29a542dc23
* feat: Record LLM token metrics via Prometheus Wire up the existing token metrics infrastructure to actually record token usage from LLM calls. The MetricsCollector already had record_tokens() method and Prometheus counters (hindsight.tokens.input, hindsight.tokens.output), but they were never being populated. Changes: - Import get_metrics_collector in llm_wrapper.py - Call record_tokens() after successful LLM calls for: - OpenAI/Groq (using response.usage.prompt_tokens, completion_tokens) - Anthropic (using response.usage.input_tokens, output_tokens) - Gemini (using response.usage_metadata.prompt_token_count, candidates_token_count) - Add test file to verify token metrics are recorded Note: Ollama's native API doesn't return token usage, so metrics are not recorded for that provider. The token metrics will now be available via /metrics endpoint: - hindsight_tokens_input_total - hindsight_tokens_output_total * feat: add per-request token usage tracking to retain and reflect endpoints - Add TokenUsage model with input_tokens, output_tokens, total_tokens - Return usage metrics in retain response (sync operations only) - Return usage metrics in reflect response - Update Python, TypeScript, and Rust clients - Add API documentation for usage fields - Add changelog entry |
||
|---|---|---|
| .. | ||
| api | ||
| admin-cli.md | ||
| configuration.md | ||
| development.md | ||
| extensions.md | ||
| index.md | ||
| installation.md | ||
| mcp-server.md | ||
| metrics.md | ||
| models.md | ||
| performance.md | ||
| rag-vs-hindsight.md | ||
| reflect.md | ||
| retain.md | ||
| retrieval.md | ||
| services.md | ||
| storage.md | ||