
+            entry["latency_ms"],
+        ))