docs: update README — 1,454 tests, 12 MCP tools, entity contracts#120
docs: update README — 1,454 tests, 12 MCP tools, entity contracts#120
Conversation
…rarchy Add entity contract system for evaluating knowledge graph entity completeness: - SQL schema: entity_contracts, entity_health, entity_type_hierarchy tables - ALTER: kg_entities +entity_subtype +status; kg_entity_chunks +relation_tier +weight - ALTER: kg_entity_aliases +valid_from +valid_to - contracts/entity-types.yaml: YAML contracts for agent, person, tool, project, concept - scripts/score_entity_health.py: 5-dimension weighted scoring (required 40%, expected 25%, relationships 15%, chunks 10%, recency 10%) with Wikidata 5-tier health levels - brain_entity response now includes completeness_score and health_level - Type hierarchy seeded with core types + subtypes (golem->agent, platform->tool, etc.) - 39 new tests (TDD: RED first, all GREEN), 0 regressions on existing suite R49 research: brainbar-b917124c-913 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
…tures Tests: 1,204 -> 1,454 Python. MCP tools: 11 -> 12 (brain_enrich added). Chunks: 281K+ -> 284K+. Added entity contracts, health scoring, MLX backend. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
![greptile-apps[bot]](https://avatars.githubusercontent.com/in/867647?s=60&v=4){kind=small}
There was a problem hiding this comment.
Your free trial has ended. If you'd like to continue receiving code reviews, you can add a payment method here.
📝 WalkthroughWalkthroughThis PR implements R49 Phase 1 entity contract and health scoring infrastructure. It introduces an entity type contracts schema ( Changes
Sequence Diagram(s)sequenceDiagram
actor User
participant digest as entity_lookup<br/>(pipeline)
participant store as VectorStore<br/>(DB)
participant health as entity_health<br/>(table)
participant scorer as score_entity_health<br/>(script)
User->>digest: entity_lookup(entity_name)
digest->>store: query FTS/semantic search
store-->>digest: entity results + relations
digest->>store: fetch health metadata
store->>health: SELECT completeness_score,<br/>health_level by entity_name
health-->>store: score data (or NULL)
store-->>digest: completeness_score, health_level
digest-->>User: enriched entity result
User->>scorer: run score_entity_health.py
scorer->>store: load contracts from YAML
scorer->>store: iterate kg_entities
loop per entity
scorer->>store: fetch fields, relationships, chunks, updated_at
scorer->>scorer: compute weighted completeness<br/>(fields + relations + chunks + recency)
scorer->>scorer: classify health level (1-5)
end
scorer->>store: upsert entity_health records
store-->>scorer: persist (commit)
scorer-->>User: print health distribution counts
Estimated code review effort🎯 4 (Complex) | ⏱️ ~45 minutes Possibly related PRs
Poem
🚥 Pre-merge checks | ✅ 2 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (2 passed)
✏️ Tip: You can configure your own custom pre-merge checks in the settings. ✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. Comment |
![macroscopeapp[bot]](https://avatars.githubusercontent.com/in/900172?s=60&v=4){kind=small}
| def test_missing_required_detected(self, populated_store, contracts_path): | ||
| """coachClaude doesn't have all required fields populated — detect gaps.""" | ||
| from scripts.score_entity_health import score_entity, load_contracts | ||
| contracts = load_contracts(str(contracts_path)) | ||
| result = score_entity( | ||
| populated_store, "ent-coach", "agent", contracts["agent"] | ||
| ) | ||
| # capabilities is partially there in metadata, memory_domains is missing | ||
| assert isinstance(result["missing_required"], list) |
There was a problem hiding this comment.
🟡 Medium tests/test_entity_contracts.py:395
test_missing_required_detected only asserts that missing_required is a list, which passes even when empty. The docstring and comment claim memory_domains should be detected as missing, but the test never verifies this field (or any field) actually appears in the list. Consider adding an assertion that specific expected fields are present in missing_required.
assert isinstance(result["missing_required"], list)
+ # Verify specific missing fields are detected
+ assert "memory_domains" in result["missing_required"]🚀 Reply "fix it for me" or copy this AI Prompt for your agent:
In file tests/test_entity_contracts.py around lines 395-403:
`test_missing_required_detected` only asserts that `missing_required` is a list, which passes even when empty. The docstring and comment claim `memory_domains` should be detected as missing, but the test never verifies this field (or any field) actually appears in the list. Consider adding an assertion that specific expected fields are present in `missing_required`.
Evidence trail:
tests/test_entity_contracts.py lines 395-403 at REVIEWED_COMMIT: The test function `test_missing_required_detected` has docstring claiming detection of missing required fields, comment specifically mentioning `memory_domains is missing`, but the only assertion is `assert isinstance(result["missing_required"], list)` with no check for specific field presence.
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 2
Caution
Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (2)
README.md (2)
94-110:⚠️ Potential issue | 🟡 MinorInconsistency: Update architecture diagram to 12 tools.
The Mermaid diagram label still shows "11 tools" while the rest of the document reflects 12.
📝 Proposed fix
- A["Claude Code / Cursor / Zed"] -->|MCP| B["BrainLayer MCP Server<br/>11 tools"] + A["Claude Code / Cursor / Zed"] -->|MCP| B["BrainLayer MCP Server<br/>12 tools"]🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@README.md` around lines 94 - 110, Update the Mermaid diagram node B that currently reads "BrainLayer MCP Server<br/>11 tools" to reflect "12 tools" so the architecture diagram matches the rest of the doc; locate the graph LR block and change the label for node B (symbol B / "BrainLayer MCP Server") to show 12 tools.
3-3:⚠️ Potential issue | 🟡 MinorInconsistency: Update tagline to 12 MCP tools.
The opening tagline still mentions "11 MCP tools" but the rest of the README (badge, stats line, section header, comparison table) now reflects 12 tools.
📝 Proposed fix
-> Persistent memory and knowledge graph for AI agents — 11 MCP tools, real-time JSONL watcher, Axiom telemetry, and a native macOS daemon for always-on recall across every conversation. +> Persistent memory and knowledge graph for AI agents — 12 MCP tools, real-time JSONL watcher, Axiom telemetry, and a native macOS daemon for always-on recall across every conversation.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@README.md` at line 3, Update the README opening tagline to reflect "12 MCP tools" instead of "11 MCP tools" so it matches the badges, stats line, section header, and comparison table; locate the tagline string in the README (the first paragraph/heading that currently reads "Persistent memory and knowledge graph for AI agents — 11 MCP tools, real-time JSONL watcher, Axiom telemetry, and a native macOS daemon for always-on recall across every conversation.") and replace "11 MCP tools" with "12 MCP tools".
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@scripts/score_entity_health.py`:
- Around line 240-245: The tuple indexing for the parent type lookup is
incorrect/overly complex: the SELECT returns single-column rows so parent_row[0]
is like (parent_type,) and the ternary with len(parent_row[0]) > 1 is useless;
change the logic that follows the cursor.execute call to extract the parent_type
as parent_row[0][0] (or assign parent_type = parent_row[0][0]) and then use
contracts.get(parent_type) instead of the current conditional expression so the
lookup is correct and readable (refer to variables parent_row, cursor.execute
and contracts in this change).
In `@tests/test_entity_contracts.py`:
- Around line 17-20: The file imports unused symbols subprocess and patch;
remove these unused imports from the top of tests/test_entity_contracts.py so
only needed imports (sys, Path) remain, or alternatively use them in tests if
intended; specifically edit the import line(s) to drop subprocess and from
unittest.mock import patch unless patch is actually referenced by functions or
fixtures like any test_* functions.
---
Outside diff comments:
In `@README.md`:
- Around line 94-110: Update the Mermaid diagram node B that currently reads
"BrainLayer MCP Server<br/>11 tools" to reflect "12 tools" so the architecture
diagram matches the rest of the doc; locate the graph LR block and change the
label for node B (symbol B / "BrainLayer MCP Server") to show 12 tools.
- Line 3: Update the README opening tagline to reflect "12 MCP tools" instead of
"11 MCP tools" so it matches the badges, stats line, section header, and
comparison table; locate the tagline string in the README (the first
paragraph/heading that currently reads "Persistent memory and knowledge graph
for AI agents — 11 MCP tools, real-time JSONL watcher, Axiom telemetry, and a
native macOS daemon for always-on recall across every conversation.") and
replace "11 MCP tools" with "12 MCP tools".
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
ℹ️ Review info
⚙️ Run configuration
Configuration used: Organization UI
Review profile: ASSERTIVE
Plan: Pro
Run ID: d0f71f44-96c5-43aa-ad6f-7bdaf42c4493
📒 Files selected for processing (7)
README.mdcontracts/entity-types.yamlscripts/score_entity_health.pysrc/brainlayer/pipeline/digest.pysrc/brainlayer/vector_store.pytests/test_entity_contracts.pytests/test_kg_schema.py
📜 Review details
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (3)
- GitHub Check: test (3.11)
- GitHub Check: test (3.12)
- GitHub Check: test (3.13)
🧰 Additional context used
📓 Path-based instructions (5)
**/*.py
📄 CodeRabbit inference engine (AGENTS.md)
**/*.py: Flag risky DB or concurrency changes explicitly and do not hand-wave lock behavior
Enforce one-write-at-a-time concurrency constraint; reads are safe but brain_digest is write-heavy and must not run in parallel with other MCP work
Run pytest before claiming behavior changed safely; current test suite has 929 tests
Files:
tests/test_kg_schema.pysrc/brainlayer/pipeline/digest.pysrc/brainlayer/vector_store.pyscripts/score_entity_health.pytests/test_entity_contracts.py
tests/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
Use pytest for testing with configuration in pyproject.toml
Files:
tests/test_kg_schema.pytests/test_entity_contracts.py
src/brainlayer/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/brainlayer/**/*.py: Use all scripts and CLI to resolve DB path via paths.py:get_db_path() with env var override support
Preserve verbatim content for ai_code, stack_trace, and user_message fields during classification
Use AST-aware chunking via tree-sitter and never split stack traces during document chunking
Mask large tool output and skip noise entries during classification and chunking
Configure enrichment rate via BRAINLAYER_ENRICH_RATE env var with default of 0.2 (12 RPM)
Implement retry logic on SQLITE_BUSY errors with each worker using its own database connection for concurrency
Use Typer CLI framework for command-line interface implementation in src/brainlayer/
Exclude lifecycle-managed chunks from default search results; provide include_archived parameter to show historical chunks
Files:
src/brainlayer/pipeline/digest.pysrc/brainlayer/vector_store.py
src/**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
Use ruff for linting and formatting with commands: ruff check src/ && ruff format src/
Files:
src/brainlayer/pipeline/digest.pysrc/brainlayer/vector_store.py
src/brainlayer/vector_store.py
📄 CodeRabbit inference engine (CLAUDE.md)
src/brainlayer/vector_store.py: Store vector embeddings using sqlite-vec via APSW in the vector store implementation
Drop FTS triggers before bulk deletes on chunks table and recreate after to prevent performance degradation
Batch deletes into 5-10K chunks with checkpoints every 3 batches for large dataset bulk operations
Implement chunk lifecycle management with columns superseded_by, aggregated_into, and archived_at for tracking chunk relationships and soft-deletes
Files:
src/brainlayer/vector_store.py
🧠 Learnings (13)
📚 Learning: 2026-03-17T01:04:22.497Z
Learnt from: EtanHey
Repo: EtanHey/brainlayer PR: 0
File: :0-0
Timestamp: 2026-03-17T01:04:22.497Z
Learning: Applies to src/brainlayer/mcp/**/*.py and brain-bar/Sources/BrainBar/MCPRouter.swift: The 8 required MCP tools are `brain_search`, `brain_store`, `brain_recall`, `brain_entity`, `brain_expand`, `brain_update`, `brain_digest`, `brain_tags`. `brain_tags` is the 8th tool, replacing `brain_get_person`, as defined in the Phase B spec merged in PR `#72`. The Python MCP server already implements `brain_tags`. Legacy `brainlayer_*` aliases must be maintained for backward compatibility.
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/mcp/**/*.py : Implement MCP server entrypoint as brainlayer-mcp with 11 tools: brain_search, brain_store, brain_recall, brain_entity, brain_expand, brain_update, brain_digest, brain_get_person, brain_tags, brain_supersede, brain_archive
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/**/*.py : Exclude lifecycle-managed chunks from default search results; provide include_archived parameter to show historical chunks
Applied to files:
README.md
📚 Learning: 2026-03-17T01:04:22.497Z
Learnt from: EtanHey
Repo: EtanHey/brainlayer PR: 0
File: :0-0
Timestamp: 2026-03-17T01:04:22.497Z
Learning: In BrainLayer, the BrainBar daemon uses the socket path `/tmp/brainbar.sock` (NOT `/tmp/brainlayer.sock`). BrainBar is a new native Swift daemon designed to coexist with the existing Python `brainlayer-mcp` server during the migration period. Different socket paths avoid conflicts and enable A/B testing. Once BrainBar is proven stable, the Python server will be retired.
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/**/*.py : Mask large tool output and skip noise entries during classification and chunking
Applied to files:
README.md
📚 Learning: 2026-03-14T02:20:54.656Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: AGENTS.md:0-0
Timestamp: 2026-03-14T02:20:54.656Z
Learning: Be aware of known BrainLayer issues: DB locking during enrichment and WAL growth up to 4.7GB
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/enrichment_controller.py : Use Groq as primary enrichment backend with Gemini as fallback via enrichment_controller.py, overridable via BRAINLAYER_ENRICH_BACKEND env var
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/**/*.py : Configure enrichment rate via BRAINLAYER_ENRICH_RATE env var with default of 0.2 (12 RPM)
Applied to files:
README.md
📚 Learning: 2026-03-22T15:55:22.017Z
Learnt from: EtanHey
Repo: EtanHey/brainlayer PR: 100
File: src/brainlayer/enrichment_controller.py:175-199
Timestamp: 2026-03-22T15:55:22.017Z
Learning: In `src/brainlayer/enrichment_controller.py`, the `parallel` parameter in `enrich_local()` is intentionally kept in the function signature (currently unused, suppressed with `# noqa: ARG001`) for API stability. Parallel local enrichment via a thread pool or process pool is planned for a future iteration. Do not flag this as dead code requiring removal.
Applied to files:
README.md
📚 Learning: 2026-03-14T02:20:54.656Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: AGENTS.md:0-0
Timestamp: 2026-03-14T02:20:54.656Z
Learning: Applies to **/*.py : Run pytest before claiming behavior changed safely; current test suite has 929 tests
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/**/*.py : Use ruff for linting and formatting with commands: ruff check src/ && ruff format src/
Applied to files:
README.md
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/vector_store.py : Store vector embeddings using sqlite-vec via APSW in the vector store implementation
Applied to files:
src/brainlayer/vector_store.py
📚 Learning: 2026-03-28T00:26:19.473Z
Learnt from: CR
Repo: EtanHey/brainlayer PR: 0
File: CLAUDE.md:0-0
Timestamp: 2026-03-28T00:26:19.473Z
Learning: Applies to src/brainlayer/vector_store.py : Implement chunk lifecycle management with columns superseded_by, aggregated_into, and archived_at for tracking chunk relationships and soft-deletes
Applied to files:
src/brainlayer/vector_store.py
🔇 Additional comments (7)
README.md (1)
9-10: LGTM — Documentation updates are consistent.The test count (1,454), MCP tool count (12), chunk count (284K+), entity contracts feature, MLX backend addition, and
brain_enrichtool documentation all align with the PR objectives and commit messages.Also applies to: 15-15, 125-125, 128-128, 149-154, 183-183, 187-190, 201-201, 216-218, 289-289
contracts/entity-types.yaml (1)
1-162: LGTM — Well-structured entity type contracts.The contract specification is well-documented with clear requirement levels and consistent health criteria across all entity types. The hierarchical structure (required/expected/optional) provides flexibility for progressive entity completeness.
tests/test_kg_schema.py (1)
74-95: LGTM — Schema assertions updated for R49 columns.The expected column sets correctly reflect the new
entity_subtype,status,relation_tier, andweightcolumns added by the R49 migrations invector_store.py.Also applies to: 118-122
src/brainlayer/vector_store.py (1)
586-662: LGTM — R49 schema additions are idempotent and well-structured.The schema changes follow best practices:
- Table creation uses
IF NOT EXISTS- Column additions check existence via
PRAGMA table_infobeforeALTER TABLE- Type hierarchy seeding uses
INSERT OR IGNORE- All changes are safe for repeated
_init_db()callssrc/brainlayer/pipeline/digest.py (1)
847-872: LGTM — Health data integration with graceful fallback.The implementation correctly:
- Uses
_read_cursor()for thread-safe reads- Queries by
entity_namematching theentity_healthtable's primary key- Falls back to
Nonewhen the table doesn't exist or entity is unscoredThe bare
except Exceptionis appropriate here since it needs to handle bothOperationalError(table doesn't exist) and empty results, and the fallback behavior (returnNonefor both fields) is the same.scripts/score_entity_health.py (1)
1-22: LGTM — Well-documented scoring implementation.The scoring algorithm is clearly documented with:
- 5-dimension weighted scoring (40% required, 25% expected, 15% relationships, 10% chunks, 10% recency)
- Wikidata-style 5-tier health classification
- Exponential decay for recency with 90-day half-life
- Proper use of
get_db_path()for DB resolution per coding guidelines- Clean resource management with
finallyblockAlso applies to: 34-42, 45-63, 129-219, 286-318
tests/test_entity_contracts.py (1)
30-128: LGTM — Comprehensive R49 test coverage.The test suite thoroughly covers:
- Schema creation (3 new tables, altered columns with defaults)
- Contract YAML validation (structure, required fields, health criteria)
- Scoring algorithm (5-dimension weights, tier boundaries, edge cases)
- Persistence (upsert behavior, idempotency)
- Lookup integration (health fields present/absent)
- Type hierarchy seeding
Good use of the
populated_storefixture to set up realistic test data with entities, relations, and chunks.Also applies to: 134-178, 184-249, 254-265, 271-317, 323-431, 436-463, 469-508, 514-539
| parent_row = list(cursor.execute( | ||
| "SELECT parent_type FROM entity_type_hierarchy WHERE child_type = ?", | ||
| (entity_type,), | ||
| )) | ||
| if parent_row: | ||
| contract = contracts.get(parent_row[0][1] if len(parent_row[0]) > 1 else parent_row[0][0]) |
There was a problem hiding this comment.
Bug: Incorrect tuple indexing for parent type lookup.
The query SELECT parent_type FROM entity_type_hierarchy returns a single-column tuple, so parent_row[0] is (parent_type,). The condition len(parent_row[0]) > 1 will never be true for a single-column result, making this always fall through to parent_row[0][0].
The ternary is unnecessary and confusing.
🐛 Proposed fix
parent_row = list(cursor.execute(
"SELECT parent_type FROM entity_type_hierarchy WHERE child_type = ?",
(entity_type,),
))
if parent_row:
- contract = contracts.get(parent_row[0][1] if len(parent_row[0]) > 1 else parent_row[0][0])
+ contract = contracts.get(parent_row[0][0])🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@scripts/score_entity_health.py` around lines 240 - 245, The tuple indexing
for the parent type lookup is incorrect/overly complex: the SELECT returns
single-column rows so parent_row[0] is like (parent_type,) and the ternary with
len(parent_row[0]) > 1 is useless; change the logic that follows the
cursor.execute call to extract the parent_type as parent_row[0][0] (or assign
parent_type = parent_row[0][0]) and then use contracts.get(parent_type) instead
of the current conditional expression so the lookup is correct and readable
(refer to variables parent_row, cursor.execute and contracts in this change).
| import subprocess | ||
| import sys | ||
| from pathlib import Path | ||
| from unittest.mock import patch |
There was a problem hiding this comment.
🧹 Nitpick | 🔵 Trivial
Minor: Unused imports.
subprocess and patch are imported but not used in this test file.
🧹 Proposed cleanup
import json
import math
import os
-import subprocess
-import sys
from pathlib import Path
-from unittest.mock import patch
import pytest📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import subprocess | |
| import sys | |
| from pathlib import Path | |
| from unittest.mock import patch | |
| import json | |
| import math | |
| import os | |
| from pathlib import Path | |
| import pytest |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@tests/test_entity_contracts.py` around lines 17 - 20, The file imports unused
symbols subprocess and patch; remove these unused imports from the top of
tests/test_entity_contracts.py so only needed imports (sys, Path) remain, or
alternatively use them in tests if intended; specifically edit the import
line(s) to drop subprocess and from unittest.mock import patch unless patch is
actually referenced by functions or fixtures like any test_* functions.
1 participant
Summary
brain_enrich)brain_enrichtool to MCP tools tableTest plan
pytest --co -qoutputGenerated with Claude Code
Note
Add entity health scoring, contracts, and new DB schema tables
entity_healthtable.VectorStore._init_dbin vector_store.py to createentity_contracts,entity_health, andentity_type_hierarchytables, and addsentity_subtype/statuscolumns tokg_entitiesandrelation_tier/weighttokg_entity_chunks.entity_lookupin digest.py to includecompleteness_scoreandhealth_levelfrom theentity_healthtable when available.ALTER TABLEon every DB init; existing databases will be migrated silently with new default-valued columns.📊 Macroscope summarized 1ad9a62. 6 files reviewed, 3 issues evaluated, 1 issue filtered, 1 comment posted
🗂️ Filtered Issues
scripts/score_entity_health.py — 0 comments posted, 2 evaluated, 1 filtered
yaml.safe_load(f)returnsNone. The subsequentdata.get("entity_types", {})will raiseAttributeError: 'NoneType' object has no attribute 'get'. [ Skipped comment generation ]Summary by CodeRabbit
New Features
brain_enrich) available.Documentation
Tests