chore: import upstream snapshot with attribution

application/usage.py

@@ -0,0 +1,238 @@
+import logging
+import time
+
+from application.storage.db.repositories.token_usage import TokenUsageRepository
+from application.storage.db.session import db_session
+from application.utils import num_tokens_from_object_or_list, num_tokens_from_string
+
+logger = logging.getLogger(__name__)
+
+
+def _serialize_for_token_count(value):
+    """Normalize payloads into token-countable primitives."""
+    if isinstance(value, str):
+        # Avoid counting large binary payloads in data URLs as text tokens.
+        if value.startswith("data:") and ";base64," in value:
+            return ""
+        return value
+
+    if value is None:
+        return ""
+
+    # Raw binary payloads (image/file attachments arrive as ``bytes`` from
+    # ``GoogleLLM.prepare_messages_with_attachments``) — without this
+    # branch they fall through to ``str(value)`` below, which produces a
+    # multi-megabyte ``"b'\\x89PNG...'"`` repr-string and inflates
+    # ``prompt_tokens`` by orders of magnitude. Same intent as the
+    # data-URL skip above.
+    if isinstance(value, (bytes, bytearray, memoryview)):
+        return ""
+
+    if isinstance(value, list):
+        return [_serialize_for_token_count(item) for item in value]
+
+    if isinstance(value, dict):
+        serialized = {}
+        for key, raw in value.items():
+            key_lower = str(key).lower()
+
+            # Skip raw binary-like fields; keep textual tool-call fields.
+            if key_lower in {"data", "base64", "image_data"} and isinstance(raw, str):
+                continue
+            if key_lower == "url" and isinstance(raw, str) and ";base64," in raw:
+                continue
+
+            serialized[key] = _serialize_for_token_count(raw)
+        return serialized
+
+    if hasattr(value, "model_dump") and callable(getattr(value, "model_dump")):
+        return _serialize_for_token_count(value.model_dump())
+    if hasattr(value, "to_dict") and callable(getattr(value, "to_dict")):
+        return _serialize_for_token_count(value.to_dict())
+    if hasattr(value, "__dict__"):
+        return _serialize_for_token_count(vars(value))
+
+    return str(value)
+
+
+def _count_tokens(value):
+    serialized = _serialize_for_token_count(value)
+    if isinstance(serialized, str):
+        return num_tokens_from_string(serialized)
+    return num_tokens_from_object_or_list(serialized)
+
+
+def _count_prompt_tokens(messages, tools=None, usage_attachments=None, **kwargs):
+    prompt_tokens = 0
+
+    for message in messages or []:
+        if not isinstance(message, dict):
+            prompt_tokens += _count_tokens(message)
+            continue
+
+        prompt_tokens += _count_tokens(message.get("content"))
+
+        # Include tool-related message fields for providers that use OpenAI-native format.
+        prompt_tokens += _count_tokens(message.get("tool_calls"))
+        prompt_tokens += _count_tokens(message.get("tool_call_id"))
+        prompt_tokens += _count_tokens(message.get("function_call"))
+        prompt_tokens += _count_tokens(message.get("function_response"))
+
+    # Count tool schema payload passed to the model.
+    prompt_tokens += _count_tokens(tools)
+
+    # Count structured-output/schema payloads when provided.
+    prompt_tokens += _count_tokens(kwargs.get("response_format"))
+    prompt_tokens += _count_tokens(kwargs.get("response_schema"))
+
+    # Optional usage-only attachment context (not forwarded to provider).
+    prompt_tokens += _count_tokens(usage_attachments)
+
+    return prompt_tokens
+
+
+def _persist_call_usage(llm, call_usage):
+    """Write one ``token_usage`` row per LLM call. Always-on; no flag.
+
+    Source defaults to ``agent_stream`` and can be overridden per
+    instance via ``_token_usage_source`` (set on side-channel LLMs:
+    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.
+    """
+    if call_usage["prompt_tokens"] == 0 and call_usage["generated_tokens"] == 0:
+        return
+    decoded_token = getattr(llm, "decoded_token", None)
+    user_id = (
+        decoded_token.get("sub") if isinstance(decoded_token, dict) else None
+    )
+    user_api_key = getattr(llm, "user_api_key", None)
+    agent_id = getattr(llm, "agent_id", None)
+    if not user_id and not user_api_key:
+        # Repository would raise on the attribution check — log instead
+        # so operators see the gap rather than crashing the stream.
+        logger.warning(
+            "token_usage skip: no user_id/api_key on LLM instance",
+            extra={
+                "source": getattr(llm, "_token_usage_source", "agent_stream"),
+            },
+        )
+        return
+    try:
+        with db_session() as conn:
+            # ``timestamp`` is omitted so Postgres ``server_default
+            # = func.now()`` populates a tz-aware UTC value; passing
+            # naive ``datetime.now()`` would silently shift on
+            # non-UTC servers.
+            TokenUsageRepository(conn).insert(
+                user_id=user_id,
+                api_key=user_api_key,
+                agent_id=str(agent_id) if agent_id else None,
+                prompt_tokens=call_usage["prompt_tokens"],
+                generated_tokens=call_usage["generated_tokens"],
+                source=(
+                    getattr(llm, "_token_usage_source", None) or "agent_stream"
+                ),
+                request_id=getattr(llm, "_request_id", None),
+                model_id=getattr(llm, "_canonical_model_id", None),
+            )
+    except Exception:
+        logger.exception("token_usage persist failed")
+
+
+def gen_token_usage(func):
+    """Accumulate per-call token counts and write a ``token_usage`` row.
+
+    The accumulator on ``self.token_usage`` stays in place for code
+    paths that introspect it (e.g., logging, response payloads). DB
+    persistence happens here for every call so primary streams,
+    side-channel LLMs, and no-save flows all produce rows uniformly.
+
+    Mirrors ``stream_token_usage``: persistence and the
+    ``llm_gen_finished`` log fire from a ``finally`` block, so a failed
+    call still records the prompt tokens it consumed and emits a
+    ``status="error"`` finish event.
+    """
+    def wrapper(self, model, messages, stream, tools, **kwargs):
+        usage_attachments = kwargs.pop("_usage_attachments", None)
+        call_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        call_usage["prompt_tokens"] += _count_prompt_tokens(
+            messages,
+            tools=tools,
+            usage_attachments=usage_attachments,
+            **kwargs,
+        )
+        started_at = time.monotonic()
+        error: BaseException | None = None
+        try:
+            result = func(self, model, messages, stream, tools, **kwargs)
+            call_usage["generated_tokens"] += _count_tokens(result)
+            return result
+        except Exception as exc:
+            error = exc
+            raise
+        finally:
+            self.token_usage["prompt_tokens"] += call_usage["prompt_tokens"]
+            self.token_usage["generated_tokens"] += call_usage["generated_tokens"]
+            _persist_call_usage(self, call_usage)
+            emit = getattr(self, "_emit_gen_finished_log", None)
+            if callable(emit):
+                try:
+                    emit(
+                        model,
+                        prompt_tokens=call_usage["prompt_tokens"],
+                        completion_tokens=call_usage["generated_tokens"],
+                        latency_ms=int((time.monotonic() - started_at) * 1000),
+                        error=error,
+                    )
+                except Exception:
+                    logger.exception("Failed to emit llm_gen_finished")
+
+    return wrapper
+
+
+def stream_token_usage(func):
+    """Stream variant of ``gen_token_usage``. Same persistence contract."""
+    def wrapper(self, model, messages, stream, tools, **kwargs):
+        usage_attachments = kwargs.pop("_usage_attachments", None)
+        call_usage = {"prompt_tokens": 0, "generated_tokens": 0}
+        call_usage["prompt_tokens"] += _count_prompt_tokens(
+            messages,
+            tools=tools,
+            usage_attachments=usage_attachments,
+            **kwargs,
+        )
+        batch = []
+        started_at = time.monotonic()
+        error: BaseException | None = None
+        try:
+            result = func(self, model, messages, stream, tools, **kwargs)
+            for r in result:
+                batch.append(r)
+                yield r
+        except Exception as exc:
+            # ``GeneratorExit`` (consumer disconnected) and KeyboardInterrupt
+            # flow through as ``status="ok"`` — same convention as
+            # ``application.logging._consume_and_log``.
+            error = exc
+            raise
+        finally:
+            for line in batch:
+                call_usage["generated_tokens"] += _count_tokens(line)
+            self.token_usage["prompt_tokens"] += call_usage["prompt_tokens"]
+            self.token_usage["generated_tokens"] += call_usage["generated_tokens"]
+            _persist_call_usage(self, call_usage)
+            emit = getattr(self, "_emit_stream_finished_log", None)
+            if callable(emit):
+                try:
+                    emit(
+                        model,
+                        prompt_tokens=call_usage["prompt_tokens"],
+                        completion_tokens=call_usage["generated_tokens"],
+                        latency_ms=int((time.monotonic() - started_at) * 1000),
+                        error=error,
+                    )
+                except Exception:
+                    logger.exception("Failed to emit llm_stream_finished")
+
+    return wrapper