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* 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 |
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Website
This website is built using Docusaurus, a modern static website generator.
Installation
npm install
Local Development
npm start
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
Build
npm run build
This command generates static content into the build directory and can be served using any static contents hosting service.
Deployment
Using SSH:
USE_SSH=true npm run deploy
Not using SSH:
GIT_USER=<Your GitHub username> npm run deploy
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the gh-pages branch.