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Author SHA1 Message Date
sysadmin 38506a753f Merge remote-tracking branch 'prgs/master' into feat/issue-651-usage-cost-analytics
# Conflicts:
#	docs/webui-authz-audit.md
#	webui/console_authz.py
2026-07-24 08:33:35 -04:00
sysadminandClaude Opus 4.8 b179610e7f fix(webui): remediate PR #876 REQUEST_CHANGES for analytics (#651)
Address review blockers and medium/low findings:

- F1: HTML-escape all dynamic analytics fields (html.escape quote=True)
- F2: Gate POST /api/v1/analytics/usage through console_authz
  record_analytics_usage (fail closed for unauthenticated / Phase 1)
- F3: Enforce usage_events retention (max rows + max age)
- F4: Coerce None remote/org/repo to empty strings in load_analytics

Add tests for XSS escaping, unauthorized ingest deny, retention, and
None scope coercion. Document authz + retention in analytics guide.

Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
2026-07-24 08:11:12 -04:00
jcwalker3 0041542fe2 Merge branch 'master' into feat/issue-651-usage-cost-analytics 2026-07-24 07:03:24 -05:00
jcwalker3 1f144705e2 Merge branch 'master' into feat/issue-651-usage-cost-analytics 2026-07-24 06:55:03 -05:00
sysadmin 5c5c1fdf77 feat(webui): model usage, token cost, latency, and performance analytics (Closes #651) 2026-07-24 07:16:44 -04:00
11 changed files with 1450 additions and 2 deletions
+231 -1
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@@ -31,7 +31,7 @@ from typing import Any, Iterator, Sequence
import dependency_graph
SCHEMA_VERSION = 4
SCHEMA_VERSION = 5
# Assignable work kinds only — raw monitoring incidents are never work items.
WORK_KINDS = frozenset({"issue", "pr"})
@@ -186,6 +186,34 @@ CREATE INDEX IF NOT EXISTS idx_dependency_edges_target
ON dependency_edges(remote, org, repo, target_kind, target_number);
CREATE INDEX IF NOT EXISTS idx_assignments_session ON assignments(session_id, status);
CREATE INDEX IF NOT EXISTS idx_incident_gitea ON incident_links(gitea_org, gitea_repo, gitea_issue_number);
-- Model usage, token cost, latency, and performance events (#651)
CREATE TABLE IF NOT EXISTS usage_events (
usage_id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT,
remote TEXT NOT NULL DEFAULT 'dadeschools',
org TEXT NOT NULL DEFAULT '',
repo TEXT NOT NULL DEFAULT '',
project_id TEXT,
role TEXT NOT NULL DEFAULT 'unknown',
model TEXT NOT NULL DEFAULT 'unknown',
issue_number INTEGER,
pr_number INTEGER,
stage TEXT NOT NULL DEFAULT 'unknown',
input_tokens INTEGER,
output_tokens INTEGER,
total_tokens INTEGER,
estimated_cost_usd REAL,
latency_ms INTEGER,
duration_ms INTEGER,
status TEXT NOT NULL DEFAULT 'success',
metadata TEXT,
created_at TEXT NOT NULL
);
CREATE INDEX IF NOT EXISTS idx_usage_events_scope ON usage_events(remote, org, repo);
CREATE INDEX IF NOT EXISTS idx_usage_events_role_model ON usage_events(role, model);
CREATE INDEX IF NOT EXISTS idx_usage_events_stage ON usage_events(stage);
"""
@@ -339,6 +367,7 @@ class ControlPlaneDB:
self._migrate_incident_links_null_scope(conn)
self._migrate_lease_lifecycle_columns(conn)
self._migrate_session_ownership_columns(conn)
self._migrate_usage_events_table(conn)
conn.execute(
"INSERT OR REPLACE INTO schema_meta(key, value) VALUES (?, ?)",
("schema_version", str(SCHEMA_VERSION)),
@@ -518,6 +547,207 @@ class ControlPlaneDB:
f"UPDATE incident_links SET {col} = '' WHERE {col} IS NULL"
)
def _migrate_usage_events_table(self, conn: sqlite3.Connection) -> None:
"""Create usage_events table and indexes if they do not exist (#651)."""
conn.execute("""
CREATE TABLE IF NOT EXISTS usage_events (
usage_id INTEGER PRIMARY KEY AUTOINCREMENT,
session_id TEXT,
remote TEXT NOT NULL DEFAULT 'dadeschools',
org TEXT NOT NULL DEFAULT '',
repo TEXT NOT NULL DEFAULT '',
project_id TEXT,
role TEXT NOT NULL DEFAULT 'unknown',
model TEXT NOT NULL DEFAULT 'unknown',
issue_number INTEGER,
pr_number INTEGER,
stage TEXT NOT NULL DEFAULT 'unknown',
input_tokens INTEGER,
output_tokens INTEGER,
total_tokens INTEGER,
estimated_cost_usd REAL,
latency_ms INTEGER,
duration_ms INTEGER,
status TEXT NOT NULL DEFAULT 'success',
metadata TEXT,
created_at TEXT NOT NULL
);
""")
conn.execute("CREATE INDEX IF NOT EXISTS idx_usage_events_scope ON usage_events(remote, org, repo);")
conn.execute("CREATE INDEX IF NOT EXISTS idx_usage_events_role_model ON usage_events(role, model);")
conn.execute("CREATE INDEX IF NOT EXISTS idx_usage_events_stage ON usage_events(stage);")
# #651 retention: cap growth so unauthenticated or high-volume ingest
# cannot DoS the control-plane DB (PR #876 F3). Applied after every write.
USAGE_EVENTS_MAX_ROWS = 10_000
USAGE_EVENTS_RETENTION_DAYS = 90
def record_usage_event(
self,
*,
session_id: str | None = None,
remote: str = "dadeschools",
org: str = "",
repo: str = "",
project_id: str | None = None,
role: str = "unknown",
model: str = "unknown",
issue_number: int | None = None,
pr_number: int | None = None,
stage: str = "unknown",
input_tokens: int | None = None,
output_tokens: int | None = None,
total_tokens: int | None = None,
estimated_cost_usd: float | None = None,
latency_ms: int | None = None,
duration_ms: int | None = None,
status: str = "success",
metadata: str | dict[str, Any] | None = None,
created_at: str | None = None,
) -> int:
"""Record a model usage, token cost, latency, or stage performance event (#651)."""
ts = created_at or _ts()
meta_str: str | None = None
if metadata is not None:
from webui import console_redaction
redacted_meta = console_redaction.redact_payload(metadata)
if isinstance(redacted_meta, str):
meta_str = redacted_meta
else:
try:
meta_str = json.dumps(redacted_meta, default=str)
except Exception:
meta_str = str(redacted_meta)
if total_tokens is None and (input_tokens is not None or output_tokens is not None):
total_tokens = (input_tokens or 0) + (output_tokens or 0)
with self._tx(immediate=True) as conn:
cursor = conn.execute(
"""
INSERT INTO usage_events (
session_id, remote, org, repo, project_id, role, model,
issue_number, pr_number, stage, input_tokens, output_tokens,
total_tokens, estimated_cost_usd, latency_ms, duration_ms,
status, metadata, created_at
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""",
(
session_id,
remote,
org,
repo,
project_id,
role,
model,
issue_number,
pr_number,
stage,
input_tokens,
output_tokens,
total_tokens,
estimated_cost_usd,
latency_ms,
duration_ms,
status,
meta_str,
ts,
),
)
usage_id = cursor.lastrowid
self._enforce_usage_events_retention(conn)
return usage_id
def _enforce_usage_events_retention(self, conn: sqlite3.Connection) -> None:
"""Drop aged and excess usage_events rows (PR #876 F3)."""
# Age-based: ISO-8601 UTC timestamps compare lexicographically.
cutoff = (
datetime.now(timezone.utc)
- timedelta(days=int(self.USAGE_EVENTS_RETENTION_DAYS))
).strftime("%Y-%m-%dT%H:%M:%SZ")
conn.execute(
"DELETE FROM usage_events WHERE created_at < ?",
(cutoff,),
)
# Count-based: keep the newest USAGE_EVENTS_MAX_ROWS by usage_id.
max_rows = int(self.USAGE_EVENTS_MAX_ROWS)
if max_rows > 0:
conn.execute(
"""
DELETE FROM usage_events
WHERE usage_id NOT IN (
SELECT usage_id FROM usage_events
ORDER BY usage_id DESC
LIMIT ?
)
""",
(max_rows,),
)
def query_usage_events(
self,
*,
remote: str | None = None,
org: str | None = None,
repo: str | None = None,
project_id: str | None = None,
role: str | None = None,
model: str | None = None,
issue_number: int | None = None,
pr_number: int | None = None,
stage: str | None = None,
session_id: str | None = None,
limit: int = 500,
offset: int = 0,
) -> list[dict[str, Any]]:
"""Query stored usage events matching filters (#651)."""
conditions = []
params = []
if remote:
conditions.append("remote = ?")
params.append(remote)
if org:
conditions.append("org = ?")
params.append(org)
if repo:
conditions.append("repo = ?")
params.append(repo)
if project_id:
conditions.append("project_id = ?")
params.append(project_id)
if role:
conditions.append("role = ?")
params.append(role)
if model:
conditions.append("model = ?")
params.append(model)
if issue_number is not None:
conditions.append("issue_number = ?")
params.append(issue_number)
if pr_number is not None:
conditions.append("pr_number = ?")
params.append(pr_number)
if stage:
conditions.append("stage = ?")
params.append(stage)
if session_id:
conditions.append("session_id = ?")
params.append(session_id)
where_clause = f"WHERE {' AND '.join(conditions)}" if conditions else ""
sql = f"""
SELECT * FROM usage_events
{where_clause}
ORDER BY usage_id ASC
LIMIT ? OFFSET ?
"""
params.extend([limit, offset])
with self._tx(immediate=False) as conn:
cursor = conn.execute(sql, params)
rows = cursor.fetchall()
return [dict(row) for row in rows]
# ── sessions ──────────────────────────────────────────────────────────
def upsert_session(
@@ -0,0 +1,143 @@
# Model Usage, Token Cost, Latency, and Workflow Analytics (Phase 4)
- **Tracking Issue:** [#651](https://gitea.prgs.cc/Scaled-Tech-Consulting/Gitea-Tools/issues/651)
- **Parent Epic:** [#631](https://gitea.prgs.cc/Scaled-Tech-Consulting/Gitea-Tools/issues/651)
- **Console Surface:** `/analytics`, `/api/v1/analytics`, `/api/v1/analytics/usage`
## 1. Overview
The Web Console Analytics module provides durable, aggregate visibility into **model usage, token cost, latency percentiles, and workflow-stage performance** across projects, worker roles, AI models, issues, and PRs.
### Non-Goals
- No mandatory client-side telemetry that leaks prompts or secret keys.
- No third-party payment provider or billing integration.
- No automatic model routing changes without controller policy (#647).
---
## 2. Event Schema (`usage_events`)
Usage metrics are stored in the control-plane database under table `usage_events`.
| Column | Type | Description |
|---|---|---|
| `usage_id` | `INTEGER` | Primary key (autoincrement) |
| `session_id` | `TEXT` | Optional active session identifier |
| `remote` | `TEXT` | Known Gitea instance (`dadeschools` or `prgs`) |
| `org` | `TEXT` | Repository owner / organization |
| `repo` | `TEXT` | Repository name |
| `project_id` | `TEXT` | Optional project identifier |
| `role` | `TEXT` | Active worker role (`author`, `reviewer`, `merger`, `reconciler`, `controller`) |
| `model` | `TEXT` | LLM model identifier (e.g. `gemini-3.6-flash`, `claude-3-5-sonnet`) |
| `issue_number` | `INTEGER` | Correlated Gitea issue number (optional) |
| `pr_number` | `INTEGER` | Correlated Gitea PR number (optional) |
| `stage` | `TEXT` | Workflow stage (`preflight`, `implementation`, `review`, `merge`, `reconciliation`) |
| `input_tokens` | `INTEGER` | Input token count (optional / nullable) |
| `output_tokens` | `INTEGER` | Output token count (optional / nullable) |
| `total_tokens` | `INTEGER` | Total token count (optional / nullable) |
| `estimated_cost_usd` | `REAL` | Estimated USD cost (optional / nullable) |
| `latency_ms` | `INTEGER` | Request latency in milliseconds (optional / nullable) |
| `duration_ms` | `INTEGER` | Stage execution duration in milliseconds (optional / nullable) |
| `status` | `TEXT` | Outcome status (`success`, `failure`, `timeout`) |
| `metadata` | `TEXT` | Redacted metadata or summary string |
| `created_at` | `TEXT` | ISO 8601 UTC timestamp |
---
## 3. Handling of Missing Data ("Unknown" vs. Zero Fabrication)
To ensure operational metrics accurately reflect evidence:
- **Untracked or missing metrics are displayed as `Unknown`**, never zero-fabricated.
- If an event omits `estimated_cost_usd`, `latency_ms`, or token counts, the aggregator marks those fields as missing (`None`) rather than defaulting to `0` or `$0.00`.
- Summary tables and KPI cards explicitly indicate when data is unmeasured or partially reported.
---
## 4. Redaction & Security Rules
Per `#633` security policy:
- Free-text fields (`metadata`, `prompt_summary`, `session_id`) are run through `console_redaction.redact_text` before persistence and output serialization.
- Secret tokens, keychain commands, authorization headers, passwords, and JWTs are stripped automatically.
---
## 5. Opt-in Instrumentation Guide
Applications, MCP servers, and background sessions can report usage metrics through either Python API or HTTP ingestion.
### Python Ingestion
```python
from webui.analytics_loader import record_usage
record_usage(
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
role="author",
model="gemini-3.6-flash",
issue_number=651,
stage="implementation",
input_tokens=1420,
output_tokens=380,
total_tokens=1800,
estimated_cost_usd=0.00045,
latency_ms=320,
duration_ms=4500,
status="success",
metadata={"note": "Implementation of analytics module"},
)
```
### HTTP Ingestion API (authorized write)
`POST /api/v1/analytics/usage` is a **gated write**. It runs through
`console_authz` action `record_analytics_usage` (operator+, Phase 2 execution).
Unauthenticated or phase-inactive requests receive **403** and do not write.
Prefer in-process `record_usage` for MCP / session instrumentation.
```http
POST /api/v1/analytics/usage HTTP/1.1
Content-Type: application/json
# Requires authenticated principal with record_analytics_usage execution enabled
{
"remote": "dadeschools",
"org": "Scaled-Tech-Consulting",
"repo": "Gitea-Tools",
"role": "author",
"model": "gemini-3.6-flash",
"issue_number": 651,
"stage": "implementation",
"input_tokens": 1420,
"output_tokens": 380,
"total_tokens": 1800,
"estimated_cost_usd": 0.00045,
"latency_ms": 320,
"duration_ms": 4500,
"status": "success",
"metadata": "Analytics schema landed"
}
```
### Retention
`usage_events` is retained with hard caps applied on every write:
| Limit | Default |
|---|---|
| Max rows | 10,000 (`ControlPlaneDB.USAGE_EVENTS_MAX_ROWS`) |
| Max age | 90 days (`ControlPlaneDB.USAGE_EVENTS_RETENTION_DAYS`) |
Older rows (by `created_at`) and excess oldest rows (by `usage_id`) are deleted
after each insert so unbounded growth / DoS-by-volume cannot fill the DB.
---
## 6. Querying Analytics API
```http
GET /api/v1/analytics?role=author&stage=implementation HTTP/1.1
```
Returns `AnalyticsSnapshot` JSON containing aggregations (`by_model`, `by_stage`, `by_role`, `by_work_item`, `by_project`) and latency percentiles (`p50`, `p90`, `p95`, `p99`).
+1
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@@ -91,6 +91,7 @@ already define, and a regression test asserts each mapping matches.
| `close_pr` | controller | privileged | `gitea.pr.close` | Yes | No | No | 3 |
| `merge_pr` | controller | privileged | `gitea.pr.merge` | Yes | **Yes** | **Yes** | 3 |
| `delete_branch` | admin | destructive | `gitea.branch.delete` | Yes | **Yes** | **Yes** | 3 |
| `record_analytics_usage` | operator | gated_write | `runtime.record_analytics_usage` | Yes | No | No | 2 |
| `system.reload_namespace` | controller | privileged | `runtime.reload_namespace` | Yes | No | No | 2 |
| `system.restart_namespace` | admin | destructive | `runtime.restart_namespace` | Yes | **Yes** | **Yes** | 2 |
+9
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@@ -510,6 +510,15 @@ TASK_CAPABILITY_MAP: dict[str, dict[str, str]] = {
"permission": "gitea.issue.comment",
"role": "author",
},
# #651 console analytics ingest — control-plane DB write, not a Gitea API
# call. Authority comes from console RBAC (operator+) plus phase gating;
# permission string is a non-Gitea runtime capability so no Gitea profile
# can satisfy it by accident.
"record_analytics_usage": {
"permission": "runtime.record_analytics_usage",
"role": "author",
},
}
+1 -1
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@@ -36,7 +36,7 @@ class ControlPlaneDBTest(unittest.TestCase):
rows = dict(conn.execute("SELECT key, value FROM schema_meta").fetchall())
finally:
conn.close()
self.assertEqual(rows["schema_version"], "4")
self.assertEqual(rows["schema_version"], "5")
self.assertIn("DB coordinates", rows["architecture"])
self.assertIn("bridge", rows["architecture"].lower())
+252
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@@ -0,0 +1,252 @@
"""Unit and integration tests for Model Usage & Performance Analytics (#651)."""
from __future__ import annotations
import os
import tempfile
import unittest
from starlette.testclient import TestClient
import control_plane_db
from webui.analytics_loader import (
ANALYTICS_SCHEMA_VERSION,
compute_percentile,
load_analytics,
record_usage,
)
from webui.app import create_app
from webui import console_redaction
class AnalyticsLoaderTest(unittest.TestCase):
def setUp(self) -> None:
self.temp_dir = tempfile.TemporaryDirectory()
self.db_path = os.path.join(self.temp_dir.name, "test_control_plane.sqlite3")
os.environ["GITEA_CONTROL_PLANE_DB"] = self.db_path
self.db = control_plane_db.ControlPlaneDB(db_path=self.db_path)
def tearDown(self) -> None:
self.temp_dir.cleanup()
def test_compute_percentile(self) -> None:
self.assertIsNone(compute_percentile([], 50.0))
self.assertEqual(compute_percentile([100], 50.0), 100.0)
# 2 elements: [100, 200]
self.assertEqual(compute_percentile([100, 200], 50.0), 150.0)
# 100 elements: 1..100
vals = list(range(1, 101))
self.assertEqual(compute_percentile(vals, 50.0), 50.5)
self.assertAlmostEqual(compute_percentile(vals, 90.0), 90.1)
def test_record_and_aggregate_usage(self) -> None:
# Record event 1 (complete data)
u1 = record_usage(
db_path=self.db_path,
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
role="author",
model="gemini-3.6-flash",
issue_number=651,
stage="implementation",
input_tokens=1000,
output_tokens=500,
estimated_cost_usd=0.0015,
latency_ms=200,
duration_ms=3000,
metadata={"secret_key": "secret123", "note": "token=secret123"},
)
self.assertGreater(u1, 0)
# Record event 2 (missing tokens and cost -> unknown)
u2 = record_usage(
db_path=self.db_path,
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
role="reviewer",
model="claude-3-5-sonnet",
pr_number=846,
stage="review",
latency_ms=500,
duration_ms=6000,
)
self.assertGreater(u2, u1)
snapshot = load_analytics(
db_path=self.db_path,
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
)
self.assertTrue(snapshot.ok)
self.assertEqual(snapshot.schema_version, ANALYTICS_SCHEMA_VERSION)
self.assertEqual(snapshot.total_events, 2)
# Verify overall summary
summary = snapshot.overall_summary
self.assertEqual(summary.total_events, 2)
self.assertEqual(summary.events_with_tokens, 1)
self.assertEqual(summary.total_tokens, 1500)
self.assertEqual(summary.events_with_cost, 1)
self.assertEqual(summary.estimated_cost_usd, 0.0015)
self.assertEqual(summary.events_with_latency, 2)
self.assertEqual(summary.latency_p50_ms, 350.0)
# Verify missing data handling (AC 3: not zero-fabricated)
reviewer_model = snapshot.by_model.get("claude-3-5-sonnet")
self.assertIsNotNone(reviewer_model)
self.assertEqual(reviewer_model.total_events, 1)
self.assertEqual(reviewer_model.events_with_tokens, 0)
self.assertIsNone(reviewer_model.total_tokens)
self.assertEqual(reviewer_model.display_tokens, "Unknown")
self.assertEqual(reviewer_model.events_with_cost, 0)
self.assertIsNone(reviewer_model.estimated_cost_usd)
self.assertEqual(reviewer_model.display_cost, "Unknown")
# Verify redaction (AC 4)
e1 = [e for e in snapshot.events if e.usage_id == u1][0]
self.assertIsNotNone(e1.metadata)
self.assertNotIn("secret123", e1.metadata)
self.assertIn("[REDACTED]", e1.metadata)
def test_missing_db_fail_soft(self) -> None:
invalid_path = "/nonexistent_path_dir/db.sqlite3"
snapshot = load_analytics(db_path=invalid_path)
self.assertFalse(snapshot.ok)
self.assertIn("control_plane_db_unavailable", snapshot.reason)
self.assertEqual(snapshot.overall_summary.display_tokens, "Unknown")
class AnalyticsWebUITest(unittest.TestCase):
def setUp(self) -> None:
self.temp_dir = tempfile.TemporaryDirectory()
self.db_path = os.path.join(self.temp_dir.name, "test_webui.sqlite3")
os.environ["GITEA_CONTROL_PLANE_DB"] = self.db_path
self.app = create_app()
self.client = TestClient(self.app)
record_usage(
db_path=self.db_path,
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
role="author",
model="gemini-3.6-flash",
issue_number=651,
stage="implementation",
input_tokens=2000,
output_tokens=1000,
estimated_cost_usd=0.003,
latency_ms=150,
duration_ms=2500,
)
def tearDown(self) -> None:
self.temp_dir.cleanup()
def test_analytics_html_route(self) -> None:
response = self.client.get("/analytics")
self.assertEqual(response.status_code, 200)
self.assertIn("Model Usage & Performance Analytics", response.text)
self.assertIn("gemini-3.6-flash", response.text)
self.assertIn("3,000", response.text)
def test_analytics_api_route(self) -> None:
response = self.client.get("/api/v1/analytics")
self.assertEqual(response.status_code, 200)
data = response.json()
self.assertTrue(data["ok"])
self.assertEqual(data["total_events"], 1)
self.assertIn("gemini-3.6-flash", data["by_model"])
def test_analytics_ingest_unauthorized_denied(self) -> None:
"""F2: unauthenticated POST must not write the control-plane DB."""
payload = {
"remote": "dadeschools",
"org": "Scaled-Tech-Consulting",
"repo": "Gitea-Tools",
"role": "reviewer",
"model": "claude-3-5-sonnet",
"pr_number": 846,
"stage": "review",
"input_tokens": 500,
"output_tokens": 100,
"latency_ms": 400,
"metadata": "Review note token=secret456",
}
response = self.client.post("/api/v1/analytics/usage", json=payload)
self.assertEqual(response.status_code, 403)
res_json = response.json()
self.assertFalse(res_json.get("ok", True))
self.assertEqual(res_json.get("error"), "unauthorized")
authorization = res_json.get("authorization") or {}
self.assertFalse(authorization.get("allowed"))
self.assertFalse(authorization.get("execution_enabled"))
# No new row written
res2 = self.client.get("/api/v1/analytics")
self.assertEqual(res2.status_code, 200)
self.assertEqual(res2.json()["total_events"], 1)
def test_html_escapes_script_bearing_model_role_stage(self) -> None:
"""F1: stored XSS — dynamic model/role/stage must render escaped."""
xss = '<script>alert(1)</script>'
record_usage(
db_path=self.db_path,
remote="dadeschools",
org="Scaled-Tech-Consulting",
repo="Gitea-Tools",
role=xss,
model=xss,
stage=xss,
issue_number=999,
status="success",
)
response = self.client.get("/analytics")
self.assertEqual(response.status_code, 200)
# Raw tag must not appear; escaped form must.
self.assertNotIn("<script>alert(1)</script>", response.text)
self.assertIn("&lt;script&gt;alert(1)&lt;/script&gt;", response.text)
def test_load_analytics_coerces_none_scope(self) -> None:
"""F4: None remote/org/repo become empty strings, never None."""
snapshot = load_analytics(db_path=self.db_path, remote=None, org=None, repo=None)
self.assertIsInstance(snapshot.remote, str)
self.assertIsInstance(snapshot.org, str)
self.assertIsInstance(snapshot.repo, str)
self.assertEqual(snapshot.remote, "")
self.assertEqual(snapshot.org, "")
self.assertEqual(snapshot.repo, "")
def test_usage_events_retention_max_rows(self) -> None:
"""F3: record_usage_event enforces USAGE_EVENTS_MAX_ROWS."""
db = control_plane_db.ControlPlaneDB(db_path=self.db_path)
original_max = db.USAGE_EVENTS_MAX_ROWS
try:
db.USAGE_EVENTS_MAX_ROWS = 3
for i in range(5):
db.record_usage_event(
remote="dadeschools",
org="org",
repo="repo",
role="author",
model=f"model-{i}",
stage="test",
)
rows = db.query_usage_events(limit=100)
self.assertLessEqual(len(rows), 3)
# Newest three retained
models = {r["model"] for r in rows}
self.assertEqual(models, {"model-2", "model-3", "model-4"})
finally:
db.USAGE_EVENTS_MAX_ROWS = original_max
if __name__ == "__main__":
unittest.main()
+433
View File
@@ -0,0 +1,433 @@
"""Model usage, token cost, latency, and workflow-performance analytics (#651, Phase 4).
Ingests session instrumentation metrics, aggregates usage/cost/latency percentiles
by project, role, model, issue/PR, and stage, enforcing secret redaction and
explicitly rendering missing metrics as "Unknown" without zero-fabrication.
"""
from __future__ import annotations
import math
from dataclasses import asdict, dataclass
from typing import Any, Sequence
import control_plane_db
from webui import console_redaction
ANALYTICS_SCHEMA_VERSION = 1
@dataclass(frozen=True)
class UsageEvent:
usage_id: int
session_id: str | None
remote: str
org: str
repo: str
project_id: str | None
role: str
model: str
issue_number: int | None
pr_number: int | None
stage: str
input_tokens: int | None
output_tokens: int | None
total_tokens: int | None
estimated_cost_usd: float | None
latency_ms: int | None
duration_ms: int | None
status: str
metadata: str | None
created_at: str
def to_dict(self) -> dict[str, Any]:
d = asdict(self)
if d["metadata"]:
d["metadata"] = console_redaction.redact_text(d["metadata"])
return d
@dataclass(frozen=True)
class GroupMetrics:
name: str
total_events: int
events_with_tokens: int
input_tokens: int | None
output_tokens: int | None
total_tokens: int | None
events_with_cost: int
estimated_cost_usd: float | None
events_with_latency: int
latency_p50_ms: float | None
latency_p90_ms: float | None
latency_p95_ms: float | None
latency_p99_ms: float | None
latency_avg_ms: float | None
events_with_duration: int
duration_avg_ms: float | None
display_tokens: str
display_cost: str
display_latency_p50: str
display_latency_p90: str
display_duration_avg: str
def to_dict(self) -> dict[str, Any]:
return asdict(self)
@dataclass(frozen=True)
class AnalyticsSnapshot:
ok: bool
reason: str
schema_version: int
remote: str
org: str
repo: str
total_events: int
overall_summary: GroupMetrics
by_project: dict[str, GroupMetrics]
by_role: dict[str, GroupMetrics]
by_model: dict[str, GroupMetrics]
by_work_item: dict[str, GroupMetrics]
by_stage: dict[str, GroupMetrics]
events: tuple[UsageEvent, ...]
def to_dict(self) -> dict[str, Any]:
return {
"ok": self.ok,
"reason": self.reason,
"schema_version": self.schema_version,
"remote": self.remote,
"org": self.org,
"repo": self.repo,
"total_events": self.total_events,
"overall_summary": self.overall_summary.to_dict(),
"by_project": {k: v.to_dict() for k, v in self.by_project.items()},
"by_role": {k: v.to_dict() for k, v in self.by_role.items()},
"by_model": {k: v.to_dict() for k, v in self.by_model.items()},
"by_work_item": {k: v.to_dict() for k, v in self.by_work_item.items()},
"by_stage": {k: v.to_dict() for k, v in self.by_stage.items()},
"events": [e.to_dict() for e in self.events],
}
def compute_percentile(values: Sequence[float | int], percentile: float) -> float | None:
if not values:
return None
sorted_vals = sorted(values)
n = len(sorted_vals)
if n == 1:
return float(sorted_vals[0])
k = (n - 1) * (percentile / 100.0)
f = math.floor(k)
c = math.ceil(k)
if f == c:
return float(sorted_vals[int(f)])
d0 = sorted_vals[int(f)] * (c - k)
d1 = sorted_vals[int(c)] * (k - f)
return float(d0 + d1)
def aggregate_events(group_name: str, events: Sequence[UsageEvent]) -> GroupMetrics:
total_events = len(events)
if total_events == 0:
return GroupMetrics(
name=group_name,
total_events=0,
events_with_tokens=0,
input_tokens=None,
output_tokens=None,
total_tokens=None,
events_with_cost=0,
estimated_cost_usd=None,
events_with_latency=0,
latency_p50_ms=None,
latency_p90_ms=None,
latency_p95_ms=None,
latency_p99_ms=None,
latency_avg_ms=None,
events_with_duration=0,
duration_avg_ms=None,
display_tokens="Unknown",
display_cost="Unknown",
display_latency_p50="Unknown",
display_latency_p90="Unknown",
display_duration_avg="Unknown",
)
token_events = [
e for e in events
if e.total_tokens is not None or e.input_tokens is not None or e.output_tokens is not None
]
events_with_tokens = len(token_events)
if events_with_tokens > 0:
input_tokens = sum(e.input_tokens or 0 for e in token_events)
output_tokens = sum(e.output_tokens or 0 for e in token_events)
total_tokens = sum(
e.total_tokens if e.total_tokens is not None else ((e.input_tokens or 0) + (e.output_tokens or 0))
for e in token_events
)
display_tokens = f"{total_tokens:,}"
else:
input_tokens = None
output_tokens = None
total_tokens = None
display_tokens = "Unknown"
cost_events = [e for e in events if e.estimated_cost_usd is not None]
events_with_cost = len(cost_events)
if events_with_cost > 0:
estimated_cost_usd = round(sum(e.estimated_cost_usd for e in cost_events), 6)
display_cost = f"${estimated_cost_usd:.4f}"
else:
estimated_cost_usd = None
display_cost = "Unknown"
latency_vals = [e.latency_ms for e in events if e.latency_ms is not None]
events_with_latency = len(latency_vals)
if events_with_latency > 0:
latency_p50_ms = compute_percentile(latency_vals, 50.0)
latency_p90_ms = compute_percentile(latency_vals, 90.0)
latency_p95_ms = compute_percentile(latency_vals, 95.0)
latency_p99_ms = compute_percentile(latency_vals, 99.0)
latency_avg_ms = round(sum(latency_vals) / events_with_latency, 2)
display_latency_p50 = f"{round(latency_p50_ms, 1)} ms" if latency_p50_ms is not None else "Unknown"
display_latency_p90 = f"{round(latency_p90_ms, 1)} ms" if latency_p90_ms is not None else "Unknown"
else:
latency_p50_ms = None
latency_p90_ms = None
latency_p95_ms = None
latency_p99_ms = None
latency_avg_ms = None
display_latency_p50 = "Unknown"
display_latency_p90 = "Unknown"
duration_vals = [e.duration_ms for e in events if e.duration_ms is not None]
events_with_duration = len(duration_vals)
if events_with_duration > 0:
duration_avg_ms = round(sum(duration_vals) / events_with_duration, 2)
display_duration_avg = f"{round(duration_avg_ms / 1000.0, 2)} s" if duration_avg_ms >= 1000 else f"{round(duration_avg_ms, 1)} ms"
else:
duration_avg_ms = None
display_duration_avg = "Unknown"
return GroupMetrics(
name=group_name,
total_events=total_events,
events_with_tokens=events_with_tokens,
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
events_with_cost=events_with_cost,
estimated_cost_usd=estimated_cost_usd,
events_with_latency=events_with_latency,
latency_p50_ms=latency_p50_ms,
latency_p90_ms=latency_p90_ms,
latency_p95_ms=latency_p95_ms,
latency_p99_ms=latency_p99_ms,
latency_avg_ms=latency_avg_ms,
events_with_duration=events_with_duration,
duration_avg_ms=duration_avg_ms,
display_tokens=display_tokens,
display_cost=display_cost,
display_latency_p50=display_latency_p50,
display_latency_p90=display_latency_p90,
display_duration_avg=display_duration_avg,
)
def record_usage(
*,
db_path: str | None = None,
session_id: str | None = None,
remote: str = "dadeschools",
org: str = "",
repo: str = "",
project_id: str | None = None,
role: str = "unknown",
model: str = "unknown",
issue_number: int | None = None,
pr_number: int | None = None,
stage: str = "unknown",
input_tokens: int | None = None,
output_tokens: int | None = None,
total_tokens: int | None = None,
estimated_cost_usd: float | None = None,
latency_ms: int | None = None,
duration_ms: int | None = None,
status: str = "success",
metadata: str | dict[str, Any] | None = None,
created_at: str | None = None,
) -> int:
"""Ingest/record a single usage event with optional metrics."""
db = control_plane_db.ControlPlaneDB(db_path=db_path)
return db.record_usage_event(
session_id=session_id,
remote=remote,
org=org,
repo=repo,
project_id=project_id,
role=role,
model=model,
issue_number=issue_number,
pr_number=pr_number,
stage=stage,
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
estimated_cost_usd=estimated_cost_usd,
latency_ms=latency_ms,
duration_ms=duration_ms,
status=status,
metadata=metadata,
created_at=created_at,
)
def load_analytics(
*,
db_path: str | None = None,
remote: str | None = None,
org: str | None = None,
repo: str | None = None,
project_id: str | None = None,
role: str | None = None,
model: str | None = None,
stage: str | None = None,
issue_number: int | None = None,
pr_number: int | None = None,
limit: int = 500,
) -> AnalyticsSnapshot:
"""Load analytics snapshot aggregated by project, role, model, issue/PR, and stage."""
remote_filter = (remote or "").strip() or None
org_filter = (org or "").strip() or None
repo_filter = (repo or "").strip() or None
role_filter = (role or "").strip() or None
model_filter = (model or "").strip() or None
stage_filter = (stage or "").strip() or None
# F4: coerce optional scope filters to str so AnalyticsSnapshot never holds None.
scope_remote = (remote or "").strip()
scope_org = (org or "").strip()
scope_repo = (repo or "").strip()
try:
db = control_plane_db.ControlPlaneDB(db_path=db_path)
rows = db.query_usage_events(
remote=remote_filter,
org=org_filter,
repo=repo_filter,
project_id=project_id,
role=role_filter,
model=model_filter,
stage=stage_filter,
issue_number=issue_number,
pr_number=pr_number,
limit=limit,
)
except Exception as exc:
empty_summary = aggregate_events("Overall", [])
return AnalyticsSnapshot(
ok=False,
reason=f"control_plane_db_unavailable: {exc}",
schema_version=ANALYTICS_SCHEMA_VERSION,
remote=scope_remote,
org=scope_org,
repo=scope_repo,
total_events=0,
overall_summary=empty_summary,
by_project={},
by_role={},
by_model={},
by_work_item={},
by_stage={},
events=(),
)
parsed_events: list[UsageEvent] = []
for r in rows:
meta = console_redaction.redact_text(r.get("metadata")) if r.get("metadata") else None
parsed_events.append(
UsageEvent(
usage_id=r["usage_id"],
session_id=r.get("session_id"),
remote=r.get("remote") or scope_remote,
org=r.get("org") or scope_org,
repo=r.get("repo") or scope_repo,
project_id=r.get("project_id"),
role=r.get("role") or "unknown",
model=r.get("model") or "unknown",
issue_number=r.get("issue_number"),
pr_number=r.get("pr_number"),
stage=r.get("stage") or "unknown",
input_tokens=r.get("input_tokens"),
output_tokens=r.get("output_tokens"),
total_tokens=r.get("total_tokens"),
estimated_cost_usd=r.get("estimated_cost_usd"),
latency_ms=r.get("latency_ms"),
duration_ms=r.get("duration_ms"),
status=r.get("status") or "success",
metadata=meta,
created_at=r.get("created_at") or "",
)
)
overall_summary = aggregate_events("Overall", parsed_events)
# Group by project
groups_by_project: dict[str, list[UsageEvent]] = {}
for e in parsed_events:
key = e.project_id or (f"{e.org}/{e.repo}" if e.org and e.repo else "default")
groups_by_project.setdefault(key, []).append(e)
by_project = {k: aggregate_events(k, v) for k, v in groups_by_project.items()}
# Group by role
groups_by_role: dict[str, list[UsageEvent]] = {}
for e in parsed_events:
groups_by_role.setdefault(e.role, []).append(e)
by_role = {k: aggregate_events(k, v) for k, v in groups_by_role.items()}
# Group by model
groups_by_model: dict[str, list[UsageEvent]] = {}
for e in parsed_events:
groups_by_model.setdefault(e.model, []).append(e)
by_model = {k: aggregate_events(k, v) for k, v in groups_by_model.items()}
# Group by work item
groups_by_work_item: dict[str, list[UsageEvent]] = {}
for e in parsed_events:
if e.issue_number:
key = f"issue #{e.issue_number}"
elif e.pr_number:
key = f"pr #{e.pr_number}"
else:
key = "unlinked"
groups_by_work_item.setdefault(key, []).append(e)
by_work_item = {k: aggregate_events(k, v) for k, v in groups_by_work_item.items()}
# Group by stage
groups_by_stage: dict[str, list[UsageEvent]] = {}
for e in parsed_events:
groups_by_stage.setdefault(e.stage, []).append(e)
by_stage = {k: aggregate_events(k, v) for k, v in groups_by_stage.items()}
return AnalyticsSnapshot(
ok=True,
reason="ok",
schema_version=ANALYTICS_SCHEMA_VERSION,
remote=scope_remote,
org=scope_org,
repo=scope_repo,
total_events=len(parsed_events),
overall_summary=overall_summary,
by_project=by_project,
by_role=by_role,
by_model=by_model,
by_work_item=by_work_item,
by_stage=by_stage,
events=tuple(parsed_events),
)
def snapshot_to_dict(snapshot: AnalyticsSnapshot) -> dict[str, Any]:
return snapshot.to_dict()
+248
View File
@@ -0,0 +1,248 @@
"""HTML views for the Model Usage & Performance Analytics console (#651)."""
from __future__ import annotations
import html
from webui.analytics_loader import AnalyticsSnapshot, GroupMetrics, UsageEvent
from webui.layout import render_page
def _escape(text: object) -> str:
"""HTML-escape dynamic analytics fields (mirrors audit_views / project_views)."""
return html.escape(str(text), quote=True)
def _render_badge(text: str, badge_type: str = "muted") -> str:
return f'<span class="badge badge-{_escape(badge_type)}">{_escape(text)}</span>'
def _render_group_table(title: str, groups: dict[str, GroupMetrics], key_header: str = "Group") -> str:
if not groups:
return (
f"<h3>{_escape(title)}</h3>"
'<div class="card"><p class="muted">No telemetry events recorded for this dimension.</p></div>'
)
rows = []
for key, g in sorted(groups.items(), key=lambda x: x[1].total_events, reverse=True):
cost_cell = (
f'<span class="accent">{_escape(g.display_cost)}</span>'
if g.events_with_cost > 0
else _render_badge("Unknown")
)
tokens_cell = (
_escape(g.display_tokens)
if g.events_with_tokens > 0
else _render_badge("Unknown")
)
lat_p50 = (
_escape(g.display_latency_p50)
if g.events_with_latency > 0
else _render_badge("Unknown")
)
lat_p90 = (
_escape(g.display_latency_p90)
if g.events_with_latency > 0
else _render_badge("Unknown")
)
dur_avg = (
_escape(g.display_duration_avg)
if g.events_with_duration > 0
else _render_badge("Unknown")
)
rows.append(
"<tr>"
f"<td><strong>{_escape(key)}</strong></td>"
f"<td>{g.total_events}</td>"
f"<td>{tokens_cell}</td>"
f"<td>{cost_cell}</td>"
f"<td>{lat_p50}</td>"
f"<td>{lat_p90}</td>"
f"<td>{dur_avg}</td>"
"</tr>"
)
rows_html = "".join(rows)
return f"""
<h3>{_escape(title)}</h3>
<div class="card" style="overflow-x: auto;">
<table class="data-table">
<thead>
<tr>
<th>{_escape(key_header)}</th>
<th>Events</th>
<th>Total Tokens</th>
<th>Est. Cost</th>
<th>Latency (p50)</th>
<th>Latency (p90)</th>
<th>Avg Stage Duration</th>
</tr>
</thead>
<tbody>
{rows_html}
</tbody>
</table>
</div>
"""
def _render_events_table(events: tuple[UsageEvent, ...]) -> str:
if not events:
return (
"<h3>Recent Usage & Instrumentation Events</h3>"
'<div class="card"><p class="muted">No individual telemetry events recorded yet. Opt-in instrumentation via session logging or authorized POST /api/v1/analytics/usage.</p></div>'
)
rows = []
for e in list(events)[-50:]: # Display latest 50
if e.issue_number is not None:
work_item = f"issue #{e.issue_number}"
elif e.pr_number is not None:
work_item = f"pr #{e.pr_number}"
else:
work_item = "unlinked"
tokens = (
_escape(f"{e.total_tokens:,}")
if e.total_tokens is not None
else _render_badge("Unknown")
)
cost = (
_escape(f"${e.estimated_cost_usd:.4f}")
if e.estimated_cost_usd is not None
else _render_badge("Unknown")
)
latency = (
_escape(f"{e.latency_ms} ms")
if e.latency_ms is not None
else _render_badge("Unknown")
)
duration = (
_escape(f"{e.duration_ms} ms")
if e.duration_ms is not None
else _render_badge("Unknown")
)
status_badge = _render_badge(
e.status, "success" if e.status == "success" else "danger"
)
rows.append(
"<tr>"
f"<td>#{e.usage_id}</td>"
f"<td><small>{_escape(e.created_at)}</small></td>"
f"<td><span class=\"badge\">{_escape(e.role)}</span></td>"
f"<td><strong>{_escape(e.model)}</strong></td>"
f"<td>{_escape(e.stage)}</td>"
f"<td>{_escape(work_item)}</td>"
f"<td>{tokens}</td>"
f"<td>{cost}</td>"
f"<td>{latency}</td>"
f"<td>{duration}</td>"
f"<td>{status_badge}</td>"
"</tr>"
)
rows_html = "".join(rows)
return f"""
<h3>Recent Telemetry Events</h3>
<div class="card" style="overflow-x: auto;">
<table class="data-table">
<thead>
<tr>
<th>ID</th>
<th>Timestamp</th>
<th>Role</th>
<th>Model</th>
<th>Stage</th>
<th>Work Item</th>
<th>Tokens</th>
<th>Cost</th>
<th>Latency</th>
<th>Duration</th>
<th>Status</th>
</tr>
</thead>
<tbody>
{rows_html}
</tbody>
</table>
</div>
"""
def render_analytics_page(snapshot: AnalyticsSnapshot) -> str:
"""Render the main Model Usage & Performance Analytics console page."""
summary = snapshot.overall_summary
kpi_tokens = (
_escape(summary.display_tokens)
if summary.events_with_tokens > 0
else _render_badge("Unknown")
)
kpi_cost = (
_escape(summary.display_cost)
if summary.events_with_cost > 0
else _render_badge("Unknown")
)
kpi_lat_p50 = (
_escape(summary.display_latency_p50)
if summary.events_with_latency > 0
else _render_badge("Unknown")
)
kpi_dur_avg = (
_escape(summary.display_duration_avg)
if summary.events_with_duration > 0
else _render_badge("Unknown")
)
status_notice = ""
if not snapshot.ok:
status_notice = (
f'<div class="card warning-card"><strong>Degraded Data Source:</strong> '
f'{_escape(snapshot.reason)}</div>'
)
body_html = f"""
<h2>Model Usage & Performance Analytics (Phase 4)</h2>
<p class="muted">
Durable console analytics for model usage, token cost, latency percentiles, and workflow-stage performance correlated to issues, PRs, and worker roles.
</p>
{status_notice}
<div class="notice-card" style="background: rgba(91, 159, 212, 0.1); border: 1px solid var(--border); padding: 0.75rem 1rem; border-radius: 6px; margin-bottom: 1.5rem;">
<small><strong>Note on telemetry fidelity:</strong> Missing data or untracked metrics are explicitly labeled as <em>Unknown</em>. No token costs or latency metrics are zero-fabricated.</small>
</div>
<div class="card-grid" style="display: grid; grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); gap: 1rem; margin-bottom: 1.5rem;">
<div class="card">
<span class="muted" style="font-size: 0.85rem;">Total Events</span>
<h3 style="margin: 0.25rem 0 0 0;">{summary.total_events}</h3>
</div>
<div class="card">
<span class="muted" style="font-size: 0.85rem;">Total Tokens</span>
<h3 style="margin: 0.25rem 0 0 0;">{kpi_tokens}</h3>
</div>
<div class="card">
<span class="muted" style="font-size: 0.85rem;">Est. Token Cost</span>
<h3 style="margin: 0.25rem 0 0 0;">{kpi_cost}</h3>
</div>
<div class="card">
<span class="muted" style="font-size: 0.85rem;">Latency (p50)</span>
<h3 style="margin: 0.25rem 0 0 0;">{kpi_lat_p50}</h3>
</div>
<div class="card">
<span class="muted" style="font-size: 0.85rem;">Avg Stage Duration</span>
<h3 style="margin: 0.25rem 0 0 0;">{kpi_dur_avg}</h3>
</div>
</div>
{_render_group_table("Usage & Cost by Model", snapshot.by_model, "Model")}
{_render_group_table("Performance by Workflow Stage", snapshot.by_stage, "Stage")}
{_render_group_table("Usage & Cost by Role", snapshot.by_role, "Role")}
{_render_group_table("Work Item Analytics", snapshot.by_work_item, "Work Item")}
{_render_events_table(snapshot.events)}
"""
return render_page(title="Model Usage & Performance Analytics", body_html=body_html)
+118
View File
@@ -47,6 +47,12 @@ from webui.worktree_views import render_worktrees_page
from webui.runtime_health import load_runtime_snapshot, snapshot_to_dict as runtime_snapshot_to_dict
from webui.runtime_views import render_runtime_page
from webui.timeline import load_timeline, snapshot_to_dict as timeline_snapshot_to_dict
from webui.analytics_loader import (
load_analytics,
record_usage,
snapshot_to_dict as analytics_snapshot_to_dict,
)
from webui.analytics_views import render_analytics_page
from webui.system_health import (
API_PATH as SYSTEM_HEALTH_API_PATH,
load_system_health,
@@ -567,6 +573,114 @@ async def api_v1_timeline(request: Request) -> JSONResponse:
return JSONResponse(timeline_snapshot_to_dict(snapshot), status_code=status_code)
async def analytics(request: Request) -> HTMLResponse:
"""Read-only model usage, token cost, latency, and performance analytics HTML view (#651)."""
snapshot = load_analytics(
remote=request.query_params.get("remote"),
org=request.query_params.get("org"),
repo=request.query_params.get("repo"),
role=request.query_params.get("role"),
model=request.query_params.get("model"),
stage=request.query_params.get("stage"),
issue_number=_query_int(request, "issue"),
pr_number=_query_int(request, "pr"),
limit=_query_int(request, "limit") or 200,
)
return HTMLResponse(render_analytics_page(snapshot))
async def api_v1_analytics(request: Request) -> JSONResponse:
"""Read-only model usage, token cost, latency, and performance analytics API (#651)."""
snapshot = load_analytics(
remote=request.query_params.get("remote"),
org=request.query_params.get("org"),
repo=request.query_params.get("repo"),
role=request.query_params.get("role"),
model=request.query_params.get("model"),
stage=request.query_params.get("stage"),
issue_number=_query_int(request, "issue"),
pr_number=_query_int(request, "pr"),
limit=_query_int(request, "limit") or 500,
)
status_code = 200 if snapshot.ok else 500
return JSONResponse(analytics_snapshot_to_dict(snapshot), status_code=status_code)
async def api_v1_analytics_ingest(request: Request) -> JSONResponse:
"""Optional session instrumentation ingestion endpoint (#651).
Fail-closed write: every request is authorized through console_authz
(``record_analytics_usage``) before any control-plane DB mutation. Phase 1
keeps ``execution_enabled=False`` and denies unauthenticated callers, so
this route cannot be used as an unauthenticated write or XSS injection
vector (PR #876 F2).
"""
try:
body = await request.json()
except Exception:
body = {}
if not isinstance(body, dict):
body = {}
principal = resolve_principal(headers=dict(request.headers))
decision = authorize(
"record_analytics_usage", principal, for_execution=True
)
allowed = bool(decision.allowed and decision.execution_enabled)
console_audit.record_event(
action_id="record_analytics_usage",
result=(
console_audit.RESULT_ALLOWED
if allowed
else console_audit.RESULT_DENIED
),
decision=decision,
principal=principal,
target=_audit_target("record_analytics_usage", body),
request_id=_request_id(),
detail=decision.detail,
)
authorization = decision.to_dict()
if not allowed:
return JSONResponse(
{
"ok": False,
"error": "unauthorized",
"detail": (
"POST /api/v1/analytics/usage requires an authenticated "
"principal with record_analytics_usage execution enabled"
),
"authorization": authorization,
},
status_code=403,
)
usage_id = record_usage(
session_id=body.get("session_id"),
remote=body.get("remote", "dadeschools"),
org=body.get("org", ""),
repo=body.get("repo", ""),
project_id=body.get("project_id"),
role=body.get("role", "unknown"),
model=body.get("model", "unknown"),
issue_number=body.get("issue_number") or body.get("issue"),
pr_number=body.get("pr_number") or body.get("pr"),
stage=body.get("stage", "unknown"),
input_tokens=body.get("input_tokens"),
output_tokens=body.get("output_tokens"),
total_tokens=body.get("total_tokens"),
estimated_cost_usd=body.get("estimated_cost_usd"),
latency_ms=body.get("latency_ms"),
duration_ms=body.get("duration_ms"),
status=body.get("status", "success"),
metadata=body.get("metadata"),
)
return JSONResponse(
{"ok": True, "usage_id": usage_id, "authorization": authorization},
status_code=201,
)
async def method_not_allowed(request: Request, _exc: Exception) -> Response:
path = request.url.path
if path in _AUDIT_MUTATION_PATHS and request.method == "POST":
@@ -608,6 +722,10 @@ def create_app(*, bind_host: str | None = None) -> Starlette:
Route("/runtime", runtime, methods=["GET"]),
Route("/api/runtime", api_runtime, methods=["GET"]),
Route("/api/v1/timeline", api_v1_timeline, methods=["GET"]),
Route("/analytics", analytics, methods=["GET"]),
Route("/api/analytics", api_v1_analytics, methods=["GET"]),
Route("/api/v1/analytics", api_v1_analytics, methods=["GET"]),
Route("/api/v1/analytics/usage", api_v1_analytics_ingest, methods=["POST"]),
Route("/audit", audit, methods=["GET", "POST"]),
Route("/api/audit", api_audit, methods=["GET", "POST"]),
Route("/worktrees", worktrees, methods=["GET"]),
+13
View File
@@ -236,6 +236,19 @@ _ACTION_SPECS: tuple[ConsoleAction, ...] = (
phase=3,
summary="Remove a remote feature branch.",
),
# #651 analytics ingest: local control-plane write, not a Gitea mutation.
# Phase 2 gated write so Phase 1 (ACTIVE_PHASE=1) fails closed on execution.
ConsoleAction(
action_id="record_analytics_usage",
task_key="record_analytics_usage",
action_class=CLASS_WRITE,
minimum_role=OPERATOR,
requires_confirmation=True,
dual_control=False,
break_glass=False,
phase=2,
summary="Ingest a model-usage / latency analytics event into the control-plane DB.",
),
# #642: sanctioned daemon lifecycle. These exist so operators have an
# audited path off `pkill -f mcp_server.py` (#630). Restart drops every
# in-flight request on a namespace, so it carries the same dual-control and
+1
View File
@@ -65,6 +65,7 @@ NAV_GROUPS: tuple[NavGroup, ...] = (
)),
NavGroup("Insights", (
NavItem("/insights", "Insights", "stub"),
NavItem("/analytics", "Analytics"),
NavItem("/audit", "Audit"),
)),
)