feat(usage): persist LLM call latency

The wrappers in docsgpt/usage.py already measured how long each call took --
for the llm_gen_finished / llm_stream_finished log lines -- and then threw the
number away. Nothing in the schema recorded it, so an operator could see what
an instance spent but never how slow it was.

Adds token_usage.duration_ms and token_usage.ttft_ms, and threads the
measurements the wrappers already take into the insert. The stream wrapper now
also stamps the moment of the first yielded chunk.

Both columns are nullable, and deliberately so: ttft_ms is NULL for a
non-streaming call and for a stream that failed before yielding anything, and
every row written before this migration is NULL too. A 0 there would drag a
p50 toward an instant first token that never happened.

docsgpt/alembic/versions/0035_token_usage_latency.py

@@ -0,0 +1,42 @@
+"""0035 token usage latency — how long the call took, and time to first token.
+
+Nothing in the schema recorded how long an LLM call took. The wrappers in
+``docsgpt/usage.py`` already measured it for the ``llm_gen_finished`` /
+``llm_stream_finished`` log lines and then threw the number away, so an
+operator could see what an instance spent but never how slow it was.
+
+Two columns, both nullable because both are genuinely unknown for some rows:
+
+* ``duration_ms`` — wall-clock for the whole call.
+* ``ttft_ms`` — time to the first streamed chunk. NULL on non-streaming calls
+  and on streams that failed before yielding anything, which is the honest
+  reading: "no first token", not "instant".
+
+NULL also means "written before this migration", so historical rows never
+drag a p50 towards zero.
+
+Idempotent both ways.
+
+Revision ID: 0035_token_usage_latency
+Revises: 0034_auth_events_actor_target
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+
+revision: str = "0035_token_usage_latency"
+down_revision: Union[str, None] = "0034_auth_events_actor_target"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.execute("ALTER TABLE token_usage ADD COLUMN IF NOT EXISTS duration_ms INTEGER;")
+    op.execute("ALTER TABLE token_usage ADD COLUMN IF NOT EXISTS ttft_ms INTEGER;")
+
+
+def downgrade() -> None:
+    op.execute("ALTER TABLE token_usage DROP COLUMN IF EXISTS ttft_ms;")
+    op.execute("ALTER TABLE token_usage DROP COLUMN IF EXISTS duration_ms;")

docsgpt/storage/db/models.py

@@ -250,6 +250,11 @@ token_usage_table = Table(
     # Added in ``0033_quotas``. USD cost of the call at write time; 0 for
     # unpriced and bring-your-own models.
     Column("cost", Numeric(12, 8), nullable=False, server_default="0"),
+    # Added in ``0035_token_usage_latency``. Wall-clock for the call, and time
+    # to the first streamed chunk. ``ttft_ms`` is NULL for non-streaming calls
+    # and for streams that failed before yielding -- "no first token", not 0.
+    Column("duration_ms", Integer),
+    Column("ttft_ms", Integer),
 )
 
 user_logs_table = Table(

docsgpt/storage/db/repositories/token_usage.py

@@ -38,6 +38,8 @@ class TokenUsageRepository:
         cached_tokens: Optional[int] = None,
         cache_write_tokens: Optional[int] = None,
         cost: float = 0.0,
+        duration_ms: Optional[int] = None,
+        ttft_ms: Optional[int] = None,
     ) -> None:
         # Attribution guard: the ``token_usage_attribution_chk`` CHECK
         # constraint requires at least one of ``user_id`` / ``api_key``
@@ -64,6 +66,7 @@ class TokenUsageRepository:
                     user_id, api_key, agent_id,
                     prompt_tokens, generated_tokens,
                     cached_tokens, cache_write_tokens, cost,
+                    duration_ms, ttft_ms,
                     source, request_id, model_id, timestamp
                 )
                 VALUES (
@@ -71,6 +74,7 @@ class TokenUsageRepository:
                     CAST(:agent_id AS uuid),
                     :prompt_tokens, :generated_tokens,
                     :cached_tokens, :cache_write_tokens, :cost,
+                    :duration_ms, :ttft_ms,
                     :source, :request_id, :model_id, COALESCE(:timestamp, now())
                 )
                 """
@@ -84,6 +88,8 @@ class TokenUsageRepository:
                 "cached_tokens": cached_tokens,
                 "cache_write_tokens": cache_write_tokens,
                 "cost": cost,
+                "duration_ms": duration_ms,
+                "ttft_ms": ttft_ms,
                 "source": source,
                 "request_id": request_id,
                 "model_id": model_id,

docsgpt/usage.py

@@ -93,7 +93,7 @@ def _count_prompt_tokens(messages, tools=None, usage_attachments=None, **kwargs)
     return prompt_tokens
 
 
-def _persist_call_usage(llm, call_usage):
+def _persist_call_usage(llm, call_usage, *, duration_ms=None, ttft_ms=None):
     """Write one ``token_usage`` row per LLM call. Always-on; no flag.
 
     Source defaults to ``agent_stream`` and can be overridden per
@@ -101,6 +101,13 @@ def _persist_call_usage(llm, call_usage):
     title / compression / rag_condense / fallback). A ``_request_id``
     stamped on the LLM lets ``count_in_range`` deduplicate the multiple
     rows produced by a single multi-tool agent run.
+
+    Args:
+        llm: The LLM instance the call ran on.
+        call_usage: The call's token counts.
+        duration_ms: Wall-clock for the call, measured by the wrapper.
+        ttft_ms: Time to the first streamed chunk; None for a non-streaming
+            call and for a stream that failed before yielding anything.
     """
     if call_usage["prompt_tokens"] == 0 and call_usage["generated_tokens"] == 0:
         return
@@ -149,6 +156,8 @@ def _persist_call_usage(llm, call_usage):
                 ),
                 request_id=getattr(llm, "_request_id", None),
                 model_id=model_id,
+                duration_ms=duration_ms,
+                ttft_ms=ttft_ms,
             )
     except Exception:
         logger.exception("token_usage persist failed")
@@ -258,10 +267,12 @@ def gen_token_usage(func):
             error = exc
             raise
         finally:
+            duration_ms = int((time.monotonic() - started_at) * 1000)
             call_usage = _prefer_provider_usage(self, call_usage)
             self.token_usage["prompt_tokens"] += call_usage["prompt_tokens"]
             self.token_usage["generated_tokens"] += call_usage["generated_tokens"]
-            _persist_call_usage(self, call_usage)
+            # A non-streaming call has no first-token moment; ttft stays NULL.
+            _persist_call_usage(self, call_usage, duration_ms=duration_ms)
             emit = getattr(self, "_emit_gen_finished_log", None)
             if callable(emit):
                 try:
@@ -269,7 +280,7 @@ def gen_token_usage(func):
                         model,
                         prompt_tokens=call_usage["prompt_tokens"],
                         completion_tokens=call_usage["generated_tokens"],
-                        latency_ms=int((time.monotonic() - started_at) * 1000),
+                        latency_ms=duration_ms,
                         cached_tokens=call_usage.get("cached_tokens"),
                         cache_write_tokens=call_usage.get("cache_write_tokens"),
                         error=error,
@@ -293,10 +304,13 @@ def stream_token_usage(func):
         )
         batch = []
         started_at = time.monotonic()
+        first_chunk_at: float | None = None
         error: BaseException | None = None
         try:
             result = func(self, model, messages, stream, tools, **kwargs)
             for r in result:
+                if first_chunk_at is None:
+                    first_chunk_at = time.monotonic()
                 batch.append(r)
                 yield r
         except Exception as exc:
@@ -306,12 +320,22 @@ def stream_token_usage(func):
             error = exc
             raise
         finally:
+            duration_ms = int((time.monotonic() - started_at) * 1000)
+            # NULL, not 0, when the stream failed before yielding: "no first
+            # token" must not read as an instant one in a p50.
+            ttft_ms = (
+                int((first_chunk_at - started_at) * 1000)
+                if first_chunk_at is not None
+                else None
+            )
             for line in batch:
                 call_usage["generated_tokens"] += _count_tokens(line)
             call_usage = _prefer_provider_usage(self, call_usage)
             self.token_usage["prompt_tokens"] += call_usage["prompt_tokens"]
             self.token_usage["generated_tokens"] += call_usage["generated_tokens"]
-            _persist_call_usage(self, call_usage)
+            _persist_call_usage(
+                self, call_usage, duration_ms=duration_ms, ttft_ms=ttft_ms
+            )
             emit = getattr(self, "_emit_stream_finished_log", None)
             if callable(emit):
                 try:
@@ -319,7 +343,7 @@ def stream_token_usage(func):
                         model,
                         prompt_tokens=call_usage["prompt_tokens"],
                         completion_tokens=call_usage["generated_tokens"],
-                        latency_ms=int((time.monotonic() - started_at) * 1000),
+                        latency_ms=duration_ms,
                         cached_tokens=call_usage.get("cached_tokens"),
                         cache_write_tokens=call_usage.get("cache_write_tokens"),
                         error=error,