* feat: implement hierarchical configuration (system, tenant, bank)
* feat: implement hierarchical configuration (system, tenant, bank)
* docs: add instructions for hierarchical config in CLAUDE.md
* feat: add ENABLE_BANK_CONFIG_API flag (disabled by default)
- Add HINDSIGHT_API_ENABLE_BANK_CONFIG_API env var (default: false)
- Return 403 Forbidden from bank config endpoints when disabled
- Update tests to enable the flag
- Update CLAUDE.md documentation
This provides security control over the bank configuration API,
ensuring it's only accessible when explicitly enabled.
* docs: add hierarchical configuration section
* feat(cli): add bank config commands (config, set-config, reset-config)
- Add 'hindsight bank config' to view bank configuration
- Add 'hindsight bank set-config' to update LLM settings per bank
- Add 'hindsight bank reset-config' to reset to defaults
- Implements client API calls to new bank config endpoints
* fix(cli): fix compilation errors in bank config commands
- Fix type signature: use ApiClient instead of api::Client
- Fix confirmation: use ui::prompt_confirmation instead of ui::confirm
- Fix error handling: use anyhow! macro instead of errors::Error
- Fix type conversion: convert HashMap to serde_json::Map for API call
* feat: implement type-safe hierarchical config with bank overrides
Implements a production-ready hierarchical configuration system that prevents
accidentally using global defaults when bank-specific overrides exist.
- Created StaticConfigProxy that wraps HindsightConfig
- get_config() now returns proxy that blocks access to bank-configurable fields
- Raises ConfigFieldAccessError with clear message when accessing configurable fields
- Added _get_raw_config() for internal use only
- Forces developers to use resolve_full_config(bank_id, context) for bank settings
- Added resolve_full_config() method that returns complete HindsightConfig
- Resolves hierarchy: Global (env) → Tenant → Bank
- No caching to support multi-server deployments (always fresh from DB)
- LLM provider pooling handles expensive operations separately
- Updated entire retain pipeline to pass resolved config through call chain
- memory_engine.py: Resolves config at top level where bank_id/context available
- orchestrator.py: Accepts and passes config to fact_extraction
- fact_extraction.py: Uses passed config instead of get_config()
- utils.py: Added optional config param for backward compatibility
- consolidator.py: Uses resolve_full_config() for enable_observations check
- memory_engine.py: Resolves config before triggering consolidation
- Renamed "Memory Bank" to "Bank Configuration" with tabs
- Combined Stats and Operations into "General" tab
- Consolidated Profile and Configuration into "Configuration" tab
- Moved Actions dropdown to page level (outside tabs)
- Created new component for managing bank-specific config
- Displays configurable fields: retain_chunk_size, retain_extraction_mode, etc.
- Edit via dialog with form validation
- Reset to defaults via AlertDialog confirmation
- Shows field IDs in monospace for clarity
- Visual separation with borders and hover effects
- Removed inline edit mode, switched to dialog-based editing
- Separate dialogs for Disposition and Mission editing
- Read-only display with clear edit buttons
- Removed duplicate stats cards and operations
- bank-stats-view.tsx: Overview statistics (memories, links, documents, pending ops)
- bank-operations-view.tsx: Background operations table with filtering
**Problem**: Consolidation always used global enable_observations, ignoring bank overrides
**Root Cause**: consolidator.py called get_config() instead of resolving bank-specific config
**Solution**: Pass resolved config through the entire pipeline
**Problem**: asyncpg returning JSONB as JSON string instead of parsed dict
**Solution**: Explicit JSON parsing in config_resolver.py with type checking
- All 19 API integration tests pass
- All 10 hierarchical config tests pass
- Retain operations work correctly with bank-specific config
- Consolidation respects bank-specific enable_observations setting
- Updated developer/configuration.md with type-safe config access pattern
- Added examples showing correct usage patterns
- Documented ConfigFieldAccessError and resolution methods
- get_config() now returns StaticConfigProxy (blocks configurable field access)
- Code accessing bank-configurable fields must use resolve_full_config()
- Clear migration path with helpful error messages
Fixes hierarchical configuration to be production-ready with proper type safety.
* refactor: remove LLM client pool and simplify config resolver
Since LLM config (provider, model, api_key) is now static and not
bank-configurable, the LLMClientPool is no longer needed.
Changes:
- Remove hindsight_api/llm_client_pool.py (no longer needed)
- Remove memory_engine._get_bank_llm_config() (dead code, never called)
- Simplify config_resolver.py by eliminating duplication between
resolve_full_config() and get_bank_config()
- get_bank_config() now calls resolve_full_config() and filters results
- Remove outdated "LLM provider pooling" comments from docstrings
All tests pass (10 hierarchical config tests, 19 API integration tests)
* fix: update tests to use _get_raw_config() for configurable fields
Fixed test fixtures that were accessing configurable fields (like
enable_observations) from get_config(), which now raises
ConfigFieldAccessError due to type-safe config access.
Changes:
- test_consolidation.py: Changed enable_observations fixture to use
_get_raw_config() instead of get_config()
- test_consolidation.py: Updated test_consolidation_returns_disabled_status
to set bank config instead of mocking get_config()
- test_link_expansion_retrieval.py: Changed fixture to use _get_raw_config()
- test_observations.py: Changed disable_observations fixture to use
_get_raw_config()
- Regenerated OpenAPI spec and clients
All 39 previously failing tests now pass.
* fix: add missing config parameter to test calls of extract_facts_from_text()
Fixed 45 test failures where tests were calling extract_facts_from_text()
without the new required config parameter.
Changes:
- Added config=_get_raw_config() to all extract_facts_from_text() calls
- Fixed test_main_module.py to patch _get_raw_config instead of get_config
- Updated 6 test files with 37 function call sites
All tests should now pass.
* fix: add missing config parameter to test_skip_podcast_meta_commentary
One more test was missing the config parameter for extract_facts_from_text().
* feat: ai sdk integration
* more fixes
* fix(security): mental model refresh tag-based security
- Mental model refresh now passes tags with all_strict matching
- Consolidation only triggers refresh for mental models with matching tags
- Consolidation filters related observations by tags (all_strict)
- Added tests to verify tag-based security boundaries
- Updated OpenAPI spec to include tags and text_preview in list_documents
- Added tags column to documents UI table
* chore: regenerate OpenAPI spec after rebase
* fix: improve consolidation prompt for contradiction handling and mental model refresh security
- Enhanced consolidation prompt to be more explicit about capturing temporal changes in contradictions
- Fixed mental model refresh security: tagged memories now only trigger refresh of mental models with matching tags
- Added stricter tag filtering to prevent cross-scope mental model refreshes
Fixes test_consolidation_merges_contradictions by improving LLM instructions to use temporal markers like "used to X, now Y" when merging contradictory facts.
Note: test_refresh_with_tags_only_accesses_same_tagged_models still needs investigation - REFLECT operation may need additional tag filtering.
* fix: mental model refresh security - proper tag filtering in search
Fixed tool_search_mental_models to properly handle all_strict tag matching mode by using the centralized build_tags_where_clause function. Previously, the function only handled "all" vs "any" modes and always included untagged mental models when using non-"all" modes.
This ensures that when a tagged mental model is refreshed with all_strict matching, it cannot access untagged mental models, preventing cross-scope information leakage.
Fixes test_refresh_with_tags_only_accesses_same_tagged_models.
Note: test_sensory_dimension_preservation is failing but this is a pre-existing issue on main branch - the LLM model (gpt-oss-20b) is not extracting facts from sensory text. Not related to security changes.
* chore: apply formatting from pre-commit hook
* fix: allow untagged mental models to be refreshed by any consolidation
Untagged mental models are considered "global" and should be refreshed
by any consolidation, regardless of whether tagged or untagged memories
were consolidated. This maintains security boundaries while allowing
global mental models to stay fresh.
When tagged memories are consolidated:
- Refresh mental models with matching tags (security boundary)
- Also refresh untagged mental models (they're global)
- DO NOT refresh mental models with different tags
When untagged memories are consolidated:
- Only refresh untagged mental models
- DO NOT refresh tagged mental models (security boundary)
Fixes test_consolidation_only_refreshes_matching_tagged_models.
* feat: improve mental models ux on control plane
* feat: improve mental models ux on control plane
* gen
* feat(cli): add --id flag to mental model create command
* fix(cli): revert unused variable underscore prefix that breaks compilation
The underscore prefix on stdout/stderr variables was added to suppress
warnings, but these variables are actually used in assert messages,
causing compilation errors. Reverting to original names.
* feat: support vertex as llm provider
* fix
* fix: add uv index-strategy to resolve dependency conflicts with pytorch index
When using pytorch index for faster torch downloads in CI,
filelock dependency resolution was failing because pytorch index
only has older versions. Adding unsafe-best-match strategy allows
uv to search all configured indexes.
Also fix type checking warnings from ty.
* fix: add index-strategy to root pyproject.toml for workspace-level uv resolution
* chore: regenerate client SDKs after Vertex AI support
* fix: misc fixes for observations and mental models
* feat: improve graph retrieval for observations
- Update LinkExpansionRetriever to traverse through source_memory_ids
for observation entity connections (avoiding data duplication)
- Remove entity link copy from world facts to observations in consolidator
- Add tests for link expansion graph retrieval
- Add directives_applied field to ReflectResult
- Include user's other changes (CLI, docs, client updates)
* fix: CI test failures
- Add mental_model_id parameter to create_mental_model function
- Fix ToolCallTrace not including reason field from ToolCall
- Improve test_link_expansion_observation_graph_retrieval to wait for consolidation with retry
* chore: reduce link expansion log verbosity
* Revert "chore: reduce link expansion log verbosity"
This reverts commit 3ce759391cead1012157785fa78fef16ef9bfe3b.
* feat: add semantic/temporal/entity links as fallback in graph retrieval
- Add fallback query for semantic, temporal, and entity links from memory_links
- Check both directions (outgoing and incoming links)
- Weight fallback results at 0.5x to prioritize entity links via unit_entities
- Fixes graph retrieval returning 0 when data has cross-cluster temporal connections
* fix: enable observations fixture for link expansion test
- Add enable_observations fixture to ensure observations are created
- Increase wait time from 10 to 30 seconds for CI reliability
* chore: run benchmarks with reflect mode
* chore: run benchmarks with reflect mode
* fixes
* new mm
* bunch of fixes
* initial commit
* fixes
* fixes
* fixes
* fix: sometimes memories gets extracted in the wrong language
* 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
