Closes#15
## Summary
- Add nullable `tokens_in` / `tokens_out` `IntegerField`s to `PromptMetric` to record real prompt/completion token counts per turn.
- New `extract_token_usage()` helper parses provider usage payloads (LangChain `usage_metadata`, OpenAI-style `prompt_tokens`/`completion_tokens`, Ollama `prompt_eval_count`/`eval_count`). When a provider reports no usage, the fields stay **null** — counts are never estimated/fabricated.
- `create_prompt_metric` / `finish_prompt_metric` in both `consumers.py` and `consumers_graph.py` accept and persist optional `tokens_in` / `tokens_out` (added to `update_fields` only when present).
- Admin panel (this ticket's deliverable):
- `PromptMetricAdmin` lists `tokens_in` / `tokens_out` and adds `event` / `model_name` / `has_file` filters.
- `ConversationAdmin` shows summed `tokens_in` / `tokens_out` / `tokens_total` per conversation.
- Migration `0023_promptmetric_tokens_in_promptmetric_tokens_out` (existing rows remain valid — null).
## Note on live capture
The streaming chat path uses LangChain `StrOutputParser`, which yields plain string chunks with no usage metadata, so live turns currently persist `null` tokens (honest, per acceptance criteria — no fabricated counts). The plumbing + helper are in place so wiring real provider usage is a drop-in once the services expose it.
## Follow-ups
- #16 — Show token in/out in chat web app UI (FE + API exposure)
- #17 — Token-based billing, quotas, and enforcement
## Test plan
- [x] `uv run python manage.py test` — full suite green (266 tests, 6 skipped)
- [x] Model: token fields default null + persist when set
- [x] `extract_token_usage`: LangChain / OpenAI / Ollama key variants, attribute sources, bool/float handling, missing usage → (None, None)
- [x] Metric lifecycle: tokens persist when provided, stay null when absent (both consumers)
- [x] Admin: conversation token totals sum across metrics and ignore other conversationsReviewed-on: #18
westfarn