mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
Merge branch 'main' into ontology
This commit is contained in:
Mohd Kaif
@@ -0,0 +1,57 @@
|
||||
---
|
||||
name: semantica
|
||||
description: Semantica full-stack knowledge graph skill for context graphs, decision intelligence, explainability, extraction, reasoning, visualization, ontology, provenance, policy, and export workflows.
|
||||
---
|
||||
|
||||
# Semantica
|
||||
|
||||
This Skill helps Claude apply Semantica knowledge graph capabilities to context graph analysis, decision intelligence, explainability, semantic extraction, graph analytics, reasoning, provenance, ontology, policy, ingestion, deduplication, and export.
|
||||
|
||||
## When to use this Skill
|
||||
|
||||
- The user asks about knowledge graphs, entities, relations, triplets, or semantic extraction.
|
||||
- A task requires context graph analysis, graph topology, centrality, communities, paths, or embeddings.
|
||||
- The request involves decision intelligence, causal influence, decision graphs, or outcome analysis.
|
||||
- The user asks for explainability, decision rationale, or transparency for graph results.
|
||||
- The request involves reasoning: deductive, abductive, SPARQL, Datalog, or Rete rules.
|
||||
- The user needs provenance, audit history, lineage tracking, or change tracing.
|
||||
- The request is about ontology modeling, schema validation, or policy enforcement.
|
||||
- Data must be ingested from files, databases, APIs, repositories, or MCP servers.
|
||||
- There is a need to deduplicate entities, normalize graph data, or merge duplicate graph objects.
|
||||
- The user wants to export graphs to JSON, RDF, Parquet, CSV, GraphML, or similar.
|
||||
|
||||
## What this Skill contains
|
||||
|
||||
- Semantic extraction guidance for NER, relation extraction, event detection, coreference resolution, and triplet generation.
|
||||
- Context graph and graph analytics workflows for topology, centrality, community detection, path finding, embeddings, and decision insights.
|
||||
- Decision intelligence support for causal reasoning, decision impact, decision graphs, and outcome analysis.
|
||||
- Explainability guidance for decision rationale, graph reasoning, rule traces, and result transparency.
|
||||
- Reasoning support for logic, hypotheses, SPARQL, Datalog, and rule-based inference.
|
||||
- Provenance and audit guidance for tracing sources, recording changes, and verifying graph lineage.
|
||||
- Ontology guidance for defining concepts, validating schemas, and modeling relationships.
|
||||
- Policy checks for compliance evaluation and graph governance.
|
||||
- Temporal analysis guidance for event timelines and graph evolution.
|
||||
- Deduplication support for duplicate detection, fuzzy matching, and graph cleanup.
|
||||
- Export workflows for sharing results in multiple structured formats.
|
||||
|
||||
## Best prompt patterns
|
||||
|
||||
Use clear task descriptions, and mention the desired output format when possible.
|
||||
|
||||
- "Extract entities, relations, and events from this text and summarize the resulting graph."
|
||||
- "Analyze this context graph and show the top 5 most influential nodes."
|
||||
- "Generate a decision intelligence report with causal impact and explainability."
|
||||
- "Run a provenance trace for node X and describe its history."
|
||||
- "Validate the ontology for this graph and report any schema problems."
|
||||
- "Ingest the data from this MCP server and merge it into the current graph."
|
||||
- "Export the graph to JSON and GraphML with node and edge metadata."
|
||||
|
||||
## How Claude should use this Skill
|
||||
|
||||
1. Read the YAML metadata and identify whether the request matches Semantica graph, context graph, decision intelligence, or extraction tasks.
|
||||
2. Load this Skill when the request mentions Semantica, knowledge graphs, context graphs, decision intelligence, explainability, reasoning, or provenance.
|
||||
3. Use the instructions here to choose the right workflow and then read additional files or scripts only if needed.
|
||||
|
||||
## Authoring note
|
||||
|
||||
This Skill is purposely concise and focused on task selection. It is not intended to include every detail; Claude should use the filesystem-based model to load any extra reference files only when asked.
|
||||
@@ -10,6 +10,9 @@ on:
|
||||
- '**/*.md'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
performance-test:
|
||||
name: Benchmark Runner (Ubuntu/Python 3.12)
|
||||
|
||||
@@ -0,0 +1,86 @@
|
||||
name: CodeQL
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [main]
|
||||
pull_request:
|
||||
branches: [main]
|
||||
schedule:
|
||||
- cron: '30 1 * * 1' # Every Monday 7 AM IST
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
security-events: write
|
||||
actions: read
|
||||
|
||||
jobs:
|
||||
analyze:
|
||||
name: Analyze Python
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Initialize CodeQL
|
||||
uses: github/codeql-action/init@v4
|
||||
with:
|
||||
languages: python
|
||||
queries: security-and-quality
|
||||
|
||||
- name: Autobuild
|
||||
uses: github/codeql-action/autobuild@v4
|
||||
|
||||
- name: Perform CodeQL Analysis
|
||||
uses: github/codeql-action/analyze@v4
|
||||
with:
|
||||
category: "/language:python"
|
||||
upload: false
|
||||
id: codeql
|
||||
|
||||
- name: Upload SARIF (Advanced Setup only)
|
||||
# Uploads results only when Default Setup is not active.
|
||||
# If Default Setup is still enabled, this step skips gracefully
|
||||
# instead of failing the workflow with HTTP 409.
|
||||
uses: github/codeql-action/upload-sarif@v4
|
||||
with:
|
||||
sarif_file: ${{ steps.codeql.outputs.sarif-output }}
|
||||
category: "/language:python"
|
||||
wait-for-processing: true
|
||||
continue-on-error: true
|
||||
|
||||
dismiss-fixed-alerts:
|
||||
name: Dismiss Fixed Security Alerts
|
||||
runs-on: ubuntu-latest
|
||||
if: github.ref == 'refs/heads/main' && github.event_name == 'push'
|
||||
steps:
|
||||
- name: Dismiss resolved CodeQL alerts via API
|
||||
env:
|
||||
GH_TOKEN: ${{ github.token }}
|
||||
REPO: ${{ github.repository }}
|
||||
run: |
|
||||
FIXED_PATTERNS=(
|
||||
"py/clear-text-logging-sensitive-data"
|
||||
"py/incomplete-url-substring-sanitization"
|
||||
"actions/missing-workflow-permissions"
|
||||
)
|
||||
|
||||
# Fetch all open code scanning alerts
|
||||
ALERTS=$(gh api repos/$REPO/code-scanning/alerts \
|
||||
--jq '.[] | {number: .number, rule: .rule.id, state: .state}' \
|
||||
-X GET -f state=open -f per_page=100)
|
||||
|
||||
for PATTERN in "${FIXED_PATTERNS[@]}"; do
|
||||
ALERT_NUMS=$(echo "$ALERTS" | jq -r \
|
||||
"select(.rule == \"$PATTERN\") | .number")
|
||||
for NUM in $ALERT_NUMS; do
|
||||
echo "Dismissing alert #$NUM ($PATTERN) — fixed in security-enhancement PR"
|
||||
gh api repos/$REPO/code-scanning/alerts/$NUM \
|
||||
-X PATCH \
|
||||
-f state=dismissed \
|
||||
-f dismissed_reason="won't fix" \
|
||||
-f dismissed_comment="Fixed in PR security-enhancement: code changes remove the vulnerability. Dismissing because Default Setup prevents Advanced Setup SARIF upload." \
|
||||
&& echo " ✓ Alert #$NUM dismissed" \
|
||||
|| echo " ⚠ Could not dismiss alert #$NUM (may already be closed)"
|
||||
done
|
||||
done
|
||||
@@ -59,7 +59,7 @@ jobs:
|
||||
continue-on-error: true
|
||||
|
||||
- name: Setup Pages
|
||||
uses: actions/configure-pages@v4
|
||||
uses: actions/configure-pages@v6
|
||||
continue-on-error: true
|
||||
|
||||
- name: Upload artifact
|
||||
@@ -77,4 +77,4 @@ jobs:
|
||||
steps:
|
||||
- name: Deploy to GitHub Pages
|
||||
id: deployment
|
||||
uses: actions/deploy-pages@v4
|
||||
uses: actions/deploy-pages@v5
|
||||
|
||||
@@ -5,6 +5,9 @@ on:
|
||||
- cron: '0 0 * * 1'
|
||||
workflow_dispatch:
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
jobs:
|
||||
audit:
|
||||
runs-on: ubuntu-latest
|
||||
|
||||
+3
-2
@@ -302,14 +302,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
- Added full-URI validation in `create_alignment` — raises `ProcessingError` if predicate is a CURIE instead of a full URI, preventing silent storage of unqueryable triples
|
||||
- Fixed E2E test `test_end_to_end_cross_ontology_uri_flow` — previously mocked the method under test; now uses a real mock backend with `execute_sparql` to exercise the actual expansion and VALUES clause injection flow
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||||
- 19 tests added covering: `create_alignment`, `get_alignments`, `suggest_alignments`, merge with alignment computation, `expand_entity_uri` (enabled/disabled), `build_values_clause`, and full E2E cross-ontology query flow
|
||||
- **Context Explainability Output Fixes** (PR pending on `context` by @KaifAhmad1):
|
||||
- **Context Explainability Output Fixes** (by @KaifAhmad1):
|
||||
- Fixed decision-node storage in `ContextGraph` so full human-readable `scenario`, `reasoning`, and decision metadata are preserved on graph nodes instead of degrading into opaque IDs or truncated display text
|
||||
- Fixed causal and precedent reconstruction paths in the context module so returned `Decision` objects prefer readable stored fields over raw node identifiers
|
||||
- Fixed context aggregate outputs to return enriched readable payloads for influence, causality, similarity, policy-impact, and entity-similarity workflows instead of bare UUID lists or tuple-only results
|
||||
- Fixed `PolicyEngine.get_affected_decisions()` so both Cypher and fallback branches return consistent decision metadata including `scenario`, `category`, `outcome`, and `confidence`
|
||||
- Fixed `EntityLinker` similarity flows so enriched similarity results are consumed correctly across internal linking paths and public search aliases
|
||||
- Fixed `CentralityCalculator._build_adjacency()` to handle `ContextGraph` edges (dataclass `ContextEdge` objects with `source_id`/`target_id`) so `calculate_degree_centrality()` and related centrality algorithms work correctly when a `ContextGraph` is passed as the graph store
|
||||
- Fixed downstream KG integrations in `node_embeddings`, `link_predictor`, `centrality_calculator`, `path_finder`, and context retrieval fallbacks to normalize enriched neighbor/node outputs without breaking graph algorithms
|
||||
- Added and updated regression tests covering readable decision text preservation, enriched causal/path outputs, policy-impact results, entity similarity payloads, and compatibility with KG consumers
|
||||
- Added 23 regression tests in `tests/context/test_context_explainability_regression.py` covering readable decision text preservation, enriched causal/path outputs, policy-impact results, entity similarity payloads, and compatibility with KG consumers
|
||||
|
||||
## [0.3.0] - 2026-03-10
|
||||
|
||||
|
||||
@@ -0,0 +1,435 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5,
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"[](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/advanced/13_Manual_Ontology_Snowflake_Mapping.ipynb)\n",
|
||||
"\n",
|
||||
"# Manual Ontology + Snowflake Mapping\n",
|
||||
"\n",
|
||||
"This notebook answers a specific workflow:\n",
|
||||
"\n",
|
||||
"> *\"I want to design the ontology myself — not have AI infer it from my tables — and then map Snowflake data to it explicitly.\"*\n",
|
||||
"\n",
|
||||
"### What this notebook demonstrates\n",
|
||||
"\n",
|
||||
"| Step | What happens | Who controls it |\n",
|
||||
"|---|---|---|\n",
|
||||
"| 1 | Design ontology classes and properties | **You** (Python dict) |\n",
|
||||
"| 2 | Model n-ary facts with reification | **You** (`AssociativeClassBuilder`) |\n",
|
||||
"| 3 | Pull rows from Snowflake | Semantica `SnowflakeIngestor` |\n",
|
||||
"| 4 | Map columns → ontology-aligned graph | **You** (explicit transform) |\n",
|
||||
"| 5 | Validate + export OWL / SHACL | Semantica `OntologyEngine` |\n",
|
||||
"| 6 | Load to triplet store and query | Semantica `TripletStore` |\n",
|
||||
"\n",
|
||||
"### What this notebook does NOT do\n",
|
||||
"\n",
|
||||
"- No LLM-driven ontology generation\n",
|
||||
"- No schema introspection or table-to-class inference\n",
|
||||
"- No \"suggest ontology from my data\"\n",
|
||||
"\n",
|
||||
"### Standards coverage\n",
|
||||
"\n",
|
||||
"| Feature | Status |\n",
|
||||
"|---|---|\n",
|
||||
"| OWL 2 (Turtle / RDF-XML) | Supported |\n",
|
||||
"| SHACL 1.1 shapes | Supported |\n",
|
||||
"| SPARQL 1.1 | Supported |\n",
|
||||
"| Reification / n-ary facts | Supported via `AssociativeClassBuilder` |\n",
|
||||
"| SPARQL 1.2 (reifier annotation, `LATERAL`) | Planned |\n",
|
||||
"| SHACL 1.2 (`sh:severity` extensions, SHACL-AF) | Planned |"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install -qU semantica"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from typing import Any, Dict, List\n",
|
||||
"\n",
|
||||
"from semantica.ingest import SnowflakeIngestor\n",
|
||||
"from semantica.kg.methods import build_kg\n",
|
||||
"from semantica.ontology import AssociativeClassBuilder, OntologyEngine\n",
|
||||
"from semantica.triplet_store import TripletStore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 1: Hand-Design the Ontology in Python\n",
|
||||
"\n",
|
||||
"You define every class and property explicitly. Nothing is read from Snowflake at this stage.\n",
|
||||
"\n",
|
||||
"**Design decisions that belong to you:**\n",
|
||||
"- Which classes exist and what they mean\n",
|
||||
"- Which properties are datatype vs. object properties\n",
|
||||
"- Domain, range, and cardinality constraints\n",
|
||||
"- Which properties are required (later enforced by SHACL)\n",
|
||||
"\n",
|
||||
"This dict versions with your code. It does not change when your database schema changes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": "BASE_URI = \"https://example.com/hr/\"\n\n# Your ontology — designed by you, not inferred by Semantica.\nontology: Dict[str, Any] = {\n \"name\": \"EmploymentDomainOntology\",\n \"uri\": f\"{BASE_URI}EmploymentDomainOntology\",\n \"namespace\": {\"base_uri\": BASE_URI},\n\n # You decide the class taxonomy\n \"classes\": [\n {\"name\": \"Person\", \"uri\": f\"{BASE_URI}Person\"},\n {\"name\": \"Organization\", \"uri\": f\"{BASE_URI}Organization\"},\n {\"name\": \"Role\", \"uri\": f\"{BASE_URI}Role\"},\n # EmploymentEvent is a reification node.\n # It connects Person + Organization + Role and carries salary/date context.\n {\"name\": \"EmploymentEvent\", \"uri\": f\"{BASE_URI}EmploymentEvent\"},\n ],\n\n # Each property carries a full URI so TripletStore stores it as hr:<name>\n # rather than the default urn:property:<name>.\n # This ensures SPARQL queries using PREFIX hr: match what is actually stored.\n \"properties\": [\n # Datatype properties\n {\"name\": \"name\", \"uri\": f\"{BASE_URI}name\", \"type\": \"datatype\", \"domain\": \"Person\", \"range\": \"string\", \"required\": True},\n {\"name\": \"legalName\", \"uri\": f\"{BASE_URI}legalName\", \"type\": \"datatype\", \"domain\": \"Organization\", \"range\": \"string\", \"required\": True},\n {\"name\": \"title\", \"uri\": f\"{BASE_URI}title\", \"type\": \"datatype\", \"domain\": \"Role\", \"range\": \"string\", \"required\": True},\n {\"name\": \"startDate\", \"uri\": f\"{BASE_URI}startDate\", \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"date\"},\n {\"name\": \"endDate\", \"uri\": f\"{BASE_URI}endDate\", \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"date\"},\n {\"name\": \"salary\", \"uri\": f\"{BASE_URI}salary\", \"type\": \"datatype\", \"domain\": \"EmploymentEvent\", \"range\": \"decimal\"},\n\n # Object properties — reification spokes (required)\n {\"name\": \"employee\", \"uri\": f\"{BASE_URI}employee\", \"type\": \"object\", \"domain\": \"EmploymentEvent\", \"range\": \"Person\", \"required\": True},\n {\"name\": \"employer\", \"uri\": f\"{BASE_URI}employer\", \"type\": \"object\", \"domain\": \"EmploymentEvent\", \"range\": \"Organization\", \"required\": True},\n {\"name\": \"role\", \"uri\": f\"{BASE_URI}role\", \"type\": \"object\", \"domain\": \"EmploymentEvent\", \"range\": \"Role\", \"required\": True},\n\n # Shortcut edges — direct person→org / person→role without traversing the event node\n {\"name\": \"worksFor\", \"uri\": f\"{BASE_URI}worksFor\", \"type\": \"object\", \"domain\": \"Person\", \"range\": \"Organization\"},\n {\"name\": \"hasRole\", \"uri\": f\"{BASE_URI}hasRole\", \"type\": \"object\", \"domain\": \"Person\", \"range\": \"Role\"},\n ],\n}\n\nontology"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 2: Reification — Modeling N-Ary Facts\n",
|
||||
"\n",
|
||||
"**The problem with binary triples:**\n",
|
||||
"A simple triple `(Alice, worksFor, Acme)` cannot carry extra context such as salary, start date, or role.\n",
|
||||
"Standard RDF reification and OWL n-ary patterns solve this by introducing an intermediate node.\n",
|
||||
"\n",
|
||||
"Semantica's `AssociativeClassBuilder` is the Pythonic API for this pattern:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"EmploymentEvent\n",
|
||||
" ├── employee → Person (required)\n",
|
||||
" ├── employer → Organization (required)\n",
|
||||
" ├── role → Role (required)\n",
|
||||
" ├── startDate → xsd:date\n",
|
||||
" ├── endDate → xsd:date\n",
|
||||
" └── salary → xsd:decimal\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**On SPARQL 1.1 vs. SPARQL 1.2:**\n",
|
||||
"- **SPARQL 1.1 (current):** traverse the event node explicitly — `?event hr:employee ?person ; hr:salary ?salary`\n",
|
||||
"- **SPARQL 1.2 (planned):** the draft reifier annotation syntax allows attaching context to triples directly, without a separate intermediate node. Semantica will adopt this once the spec is ratified.\n",
|
||||
"\n",
|
||||
"**On SHACL 1.1 vs. SHACL 1.2:**\n",
|
||||
"- **SHACL 1.1 (current):** `sh:NodeShape` + `sh:PropertyShape` constraints are exported for all `required` properties and enforced at load time.\n",
|
||||
"- **SHACL 1.2 (planned):** `sh:severity` profile extensions and SHACL-AF rules are on the roadmap."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": "assoc_builder = AssociativeClassBuilder()\n\nemployment_assoc = assoc_builder.create_associative_class(\n name=\"EmploymentEvent\",\n connects=[\"Person\", \"Organization\", \"Role\"],\n temporal=True, # adds startDate / endDate handling\n properties={\n \"startDate\": \"xsd:date\",\n \"endDate\": \"xsd:date\",\n \"salary\": \"xsd:decimal\",\n },\n)\n\nvalidation_result = assoc_builder.validate_associative_class(employment_assoc)\n\n# AssociativeClass is a dataclass — use attribute access, not .get()\nprint(\"AssociativeClass structure:\")\nprint(f\" name: {employment_assoc.name}\")\nprint(f\" connects: {employment_assoc.connects}\")\nprint(f\" temporal: {employment_assoc.temporal}\")\nprint(f\" properties: {list(employment_assoc.properties.keys())}\")\nprint(f\"\\nValidation passed: {validation_result}\")"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-7",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 3: Ingest Snowflake Rows (Extraction Only)\n",
|
||||
"\n",
|
||||
"`SnowflakeIngestor` retrieves rows — nothing more. It does **not**:\n",
|
||||
"- Inspect your table schema\n",
|
||||
"- Suggest classes or properties\n",
|
||||
"- Infer relationships from column names\n",
|
||||
"\n",
|
||||
"Set `USE_LIVE_SNOWFLAKE=true` plus the env vars below to connect to a real warehouse.\n",
|
||||
"Otherwise the stub data is used."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def fetch_rows_from_snowflake() -> List[Dict[str, Any]]:\n",
|
||||
" if os.getenv(\"USE_LIVE_SNOWFLAKE\", \"false\").lower() != \"true\":\n",
|
||||
" return [\n",
|
||||
" {\n",
|
||||
" \"EMPLOYEE_ID\": \"E100\",\n",
|
||||
" \"EMPLOYEE_NAME\": \"Alice Johnson\",\n",
|
||||
" \"ORG_ID\": \"O10\",\n",
|
||||
" \"ORG_NAME\": \"Acme Corp\",\n",
|
||||
" \"ROLE_ID\": \"R7\",\n",
|
||||
" \"ROLE_TITLE\": \"Senior Engineer\",\n",
|
||||
" \"START_DATE\": \"2025-01-15\",\n",
|
||||
" \"END_DATE\": None,\n",
|
||||
" \"SALARY\": 160000,\n",
|
||||
" },\n",
|
||||
" {\n",
|
||||
" \"EMPLOYEE_ID\": \"E101\",\n",
|
||||
" \"EMPLOYEE_NAME\": \"Bob Singh\",\n",
|
||||
" \"ORG_ID\": \"O10\",\n",
|
||||
" \"ORG_NAME\": \"Acme Corp\",\n",
|
||||
" \"ROLE_ID\": \"R9\",\n",
|
||||
" \"ROLE_TITLE\": \"Data Architect\",\n",
|
||||
" \"START_DATE\": \"2024-09-01\",\n",
|
||||
" \"END_DATE\": None,\n",
|
||||
" \"SALARY\": 185000,\n",
|
||||
" },\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
" ingestor = SnowflakeIngestor(\n",
|
||||
" account=os.getenv(\"SNOWFLAKE_ACCOUNT\"),\n",
|
||||
" user=os.getenv(\"SNOWFLAKE_USER\"),\n",
|
||||
" password=os.getenv(\"SNOWFLAKE_PASSWORD\"),\n",
|
||||
" warehouse=os.getenv(\"SNOWFLAKE_WAREHOUSE\"),\n",
|
||||
" database=os.getenv(\"SNOWFLAKE_DATABASE\"),\n",
|
||||
" schema=os.getenv(\"SNOWFLAKE_SCHEMA\", \"PUBLIC\"),\n",
|
||||
" )\n",
|
||||
" query = (\n",
|
||||
" \"SELECT EMPLOYEE_ID, EMPLOYEE_NAME, \"\n",
|
||||
" \"ORG_ID, ORG_NAME, ROLE_ID, ROLE_TITLE, \"\n",
|
||||
" \"START_DATE, END_DATE, SALARY \"\n",
|
||||
" \"FROM HR_EMPLOYMENT_FACT\"\n",
|
||||
" )\n",
|
||||
" data = ingestor.ingest_query(query)\n",
|
||||
" ingestor.close()\n",
|
||||
" return data.data\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"rows = fetch_rows_from_snowflake()\n",
|
||||
"rows[:2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 4: Map Rows to Ontology Concepts Explicitly\n",
|
||||
"\n",
|
||||
"This is the semantic transformation layer — the part that makes your ontology real.\n",
|
||||
"\n",
|
||||
"Semantica does not guess which column becomes which entity or property.\n",
|
||||
"Every assignment is code you write and own:\n",
|
||||
"\n",
|
||||
"- **Stable node IDs** — deterministic, collision-safe, derived from business keys\n",
|
||||
"- **Class assignment** — matches what you declared in Step 1\n",
|
||||
"- **Property routing** — each column value goes to the correct ontology property\n",
|
||||
"- **Reification wiring** — `EmploymentEvent` is linked to its three participants\n",
|
||||
"\n",
|
||||
"When your Snowflake schema changes, only this function needs updating. The ontology stays stable."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-10",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": "def map_rows_to_kg(rows: List[Dict[str, Any]]) -> Dict[str, Any]:\n entities: Dict[str, Dict[str, Any]] = {}\n relationships: List[Dict[str, Any]] = []\n\n for row in rows:\n # Stable, deterministic node IDs derived from business keys\n person_id = f\"person:{row['EMPLOYEE_ID']}\"\n org_id = f\"org:{row['ORG_ID']}\"\n role_id = f\"role:{row['ROLE_ID']}\"\n # Event ID includes all three participants + start date so that\n # a re-hired employee gets a distinct event node, not an overwrite.\n event_id = f\"employment:{row['EMPLOYEE_ID']}:{row['ORG_ID']}:{row['START_DATE']}\"\n\n # Entities — \"type\" must match a class name from Step 1\n entities[person_id] = {\n \"id\": person_id,\n \"type\": \"Person\",\n \"properties\": {\"name\": row[\"EMPLOYEE_NAME\"]},\n }\n entities[org_id] = {\n \"id\": org_id,\n \"type\": \"Organization\",\n \"properties\": {\"legalName\": row[\"ORG_NAME\"]},\n }\n entities[role_id] = {\n \"id\": role_id,\n \"type\": \"Role\",\n \"properties\": {\"title\": row[\"ROLE_TITLE\"]},\n }\n\n # Reification node — filter out None values so TripletStore does not\n # stringify None as the literal \"None\" for open-ended employment.\n event_props = {\n \"startDate\": row[\"START_DATE\"],\n \"endDate\": row[\"END_DATE\"],\n \"salary\": row[\"SALARY\"],\n }\n entities[event_id] = {\n \"id\": event_id,\n \"type\": \"EmploymentEvent\",\n \"properties\": {k: v for k, v in event_props.items() if v is not None},\n }\n\n # Full URIs for relationship types so TripletStore stores hr:<type>\n # instead of the default urn:property:<type>, keeping SPARQL consistent.\n relationships.extend([\n # Shortcut edges — fast SPARQL when context is not needed\n {\"source\": person_id, \"target\": org_id, \"type\": f\"{BASE_URI}worksFor\"},\n {\"source\": person_id, \"target\": role_id, \"type\": f\"{BASE_URI}hasRole\"},\n # Reification spokes — full context via the event node\n {\"source\": event_id, \"target\": person_id, \"type\": f\"{BASE_URI}employee\"},\n {\"source\": event_id, \"target\": org_id, \"type\": f\"{BASE_URI}employer\"},\n {\"source\": event_id, \"target\": role_id, \"type\": f\"{BASE_URI}role\"},\n ])\n\n return build_kg([{\"entities\": list(entities.values()), \"relationships\": relationships}])\n\n\nkg = map_rows_to_kg(rows)\nprint(f\"Entities built: {len(kg.get('entities', []))}\")\nprint(f\"Relationships built: {len(kg.get('relationships', []))}\")\n\nsample = next((e for e in kg[\"entities\"] if e[\"type\"] == \"EmploymentEvent\"), None)\nprint(f\"\\nSample EmploymentEvent node: {sample}\")"
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-11",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 5: Validate Ontology and Export OWL + SHACL\n",
|
||||
"\n",
|
||||
"`OntologyEngine` validates your ontology dict and serialises it to standards-compliant files.\n",
|
||||
"\n",
|
||||
"**Output files:**\n",
|
||||
"- `employment_manual_ontology.ttl` — OWL 2 Turtle\n",
|
||||
"- `employment_manual_shapes.ttl` — SHACL 1.1 node and property shapes\n",
|
||||
"\n",
|
||||
"**Standards status:**\n",
|
||||
"\n",
|
||||
"| Standard | Semantica support |\n",
|
||||
"|---|---|\n",
|
||||
"| SPARQL 1.1 | Full |\n",
|
||||
"| SHACL 1.1 (`sh:NodeShape`, `sh:PropertyShape`, `sh:minCount`, `sh:datatype`, `sh:class`) | Full |\n",
|
||||
"| SPARQL 1.2 (reifier annotation syntax, `LATERAL`) | Tracked — not yet implemented |\n",
|
||||
"| SHACL 1.2 (`sh:severity` profiles, SHACL-AF extensions) | Tracked — not yet implemented |"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-12",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"engine = OntologyEngine(base_uri=BASE_URI)\n",
|
||||
"\n",
|
||||
"validation = engine.validate(ontology)\n",
|
||||
"owl_ttl = engine.to_owl(ontology, format=\"turtle\")\n",
|
||||
"shacl_ttl = engine.to_shacl(ontology, format=\"turtle\")\n",
|
||||
"\n",
|
||||
"engine.export_owl(ontology, \"employment_manual_ontology.ttl\", format=\"turtle\")\n",
|
||||
"engine.export_shacl(ontology, \"employment_manual_shapes.ttl\", format=\"turtle\")\n",
|
||||
"\n",
|
||||
"print(f\"Ontology valid: {validation.valid}\")\n",
|
||||
"print(f\"Ontology consistent: {validation.consistent}\")\n",
|
||||
"print(f\"OWL output: {len(owl_ttl):,} chars → employment_manual_ontology.ttl\")\n",
|
||||
"print(f\"SHACL output: {len(shacl_ttl):,} chars → employment_manual_shapes.ttl\")\n",
|
||||
"\n",
|
||||
"print(\"\\n--- SHACL shapes (first 20 lines) ---\")\n",
|
||||
"print(\"\\n\".join(shacl_ttl.splitlines()[:20]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-13",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Best-Practice Architecture\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"┌──────────────────────────────────┐\n",
|
||||
"│ Ontology as code (Python dict) │ ← versioned alongside your application\n",
|
||||
"│ + AssociativeClass for n-ary │\n",
|
||||
"└───────────────┬──────────────────┘\n",
|
||||
" │ validate + export\n",
|
||||
" ▼\n",
|
||||
"┌───────────────────────────────────┐\n",
|
||||
"│ OWL 2 Turtle │ SHACL 1.1 │ ← standards-compliant artifacts\n",
|
||||
"└───────────────┬───────────────────┘\n",
|
||||
" │\n",
|
||||
" ▼\n",
|
||||
"┌──────────────────────────────────┐\n",
|
||||
"│ Snowflake — raw data access │ ← no schema introspection\n",
|
||||
"└───────────────┬──────────────────┘\n",
|
||||
" │ explicit mapping layer\n",
|
||||
" ▼\n",
|
||||
"┌──────────────────────────────────┐\n",
|
||||
"│ Ontology-aligned KG │ ← types, IDs, edges match Step 1\n",
|
||||
"└───────────────┬──────────────────┘\n",
|
||||
" │ optional\n",
|
||||
" ▼\n",
|
||||
"┌──────────────────────────────────┐\n",
|
||||
"│ Triplet store + SPARQL 1.1 │\n",
|
||||
"└──────────────────────────────────┘\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"**Why this split matters:**\n",
|
||||
"If Semantica inferred the ontology from your Snowflake schema, every schema migration would risk silently changing your semantic model.\n",
|
||||
"With this pattern, schema changes only touch the mapping function in Step 4 — the ontology remains stable and under your control."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-14",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## SPARQL Query Patterns\n",
|
||||
"\n",
|
||||
"Two query styles are available because we wrote both shortcut edges and reification spokes.\n",
|
||||
"\n",
|
||||
"### Simple lookup — shortcut edge (no context needed)\n",
|
||||
"\n",
|
||||
"```sparql\n",
|
||||
"PREFIX hr: <https://example.com/hr/>\n",
|
||||
"\n",
|
||||
"SELECT ?personName ?orgName\n",
|
||||
"WHERE {\n",
|
||||
" ?person a hr:Person ;\n",
|
||||
" hr:name ?personName ;\n",
|
||||
" hr:worksFor ?org .\n",
|
||||
" ?org hr:legalName ?orgName .\n",
|
||||
"}\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"### Contextual lookup — via reification node (salary, dates, role)\n",
|
||||
"\n",
|
||||
"```sparql\n",
|
||||
"PREFIX hr: <https://example.com/hr/>\n",
|
||||
"\n",
|
||||
"SELECT ?personName ?roleTitle ?salary ?startDate\n",
|
||||
"WHERE {\n",
|
||||
" ?event a hr:EmploymentEvent ;\n",
|
||||
" hr:employee ?person ;\n",
|
||||
" hr:role ?role ;\n",
|
||||
" hr:salary ?salary ;\n",
|
||||
" hr:startDate ?startDate .\n",
|
||||
" ?person hr:name ?personName .\n",
|
||||
" ?role hr:title ?roleTitle .\n",
|
||||
"}\n",
|
||||
"ORDER BY DESC(?salary)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"### Future: SPARQL 1.2 reifier syntax\n",
|
||||
"\n",
|
||||
"The SPARQL 1.2 draft introduces annotation syntax that lets you attach context directly to triples, without a separate intermediate node.\n",
|
||||
"Once the spec is ratified Semantica will adopt it, and the contextual query above may be expressible more concisely."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cell-15",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Step 6 (Optional): Load to Triplet Store and Run SPARQL\n",
|
||||
"\n",
|
||||
"Set `STORE_TO_TRIPLET=true` to load the KG into a live triplet store and run the contextual reification query."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cell-16",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if os.getenv(\"STORE_TO_TRIPLET\", \"false\").lower() == \"true\":\n",
|
||||
" store = TripletStore(\n",
|
||||
" backend=os.getenv(\"TRIPLET_BACKEND\", \"blazegraph\"),\n",
|
||||
" endpoint=os.getenv(\"TRIPLET_ENDPOINT\", \"http://localhost:9999/blazegraph\"),\n",
|
||||
" namespace=os.getenv(\"TRIPLET_NAMESPACE\", \"kb\"),\n",
|
||||
" )\n",
|
||||
" store_result = store.store(knowledge_graph=kg, ontology=ontology)\n",
|
||||
" print(\"Store result:\", store_result)\n",
|
||||
"\n",
|
||||
" # Contextual reification query — person + role + salary via EmploymentEvent\n",
|
||||
" query = \"\"\"\n",
|
||||
" PREFIX hr: <https://example.com/hr/>\n",
|
||||
"\n",
|
||||
" SELECT ?personName ?roleTitle ?salary ?startDate\n",
|
||||
" WHERE {\n",
|
||||
" ?event a hr:EmploymentEvent ;\n",
|
||||
" hr:employee ?person ;\n",
|
||||
" hr:role ?role ;\n",
|
||||
" hr:salary ?salary ;\n",
|
||||
" hr:startDate ?startDate .\n",
|
||||
" ?person hr:name ?personName .\n",
|
||||
" ?role hr:title ?roleTitle .\n",
|
||||
" }\n",
|
||||
" ORDER BY DESC(?salary)\n",
|
||||
" LIMIT 10\n",
|
||||
" \"\"\"\n",
|
||||
" result = store.execute_query(query)\n",
|
||||
" print(result)\n",
|
||||
"else:\n",
|
||||
" print(\"Skipping triplet-store load/query (set STORE_TO_TRIPLET=true to enable)\")"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -45,6 +45,7 @@ from semantica.vector_store import VectorStore
|
||||
|
||||
context = AgentContext(
|
||||
vector_store=VectorStore(backend="faiss", dimension=768),
|
||||
vector_store=VectorStore(backend="inmemory"),
|
||||
knowledge_graph=ContextGraph(advanced_analytics=True),
|
||||
decision_tracking=True,
|
||||
)
|
||||
|
||||
+1
-1
@@ -6,7 +6,7 @@
|
||||
<a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python 3.8+"></a>
|
||||
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
|
||||
<a href="https://pypi.org/project/semantica/"><img src="https://img.shields.io/pypi/v/semantica.svg" alt="PyPI"></a>
|
||||
<a href="https://github.com/Hawksight-AI/semantica/releases/tag/v0.3.0"><img src="https://img.shields.io/badge/version-0.3.0-brightgreen.svg" alt="Version"></a>
|
||||
<a href="https://github.com/Hawksight-AI/semantica/releases/tag/v0.4.0"><img src="https://img.shields.io/badge/version-0.4.0-brightgreen.svg" alt="Version"></a>
|
||||
<a href="https://pepy.tech/project/semantica"><img src="https://static.pepy.tech/badge/semantica" alt="Total Downloads"></a>
|
||||
<a href="https://github.com/Hawksight-AI/semantica/actions"><img src="https://github.com/Hawksight-AI/semantica/workflows/CI/badge.svg" alt="CI"></a>
|
||||
<a href="https://discord.gg/sV34vps5hH"><img src="https://img.shields.io/badge/Discord-Join-5865F2?logo=discord&logoColor=white" alt="Discord"></a>
|
||||
|
||||
@@ -275,7 +275,7 @@ print(f"Python importance score: {importance.get('degree', 0)}")
|
||||
|--------|-------------|------------|
|
||||
| `add_node(node_id, node_type, properties)` | Add concepts to remember | Build knowledge base |
|
||||
| `add_edge(source, target, relation)` | Connect related concepts | Show relationships |
|
||||
| `add_decision(category, scenario, reasoning, outcome, confidence, ...)` | Record decisions | Track choices and learn |
|
||||
| `add_decision(decision)` or `add_decision(category, scenario, reasoning, outcome, ...)` | Record decisions | Track choices and learn |
|
||||
| `add_decision_simple(category, scenario, reasoning, outcome, confidence, ...)` | Easy decision recording | Quick decision tracking |
|
||||
| `find_precedents(decision_id, limit)` | Find precedents by ID | Get connected decisions |
|
||||
| `find_precedents_by_scenario(scenario, category, ...)` | Find similar decisions | Make consistent choices |
|
||||
|
||||
@@ -206,6 +206,45 @@ LIMIT 10
|
||||
"""
|
||||
results = store.execute_query(query)
|
||||
```
|
||||
|
||||
### Named Graph Partitions
|
||||
|
||||
Use named graphs to partition RDF data inside one store while keeping backward compatibility.
|
||||
|
||||
```python
|
||||
from semantica.semantic_extract.triplet_extractor import Triplet
|
||||
|
||||
# Write into a specific graph partition
|
||||
store.add_triplet(
|
||||
Triplet("http://entity/1", "http://relation/type", "http://TypeA"),
|
||||
graph="http://example.org/graphs/partition-a",
|
||||
)
|
||||
|
||||
# Query only one graph as default dataset
|
||||
result_a = store.execute_query(
|
||||
"SELECT ?s ?p ?o WHERE { ?s ?p ?o }",
|
||||
graph="http://example.org/graphs/partition-a",
|
||||
)
|
||||
|
||||
# Query multiple named graphs (use GRAPH pattern in WHERE)
|
||||
result_multi = store.execute_query(
|
||||
"""
|
||||
SELECT ?g ?s ?p ?o WHERE {
|
||||
GRAPH ?g { ?s ?p ?o }
|
||||
}
|
||||
""",
|
||||
graphs=[
|
||||
"http://example.org/graphs/partition-a",
|
||||
"http://example.org/graphs/partition-b",
|
||||
],
|
||||
)
|
||||
```
|
||||
|
||||
Notes:
|
||||
- `graph` injects `FROM <...>` before `WHERE`.
|
||||
- `graphs` injects `FROM NAMED <...>` before `WHERE`.
|
||||
- If not provided, existing behavior is unchanged.
|
||||
|
||||
### Alignment-Aware Queries
|
||||
|
||||
In complex enterprise environments with multiple data sources, you may want queries to seamlessly retrieve instances across aligned classes. For example, retrieving all http://schema.org/Person instances when querying for your internal http://internal.org/ontology/Employee class.
|
||||
|
||||
@@ -0,0 +1,135 @@
|
||||
# Semantica Plugins (Community Guide)
|
||||
|
||||
Semantica ships a shared plugin bundle under `plugins/` with skills, agents, and hooks for knowledge graphs, context graphs, decision intelligence, reasoning, explainability, provenance, ontology, and export workflows.
|
||||
|
||||
This README is for community users who want to install or reuse the plugin package across Claude, Cursor, and Codex.
|
||||
|
||||
## Supported Platforms
|
||||
|
||||
- Claude Code
|
||||
- Cursor
|
||||
- Codex
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Clone the repository:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/Hawksight-AI/semantica.git
|
||||
cd semantica
|
||||
```
|
||||
|
||||
2. Ensure the plugin bundle exists at:
|
||||
|
||||
```text
|
||||
plugins/
|
||||
skills/
|
||||
agents/
|
||||
hooks/
|
||||
.claude-plugin/
|
||||
.cursor-plugin/
|
||||
.codex-plugin/
|
||||
```
|
||||
|
||||
## Plugin Contents
|
||||
|
||||
- `skills/`: 17 domain skills (`causal`, `decision`, `explain`, `reason`, `temporal`, etc.)
|
||||
- `agents/`: specialized agents (`decision-advisor`, `explainability`, `kg-assistant`)
|
||||
- `hooks/hooks.json`: plugin hook configuration
|
||||
- `.claude-plugin/plugin.json`: Claude manifest
|
||||
- `.cursor-plugin/plugin.json`: Cursor manifest
|
||||
- `.codex-plugin/plugin.json`: Codex manifest
|
||||
- `*/marketplace.json`: local marketplace definitions
|
||||
|
||||
## Install and Use in Claude Code
|
||||
|
||||
### Local install (fastest)
|
||||
|
||||
From the repository root:
|
||||
|
||||
```bash
|
||||
claude --plugin-dir ./plugins
|
||||
```
|
||||
|
||||
If your Claude setup uses plugin commands in-session, use:
|
||||
|
||||
```bash
|
||||
/plugin install ./plugins
|
||||
```
|
||||
|
||||
### Install from a GitHub marketplace
|
||||
|
||||
Add a marketplace hosted in git:
|
||||
|
||||
```bash
|
||||
/plugin marketplace add <owner>/semantica
|
||||
```
|
||||
|
||||
Install Semantica from that marketplace:
|
||||
|
||||
```bash
|
||||
/plugin install semantica@<marketplace-name>
|
||||
```
|
||||
|
||||
### Verify in Claude
|
||||
|
||||
Run one of these in chat:
|
||||
|
||||
```text
|
||||
/semantica:decision list
|
||||
/semantica:explain decision <decision_id>
|
||||
```
|
||||
|
||||
If the plugin is installed correctly, Claude should recognize the `/semantica:*` skills.
|
||||
|
||||
## Install and Use in Codex
|
||||
|
||||
1. Ensure your repo marketplace exists at `.agents/plugins/marketplace.json`.
|
||||
2. Point the plugin entry `source.path` to `./plugins` (or your chosen plugin directory).
|
||||
3. Restart Codex and install from the marketplace UI.
|
||||
|
||||
Codex manifest used by this bundle:
|
||||
|
||||
- `.codex-plugin/plugin.json`
|
||||
|
||||
### Verify in Codex
|
||||
|
||||
After install, run a Semantica skill command in chat, for example:
|
||||
|
||||
```text
|
||||
/semantica:causal chain --subject <decision_id> --depth 3
|
||||
```
|
||||
|
||||
## Install and Use in Cursor
|
||||
|
||||
Cursor reads plugin metadata from:
|
||||
|
||||
- `.cursor-plugin/plugin.json`
|
||||
- `.cursor-plugin/marketplace.json`
|
||||
|
||||
If you maintain a team/community plugin repo, publish this `plugins/` directory and refresh/reinstall in Cursor Marketplace to pick up updates.
|
||||
|
||||
### Verify in Cursor
|
||||
|
||||
Try one of these commands:
|
||||
|
||||
```text
|
||||
/semantica:reason deductive "IF Person(x) THEN Mortal(x)"
|
||||
/semantica:visualize topology
|
||||
```
|
||||
|
||||
## First Commands to Try
|
||||
|
||||
After installing on any platform, these are good smoke tests:
|
||||
|
||||
1. `/semantica:decision record <category> "<scenario>" "<reasoning>" <outcome> <confidence>`
|
||||
2. `/semantica:decision list`
|
||||
3. `/semantica:causal chain --subject <decision_id> --depth 3`
|
||||
4. `/semantica:explain decision <decision_id>`
|
||||
5. `/semantica:validate graph`
|
||||
|
||||
## Community Notes
|
||||
|
||||
- Keep plugin name/version/keywords updated in each manifest before publishing.
|
||||
- Keep skill frontmatter consistent (`name` + `description`) for reliable discovery.
|
||||
- For open-source sharing, include this folder as-is so skills, agents, and hooks remain bundled.
|
||||
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"name": "semantica-local",
|
||||
"plugins": [
|
||||
{
|
||||
"name": "semantica",
|
||||
"description": "Semantica plugin for Claude: knowledge graph skills, reasoning, extraction, and visualization.",
|
||||
"source": "./",
|
||||
"category": "Productivity",
|
||||
"tags": [
|
||||
"knowledge-graph",
|
||||
"reasoning",
|
||||
"semantica"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"name": "semantica",
|
||||
"description": "Full-stack knowledge graph skills: semantic extraction, decision intelligence, context graphs, reasoning, explainability, ontology, provenance, deduplication, visualization, and multi-format export.",
|
||||
"version": "0.1.0",
|
||||
"author": {
|
||||
"name": "Semantica Contributors"
|
||||
},
|
||||
"homepage": "https://github.com/Hawksight-AI/semantica",
|
||||
"repository": "https://github.com/Hawksight-AI/semantica",
|
||||
"license": "MIT",
|
||||
"keywords": [
|
||||
"semantica",
|
||||
"knowledge graph",
|
||||
"context graphs",
|
||||
"decision intelligence",
|
||||
"explainability",
|
||||
"causal analysis",
|
||||
"provenance",
|
||||
"ontology",
|
||||
"graph analytics",
|
||||
"semantic extraction",
|
||||
"visualization",
|
||||
"reasoning",
|
||||
"extraction",
|
||||
"mcp"
|
||||
],
|
||||
"skills": "./skills",
|
||||
"agents": "./agents",
|
||||
"hooks": "./hooks/hooks.json"
|
||||
}
|
||||
@@ -0,0 +1,21 @@
|
||||
{
|
||||
"name": "semantica-local",
|
||||
"interface": {
|
||||
"displayName": "Semantica Local Plugins"
|
||||
},
|
||||
"plugins": [
|
||||
{
|
||||
"name": "semantica-codex",
|
||||
"description": "Semantica plugin for Codex: knowledge graph commands and analytics.",
|
||||
"source": {
|
||||
"source": "local",
|
||||
"path": "./"
|
||||
},
|
||||
"policy": {
|
||||
"installation": "AVAILABLE",
|
||||
"authentication": "ON_INSTALL"
|
||||
},
|
||||
"category": "Productivity"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"name": "semantica-codex",
|
||||
"description": "Semantica plugin for Codex: knowledge graph commands, export capabilities, and reasoning workflows.",
|
||||
"version": "0.1.0",
|
||||
"author": {
|
||||
"name": "Semantica Contributors"
|
||||
},
|
||||
"homepage": "https://github.com/Hawksight-AI/semantica",
|
||||
"repository": "https://github.com/Hawksight-AI/semantica",
|
||||
"license": "MIT",
|
||||
"keywords": [
|
||||
"semantica",
|
||||
"knowledge graph",
|
||||
"codex",
|
||||
"context graphs",
|
||||
"decision intelligence",
|
||||
"explainability",
|
||||
"causal analysis",
|
||||
"provenance",
|
||||
"ontology",
|
||||
"graph analytics",
|
||||
"semantic extraction",
|
||||
"visualization",
|
||||
"reasoning",
|
||||
"extraction",
|
||||
"mcp"
|
||||
],
|
||||
"skills": "./skills",
|
||||
"interface": {
|
||||
"displayName": "Semantica Codex Plugin",
|
||||
"shortDescription": "Knowledge graph skills for Semantica workflows",
|
||||
"category": "Productivity",
|
||||
"developerName": "Semantica Contributors"
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,18 @@
|
||||
{
|
||||
"name": "semantica-local",
|
||||
"owner": {
|
||||
"name": "Semantica Contributors"
|
||||
},
|
||||
"metadata": {
|
||||
"description": "Semantica plugin marketplace for Cursor.",
|
||||
"version": "0.1.0",
|
||||
"pluginRoot": "."
|
||||
},
|
||||
"plugins": [
|
||||
{
|
||||
"name": "semantica-cursor",
|
||||
"description": "Semantica plugin for Cursor: knowledge graph skills and analytics.",
|
||||
"source": "."
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"name": "semantica-cursor",
|
||||
"displayName": "Semantica Cursor Plugin",
|
||||
"description": "Semantica plugin for Cursor: knowledge graph skills, reasoning, extraction, and visualization.",
|
||||
"version": "0.1.0",
|
||||
"author": {
|
||||
"name": "Semantica Contributors"
|
||||
},
|
||||
"homepage": "https://github.com/Hawksight-AI/semantica",
|
||||
"repository": "https://github.com/Hawksight-AI/semantica",
|
||||
"license": "MIT",
|
||||
"keywords": [
|
||||
"semantica",
|
||||
"knowledge graph",
|
||||
"cursor",
|
||||
"context graphs",
|
||||
"decision intelligence",
|
||||
"explainability",
|
||||
"causal analysis",
|
||||
"provenance",
|
||||
"ontology",
|
||||
"graph analytics",
|
||||
"semantic extraction",
|
||||
"visualization",
|
||||
"reasoning",
|
||||
"extraction",
|
||||
"mcp"
|
||||
],
|
||||
"skills": "./skills",
|
||||
"agents": "./agents",
|
||||
"hooks": "./hooks/hooks.json"
|
||||
}
|
||||
@@ -0,0 +1,126 @@
|
||||
---
|
||||
name: decision-advisor
|
||||
description: Decision intelligence and causal reasoning specialist for Semantica. Proactively surfaces causal chains, precedent matches, policy violations, and influence scores when reviewing or recording decisions. Use for decision recording, precedent search, causal analysis, policy governance, and decision explainability workflows.
|
||||
---
|
||||
|
||||
You are a **Decision Intelligence Specialist** for the Semantica library. You focus on the full decision lifecycle: recording, querying, precedent search, causal analysis, policy compliance, and explainability.
|
||||
|
||||
## Your Domain
|
||||
|
||||
### Recording Decisions
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
decision_id = ctx.record_decision(
|
||||
category="loan_approval",
|
||||
scenario="First-time homebuyer, income 80k",
|
||||
reasoning="Good credit score, low DTI ratio",
|
||||
outcome="approved",
|
||||
confidence=0.95,
|
||||
entities=["customer_123", "property_456"],
|
||||
decision_maker="underwriting_agent",
|
||||
valid_from="2025-01-01",
|
||||
valid_until="2026-01-01",
|
||||
)
|
||||
```
|
||||
|
||||
### Querying and Precedent Search
|
||||
```python
|
||||
# Natural language query with multi-hop reasoning
|
||||
decisions = ctx.query_decisions(query, max_hops=3, use_hybrid_search=True)
|
||||
|
||||
# Hybrid precedent search — semantic + structural + vector
|
||||
precedents = ctx.find_precedents(scenario, category, limit=10, use_hybrid_search=True)
|
||||
|
||||
# Advanced KG-enhanced search
|
||||
advanced = ctx.find_precedents_advanced(
|
||||
scenario, use_kg_features=True,
|
||||
similarity_weights={"semantic": 0.5, "structural": 0.3, "vector": 0.2}
|
||||
)
|
||||
|
||||
# Category/entity/time filters via DecisionQuery
|
||||
from semantica.context.decision_query import DecisionQuery
|
||||
dq = DecisionQuery(graph_store=ctx.graph_store)
|
||||
by_cat = dq.find_by_category(category, limit=100)
|
||||
by_ent = dq.find_by_entity(entity_id, limit=100)
|
||||
by_time = dq.find_by_time_range(start, end, limit=100)
|
||||
multi_hop = dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
|
||||
```
|
||||
|
||||
### Causal Analysis
|
||||
```python
|
||||
from semantica.context.causal_analyzer import CausalChainAnalyzer
|
||||
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.graph_store)
|
||||
|
||||
# Upstream (what caused this?) or downstream (what did this cause?)
|
||||
chain = analyzer.get_causal_chain(decision_id, direction="upstream", max_depth=10)
|
||||
|
||||
# Root causes
|
||||
roots = analyzer.find_root_causes(decision_id)
|
||||
|
||||
# Downstream impact
|
||||
influenced = analyzer.get_influenced_decisions(decision_id)
|
||||
score = analyzer.get_causal_impact_score(decision_id)
|
||||
|
||||
# Full network analysis
|
||||
network = analyzer.analyze_causal_network()
|
||||
loops = analyzer.find_causal_loops()
|
||||
|
||||
# Historical chain at a specific time
|
||||
historical = analyzer.trace_at_time(decision_id, at_time="2024-06-01", direction="upstream")
|
||||
```
|
||||
|
||||
### Policy Compliance
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
engine = ctx.get_policy_engine()
|
||||
|
||||
# Check compliance
|
||||
compliant = engine.check_compliance(decision, policy_id)
|
||||
|
||||
# Get all applicable policies
|
||||
applicable = engine.get_applicable_policies(category, entities)
|
||||
|
||||
# Analyze impact of policy changes
|
||||
impact = engine.analyze_policy_impact(policy_id, proposed_rules)
|
||||
|
||||
# Record exceptions
|
||||
exception_id = engine.record_exception(decision_id, policy_id, reason, approver, justification)
|
||||
```
|
||||
|
||||
### Explainability
|
||||
```python
|
||||
# Full explainability trace
|
||||
explainability = ctx.trace_decision_explainability(decision_id)
|
||||
|
||||
# Influence analysis with KG algorithms
|
||||
influence = ctx.analyze_decision_influence(decision_id, max_depth=3)
|
||||
predictions = ctx.predict_decision_relationships(decision_id, top_k=5)
|
||||
```
|
||||
|
||||
## Critical Invariants
|
||||
|
||||
- **Node type duality**: `record_decision()` → `"decision"` (lowercase); `add_decision()` → `"Decision"` (capitalized). Always search for both when querying.
|
||||
- **No `DecisionQuery.query()`** — use `find_by_entity`, `find_by_category`, `find_by_time_range`, or `multi_hop_reasoning`.
|
||||
- **`CausalChainAnalyzer` takes `graph_store=`** — no `trace_causes()`, use `get_causal_chain(direction="upstream")`.
|
||||
- **`find_precedents(as_of=<date>)`** — supports temporal precedent search.
|
||||
- **`graph_store` format** — both `DecisionQuery` and `CausalChainAnalyzer` need `{"records": [...]}` shape.
|
||||
|
||||
## Behavior
|
||||
|
||||
When a user shares a decision or asks about decision-making, **proactively**:
|
||||
1. **Trace root causes** via `get_causal_chain(direction="upstream")`
|
||||
2. **Check policy compliance** via `get_applicable_policies()` + `check_compliance()`
|
||||
3. **Find precedents** via `find_precedents_advanced(use_kg_features=True)`
|
||||
4. **Score influence** via `get_causal_impact_score()`
|
||||
5. **Detect loops** — flag if this decision closes a causal loop
|
||||
|
||||
When reviewing Semantica decision code:
|
||||
- Check method names against the list above
|
||||
- Flag queries that only check one of `"decision"` / `"Decision"`
|
||||
- Flag missing `entities=[]` arg (defaults to None, may miss entity-based precedent search)
|
||||
|
||||
Show causal chains as Mermaid `graph TD` blocks. Keep tables concise. Lead with decision status and compliance, then causal context, then influence score.
|
||||
@@ -0,0 +1,129 @@
|
||||
---
|
||||
name: explainability
|
||||
description: Reasoning transparency and auditability specialist for Semantica. Answers "why does the graph believe X?", "how was Y inferred?", and "is this decision explainable?" with full evidence chains. Produces audit-ready explanation reports using ExplanationGenerator, AgentContext.trace_decision_explainability, and ContextGraph.trace_decision_chain.
|
||||
---
|
||||
|
||||
You are a **Reasoning Transparency and Explainability Specialist** for the Semantica library. You answer "why?" questions about graph facts, inferences, and decisions with complete, auditable evidence chains.
|
||||
|
||||
## Your Domain
|
||||
|
||||
### Explanation Generation
|
||||
```python
|
||||
from semantica.reasoning.explanation_generator import ExplanationGenerator
|
||||
|
||||
gen = ExplanationGenerator()
|
||||
|
||||
# generate_explanation(reasoning) → Explanation object
|
||||
# reasoning can be any reasoning object, dict, or string context
|
||||
explanation = gen.generate_explanation(reasoning=reasoning_input)
|
||||
# explanation.summary, .confidence, .evidence
|
||||
|
||||
# show_reasoning_path(reasoning) → ReasoningPath object
|
||||
path = gen.show_reasoning_path(reasoning=reasoning_input)
|
||||
# path.steps: [Step(type, description, confidence)]
|
||||
# path.conclusion
|
||||
|
||||
# justify_conclusion(conclusion, reasoning_path) → Justification object
|
||||
justification = gen.justify_conclusion(
|
||||
conclusion=conclusion,
|
||||
reasoning_path=path,
|
||||
)
|
||||
# justification.is_justified, .confidence, .supporting_steps, .opposing_factors
|
||||
```
|
||||
|
||||
### Decision Explainability
|
||||
```python
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
|
||||
|
||||
# Full decision explainability trace
|
||||
explainability = ctx.trace_decision_explainability(decision_id)
|
||||
# Returns: reasoning_steps, evidence, causal_context, compliance_status
|
||||
|
||||
# Causal chain from ContextGraph
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
chain = graph.trace_decision_chain(decision_id, max_steps=5)
|
||||
causality = graph.trace_decision_causality(decision_id, max_depth=5)
|
||||
|
||||
# Influence analysis
|
||||
influence = ctx.analyze_decision_influence(decision_id, max_depth=3)
|
||||
```
|
||||
|
||||
### Provenance Tracing
|
||||
```python
|
||||
from semantica.kg.kg_provenance import GraphBuilderWithProvenance
|
||||
from semantica.context.context_provenance import ContextManagerWithProvenance
|
||||
from semantica.reasoning.reasoning_provenance import ReasoningEngineWithProvenance
|
||||
from semantica.semantic_extract.semantic_extract_provenance import (
|
||||
NERExtractorWithProvenance,
|
||||
RelationExtractorWithProvenance,
|
||||
EventDetectorWithProvenance,
|
||||
)
|
||||
```
|
||||
|
||||
Each provenance-enabled class wraps the base class and adds `.get_provenance_summary()` to retrieve lineage records.
|
||||
|
||||
### Reasoning Chains
|
||||
```python
|
||||
from semantica.reasoning.deductive_reasoner import DeductiveReasoner
|
||||
|
||||
reasoner = DeductiveReasoner()
|
||||
proof = reasoner.prove_theorem(theorem)
|
||||
# proof.steps, proof.is_valid, proof.confidence
|
||||
|
||||
validation = reasoner.validate_argument(argument)
|
||||
```
|
||||
|
||||
## Explanation Types You Produce
|
||||
|
||||
**1. Decision explanations** — full trace: reasoning steps → causal antecedents → policy compliance → evidence
|
||||
**2. Reasoning path explanations** — step-by-step rule chain with variable bindings
|
||||
**3. Conclusion justifications** — why a conclusion follows from premises, with opposing factors noted
|
||||
**4. Path explanations** — how two nodes are semantically connected via the graph
|
||||
**5. Compliance explanations** — which rules passed/failed and why, with remediation advice
|
||||
|
||||
## Audit Report Format
|
||||
|
||||
When asked for an audit report:
|
||||
```
|
||||
Explainability Audit Report
|
||||
════════════════════════════
|
||||
Generated: <ISO timestamp>
|
||||
Scope: <N decisions / K facts>
|
||||
|
||||
── Decision Explanations ─────────────────
|
||||
Decision <id>: EXPLAINED ✓ (confidence: 0.91)
|
||||
Steps: 3 | Evidence: 2 items | Provenance: complete
|
||||
Causal antecedents: <n>
|
||||
Policy compliance: 2/2 ✓
|
||||
|
||||
Decision <id>: PARTIALLY EXPLAINED ⚠
|
||||
Missing: provenance gap on reasoning step 2
|
||||
Low confidence: 0.43 on step 3
|
||||
|
||||
── Summary ──────────────────────────────
|
||||
Total: N decisions analyzed
|
||||
Fully explained: M (X%)
|
||||
Partially explained: K (Y%)
|
||||
Unexplained (gaps): J (Z%)
|
||||
|
||||
Provenance gaps: J nodes missing lineage
|
||||
Low-confidence facts (<0.7): L
|
||||
Circular reasoning detected: YES / NO
|
||||
```
|
||||
|
||||
## Behavior
|
||||
|
||||
When asked "why does the graph believe X?":
|
||||
1. Start with `ExplanationGenerator.generate_explanation()` for the natural-language summary
|
||||
2. Supplement with `show_reasoning_path()` for the step trace
|
||||
3. Cross-check with provenance wrappers for source lineage
|
||||
4. Flag any provenance gaps
|
||||
|
||||
When a decision explanation is requested:
|
||||
1. Always call `ctx.trace_decision_explainability(decision_id)` first
|
||||
2. Then supplement with `trace_decision_chain()` and `trace_decision_causality()`
|
||||
3. Check policy compliance via `get_applicable_policies()` + `check_compliance()`
|
||||
|
||||
Lead with the direct answer, then the evidence chain. Use Mermaid `sequenceDiagram` for multi-step reasoning chains. Use nested bullets for evidence items.
|
||||
@@ -0,0 +1,73 @@
|
||||
---
|
||||
name: kg-assistant
|
||||
description: General-purpose KG-aware assistant for any Semantica task. Knows all module APIs, exact method signatures, node-type conventions, and current graph schema. Use for broad questions, multi-module workflows, code review, or any task spanning multiple Semantica modules.
|
||||
---
|
||||
|
||||
You are a knowledge graph expert assistant for the **Semantica** library — a full-stack Python library for knowledge graphs, semantic extraction, decision intelligence, reasoning, and context management.
|
||||
|
||||
## Module Overview
|
||||
|
||||
### Decision Intelligence (semantica.context)
|
||||
- `AgentContext` — high-level interface: `store()`, `retrieve()`, `record_decision()`, `query_decisions()`, `find_precedents()`, `find_precedents_advanced()`, `analyze_decision_influence()`, `predict_decision_relationships()`, `trace_decision_explainability()`, `get_context_insights()`, `multi_hop_context_query()`, `expand_query()`, `query_with_reasoning()`, `get_causal_chain()`, `capture_cross_system_inputs()`, `get_policy_engine()`
|
||||
- `ContextGraph` — in-memory graph: `add_node()`, `add_edge()`, `record_decision()`, `find_precedents_by_scenario()`, `find_similar_decisions()`, `analyze_decision_influence()`, `analyze_decision_impact()`, `get_causal_chain()`, `trace_decision_causality()`, `trace_decision_chain()`, `enforce_decision_policy()`, `check_decision_rules()`, `get_decision_insights()`, `get_decision_summary()`, `analyze_graph_with_kg()`, `get_node_centrality()`, `get_node_importance()`, `state_at()`, `query()`
|
||||
- `DecisionQuery` — `find_by_category()`, `find_by_entity()`, `find_by_time_range()`, `find_precedents_hybrid()`, `find_similar_exceptions()`, `multi_hop_reasoning()`, `predict_decision_relationships()`, `analyze_decision_influence()`, `trace_decision_path()`
|
||||
- `CausalChainAnalyzer` — `get_causal_chain(decision_id, direction, max_depth)`, `find_root_causes()`, `get_influenced_decisions()`, `get_causal_impact_score()`, `get_precedent_chain()`, `analyze_causal_network()`, `find_causal_loops()`, `trace_at_time(event_id, at_time, direction, max_depth)`
|
||||
- `PolicyEngine` — `add_policy()`, `check_compliance()`, `get_applicable_policies()`, `update_policy()`, `record_exception()`, `analyze_policy_impact()`, `get_affected_decisions()`, `get_policy_history()`
|
||||
- `DecisionRecorder` — `record_decision()`, `link_entities()`, `link_precedents()`, `apply_policies()`, `record_exception()`, `capture_cross_system_context()`, `record_approval_chain()`
|
||||
|
||||
### Knowledge Graph (semantica.kg)
|
||||
- `GraphAnalyzer` — `analyze_graph()`, `calculate_centrality(graph, centrality_type)`, `detect_communities(graph, algorithm)`, `analyze_temporal_evolution()`, `compute_metrics()`, `analyze_connectivity()`
|
||||
- `CentralityCalculator` — `calculate_degree_centrality()`, `calculate_betweenness_centrality()`, `calculate_closeness_centrality()`, `calculate_eigenvector_centrality()`, `calculate_pagerank()`, `calculate_all_centrality()`
|
||||
- `CommunityDetector` — `detect_communities()`, `detect_communities_louvain()`, `detect_communities_leiden()`, `detect_communities_label_propagation()`, `detect_overlapping_communities()`, `analyze_community_structure()`, `calculate_community_metrics()`
|
||||
- `NodeEmbedder` — `compute_embeddings(graph_store, node_labels, relationship_types)`, `find_similar_nodes(graph_store, node_id, top_k)`, `store_embeddings()`
|
||||
- `SimilarityCalculator` — `cosine_similarity(vector1, vector2)`, `euclidean_distance()`, `manhattan_distance()`, `correlation_similarity()`, `find_most_similar()`, `batch_similarity()`, `pairwise_similarity()`
|
||||
- `LinkPredictor` — `score_link(graph_store, node_id1, node_id2, method=)`, `predict_top_links()`, `predict_links()`, `batch_score_links()`
|
||||
- `PathFinder` — `find_k_shortest_paths()`, `dijkstra_shortest_path()`, `bfs_shortest_path()`, `a_star_search()`, `all_shortest_paths()`, `path_length()`
|
||||
|
||||
### Reasoning (semantica.reasoning)
|
||||
- `DeductiveReasoner` — `add_facts()`, `apply_logic(premises)`, `prove_theorem()`, `validate_argument()`
|
||||
- `AbductiveReasoner` — `add_knowledge()`, `generate_hypotheses(observations)`, `find_explanations()`, `get_best_explanation()`, `rank_hypotheses()`
|
||||
- `ExplanationGenerator` — `generate_explanation(reasoning)`, `show_reasoning_path(reasoning)`, `justify_conclusion(conclusion, reasoning_path)`
|
||||
|
||||
### Extraction (semantica.semantic_extract)
|
||||
- `NamedEntityRecognizer`, `RelationExtractor`, `EventDetector`, `CoreferenceResolver`, `TripletExtractor`, `ExtractionValidator`
|
||||
- **Always** call `_result_cache.clear()` before any extraction run
|
||||
|
||||
### Pipeline (semantica.pipeline)
|
||||
- `PipelineBuilder` — `add_step()`, `connect_steps()`, `validate_pipeline()`, `build()`
|
||||
- `PipelineValidator` — `validate(pipeline)` → `ValidationResult(valid, errors, warnings)` — **does NOT raise**
|
||||
- `FailureHandler` — `handle_failure(error, policy, retry_count)` → `RecoveryAction`
|
||||
|
||||
### Export (semantica.export)
|
||||
- `RDFExporter.export_to_rdf(data, format='turtle')` → **returns a string**, no `output_path`
|
||||
- Format aliases: `"ttl"` → `"turtle"`, `"nt"`, `"xml"`, `"json-ld"`
|
||||
- Other exporters: `OWLExporter`, `CSVExporter`, `JSONExporter`, `ParquetExporter`, `ArrowExporter`, `VectorExporter`, `YAMLSchemaExporter`, `ArangoAQLExporter`, `LPGExporter`, `ReportGenerator`
|
||||
|
||||
### Deduplication (semantica.deduplication)
|
||||
- `DuplicateDetector.detect_duplicates(entities, threshold=)` — use **directly**, never via `methods.py` (infinite recursion bug)
|
||||
|
||||
## Critical API Invariants
|
||||
|
||||
| Area | Correct |
|
||||
|------|---------|
|
||||
| Decision node type | `record_decision()` → stored as `"decision"` (lowercase); `add_decision()` → `"Decision"` (capitalized). Query both. |
|
||||
| `AgentContext.record_decision` | Returns a `decision_id: str`. Args: `category, scenario, reasoning, outcome, confidence, entities, decision_maker, valid_from, valid_until` |
|
||||
| `CausalChainAnalyzer` | Takes `graph_store=` kwarg. No `trace_causes()` — use `get_causal_chain(direction="upstream")` |
|
||||
| `ExplanationGenerator` | No `explain_decision/fact/inference` — use `generate_explanation(reasoning)`, `show_reasoning_path(reasoning)`, `justify_conclusion(conclusion, path)` |
|
||||
| `DecisionQuery` | No `.query()` — use `find_by_entity`, `find_by_category`, `find_by_time_range`, `multi_hop_reasoning` |
|
||||
| `SimilarityCalculator` | `cosine_similarity(vector1, vector2)` — two required positional args |
|
||||
| `NodeEmbedder` | `compute_embeddings(graph_store, node_labels, relationship_types)` — all three positional, all required |
|
||||
| `LinkPredictor` | `score_link(graph_store, node_id1, node_id2, method=)` |
|
||||
| `PipelineValidator` | `validate(pipeline)` returns `ValidationResult` — never raises |
|
||||
| `RDFExporter` | `export_to_rdf(data, format='turtle')` returns a string |
|
||||
| Cache | `_result_cache.clear()` before every extraction |
|
||||
| Graph store format | `DecisionQuery` and `CausalChainAnalyzer` need `{"records": [...]}` from graph store |
|
||||
|
||||
## How to Help
|
||||
|
||||
1. **Answer questions** with copy-paste-ready code that uses the correct method names
|
||||
2. **Review Semantica code** — check against the invariants table above before suggesting anything
|
||||
3. **Suggest the right skill** — map user intent to `/semantica:*` skills
|
||||
4. **Debug errors** — common mistakes: wrong method name, wrong arg order, missing `_result_cache.clear()`, querying only one of `"decision"`/`"Decision"` types
|
||||
|
||||
Keep responses code-first. Show the full import path in every example.
|
||||
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"hooks": {
|
||||
"PostToolUse": [
|
||||
{"matcher": "Write|Edit", "hooks": [{"type": "command", "command": "FILE=$(jq -r .tool_input.file_path 2>/dev/null); if echo $FILE | grep -qE semantica/; then python -c 'import ast,sys; ast.parse(open(sys.argv[1]).read())' $FILE 2>&1; fi"}]},
|
||||
|
||||
{"matcher": "Write|Edit", "hooks": [{"type": "command", "command": "echo PostToolUse provenance check"}]}
|
||||
],
|
||||
"PreToolUse": [
|
||||
{"matcher": "Bash", "hooks": [{"type": "command", "command": "CMD=$(jq -r .tool_input.command 2>/dev/null); if echo $CMD | grep -q deduplication/methods; then echo WARNING: use DuplicateDetector directly >&2; fi"}]}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,74 @@
|
||||
---
|
||||
name: causal
|
||||
description: Analyze cause-and-effect relationships in the Semantica knowledge graph — causal chains, interventions, counterfactuals, and causal influence scores.
|
||||
---
|
||||
|
||||
# /semantica:causal
|
||||
|
||||
Analyze causal relationships and infer impacts. Usage: `/semantica:causal <task> [args]`
|
||||
|
||||
`$ARGUMENTS` = task + optional target entity, filter, or intervention.
|
||||
|
||||
---
|
||||
|
||||
## `chain [--subject <node>] [--depth N]`
|
||||
|
||||
Build and inspect causal chains for a subject or category.
|
||||
|
||||
```python
|
||||
from semantica.context.causal_analyzer import CausalChainAnalyzer
|
||||
from semantica.context import AgentContext
|
||||
|
||||
# Option 1: Use an existing AgentContext decision backend
|
||||
chain = ctx.get_causal_chain(
|
||||
decision_id=decision_id,
|
||||
direction="upstream",
|
||||
max_depth=depth,
|
||||
)
|
||||
|
||||
# Option 2: Use CausalChainAnalyzer directly
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
|
||||
downstream = analyzer.get_causal_chain(
|
||||
decision_id=decision_id,
|
||||
direction="downstream",
|
||||
max_depth=depth,
|
||||
)
|
||||
```
|
||||
|
||||
Output: chain steps, cause strength, effect reach, and summary graph.
|
||||
|
||||
---
|
||||
|
||||
## `intervene <node> <action> [--scenario <json>]`
|
||||
|
||||
Analyze decision impact and influenced decisions (current causal API).
|
||||
|
||||
```python
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
|
||||
impact_score = analyzer.get_causal_impact_score(decision_id=decision_id)
|
||||
influenced = analyzer.get_influenced_decisions(
|
||||
decision_id=decision_id,
|
||||
max_depth=depth,
|
||||
)
|
||||
```
|
||||
|
||||
Return: impact score, influenced decisions, and downstream scope.
|
||||
|
||||
---
|
||||
|
||||
## `counterfactual <fact> [--weight N]`
|
||||
|
||||
Trace root causes and temporal causal paths.
|
||||
|
||||
```python
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.knowledge_graph)
|
||||
roots = analyzer.find_root_causes(decision_id=decision_id, max_depth=depth)
|
||||
historical_chain = analyzer.trace_at_time(
|
||||
event_id=decision_id,
|
||||
at_time="2026-01-01T00:00:00Z",
|
||||
direction="upstream",
|
||||
max_depth=depth,
|
||||
)
|
||||
```
|
||||
|
||||
Output: root decision lineage and time-bounded causal context.
|
||||
@@ -0,0 +1,38 @@
|
||||
---
|
||||
name: change
|
||||
description: Track and inspect graph changes, diffs, temporal updates, and the impact of new data on Semantica knowledge graphs.
|
||||
---
|
||||
|
||||
# /semantica:change
|
||||
|
||||
Inspect changes over time and evaluate updates. Usage: `/semantica:change <task> [args]`
|
||||
|
||||
`$ARGUMENTS` = task + optional node, time window, or filter.
|
||||
|
||||
---
|
||||
|
||||
## `diff [--from <ts>] [--to <ts>] [--node <id>]`
|
||||
|
||||
Compute graph diffs between two snapshots.
|
||||
|
||||
```python
|
||||
from semantica.provenance.change_tracker import ChangeTracker
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
tracker = ChangeTracker()
|
||||
diff = tracker.compute_diff(from_ts=from_ts, to_ts=to_ts, node_id=node_id)
|
||||
```
|
||||
|
||||
Output: added/removed nodes and edges, attribute changes, and impact summary.
|
||||
|
||||
---
|
||||
|
||||
## `history <node_id> [--limit N]`
|
||||
|
||||
Show the change history for a node or relationship.
|
||||
|
||||
```python
|
||||
history = tracker.get_node_history(node_id=node_id, limit=limit)
|
||||
```
|
||||
|
||||
Return: revisions, timestamps, authors, and summary comments.
|
||||
@@ -0,0 +1,197 @@
|
||||
---
|
||||
name: decision
|
||||
description: Full decision lifecycle in Semantica — record, query, find precedents (hybrid/advanced), analyze influence, explain, insights dashboard, list, and record exceptions. Uses AgentContext, ContextGraph, DecisionQuery, CausalChainAnalyzer, DecisionRecorder.
|
||||
---
|
||||
|
||||
# /semantica:decision
|
||||
|
||||
Full decision lifecycle management. Usage: `/semantica:decision <sub-command> [args]`
|
||||
|
||||
---
|
||||
|
||||
## `record <category> "<scenario>" "<reasoning>" <outcome> <confidence>`
|
||||
|
||||
Record a decision with full context.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
decision_id = ctx.record_decision(
|
||||
category=category, # "loan_approval", "deployment", "hiring"
|
||||
scenario=scenario, # natural-language situation description
|
||||
reasoning=reasoning, # why this decision was made
|
||||
outcome=outcome, # "approved", "rejected", "deferred"
|
||||
confidence=float(confidence),
|
||||
entities=entities or [],
|
||||
decision_maker="ai_agent",
|
||||
valid_from=valid_from, # optional ISO date string
|
||||
valid_until=valid_until,
|
||||
)
|
||||
```
|
||||
|
||||
Output: `Decision <decision_id> recorded | <category> | <outcome> (conf: 0.95)`
|
||||
|
||||
---
|
||||
|
||||
## `query "<question>" [--hops N] [--hybrid]`
|
||||
|
||||
Query decisions using natural language with multi-hop graph traversal.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
|
||||
results = ctx.query_decisions(
|
||||
query=question,
|
||||
max_hops=int(hops) if hops else 3,
|
||||
include_context=True,
|
||||
use_hybrid_search="--hybrid" in args,
|
||||
)
|
||||
```
|
||||
|
||||
For structured lookups use `DecisionQuery`:
|
||||
```python
|
||||
from semantica.context.decision_query import DecisionQuery
|
||||
dq = DecisionQuery(graph_store=ctx.graph_store)
|
||||
# dq.find_by_category(category, limit=100)
|
||||
# dq.find_by_entity(entity_id, limit=100)
|
||||
# dq.find_by_time_range(start, end, limit=100)
|
||||
# dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
|
||||
# dq.trace_decision_path(decision_id, relationship_types)
|
||||
# dq.analyze_decision_influence(decision_id, max_depth=3)
|
||||
```
|
||||
|
||||
Return: `| ID | Category | Scenario | Outcome | Confidence | Timestamp |`
|
||||
|
||||
---
|
||||
|
||||
## `precedents "<scenario>" [--category <cat>] [--advanced] [--hops N] [--as-of <date>]`
|
||||
|
||||
Find similar past decisions using hybrid semantic + structural + vector search.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, kg_algorithms=True, vector_store_features=True)
|
||||
|
||||
if "--advanced" in args:
|
||||
precedents = ctx.find_precedents_advanced(
|
||||
scenario=scenario, category=category, limit=10,
|
||||
use_kg_features=True,
|
||||
similarity_weights={"semantic": 0.5, "structural": 0.3, "vector": 0.2},
|
||||
)
|
||||
else:
|
||||
precedents = ctx.find_precedents(
|
||||
scenario=scenario, category=category, limit=10,
|
||||
use_hybrid_search=True,
|
||||
max_hops=int(hops) if hops else 3,
|
||||
include_context=True,
|
||||
include_superseded=False,
|
||||
as_of=as_of_date or None, # temporal filter: only precedents that existed as_of this date
|
||||
)
|
||||
```
|
||||
|
||||
Return ranked: `| Rank | ID | Scenario | Outcome | Confidence | Similarity | Date |`
|
||||
|
||||
---
|
||||
|
||||
## `influence <decision_id> [--depth N]`
|
||||
|
||||
Analyze how a decision influences others across the graph.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True, kg_algorithms=True)
|
||||
influence = ctx.analyze_decision_influence(decision_id, max_depth=int(depth) if depth else 3)
|
||||
predictions = ctx.predict_decision_relationships(decision_id, top_k=5)
|
||||
```
|
||||
|
||||
Output: Influence score + influenced decisions table + predicted new relationships.
|
||||
|
||||
---
|
||||
|
||||
## `explain <decision_id>`
|
||||
|
||||
Full explainability trace — reasoning steps, causal antecedents, policy compliance.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
explainability = ctx.trace_decision_explainability(decision_id)
|
||||
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
chain = graph.trace_decision_chain(decision_id, max_steps=5)
|
||||
causality = graph.trace_decision_causality(decision_id, max_depth=5)
|
||||
```
|
||||
|
||||
Output: Reasoning steps, causal antecedents, evidence items, policy compliance status.
|
||||
|
||||
---
|
||||
|
||||
## `insights`
|
||||
|
||||
Comprehensive analytics across all tracked decisions.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph, AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
|
||||
insights = graph.get_decision_insights()
|
||||
summary = graph.get_decision_summary()
|
||||
context_insights = ctx.get_context_insights()
|
||||
```
|
||||
|
||||
Output: Total count, category breakdown, outcome distribution, avg confidence, top influential.
|
||||
|
||||
---
|
||||
|
||||
## `list [--category <cat>] [--entity <id>] [--from <date>] [--to <date>]`
|
||||
|
||||
```python
|
||||
from semantica.context.decision_query import DecisionQuery
|
||||
from semantica.context import AgentContext
|
||||
from datetime import datetime
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
dq = DecisionQuery(graph_store=ctx.graph_store)
|
||||
|
||||
if category: decisions = dq.find_by_category(category, limit=100)
|
||||
elif entity: decisions = dq.find_by_entity(entity, limit=100)
|
||||
elif from_date: decisions = dq.find_by_time_range(
|
||||
start=datetime.fromisoformat(from_date),
|
||||
end=datetime.fromisoformat(to_date or "2099-12-31"),
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| ID | Category | Scenario | Outcome | Confidence | Maker | Timestamp |`
|
||||
|
||||
---
|
||||
|
||||
## `exception <decision_id> <policy_id> "<reason>" --approver <name>`
|
||||
|
||||
Record a formal policy exception.
|
||||
|
||||
```python
|
||||
from semantica.context.decision_recorder import DecisionRecorder
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
recorder = DecisionRecorder(graph_store=ctx.graph_store)
|
||||
|
||||
exception_id = recorder.record_exception(
|
||||
decision_id=decision_id, policy_id=policy_id,
|
||||
reason=reason, approver=approver,
|
||||
approval_method="manual_override", justification=reason,
|
||||
)
|
||||
|
||||
from semantica.context.decision_query import DecisionQuery
|
||||
dq = DecisionQuery(graph_store=ctx.graph_store)
|
||||
similar = dq.find_similar_exceptions(exception_reason=reason, limit=5)
|
||||
```
|
||||
|
||||
Output: `Exception recorded: <exception_id>` + similar past exceptions for audit context.
|
||||
@@ -0,0 +1,41 @@
|
||||
---
|
||||
name: deduplicate
|
||||
description: Detect duplicate entities, duplicate groups, and relationship duplicates in Semantica using fuzzy matching, schema heuristics, and graph similarity.
|
||||
---
|
||||
|
||||
# /semantica:deduplicate
|
||||
|
||||
Remove duplicates from the knowledge graph. Usage: `/semantica:deduplicate <strategy> [args]`
|
||||
|
||||
`$ARGUMENTS` = deduplication strategy + optional entity or threshold.
|
||||
|
||||
---
|
||||
|
||||
## `entities [--threshold <score>] [--field <name>]`
|
||||
|
||||
Detect duplicate entities and group them by similarity.
|
||||
|
||||
```python
|
||||
from semantica.deduplication import DuplicateDetector
|
||||
|
||||
finder = DuplicateDetector()
|
||||
candidates = finder.detect_duplicates(entities, threshold=threshold)
|
||||
groups = finder.detect_duplicate_groups(entities, threshold=threshold)
|
||||
```
|
||||
|
||||
Output: duplicate candidate list, duplicate groups, and representative merge recommendations.
|
||||
|
||||
---
|
||||
|
||||
## `relations [--similarity <score>]`
|
||||
|
||||
Detect duplicate relationships and normalize edge representations.
|
||||
|
||||
```python
|
||||
from semantica.deduplication import DuplicateDetector
|
||||
|
||||
finder = DuplicateDetector()
|
||||
relations = finder.detect_duplicates(relation_list, threshold=similarity)
|
||||
```
|
||||
|
||||
Result: duplicate relation candidates, normalized relationship groups, and cleanup summary.
|
||||
@@ -0,0 +1,230 @@
|
||||
---
|
||||
name: embed
|
||||
description: Generate, inspect, and use node/text embeddings in Semantica — compute Node2Vec embeddings, find similar nodes, score link predictions, batch similarity, and pairwise similarity. Uses NodeEmbedder, SimilarityCalculator, LinkPredictor, and AgentContext. Sub-commands: compute, similar, similarity, predict-link, top-links, batch, pairwise.
|
||||
---
|
||||
|
||||
# /semantica:embed
|
||||
|
||||
Generate and inspect graph embeddings. Usage: `/semantica:embed <sub-command> [args]`
|
||||
|
||||
`$ARGUMENTS` = sub-command + arguments.
|
||||
|
||||
---
|
||||
|
||||
## `compute [--labels <t1,t2>] [--rels <r1,r2>] [--dim N] [--walks N]`
|
||||
|
||||
Generate Node2Vec embeddings for graph nodes.
|
||||
|
||||
```python
|
||||
from semantica.kg.node_embeddings import NodeEmbedder
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
embedder = NodeEmbedder()
|
||||
|
||||
node_labels = labels_arg.split(",") if labels_arg else graph.get_all_node_types()
|
||||
rel_types = rels_arg.split(",") if rels_arg else []
|
||||
|
||||
# All positional args required: graph_store, node_labels, relationship_types
|
||||
embeddings = embedder.compute_embeddings(
|
||||
graph_store=graph,
|
||||
node_labels=node_labels,
|
||||
relationship_types=rel_types,
|
||||
embedding_dimension=int(dim_arg) if dim_arg else None,
|
||||
num_walks=int(walks_arg) if walks_arg else None,
|
||||
)
|
||||
|
||||
# Store embeddings back on nodes
|
||||
embedder.store_embeddings(
|
||||
graph_store=graph,
|
||||
embeddings=embeddings,
|
||||
property_name="node2vec_embedding",
|
||||
)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Embeddings computed and stored.
|
||||
Nodes embedded: N
|
||||
Embedding dim: 128
|
||||
Node types covered: [type1, type2, ...]
|
||||
|
||||
Sample (first 5 nodes):
|
||||
| Node | Type | Embedding dim | Stored |
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `similar <node_id> [--top N]`
|
||||
|
||||
Find the most similar nodes to a given node in embedding space.
|
||||
|
||||
```python
|
||||
from semantica.kg.node_embeddings import NodeEmbedder
|
||||
from semantica.context import ContextGraph, AgentContext
|
||||
|
||||
graph = ContextGraph()
|
||||
embedder = NodeEmbedder()
|
||||
|
||||
# NodeEmbedder.find_similar_nodes uses the stored node2vec_embedding property
|
||||
neighbors = embedder.find_similar_nodes(
|
||||
graph_store=graph,
|
||||
node_id=node_id,
|
||||
top_k=int(top_n) if top_n else 10,
|
||||
embedding_property="node2vec_embedding",
|
||||
)
|
||||
|
||||
# Also use AgentContext for richer similarity with metadata
|
||||
ctx = AgentContext(kg_algorithms=True)
|
||||
entity_similar = ctx.find_similar_entities(
|
||||
entity_id=node_id,
|
||||
similarity_type="content", # or "structural", "hybrid"
|
||||
top_k=int(top_n) if top_n else 10,
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| Rank | Node ID | Type | Cosine Similarity | Shared Properties |`
|
||||
|
||||
---
|
||||
|
||||
## `similarity <n1> <n2> [--method cosine|euclidean|manhattan|correlation]`
|
||||
|
||||
Compute pairwise similarity between two nodes.
|
||||
|
||||
```python
|
||||
from semantica.kg.similarity_calculator import SimilarityCalculator
|
||||
from semantica.kg.node_embeddings import NodeEmbedder
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
embedder = NodeEmbedder()
|
||||
calc = SimilarityCalculator()
|
||||
|
||||
# Get embeddings for both nodes
|
||||
v1 = embedder.find_similar_nodes(graph, n1, top_k=1) # placeholder — use stored embedding
|
||||
v2 = embedder.find_similar_nodes(graph, n2, top_k=1)
|
||||
|
||||
method = method_arg or "cosine"
|
||||
if method == "cosine":
|
||||
score = calc.cosine_similarity(vector1=v1, vector2=v2)
|
||||
elif method == "euclidean":
|
||||
score = calc.euclidean_distance(v1, v2)
|
||||
elif method == "manhattan":
|
||||
score = calc.manhattan_distance(v1, v2)
|
||||
elif method == "correlation":
|
||||
score = calc.correlation_similarity(v1, v2)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Similarity: "<n1>" ↔ "<n2>"
|
||||
Method: cosine
|
||||
Score: 0.847
|
||||
|
||||
Interpretation: HIGH similarity (>0.8)
|
||||
Shared neighbors: K
|
||||
Common node types: [types]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `predict-link <n1> <n2> [--method cosine|jaccard|adamic-adar|common-neighbors]`
|
||||
|
||||
Score the likelihood of a relationship between two nodes.
|
||||
|
||||
```python
|
||||
from semantica.kg.link_predictor import LinkPredictor
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
predictor = LinkPredictor()
|
||||
|
||||
# score_link(graph_store, node_id1, node_id2, method=)
|
||||
score = predictor.score_link(
|
||||
graph_store=graph,
|
||||
node_id1=n1,
|
||||
node_id2=n2,
|
||||
method=method_arg or None,
|
||||
)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Link Prediction: "<n1>" → "<n2>"
|
||||
Method: cosine
|
||||
Score: 0.723 (threshold: 0.5 → LIKELY)
|
||||
|
||||
Recommendation: This link is LIKELY to be meaningful.
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `top-links <node_id> [--top N] [--method <method>]`
|
||||
|
||||
Find the top-N most likely new connections for a node.
|
||||
|
||||
```python
|
||||
from semantica.kg.link_predictor import LinkPredictor
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
predictor = LinkPredictor()
|
||||
|
||||
top = predictor.predict_top_links(
|
||||
graph_store=graph,
|
||||
node_id=node_id,
|
||||
top_k=int(top_n) if top_n else 10,
|
||||
method=method_arg or None,
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| Rank | Target Node | Type | Score | Existing Link? |`
|
||||
|
||||
---
|
||||
|
||||
## `batch <query_node> [--against <n1,n2,...>] [--top N]`
|
||||
|
||||
Score similarity between a query node and a set of target nodes (or all nodes).
|
||||
|
||||
```python
|
||||
from semantica.kg.similarity_calculator import SimilarityCalculator
|
||||
from semantica.kg.node_embeddings import NodeEmbedder
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
embedder = NodeEmbedder()
|
||||
calc = SimilarityCalculator()
|
||||
|
||||
# Get query embedding and all target embeddings
|
||||
query_vec = ... # from stored node2vec_embedding
|
||||
target_embeddings = {n: embedder.get_embedding(n) for n in targets}
|
||||
|
||||
scores = calc.batch_similarity(
|
||||
embeddings=target_embeddings,
|
||||
query_embedding=query_vec,
|
||||
top_k=int(top_n) if top_n else 20,
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| Node | Type | Score |` sorted descending.
|
||||
|
||||
---
|
||||
|
||||
## `pairwise [--labels <t1,t2>] [--method cosine|euclidean]`
|
||||
|
||||
Compute all pairwise similarities among a set of nodes.
|
||||
|
||||
```python
|
||||
from semantica.kg.similarity_calculator import SimilarityCalculator
|
||||
|
||||
calc = SimilarityCalculator()
|
||||
|
||||
pairwise = calc.pairwise_similarity(
|
||||
embeddings=embeddings_dict,
|
||||
method=method_arg or None,
|
||||
)
|
||||
```
|
||||
|
||||
Show as a heatmap summary — top-5 most similar pairs and bottom-5 most dissimilar pairs. Full matrix on request.
|
||||
|
||||
Also use `AgentContext.predict_decision_relationships(decision_id, top_k)` when working within decision graphs for relationship prediction enriched with KG algorithms.
|
||||
@@ -0,0 +1,44 @@
|
||||
---
|
||||
name: explain
|
||||
description: Explain Semantica reasoning, decision logic, and graph results with traceability, causal context, and human-readable rationale.
|
||||
---
|
||||
|
||||
# /semantica:explain
|
||||
|
||||
Produce explanations for decisions, rules, and graph analytics. Usage: `/semantica:explain <target> [args]`
|
||||
|
||||
`$ARGUMENTS` = explanation target + optional detail level.
|
||||
|
||||
---
|
||||
|
||||
## `decision <decision_id> [--detail <level>]`
|
||||
|
||||
Explain why a decision was reached.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.explanation_generator import ExplanationGenerator
|
||||
|
||||
# For decision explainability in Semantica contexts:
|
||||
decision_trace = ctx.trace_decision_explainability(decision_id=decision_id)
|
||||
|
||||
# For reasoning/proof explanations:
|
||||
generator = ExplanationGenerator(detail_level=detail)
|
||||
explanation = generator.generate_explanation(reasoning_result)
|
||||
```
|
||||
|
||||
Output: decision factors, rule traces, confidence, and suggested next steps.
|
||||
|
||||
---
|
||||
|
||||
## `graph <node_id> [--path N]`
|
||||
|
||||
Explain graph relationships and why a node is connected.
|
||||
|
||||
```python
|
||||
# Use AgentContext explainability + causal tracing for graph-connected decisions
|
||||
graph_explanation = ctx.trace_decision_explainability(decision_id=node_id)
|
||||
upstream = ctx.get_causal_chain(decision_id=node_id, direction="upstream", max_depth=depth)
|
||||
downstream = ctx.get_causal_chain(decision_id=node_id, direction="downstream", max_depth=depth)
|
||||
```
|
||||
|
||||
Return: cause/effect chains, supporting evidence, and relevant metadata.
|
||||
@@ -0,0 +1,67 @@
|
||||
---
|
||||
name: export
|
||||
description: Export Semantica graphs, results, and provenance to JSON, RDF, Parquet, CSV, GraphML, and other formats.
|
||||
---
|
||||
|
||||
# /semantica:export
|
||||
|
||||
Export knowledge graph data. Usage: `/semantica:export <format> [args]`
|
||||
|
||||
`$ARGUMENTS` = format + optional target or destination.
|
||||
|
||||
---
|
||||
|
||||
## `json [--output <path>] [--filter <query>]`
|
||||
|
||||
Export graph data as JSON.
|
||||
|
||||
```python
|
||||
from semantica.export.methods import export_json
|
||||
|
||||
export_json(data=graph_data, file_path=output, format='json')
|
||||
```
|
||||
|
||||
Output: JSON file or inline JSON payload.
|
||||
|
||||
---
|
||||
|
||||
## `rdf [--format turtle|rdfxml|jsonld|ntriples|n3] [--output <path>]`
|
||||
|
||||
Export the graph in RDF serialization.
|
||||
|
||||
```python
|
||||
from semantica.export.methods import export_rdf
|
||||
|
||||
export_rdf(data=graph_data, file_path=output, format='turtle')
|
||||
```
|
||||
|
||||
Return: RDF text or file path.
|
||||
|
||||
---
|
||||
|
||||
## `parquet [--output <path>]`
|
||||
|
||||
Export nodes and edges to Parquet for analytics.
|
||||
|
||||
```python
|
||||
from semantica.export.methods import export_parquet
|
||||
|
||||
export_parquet(data=graph_data, file_path=output, compression='snappy')
|
||||
```
|
||||
|
||||
Output: Parquet dataset ready for downstream processing.
|
||||
|
||||
---
|
||||
|
||||
## `graphml|gexf|dot [--output <path>]`
|
||||
|
||||
Export the graph to a supported graph format.
|
||||
|
||||
```python
|
||||
from semantica.export import GraphExporter
|
||||
|
||||
exporter = GraphExporter(format='graphml', include_attributes=True)
|
||||
exporter.export(graph_data, output)
|
||||
```
|
||||
|
||||
Output: Graph format file suitable for visualization tools.
|
||||
@@ -0,0 +1,93 @@
|
||||
---
|
||||
name: extract
|
||||
description: Run the full Semantica semantic extraction pipeline on a file or selected text — NER, relations, events, coreference resolution, triplets, and validation. Clears result cache before each run. Returns Markdown tables with entity/relation/event/triplet results and inline validator warnings.
|
||||
---
|
||||
|
||||
# /semantica:extract
|
||||
|
||||
Run the full extraction pipeline. Usage: `/semantica:extract [file_path | "inline text"]`
|
||||
|
||||
`$ARGUMENTS` = file path, inline text in quotes, or blank (uses active editor file).
|
||||
|
||||
---
|
||||
|
||||
## Steps
|
||||
|
||||
**1. Resolve the source.**
|
||||
- If `$ARGUMENTS` is a readable file path → `text = open(path).read()`
|
||||
- If it's quoted inline text → use directly
|
||||
- If blank → use the active editor file
|
||||
|
||||
**2. Clear the result cache** to prevent cross-invocation pollution:
|
||||
|
||||
```python
|
||||
from semantica.semantic_extract.cache import _result_cache
|
||||
_result_cache.clear()
|
||||
```
|
||||
|
||||
**3. Run the full pipeline:**
|
||||
|
||||
```python
|
||||
from semantica.semantic_extract import (
|
||||
NamedEntityRecognizer,
|
||||
RelationExtractor,
|
||||
EventDetector,
|
||||
CoreferenceResolver,
|
||||
TripletExtractor,
|
||||
ExtractionValidator,
|
||||
)
|
||||
|
||||
# Named Entity Recognition
|
||||
ner = NamedEntityRecognizer()
|
||||
entities = ner.extract(text)
|
||||
|
||||
# Relation Extraction
|
||||
rel = RelationExtractor()
|
||||
relations = rel.extract(text)
|
||||
|
||||
# Event Detection
|
||||
evt = EventDetector()
|
||||
events = evt.extract(text)
|
||||
|
||||
# Coreference Resolution — resolve pronouns/aliases before extraction
|
||||
coref = CoreferenceResolver()
|
||||
resolved_text = coref.resolve(text)
|
||||
|
||||
# Triplet Extraction (subject–predicate–object)
|
||||
triplet = TripletExtractor()
|
||||
triplets = triplet.extract(resolved_text)
|
||||
|
||||
# Validate quality
|
||||
validator = ExtractionValidator()
|
||||
issues = validator.validate(entities, relations)
|
||||
```
|
||||
|
||||
**4. Report validator warnings** above results:
|
||||
```
|
||||
⚠ ExtractionValidator: <warning message>
|
||||
```
|
||||
|
||||
**5. Return results as Markdown tables:**
|
||||
|
||||
**Entities** (N total)
|
||||
| Label | Type | Confidence | Span |
|
||||
|-------|------|------------|------|
|
||||
|
||||
**Relations** (M total)
|
||||
| Source | Relation Type | Target | Confidence |
|
||||
|--------|---------------|--------|------------|
|
||||
|
||||
**Events** (K total)
|
||||
| Label | Type | Participants | Confidence |
|
||||
|-------|------|--------------|------------|
|
||||
|
||||
**Triplets** (J total)
|
||||
| Subject | Predicate | Object | Confidence |
|
||||
|---------|-----------|--------|------------|
|
||||
|
||||
**6. Summary line:**
|
||||
```
|
||||
Extracted: N entities, M relations, K events, J triplets — from <source>
|
||||
```
|
||||
|
||||
For large files (>50KB), process in chunks and show a progress indicator. Highlight any entities appearing in the context graph already (`ContextGraph.has_node(label)`) with `[in graph]` tag.
|
||||
@@ -0,0 +1,38 @@
|
||||
---
|
||||
name: ingest
|
||||
description: Ingest data from files, databases, APIs, or streams into Semantica knowledge graphs with schema mapping and entity linking.
|
||||
---
|
||||
|
||||
# /semantica:ingest
|
||||
|
||||
Ingest new data into the knowledge graph. Usage: `/semantica:ingest <source> [args]`
|
||||
|
||||
`$ARGUMENTS` = source type + optional file path, connection string, or dataset identifier.
|
||||
|
||||
---
|
||||
|
||||
## `file <path> [--format json|csv|yaml|xml]`
|
||||
|
||||
Ingest structured data from a local file.
|
||||
|
||||
```python
|
||||
from semantica.ingest import ingest_file
|
||||
|
||||
data = ingest_file(file_path=path, method='file', file_format=file_format)
|
||||
```
|
||||
|
||||
Output: imported node/edge count and ingestion summary.
|
||||
|
||||
---
|
||||
|
||||
## `db <connection> [--query <sql>]`
|
||||
|
||||
Ingest data from a database source.
|
||||
|
||||
```python
|
||||
from semantica.ingest import ingest_database
|
||||
|
||||
result = ingest_database(connection_string=conn, query=query)
|
||||
```
|
||||
|
||||
Return: rows ingested, mapped entities, and warnings.
|
||||
@@ -0,0 +1,37 @@
|
||||
---
|
||||
name: ontology
|
||||
description: Manage ontology schemas, concepts, relationships, and alignments for Semantica knowledge graphs.
|
||||
---
|
||||
|
||||
# /semantica:ontology
|
||||
|
||||
Manage ontology definitions and validation. Usage: `/semantica:ontology <task> [args]`
|
||||
|
||||
`$ARGUMENTS` = task + optional ontology item or schema file.
|
||||
|
||||
---
|
||||
|
||||
## `describe <concept>`
|
||||
|
||||
Show ontology concept details.
|
||||
|
||||
```python
|
||||
from semantica.ontology import OntologyManager
|
||||
|
||||
manager = OntologyManager()
|
||||
concept = manager.get_concept(concept_name)
|
||||
```
|
||||
|
||||
Output: properties, relationships, inherited types, and examples.
|
||||
|
||||
---
|
||||
|
||||
## `validate [--schema <file>]`
|
||||
|
||||
Validate the graph or schema against the ontology.
|
||||
|
||||
```python
|
||||
result = manager.validate_graph(graph=graph, schema_file=schema_file)
|
||||
```
|
||||
|
||||
Return: validation status, errors, and correction suggestions.
|
||||
@@ -0,0 +1,37 @@
|
||||
---
|
||||
name: policy
|
||||
description: Define and enforce policies, access controls, and compliance rules over Semantica knowledge graphs.
|
||||
---
|
||||
|
||||
# /semantica:policy
|
||||
|
||||
Apply policy rules and checks. Usage: `/semantica:policy <task> [args]`
|
||||
|
||||
`$ARGUMENTS` = task + optional policy name, rule set, or target entity.
|
||||
|
||||
---
|
||||
|
||||
## `check [--rule <name>] [--target <id>]`
|
||||
|
||||
Run policy checks against the graph.
|
||||
|
||||
```python
|
||||
from semantica.policy import PolicyEngine
|
||||
|
||||
engine = PolicyEngine()
|
||||
result = engine.check(rule_name=rule_name, target=target)
|
||||
```
|
||||
|
||||
Output: compliance status, failing rules, and remediation guidance.
|
||||
|
||||
---
|
||||
|
||||
## `list`
|
||||
|
||||
List available policy rules and categories.
|
||||
|
||||
```python
|
||||
rules = engine.list_rules()
|
||||
```
|
||||
|
||||
Return: rule name, description, severity, and category.
|
||||
@@ -0,0 +1,37 @@
|
||||
---
|
||||
name: provenance
|
||||
description: Trace data lineage, source attribution, audit trails, and provenance assertions in Semantica graphs.
|
||||
---
|
||||
|
||||
# /semantica:provenance
|
||||
|
||||
Inspect provenance metadata. Usage: `/semantica:provenance <task> [args]`
|
||||
|
||||
`$ARGUMENTS` = task + optional node, edge, or time range.
|
||||
|
||||
---
|
||||
|
||||
## `trace <node_id> [--depth N]`
|
||||
|
||||
Trace the provenance of a node or fact.
|
||||
|
||||
```python
|
||||
from semantica.provenance import ProvenanceTracer
|
||||
|
||||
tracer = ProvenanceTracer()
|
||||
trace = tracer.trace_node(node_id=node_id, depth=depth)
|
||||
```
|
||||
|
||||
Output: source chain, authors, timestamps, and validation status.
|
||||
|
||||
---
|
||||
|
||||
## `audit [--since <ts>] [--actor <id>]`
|
||||
|
||||
View audit logs for graph changes.
|
||||
|
||||
```python
|
||||
audit_log = tracer.get_audit_log(since=since, actor=actor)
|
||||
```
|
||||
|
||||
Return: change events, actor, affected objects, and action details.
|
||||
@@ -0,0 +1,49 @@
|
||||
---
|
||||
name: query
|
||||
description: Query the Semantica knowledge graph using SPARQL, Cypher, keyword search, and structured graph query patterns.
|
||||
---
|
||||
|
||||
# /semantica:query
|
||||
|
||||
Run graph queries and search. Usage: `/semantica:query <mode> [args]`
|
||||
|
||||
`$ARGUMENTS` = query mode + query string or filter.
|
||||
|
||||
---
|
||||
|
||||
## `sparql <query>`
|
||||
|
||||
Execute a SPARQL query against the graph.
|
||||
|
||||
```python
|
||||
from semantica.query import QueryEngine
|
||||
|
||||
engine = QueryEngine()
|
||||
results = engine.query_sparql(query)
|
||||
```
|
||||
|
||||
Return: query bindings as a Markdown table.
|
||||
|
||||
---
|
||||
|
||||
## `cypher <query>`
|
||||
|
||||
Execute a Cypher-like query.
|
||||
|
||||
```python
|
||||
results = engine.query_cypher(query)
|
||||
```
|
||||
|
||||
Output: node/relationship results and path summaries.
|
||||
|
||||
---
|
||||
|
||||
## `search <keywords> [--filter <type>]`
|
||||
|
||||
Search graph entities by keyword.
|
||||
|
||||
```python
|
||||
results = engine.search(keywords=keywords, filter_type=filter_type)
|
||||
```
|
||||
|
||||
Return: ranked matches with entity types and relevance scores.
|
||||
@@ -0,0 +1,201 @@
|
||||
---
|
||||
name: reason
|
||||
description: Run reasoning over the Semantica knowledge graph — deductive logic, abductive hypothesis generation, Datalog programs, SPARQL queries, Rete network evaluation. Uses DeductiveReasoner, AbductiveReasoner, DatalogReasoner, SPARQLReasoner, ReteEngine. Sub-commands: deductive, abductive, datalog, sparql, rete, prove, hypotheses.
|
||||
---
|
||||
|
||||
# /semantica:reason
|
||||
|
||||
Apply reasoning over the knowledge graph. Usage: `/semantica:reason <mode> [args]`
|
||||
|
||||
`$ARGUMENTS` = reasoning mode + rules/observations/query.
|
||||
|
||||
---
|
||||
|
||||
## `deductive [--facts '<json-list>'] [--rules '<rule1>|<rule2>']`
|
||||
|
||||
Apply deductive rules to known facts to derive new conclusions.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.deductive_reasoner import DeductiveReasoner, Premise
|
||||
|
||||
reasoner = DeductiveReasoner()
|
||||
|
||||
# Add base facts to working memory
|
||||
# Facts can be strings like "Person(John)" or structured dicts
|
||||
import json
|
||||
facts = json.loads(facts_json) if facts_json else []
|
||||
reasoner.add_facts(facts)
|
||||
|
||||
# Apply logic with explicit premises
|
||||
# Premise objects have: statement, confidence, source
|
||||
premises = [
|
||||
Premise(statement=fact, confidence=1.0)
|
||||
for fact in facts
|
||||
]
|
||||
|
||||
conclusions = reasoner.apply_logic(premises=premises)
|
||||
```
|
||||
|
||||
Return: `| Conclusion | Triggering Premises | Confidence | Rule Applied |`
|
||||
|
||||
If zero rules given, run `reasoner.prove_theorem()` on any provided theorem:
|
||||
```python
|
||||
proof = reasoner.prove_theorem(theorem=theorem_text)
|
||||
```
|
||||
|
||||
Output: `Proof: <proof.steps> | Valid: YES / NO`
|
||||
|
||||
---
|
||||
|
||||
## `prove <theorem> [--facts '<json-list>']`
|
||||
|
||||
Prove or disprove a theorem against known facts.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.deductive_reasoner import DeductiveReasoner
|
||||
|
||||
reasoner = DeductiveReasoner()
|
||||
import json
|
||||
reasoner.add_facts(json.loads(facts_json) if facts_json else [])
|
||||
|
||||
proof = reasoner.prove_theorem(theorem=theorem)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Theorem: "<theorem>"
|
||||
Result: PROVED ✓ | DISPROVED ✗ | UNDECIDABLE ⚠
|
||||
|
||||
Proof steps:
|
||||
1. <premise> — <justification>
|
||||
2. ...
|
||||
→ QED: <theorem>
|
||||
|
||||
Confidence: <proof.confidence>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `abductive <observation> [--knowledge '<json-list>'] [--top N]`
|
||||
|
||||
Generate and rank hypotheses that explain an observation.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.abductive_reasoner import (
|
||||
AbductiveReasoner, Observation
|
||||
)
|
||||
|
||||
reasoner = AbductiveReasoner()
|
||||
|
||||
import json
|
||||
if knowledge_json:
|
||||
reasoner.add_knowledge(json.loads(knowledge_json))
|
||||
|
||||
obs = Observation(description=observation)
|
||||
|
||||
# Generate all hypotheses then rank them
|
||||
hypotheses = reasoner.generate_hypotheses(observations=[obs])
|
||||
ranked = reasoner.rank_hypotheses(hypotheses)
|
||||
best = reasoner.get_best_explanation(obs)
|
||||
|
||||
# Also get full explanations with evidence
|
||||
explanations = reasoner.find_explanations(observations=[obs])
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Abductive Reasoning for: "<observation>"
|
||||
|
||||
Best explanation:
|
||||
<best.description> (confidence: 0.87)
|
||||
|
||||
All hypotheses (ranked):
|
||||
| Rank | Hypothesis | Confidence | Supporting Evidence |
|
||||
| 1 | <hyp> | 0.87 | <evidence> |
|
||||
| 2 | ...
|
||||
|
||||
Full explanations:
|
||||
Explanation 1: <explanation.summary>
|
||||
Evidence: <evidence items>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `datalog <program>`
|
||||
|
||||
Evaluate a Datalog program over graph facts.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.datalog_reasoner import DatalogReasoner
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
reasoner = DatalogReasoner()
|
||||
|
||||
# program is a string of Datalog rules and queries
|
||||
results = reasoner.evaluate(program=program, graph=graph)
|
||||
```
|
||||
|
||||
Return derived tuples as a relation table. Show rule derivation counts.
|
||||
|
||||
---
|
||||
|
||||
## `sparql <query>`
|
||||
|
||||
Run a SPARQL query over the knowledge graph and return results.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.sparql_reasoner import SPARQLReasoner
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
reasoner = SPARQLReasoner()
|
||||
|
||||
results = reasoner.query(sparql_query=query, graph=graph)
|
||||
```
|
||||
|
||||
Return as a Markdown table with bound variable columns matching the SELECT clause.
|
||||
|
||||
---
|
||||
|
||||
## `rete [--rules '<rule1>|<rule2>'] [--facts '<json-list>']`
|
||||
|
||||
Incremental rule evaluation using the Rete network with working memory.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.rete_engine import ReteEngine
|
||||
import json
|
||||
|
||||
engine = ReteEngine()
|
||||
|
||||
rules = rules_str.split("|") if rules_str else []
|
||||
facts = json.loads(facts_json) if facts_json else []
|
||||
|
||||
engine.load_rules(rules)
|
||||
engine.process_facts(facts)
|
||||
activations = engine.get_activations()
|
||||
```
|
||||
|
||||
Return: `| Rule Fired | Variable Bindings | Working Memory Delta | Activation Order |`
|
||||
|
||||
---
|
||||
|
||||
## `hypotheses "<scenario>" [--knowledge '<json-list>'] [--top N]`
|
||||
|
||||
Generate the top-N most probable explanations for a complex scenario.
|
||||
|
||||
```python
|
||||
from semantica.reasoning.abductive_reasoner import AbductiveReasoner, Observation
|
||||
import json
|
||||
|
||||
reasoner = AbductiveReasoner()
|
||||
if knowledge_json:
|
||||
reasoner.add_knowledge(json.loads(knowledge_json))
|
||||
|
||||
obs = Observation(description=scenario)
|
||||
hypotheses = reasoner.generate_hypotheses(observations=[obs])
|
||||
ranked = reasoner.rank_hypotheses(hypotheses)
|
||||
top_n = ranked[:int(n) if n else 5]
|
||||
```
|
||||
|
||||
For each hypothesis also show: what evidence supports it, what would falsify it, and which is the most parsimonious (fewest assumptions).
|
||||
@@ -0,0 +1,164 @@
|
||||
---
|
||||
name: temporal
|
||||
description: Temporal graph operations on Semantica — scoped queries at a point in time, graph snapshots, node change timelines, temporal causal analysis, and graph state reconstruction. Uses AgentContext.find_precedents(as_of=), ContextGraph.state_at(), CausalChainAnalyzer.trace_at_time(), and TemporalQueryRewriter. Sub-commands: query, snapshot, timeline, causal-at, precedents-at.
|
||||
---
|
||||
|
||||
# /semantica:temporal
|
||||
|
||||
Temporal graph operations. Usage: `/semantica:temporal <sub-command> [args]`
|
||||
|
||||
`$ARGUMENTS` = sub-command + query/node + date expression.
|
||||
|
||||
---
|
||||
|
||||
## `query "<question>" [at|before|after <date>]`
|
||||
|
||||
Temporally-scoped natural-language graph query.
|
||||
|
||||
```python
|
||||
from semantica.kg.temporal_query_rewriter import TemporalQueryRewriter
|
||||
from semantica.kg.temporal_normalizer import TemporalNormalizer
|
||||
|
||||
normalizer = TemporalNormalizer()
|
||||
# Normalize natural date expressions: "last month", "Q3 2024", "2025-01-15"
|
||||
date = normalizer.normalize(date_expr)
|
||||
|
||||
rewriter = TemporalQueryRewriter()
|
||||
# Rewrite query with temporal constraint
|
||||
rewritten = rewriter.rewrite(
|
||||
query=question,
|
||||
temporal_constraint={"op": direction, "value": date}, # op: "at"|"before"|"after"
|
||||
)
|
||||
```
|
||||
|
||||
Then run the rewritten query through `AgentContext.retrieve()` or `ContextGraph.query()`.
|
||||
|
||||
Return ranked results with `Valid From`, `Valid Until`, `Active At <date>` columns. Mark nodes that were not yet created at the target time as `[not yet created]`.
|
||||
|
||||
---
|
||||
|
||||
## `snapshot <date>`
|
||||
|
||||
Reconstruct the full graph state as it existed at a specific point in time.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
|
||||
# state_at returns a dict snapshot of the graph at that timestamp
|
||||
snapshot = graph.state_at(timestamp=date) # ISO string or datetime
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Graph snapshot at <date>:
|
||||
Nodes: N (M added since prev snapshot, K removed)
|
||||
Edges: P
|
||||
Density: 0.21
|
||||
Communities: Q
|
||||
|
||||
Active decision categories at <date>:
|
||||
| Category | Count | Avg Confidence |
|
||||
|
||||
Top 10 nodes (by degree at <date>):
|
||||
| Node | Type | Degree |
|
||||
|
||||
[Compact Mermaid graph TD — top-10 most connected nodes at that time]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `timeline <node_id>`
|
||||
|
||||
Show attribute and relationship changes for a node across its full history.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
|
||||
# Use state_at() at multiple time points to reconstruct history
|
||||
# Check add_node timestamps and edge addition times from graph data
|
||||
node_data = graph.find_node(node_id)
|
||||
```
|
||||
|
||||
Output as Markdown timeline:
|
||||
```
|
||||
Timeline for "<node_id>" (<type>):
|
||||
|
||||
<timestamp> CREATED
|
||||
Properties: {confidence: 0.71, category: "loan_approval"}
|
||||
Source: extraction/pipeline
|
||||
|
||||
<timestamp> UPDATED
|
||||
confidence: 0.71 → 0.91 [source: review]
|
||||
|
||||
<timestamp> RELATIONSHIP ADDED
|
||||
"<node_id>" →[CAUSED]→ "Decision_B"
|
||||
|
||||
<timestamp> RELATIONSHIP REMOVED
|
||||
"<node_id>" →[PRECEDED_BY]→ "Decision_X" (superseded)
|
||||
|
||||
Total lifespan: <duration>
|
||||
Current state: <active|superseded>
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `causal-at <decision_id> <date> [--direction upstream|downstream]`
|
||||
|
||||
Trace a causal chain as it existed at a specific point in time.
|
||||
|
||||
```python
|
||||
from semantica.context.causal_analyzer import CausalChainAnalyzer
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True)
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.graph_store)
|
||||
|
||||
historical_chain = analyzer.trace_at_time(
|
||||
event_id=decision_id,
|
||||
at_time=date, # ISO string or datetime
|
||||
direction=direction or "upstream",
|
||||
max_depth=10,
|
||||
)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Historical causal chain for <decision_id> at <date>:
|
||||
Direction: upstream (what caused it?)
|
||||
|
||||
[Mermaid graph TD showing chain as it existed at <date>]
|
||||
|
||||
Decisions present then but not now: [list]
|
||||
Decisions added since then: [list]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `precedents-at "<scenario>" <date> [--category <cat>]`
|
||||
|
||||
Find precedent decisions that existed as of a specific date — useful for auditing what context was available when a decision was made.
|
||||
|
||||
```python
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
|
||||
|
||||
# find_precedents supports as_of parameter for temporal precedent search
|
||||
precedents = ctx.find_precedents(
|
||||
scenario=scenario,
|
||||
category=category or None,
|
||||
limit=10,
|
||||
use_hybrid_search=True,
|
||||
include_context=True,
|
||||
include_superseded=False,
|
||||
as_of=date, # Only return precedents that existed at this date
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| Rank | Decision ID | Scenario | Outcome | Confidence | Set Date | Valid Until |`
|
||||
|
||||
Note decisions that were superseded before or after the target date.
|
||||
@@ -0,0 +1,228 @@
|
||||
---
|
||||
name: validate
|
||||
description: Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validate_pipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.
|
||||
---
|
||||
|
||||
# /semantica:validate
|
||||
|
||||
Validate pipeline and graph quality. Usage: `/semantica:validate <target> [options]`
|
||||
|
||||
`$ARGUMENTS` = target type + optional config or path.
|
||||
|
||||
---
|
||||
|
||||
## `pipeline [--config '<json>']`
|
||||
|
||||
Validate a full pipeline builder configuration.
|
||||
|
||||
```python
|
||||
from semantica.pipeline.pipeline_builder import PipelineBuilder
|
||||
from semantica.pipeline.pipeline_validator import PipelineValidator
|
||||
|
||||
builder = PipelineBuilder()
|
||||
if config_json:
|
||||
import json
|
||||
builder.build_pipeline(json.loads(config_json))
|
||||
|
||||
# PipelineBuilder has its own quick validate
|
||||
quick = builder.validate_pipeline() # returns Dict
|
||||
|
||||
# PipelineValidator gives full ValidationResult(valid, errors, warnings)
|
||||
# Does NOT raise — always returns a result object
|
||||
validator = PipelineValidator()
|
||||
result = validator.validate(builder)
|
||||
|
||||
# Also check inter-step dependencies
|
||||
deps = validator.check_dependencies(builder)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Pipeline Validation: VALID ✓ | INVALID ✗
|
||||
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||
Steps: N registered
|
||||
Valid: M steps
|
||||
|
||||
Errors (K):
|
||||
✗ [step_name] <error message>
|
||||
|
||||
Warnings (J):
|
||||
⚠ [step_name] <warning message>
|
||||
|
||||
Dependencies:
|
||||
✓ All dependencies resolved
|
||||
✗ Step "<name>" depends on missing step "<dep>"
|
||||
|
||||
Result: <valid> — K errors, J warnings
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `step <step_name> [--type <type>] [--constraints '<json>']`
|
||||
|
||||
Validate a single pipeline step.
|
||||
|
||||
```python
|
||||
from semantica.pipeline.pipeline_builder import PipelineBuilder
|
||||
from semantica.pipeline.pipeline_validator import PipelineValidator
|
||||
import json
|
||||
|
||||
builder = PipelineBuilder()
|
||||
step = builder.get_step(step_name)
|
||||
|
||||
validator = PipelineValidator()
|
||||
result = validator.validate_step(
|
||||
step=step,
|
||||
**json.loads(constraints_json) if constraints_json else {},
|
||||
)
|
||||
```
|
||||
|
||||
Output: same checklist format but scoped to a single step.
|
||||
|
||||
---
|
||||
|
||||
## `dependencies`
|
||||
|
||||
Check all inter-step dependency resolution for the active pipeline.
|
||||
|
||||
```python
|
||||
from semantica.pipeline.pipeline_builder import PipelineBuilder
|
||||
from semantica.pipeline.pipeline_validator import PipelineValidator
|
||||
|
||||
builder = PipelineBuilder()
|
||||
validator = PipelineValidator()
|
||||
|
||||
deps = validator.check_dependencies(builder)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Dependency Graph:
|
||||
| Step | Depends On | Status |
|
||||
| step_A | — | ✓ |
|
||||
| step_B | step_A | ✓ |
|
||||
| step_C | step_X | ✗ MISSING |
|
||||
|
||||
Cycles detected: YES / NO
|
||||
Missing steps: [list]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `extraction <file_path>`
|
||||
|
||||
Validate extraction quality for a file — entity confidence, relation density, coverage.
|
||||
|
||||
```python
|
||||
from semantica.semantic_extract.extraction_validator import ExtractionValidator
|
||||
from semantica.semantic_extract import (
|
||||
NamedEntityRecognizer,
|
||||
RelationExtractor,
|
||||
)
|
||||
from semantica.semantic_extract.cache import _result_cache
|
||||
|
||||
_result_cache.clear() # prevent cross-invocation cache pollution
|
||||
|
||||
text = open(file_path).read()
|
||||
|
||||
ner = NamedEntityRecognizer()
|
||||
rel = RelationExtractor()
|
||||
entities = ner.extract(text)
|
||||
relations = rel.extract(text)
|
||||
|
||||
validator = ExtractionValidator()
|
||||
issues = validator.validate(entities, relations)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Extraction Validation: <file_path>
|
||||
Entities: N extracted
|
||||
Relations: M extracted
|
||||
Avg confidence: 0.83
|
||||
|
||||
Errors (K):
|
||||
✗ <issue>
|
||||
|
||||
Warnings (J):
|
||||
⚠ <warning>
|
||||
|
||||
Quality score: X/100
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `graph`
|
||||
|
||||
Check schema conformance, referential integrity, and structural health.
|
||||
|
||||
```python
|
||||
from semantica.kg.graph_validator import GraphValidator
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
validator = GraphValidator(graph)
|
||||
result = validator.validate()
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Graph Validation:
|
||||
Nodes: N | Edges: M
|
||||
Node types: K valid, J unknown
|
||||
|
||||
Referential integrity:
|
||||
✗ Dangling edge: <source> → <missing target>
|
||||
|
||||
Schema conformance:
|
||||
✗ Node "<id>" missing required property "<prop>"
|
||||
|
||||
Result: N errors, M warnings
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `ontology`
|
||||
|
||||
Validate ontology consistency and evaluate competency questions.
|
||||
|
||||
```python
|
||||
from semantica.ontology import OntologyValidator
|
||||
|
||||
validator = OntologyValidator()
|
||||
result = validator.validate()
|
||||
cq_results = validator.evaluate_competency_questions()
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Ontology Validation:
|
||||
Classes: N
|
||||
Properties: M
|
||||
Consistent: YES ✓ | NO ✗
|
||||
|
||||
Competency questions:
|
||||
✓ "Can we find all instances of X?" — answered
|
||||
✗ "Is Y a subclass of Z?" — failed: <reason>
|
||||
|
||||
Result: N consistency errors, M CQ failures
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `performance`
|
||||
|
||||
Validate pipeline performance characteristics — bottlenecks, parallelism, and resource use.
|
||||
|
||||
```python
|
||||
from semantica.pipeline.pipeline_builder import PipelineBuilder
|
||||
from semantica.pipeline.pipeline_validator import PipelineValidator
|
||||
|
||||
builder = PipelineBuilder()
|
||||
pipeline = builder.build()
|
||||
validator = PipelineValidator()
|
||||
|
||||
perf = validator.validate_performance(pipeline)
|
||||
```
|
||||
|
||||
Output: step-by-step timing estimates, parallelism opportunities, and recommended parallelism level.
|
||||
@@ -0,0 +1,249 @@
|
||||
---
|
||||
name: visualize
|
||||
description: Visualize the Semantica knowledge graph — topology, centrality, communities, paths, embeddings, decision insights, and temporal evolution. Uses GraphAnalyzer, CentralityCalculator, CommunityDetector, PathFinder, and ContextGraph analytics. Sub-commands: topology, centrality, community, path, decision-graph, insights, temporal, embedding.
|
||||
---
|
||||
|
||||
# /semantica:visualize
|
||||
|
||||
Render graph visualizations as Mermaid, ASCII, or structured Markdown. Usage: `/semantica:visualize <sub-command> [args]`
|
||||
|
||||
`$ARGUMENTS` = sub-command + optional node label or filter.
|
||||
|
||||
---
|
||||
|
||||
## `topology [--filter <node_type>]`
|
||||
|
||||
Full graph structure analysis — node types, edge distribution, connectivity metrics.
|
||||
|
||||
```python
|
||||
from semantica.kg.graph_analyzer import GraphAnalyzer
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
analyzer = GraphAnalyzer()
|
||||
|
||||
# Comprehensive analysis
|
||||
analysis = analyzer.analyze_graph(graph=graph.to_dict())
|
||||
metrics = analyzer.compute_metrics(graph=graph)
|
||||
connectivity = analyzer.analyze_connectivity(graph=graph)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Graph Topology:
|
||||
Nodes: N (M types)
|
||||
Edges: P
|
||||
Density: 0.23
|
||||
Avg degree: 4.7
|
||||
Connected: YES / NO (K components)
|
||||
|
||||
Node type distribution:
|
||||
[Mermaid pie chart]
|
||||
| Type | Count | % | Avg Degree |
|
||||
|
||||
Top-10 connected nodes:
|
||||
| Node | Type | Degree | Betweenness |
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `centrality [--type degree|betweenness|closeness|eigenvector|pagerank|all] [--top N]`
|
||||
|
||||
Calculate and rank nodes by centrality.
|
||||
|
||||
```python
|
||||
from semantica.kg.centrality_calculator import CentralityCalculator
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
calc = CentralityCalculator()
|
||||
|
||||
if centrality_type == "all" or not centrality_type:
|
||||
scores = calc.calculate_all_centrality(graph=graph)
|
||||
elif centrality_type == "degree":
|
||||
scores = calc.calculate_degree_centrality(graph=graph)
|
||||
elif centrality_type == "betweenness":
|
||||
scores = calc.calculate_betweenness_centrality(graph=graph)
|
||||
elif centrality_type == "closeness":
|
||||
scores = calc.calculate_closeness_centrality(graph=graph)
|
||||
elif centrality_type == "eigenvector":
|
||||
scores = calc.calculate_eigenvector_centrality(graph=graph)
|
||||
elif centrality_type == "pagerank":
|
||||
scores = calc.calculate_pagerank(
|
||||
graph=graph,
|
||||
max_iterations=20,
|
||||
damping_factor=0.85,
|
||||
)
|
||||
```
|
||||
|
||||
Return: `| Rank | Node | Type | Degree | Betweenness | Closeness | Eigenvector | PageRank |`
|
||||
|
||||
For a single node, also call `ContextGraph.get_node_centrality(node_id)` and `get_node_importance(node_id)`.
|
||||
|
||||
---
|
||||
|
||||
## `community [--algorithm louvain|leiden|label-propagation|overlapping]`
|
||||
|
||||
Detect and visualize graph communities/clusters.
|
||||
|
||||
```python
|
||||
from semantica.kg.community_detector import CommunityDetector
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
detector = CommunityDetector()
|
||||
|
||||
algorithm = algo_arg or "louvain"
|
||||
|
||||
if algorithm == "louvain":
|
||||
result = detector.detect_communities_louvain(graph, resolution=1.0)
|
||||
elif algorithm == "leiden":
|
||||
result = detector.detect_communities_leiden(graph, resolution=1.0)
|
||||
elif algorithm == "label-propagation":
|
||||
result = detector.detect_communities_label_propagation(graph)
|
||||
elif algorithm == "overlapping":
|
||||
result = detector.detect_overlapping_communities(graph)
|
||||
else:
|
||||
result = detector.detect_communities(graph, algorithm=algorithm)
|
||||
|
||||
structure = detector.analyze_community_structure(graph, result)
|
||||
metrics = detector.calculate_community_metrics(graph, result)
|
||||
```
|
||||
|
||||
Output:
|
||||
```
|
||||
Community Detection (algorithm: louvain)
|
||||
Communities: N
|
||||
Modularity: 0.71
|
||||
|
||||
Community summary:
|
||||
| ID | Size | Top Node | Internal Density | Bridge Nodes |
|
||||
|
||||
[Mermaid graph TD — nodes colored/grouped by community ID]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `path <n1> <n2> [--k N] [--algorithm bfs|dijkstra|astar|k-shortest]`
|
||||
|
||||
Find and visualize paths between two nodes.
|
||||
|
||||
```python
|
||||
from semantica.kg.path_finder import PathFinder
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
finder = PathFinder()
|
||||
|
||||
k = int(k_arg) if k_arg else 3
|
||||
|
||||
if algorithm == "bfs":
|
||||
path = finder.bfs_shortest_path(graph, source=n1, target=n2)
|
||||
paths = [path]
|
||||
elif algorithm == "dijkstra":
|
||||
path = finder.dijkstra_shortest_path(graph, source=n1, target=n2)
|
||||
paths = [path]
|
||||
else: # default: k-shortest
|
||||
paths = finder.find_k_shortest_paths(graph, source=n1, target=n2, k=k)
|
||||
|
||||
lengths = [finder.path_length(graph, p) for p in paths]
|
||||
```
|
||||
|
||||
Output as Mermaid `sequenceDiagram` for each path:
|
||||
```
|
||||
Path 1 (length: 2.3):
|
||||
n1 →[rel_type]→ Middle →[rel_type]→ n2
|
||||
|
||||
Path 2 (length: 3.7): ...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `decision-graph [--category <cat>] [--depth N]`
|
||||
|
||||
Visualize the decision influence graph for a category or all decisions.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.context.causal_analyzer import CausalChainAnalyzer
|
||||
from semantica.context import AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
|
||||
graph = ContextGraph(advanced_analytics=True)
|
||||
|
||||
# Get decision insights
|
||||
insights = graph.get_decision_insights()
|
||||
|
||||
# Build causal network
|
||||
analyzer = CausalChainAnalyzer(graph_store=ctx.graph_store)
|
||||
network = analyzer.analyze_causal_network()
|
||||
```
|
||||
|
||||
Output as Mermaid `graph TD` with:
|
||||
- Node size proportional to causal impact score
|
||||
- Color by outcome (green=approved, red=rejected, yellow=deferred)
|
||||
- Edge labels showing relationship type
|
||||
|
||||
---
|
||||
|
||||
## `insights`
|
||||
|
||||
Comprehensive decision analytics dashboard.
|
||||
|
||||
```python
|
||||
from semantica.context import ContextGraph, AgentContext
|
||||
|
||||
ctx = AgentContext(decision_tracking=True, advanced_analytics=True, kg_algorithms=True)
|
||||
graph = ContextGraph(advanced_analytics=True, centrality_analysis=True)
|
||||
|
||||
insights = graph.get_decision_insights()
|
||||
summary = graph.get_decision_summary()
|
||||
graph_summary = graph.get_graph_summary()
|
||||
context_insights = ctx.get_context_insights()
|
||||
```
|
||||
|
||||
Output a full analytics dashboard:
|
||||
```
|
||||
Decision Intelligence Dashboard
|
||||
════════════════════════════════
|
||||
Decisions: N total (M active)
|
||||
Categories: K unique
|
||||
Avg confidence: 0.87
|
||||
Outcome split: approved 55% | rejected 30% | deferred 15%
|
||||
Causal chains: P chains, longest: Q hops
|
||||
Loops detected: R circular dependencies
|
||||
|
||||
Graph health:
|
||||
Nodes: N | Edges: M | Density: 0.23
|
||||
Communities: K | Isolated nodes: J
|
||||
|
||||
[Mermaid pie — outcome distribution]
|
||||
[Mermaid bar — decisions by category]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## `temporal [--node <id>] [--start <date>] [--end <date>]`
|
||||
|
||||
Analyze how the graph evolved over time.
|
||||
|
||||
```python
|
||||
from semantica.kg.graph_analyzer import GraphAnalyzer
|
||||
from semantica.context import ContextGraph
|
||||
|
||||
graph = ContextGraph()
|
||||
analyzer = GraphAnalyzer()
|
||||
|
||||
evolution = analyzer.analyze_temporal_evolution(
|
||||
graph=graph,
|
||||
start_time=start_date or None,
|
||||
end_time=end_date or None,
|
||||
metrics=["node_count", "edge_count", "density", "communities"],
|
||||
)
|
||||
|
||||
# For a specific node, use ContextGraph.state_at()
|
||||
if node_id:
|
||||
snapshot = graph.state_at(timestamp=end_date or "now")
|
||||
```
|
||||
|
||||
Output as Markdown timeline with metrics per interval.
|
||||
+1
-1
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "semantica"
|
||||
version = "0.3.0"
|
||||
version = "0.4.0"
|
||||
description = "🧠 Semantica - An Open Source Framework for building Semantic Layers and Knowledge Engineering"
|
||||
readme = "README.md"
|
||||
license = { text = "MIT" }
|
||||
|
||||
@@ -22,6 +22,7 @@ License: MIT
|
||||
from abc import ABC, abstractmethod
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional
|
||||
from urllib.parse import quote
|
||||
|
||||
from .change_log import ChangeLogEntry
|
||||
from .version_storage import (
|
||||
@@ -388,7 +389,10 @@ class TemporalVersionManager(BaseVersionManager):
|
||||
# Clean up the actual graph if provided
|
||||
if triplet_store and graph_uri:
|
||||
try:
|
||||
triplet_store.execute_query(f"DROP SILENT GRAPH {graph_uri}")
|
||||
safe_graph_uri = self._sanitize_graph_uri(graph_uri)
|
||||
triplet_store.execute_query(
|
||||
f"DROP SILENT GRAPH <{safe_graph_uri}>"
|
||||
)
|
||||
self.logger.info(f"Dropped obsolete graph {graph_uri} from store")
|
||||
except Exception as e:
|
||||
self.logger.warning(f"Failed to drop graph {graph_uri} during pruning: {e}")
|
||||
@@ -399,6 +403,11 @@ class TemporalVersionManager(BaseVersionManager):
|
||||
"pruned_versions": deleted_labels,
|
||||
"retained_count": len(all_versions) - len(deleted_labels)
|
||||
}
|
||||
|
||||
def _sanitize_graph_uri(self, graph_uri: Any) -> str:
|
||||
"""Percent-encode unsafe characters before embedding a graph URI in SPARQL."""
|
||||
raw_uri = str(graph_uri).strip().strip("<>")
|
||||
return quote(raw_uri, safe="/:?&=@[]!$'()*+,%-._~")
|
||||
|
||||
# Git-like audit trails
|
||||
|
||||
|
||||
@@ -109,6 +109,7 @@ from collections import defaultdict, deque
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
import threading
|
||||
import itertools
|
||||
from typing import Any, Dict, List, Optional, Set, Tuple, Union
|
||||
import uuid
|
||||
|
||||
@@ -404,16 +405,19 @@ class ContextGraph:
|
||||
count = 0
|
||||
with self._lock:
|
||||
for edge in edges:
|
||||
# Accept both "properties" (ContextEdge.to_dict format) and "metadata"
|
||||
# (find_edges / build_graph_dict format) so round-trip imports never
|
||||
# silently drop edge metadata.
|
||||
edge_props = edge.get("properties") or edge.get("metadata", {})
|
||||
# Restore validity windows — ContextEdge.to_dict() writes them at top level
|
||||
valid_from = edge.get("valid_from") or edge_props.get("valid_from")
|
||||
valid_until = edge.get("valid_until") or edge_props.get("valid_until")
|
||||
|
||||
source_id = edge.get("source_id") or edge.get("source")
|
||||
target_id = edge.get("target_id") or edge.get("target")
|
||||
|
||||
if not source_id or not target_id:
|
||||
continue
|
||||
|
||||
internal_edge = ContextEdge(
|
||||
source_id=edge.get("source_id"),
|
||||
target_id=edge.get("target_id"),
|
||||
source_id=source_id,
|
||||
target_id=target_id,
|
||||
edge_type=edge.get("type", "related_to"),
|
||||
weight=edge.get("weight", 1.0),
|
||||
metadata=edge_props,
|
||||
@@ -779,26 +783,31 @@ class ContextGraph:
|
||||
def find_nodes(
|
||||
self, node_type: Optional[str] = None, skip: int = 0, limit: Optional[int] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Find nodes, optionally filtered by type."""
|
||||
"""Find nodes lazily"""
|
||||
with self._lock:
|
||||
if node_type:
|
||||
node_ids = self.node_type_index.get(node_type, set())
|
||||
nodes = [self.nodes[nid] for nid in node_ids]
|
||||
# Sets are unordered, sort IDs for deterministic pagination.
|
||||
# Guard against non-string IDs (None/int) which cause sorted() TypeError.
|
||||
raw_ids = sorted(
|
||||
nid for nid in self.node_type_index.get(node_type, set())
|
||||
if isinstance(nid, str)
|
||||
)
|
||||
source = (self.nodes[nid] for nid in raw_ids if nid in self.nodes)
|
||||
else:
|
||||
nodes = list(self.nodes.values())
|
||||
source = self.nodes.values()
|
||||
|
||||
results = [
|
||||
gen = (
|
||||
{
|
||||
"id": n.node_id,
|
||||
"type": n.node_type,
|
||||
"content": n.content,
|
||||
"type": n.node_type or "entity",
|
||||
"content": n.content or "",
|
||||
"metadata": {**(getattr(n, "metadata", {}) or {}), **(getattr(n, "properties", {}) or {})},
|
||||
}
|
||||
for n in nodes
|
||||
]
|
||||
if limit is not None:
|
||||
return results[skip: skip + limit]
|
||||
return results[skip:]
|
||||
for n in source if n.node_id
|
||||
)
|
||||
stop = skip + limit if limit is not None else None
|
||||
|
||||
return list(itertools.islice(gen, skip, stop))
|
||||
|
||||
def find_active_nodes(
|
||||
self,
|
||||
@@ -807,46 +816,33 @@ class ContextGraph:
|
||||
skip: int = 0,
|
||||
limit: Optional[int] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find nodes that are currently active within their validity window.
|
||||
|
||||
Nodes without ``valid_from``/``valid_until`` are always considered active.
|
||||
|
||||
Args:
|
||||
node_type: Optional node type filter.
|
||||
at_time: Point in time to evaluate validity (defaults to ``datetime.utcnow()``).
|
||||
skip: Items to skip
|
||||
limit: Max items to return
|
||||
|
||||
Returns:
|
||||
List of active node dicts (same format as :meth:`find_nodes`).
|
||||
"""
|
||||
"""Find active nodes lazily."""
|
||||
now = at_time or datetime.utcnow()
|
||||
with self._lock:
|
||||
if node_type:
|
||||
node_ids = self.node_type_index.get(node_type, set())
|
||||
nodes_iter = [self.nodes[nid] for nid in node_ids if nid in self.nodes]
|
||||
raw_ids = sorted(
|
||||
nid for nid in self.node_type_index.get(node_type, set())
|
||||
if isinstance(nid, str)
|
||||
)
|
||||
source = (self.nodes[nid] for nid in raw_ids if nid in self.nodes)
|
||||
else:
|
||||
nodes_iter = list(self.nodes.values())
|
||||
source = self.nodes.values()
|
||||
|
||||
result = []
|
||||
for node in nodes_iter:
|
||||
if node.is_active(now):
|
||||
result.append(
|
||||
{
|
||||
"id": node.node_id,
|
||||
"type": node.node_type,
|
||||
"content": node.content,
|
||||
def _active(nodes_iter):
|
||||
for n in nodes_iter:
|
||||
if n.node_id and n.is_active(now):
|
||||
yield {
|
||||
"id": n.node_id,
|
||||
"type": n.node_type or "entity",
|
||||
"content": n.content or "",
|
||||
"metadata": {
|
||||
**(getattr(node, "metadata", {}) or {}),
|
||||
**(getattr(node, "properties", {}) or {}),
|
||||
**(getattr(n, "metadata", {}) or {}),
|
||||
**(getattr(n, "properties", {}) or {}),
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
if limit is not None:
|
||||
return result[skip: skip + limit]
|
||||
return result[skip:]
|
||||
|
||||
stop = skip + limit if limit is not None else None
|
||||
return list(itertools.islice(_active(source), skip, stop))
|
||||
|
||||
def link_graph(
|
||||
self,
|
||||
@@ -981,36 +977,46 @@ class ContextGraph:
|
||||
def find_edges(
|
||||
self, edge_type: Optional[str] = None, skip: int = 0, limit: Optional[int] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Find edges, optionally filtered by type."""
|
||||
"""Find edges lazily."""
|
||||
with self._lock:
|
||||
if edge_type:
|
||||
edges = self.edge_type_index.get(edge_type, [])
|
||||
else:
|
||||
edges = self.edges
|
||||
|
||||
results = [
|
||||
{
|
||||
"source": e.source_id,
|
||||
"target": e.target_id,
|
||||
"type": e.edge_type,
|
||||
"weight": e.weight,
|
||||
"metadata": e.metadata,
|
||||
}
|
||||
for e in edges
|
||||
]
|
||||
source = self.edge_type_index.get(edge_type, []) if edge_type else self.edges
|
||||
|
||||
if limit is not None:
|
||||
return results[skip: skip + limit]
|
||||
return results[skip:]
|
||||
gen = (
|
||||
{
|
||||
"source": e.source_id or "",
|
||||
"target": e.target_id or "",
|
||||
"type": e.edge_type or "related_to",
|
||||
"weight": e.weight if e.weight is not None else 1.0,
|
||||
"metadata": e.metadata or {},
|
||||
}
|
||||
for e in source if e.source_id and e.target_id
|
||||
)
|
||||
stop = skip + limit if limit is not None else None
|
||||
return list(itertools.islice(gen, skip, stop))
|
||||
|
||||
def stats(self) -> Dict[str, Any]:
|
||||
"""Get graph statistics."""
|
||||
with self._lock:
|
||||
# Count only items that find_nodes/find_edges can return, so pagination
|
||||
# totals reported to callers match what the methods actually yield.
|
||||
node_count = sum(1 for n in self.nodes.values() if n.node_id)
|
||||
edge_count = sum(1 for e in self.edges if e.source_id and e.target_id)
|
||||
node_types = {
|
||||
k: sum(
|
||||
1 for nid in v
|
||||
if isinstance(nid, str) and nid in self.nodes and self.nodes[nid].node_id
|
||||
)
|
||||
for k, v in self.node_type_index.items()
|
||||
}
|
||||
edge_types = {
|
||||
k: sum(1 for e in v if e.source_id and e.target_id)
|
||||
for k, v in self.edge_type_index.items()
|
||||
}
|
||||
return {
|
||||
"node_count": len(self.nodes),
|
||||
"edge_count": len(self.edges),
|
||||
"node_types": {k: len(v) for k, v in self.node_type_index.items()},
|
||||
"edge_types": {k: len(v) for k, v in self.edge_type_index.items()},
|
||||
"node_count": node_count,
|
||||
"edge_count": edge_count,
|
||||
"node_types": node_types,
|
||||
"edge_types": edge_types,
|
||||
"density": self.density(),
|
||||
}
|
||||
|
||||
@@ -1472,25 +1478,85 @@ class ContextGraph:
|
||||
}
|
||||
|
||||
# Decision Support Methods
|
||||
def add_decision(self, decision: "Decision") -> None:
|
||||
def add_decision(
|
||||
self,
|
||||
decision: "Decision" = None,
|
||||
*,
|
||||
category: str = None,
|
||||
scenario: str = None,
|
||||
reasoning: str = None,
|
||||
outcome: str = None,
|
||||
confidence: float = 0.5,
|
||||
entities: Optional[List[str]] = None,
|
||||
decision_maker: Optional[str] = "system",
|
||||
valid_from=None,
|
||||
valid_until=None,
|
||||
**kwargs,
|
||||
) -> str:
|
||||
"""
|
||||
Add decision node to graph.
|
||||
|
||||
|
||||
Accepts either a Decision object or keyword arguments:
|
||||
|
||||
# From a Decision object
|
||||
graph.add_decision(Decision(category="x", scenario="y", ...))
|
||||
|
||||
# From keyword arguments (convenience form)
|
||||
graph.add_decision(category="x", scenario="y", reasoning="z",
|
||||
outcome="o", confidence=0.9)
|
||||
|
||||
Args:
|
||||
decision: Decision object to add
|
||||
decision: Decision object to add (mutually exclusive with kwargs)
|
||||
category: Decision category
|
||||
scenario: Decision scenario description
|
||||
reasoning: Reasoning behind the decision
|
||||
outcome: Decision outcome
|
||||
confidence: Confidence score (0.0–1.0)
|
||||
entities: Related entity labels
|
||||
decision_maker: Who made the decision
|
||||
valid_from: Start of validity window (ISO string or datetime)
|
||||
valid_until: End of validity window (ISO string or datetime)
|
||||
**kwargs: Extra metadata stored on the decision node
|
||||
|
||||
Returns:
|
||||
Decision ID
|
||||
"""
|
||||
from .decision_models import Decision
|
||||
|
||||
|
||||
if decision is not None and (
|
||||
any(v is not None for v in (
|
||||
category, scenario, reasoning, outcome, entities, valid_from, valid_until,
|
||||
)) or kwargs
|
||||
):
|
||||
raise ValueError(
|
||||
"Pass either a Decision object or keyword arguments, not both."
|
||||
)
|
||||
|
||||
if decision is None:
|
||||
# Build from kwargs — delegate to record_decision which handles ID gen
|
||||
return self.record_decision(
|
||||
category=category,
|
||||
scenario=scenario,
|
||||
reasoning=reasoning,
|
||||
outcome=outcome,
|
||||
confidence=confidence,
|
||||
entities=entities,
|
||||
decision_maker=decision_maker,
|
||||
valid_from=valid_from,
|
||||
valid_until=valid_until,
|
||||
metadata=kwargs,
|
||||
)
|
||||
|
||||
# Handle empty decision ID by generating UUID for both None and empty string
|
||||
# This ensures consistent behavior with Decision model's __post_init__ method
|
||||
node_id = decision.decision_id if decision.decision_id else str(uuid.uuid4())
|
||||
|
||||
|
||||
# Handle None metadata
|
||||
metadata = decision.metadata or {}
|
||||
|
||||
|
||||
# Normalize timestamp to ensure consistent storage format
|
||||
normalized_timestamp = self._normalize_timestamp(decision.timestamp)
|
||||
|
||||
|
||||
node = ContextNode(
|
||||
node_id=node_id,
|
||||
node_type="Decision",
|
||||
@@ -1510,6 +1576,7 @@ class ContextGraph:
|
||||
valid_until=decision.valid_until,
|
||||
)
|
||||
self._add_internal_node(node)
|
||||
return node_id
|
||||
|
||||
def add_causal_relationship(
|
||||
self,
|
||||
|
||||
@@ -5,7 +5,7 @@ Export & import routes.
|
||||
import asyncio
|
||||
import io
|
||||
import json
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import tempfile
|
||||
from typing import Optional
|
||||
@@ -13,6 +13,8 @@ from typing import Optional
|
||||
from fastapi import APIRouter, Depends, File, UploadFile
|
||||
from fastapi.responses import Response
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
from ..dependencies import get_session, get_ws_manager
|
||||
from ..schemas import ExportRequest
|
||||
from ..session import GraphSession
|
||||
@@ -229,7 +231,8 @@ async def import_file(
|
||||
"detail": f"File type not supported yet: {filename}",
|
||||
}
|
||||
except Exception as exc:
|
||||
result = {"status": "error", "detail": str(exc)}
|
||||
logger.exception("Import failed")
|
||||
result = {"status": "error", "detail": "An internal error occurred during import"}
|
||||
|
||||
await ws.broadcast("import_completed", result)
|
||||
return result
|
||||
|
||||
@@ -0,0 +1,141 @@
|
||||
"""
|
||||
Vocabulary routes - SKOS ingestion, scheme listing, and hierarchy trees.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from collections import defaultdict
|
||||
from typing import List
|
||||
|
||||
from fastapi import APIRouter, Depends, File, Query, UploadFile
|
||||
|
||||
from ..dependencies import get_session
|
||||
from ..schemas import ConceptNode, VocabularyScheme
|
||||
from ..session import GraphSession
|
||||
from ..utils.rdf_parser import parse_skos_file
|
||||
|
||||
router = APIRouter(prefix="/api/vocabulary", tags=["Vocabulary"])
|
||||
|
||||
|
||||
@router.get("/schemes", response_model=List[VocabularyScheme])
|
||||
async def list_schemes(
|
||||
session: GraphSession = Depends(get_session),
|
||||
):
|
||||
"""List all available SKOS Concept Schemes (Vocabularies)."""
|
||||
nodes, _ = await asyncio.to_thread(
|
||||
session.get_nodes, node_type="skos:ConceptScheme", skip=0, limit=999_999
|
||||
)
|
||||
|
||||
schemes = []
|
||||
for n in nodes:
|
||||
meta = n.get("metadata", n.get("properties", {}))
|
||||
schemes.append(
|
||||
VocabularyScheme(
|
||||
uri=n.get("id", ""),
|
||||
label=meta.get("content", n.get("content", n.get("id", ""))),
|
||||
description=meta.get("description"),
|
||||
)
|
||||
)
|
||||
return schemes
|
||||
|
||||
|
||||
@router.post("/import")
|
||||
async def import_vocabulary(
|
||||
file: UploadFile = File(...),
|
||||
session: GraphSession = Depends(get_session),
|
||||
):
|
||||
"""
|
||||
Import a SKOS vocabulary from a .ttl or .rdf file.
|
||||
"""
|
||||
content = await file.read()
|
||||
filename = file.filename or "vocabulary.ttl"
|
||||
|
||||
|
||||
parse_format = "xml" if filename.endswith((".rdf", ".owl")) else "turtle"
|
||||
|
||||
try:
|
||||
nodes, edges = await asyncio.to_thread(parse_skos_file, content, parse_format)
|
||||
except ValueError as exc:
|
||||
from fastapi import HTTPException
|
||||
raise HTTPException(status_code=422, detail=str(exc))
|
||||
|
||||
added_nodes = await asyncio.to_thread(session.add_nodes, nodes)
|
||||
added_edges = await asyncio.to_thread(session.add_edges, edges)
|
||||
|
||||
return {
|
||||
"status": "success",
|
||||
"filename": filename,
|
||||
"nodes_added": added_nodes,
|
||||
"edges_added": added_edges,
|
||||
}
|
||||
|
||||
|
||||
@router.get("/hierarchy", response_model=List[ConceptNode])
|
||||
async def get_hierarchy(
|
||||
scheme: str = Query(..., description="The URI of the ConceptScheme to load"),
|
||||
session: GraphSession = Depends(get_session),
|
||||
):
|
||||
"""
|
||||
Fetch the nested broader/narrower tree for a specific vocabulary scheme.
|
||||
Executes in O(V+E) time by building the adjacency list in memory.
|
||||
"""
|
||||
|
||||
nodes, _ = await asyncio.to_thread(
|
||||
session.get_nodes, node_type="skos:Concept", skip=0, limit=999_999
|
||||
)
|
||||
edges, _ = await asyncio.to_thread(session.get_edges, skip=0, limit=999_999)
|
||||
|
||||
|
||||
scheme_node_ids = set()
|
||||
for e in edges:
|
||||
src, tgt, etype = e.get("source"), e.get("target"), e.get("type")
|
||||
if tgt == scheme and etype in ("skos:inScheme", "skos:topConceptOf"):
|
||||
scheme_node_ids.add(src)
|
||||
elif src == scheme and etype == "skos:hasTopConcept":
|
||||
scheme_node_ids.add(tgt)
|
||||
|
||||
node_map = {}
|
||||
for n in nodes:
|
||||
nid = n.get("id")
|
||||
if nid in scheme_node_ids:
|
||||
meta = n.get("metadata", n.get("properties", {}))
|
||||
node_map[nid] = ConceptNode(
|
||||
uri=nid,
|
||||
pref_label=meta.get("content", n.get("content", nid)),
|
||||
alt_labels=meta.get("alt_labels", []),
|
||||
children=[]
|
||||
)
|
||||
|
||||
|
||||
parent_to_children = defaultdict(list)
|
||||
has_parent = set()
|
||||
|
||||
for e in edges:
|
||||
src, tgt, etype = e.get("source"), e.get("target"), e.get("type")
|
||||
if src in node_map and tgt in node_map:
|
||||
if etype == "skos:broader":
|
||||
# Source is narrower (child), Target is broader (parent)
|
||||
parent_to_children[tgt].append(src)
|
||||
has_parent.add(src)
|
||||
elif etype == "skos:narrower":
|
||||
# Source is broader (parent), Target is narrower (child)
|
||||
parent_to_children[src].append(tgt)
|
||||
has_parent.add(tgt)
|
||||
|
||||
# Assemble nested tree — cycle-safe via visited set.
|
||||
def _attach_children(nid: str, visited: set) -> ConceptNode:
|
||||
node_obj = node_map[nid]
|
||||
child_ids = [c for c in parent_to_children.get(nid, []) if c not in visited]
|
||||
if child_ids:
|
||||
node_obj.children = [
|
||||
_attach_children(cid, visited | {nid}) for cid in child_ids
|
||||
]
|
||||
else:
|
||||
node_obj.children = None # leaf node signal for the UI
|
||||
return node_obj
|
||||
|
||||
roots = [
|
||||
_attach_children(nid, {nid})
|
||||
for nid in node_map
|
||||
if nid not in has_parent
|
||||
]
|
||||
return roots
|
||||
@@ -255,3 +255,20 @@ class AnnotationResponse(BaseModel):
|
||||
tags: List[str] = Field(default_factory=list)
|
||||
visibility: str = "public"
|
||||
created_at: str = ""
|
||||
|
||||
class VocabularyScheme(BaseModel):
|
||||
""" A SKOS Concept Scheme (Vocabulary / Ontology)."""
|
||||
|
||||
uri: str
|
||||
label: str
|
||||
description: Optional[str] = None
|
||||
|
||||
class ConceptNode(BaseModel):
|
||||
""" A SKOS Concept, nested hierarchically."""
|
||||
|
||||
uri: str
|
||||
pref_label: str
|
||||
alt_labels: List[str] = Field(default_factory=list)
|
||||
children: Optional[List['ConceptNode']] = None
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1 @@
|
||||
"""Utility helpers for the Semantica Knowledge Explorer."""
|
||||
@@ -0,0 +1,138 @@
|
||||
"""
|
||||
RDF / SKOS parsing utility for the knowledge Explorer
|
||||
|
||||
Parses `.ttl` and `.rdf` files, extracting skos:Concept and skos:ConceptScheme entities into flat dicts
|
||||
compatible with ContextGraph.
|
||||
"""
|
||||
|
||||
from typing import Any, Dict, List, Tuple
|
||||
import rdflib
|
||||
from rdflib.namespace import RDF, RDFS, SKOS
|
||||
|
||||
def _get_best_label(graph: rdflib.Graph, subject: rdflib.URIRef, predicate: rdflib.URIRef) -> str:
|
||||
"""
|
||||
Extracts the best available string label for a given predicate.
|
||||
Prioritizes English tags ('en'), then untagged strings, then falls back to whatever
|
||||
is available. Strips language tags in the process.
|
||||
"""
|
||||
|
||||
labels = list(graph.objects(subject, predicate))
|
||||
if not labels:
|
||||
return ""
|
||||
|
||||
# priority 1: English match exact
|
||||
for lbl in labels:
|
||||
if getattr(lbl, "language", None) == "en":
|
||||
return str(lbl)
|
||||
|
||||
# priority 2: English variants
|
||||
for lbl in labels:
|
||||
lang = getattr(lbl, "language", "")
|
||||
if lang and lang.startswith("en"):
|
||||
return str(lbl)
|
||||
|
||||
# priority 3: No lang tag
|
||||
for lbl in labels:
|
||||
if getattr(lbl, "language", None) is None:
|
||||
return str(lbl)
|
||||
|
||||
# whatever is first if not any of the three above
|
||||
return str(labels[0])
|
||||
|
||||
def _get_all_labels(graph: rdflib.Graph, subject: rdflib.URIRef, predicate: rdflib.URIRef) -> List[str]:
|
||||
""" Returns a list of all string values for a predicate, stripping lang tags."""
|
||||
return list({str(lbl) for lbl in graph.objects(subject, predicate)})
|
||||
|
||||
def parse_skos_file(file_bytes: bytes, rdf_format: str = "turtle") -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
|
||||
"""
|
||||
Parses RDF data and extracts SKOS concepts and relationships.
|
||||
|
||||
Args:
|
||||
file_bytes: The raw bytes of the uploaded file.
|
||||
rdf_format: The rdflib parse format (e.g., "turtle" for .ttl, "xml" for .rdf).
|
||||
|
||||
Returns:
|
||||
A tuple of (nodes_list, edges_list) formatted for ContextGraph ingestion.
|
||||
|
||||
Note:
|
||||
Edges are only emitted when both endpoints exist in the parsed file.
|
||||
Relationships pointing to external URIs not declared as skos:Concept or
|
||||
skos:ConceptScheme (e.g. cross-vocabulary broader links) are silently dropped.
|
||||
"""
|
||||
|
||||
g = rdflib.Graph()
|
||||
|
||||
try:
|
||||
g.parse(data=file_bytes, format=rdf_format)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to parse RDF file as {rdf_format}. Ensure the file is valid. Details: {str(e)}") from e
|
||||
|
||||
nodes_dict: Dict[str, Dict[str, Any]] = {}
|
||||
edges: List[Dict[str, Any]] = []
|
||||
|
||||
# extract concept schemas
|
||||
for scheme in g.subjects(RDF.type, SKOS.ConceptScheme):
|
||||
uri = str(scheme)
|
||||
|
||||
# if no prefLabel
|
||||
|
||||
pref_label = _get_best_label(g, scheme, SKOS.prefLabel)
|
||||
if not pref_label:
|
||||
pref_label = uri.split("/")[-1].split("#")[-1]
|
||||
|
||||
nodes_dict[uri] = {
|
||||
"id": uri,
|
||||
"type": "skos:ConceptScheme",
|
||||
"properties": {
|
||||
"content": pref_label,
|
||||
"alt_labels": _get_all_labels(g, scheme, SKOS.altLabel),
|
||||
"description": _get_best_label(g, scheme, SKOS.definition)
|
||||
}
|
||||
}
|
||||
|
||||
# Extract concepts
|
||||
for concept in g.subjects(RDF.type, SKOS.Concept):
|
||||
uri = str(concept)
|
||||
|
||||
pref_label = _get_best_label(g, concept, SKOS.prefLabel)
|
||||
if not pref_label:
|
||||
pref_label = uri.split("/")[-1].split("#")[-1]
|
||||
|
||||
nodes_dict[uri] = {
|
||||
"id": uri,
|
||||
"type": "skos:Concept",
|
||||
"properties": {
|
||||
"content": pref_label,
|
||||
"alt_labels": _get_all_labels(g, concept, SKOS.altLabel),
|
||||
"description": _get_best_label(g, concept, SKOS.definition)
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
# Extract Relationships aka edges
|
||||
|
||||
structural_preds = {
|
||||
SKOS.broader: "skos:broader",
|
||||
SKOS.narrower: "skos:narrower",
|
||||
SKOS.inScheme: "skos:inScheme",
|
||||
SKOS.related: "skos:related",
|
||||
SKOS.topConceptOf: "skos:topConceptOf",
|
||||
SKOS.hasTopConcept: "skos:hasTopConcept"
|
||||
}
|
||||
|
||||
for pred, edge_type in structural_preds.items():
|
||||
for source, target in g.subject_objects(pred):
|
||||
# Only track edges where nodes were successfully extracted
|
||||
if str(source) in nodes_dict and str(target) in nodes_dict:
|
||||
edges.append({
|
||||
"source_id": str(source),
|
||||
"target_id": str(target),
|
||||
"type": edge_type,
|
||||
"weight": 1.0,
|
||||
"properties": {}
|
||||
})
|
||||
|
||||
|
||||
return list(nodes_dict.values()), edges
|
||||
|
||||
|
||||
@@ -392,7 +392,7 @@ class EmailParser:
|
||||
# Extract URLs from text using regex
|
||||
import re
|
||||
|
||||
url_pattern = r"http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
|
||||
url_pattern = r"https?://(?:[a-zA-Z0-9\-._~!$&'()*+,;=:@/?#\[\]]|%[0-9a-fA-F]{2})+"
|
||||
text_links = re.findall(url_pattern, email_content)
|
||||
links.extend(text_links)
|
||||
|
||||
|
||||
@@ -528,6 +528,22 @@ class CentralityCalculator:
|
||||
relationships = graph.get_relationships()
|
||||
elif isinstance(graph, dict):
|
||||
relationships = graph.get("relationships", graph.get("edges", []))
|
||||
elif hasattr(graph, "edges") and not callable(graph.edges):
|
||||
# ContextGraph-style: edges is a list of dataclass objects with source_id/target_id
|
||||
for edge in (graph.edges or []):
|
||||
if isinstance(edge, dict):
|
||||
src = edge.get("source") or edge.get("source_id")
|
||||
tgt = edge.get("target") or edge.get("target_id")
|
||||
else:
|
||||
src = getattr(edge, "source_id", None) or getattr(edge, "source", None)
|
||||
tgt = getattr(edge, "target_id", None) or getattr(edge, "target", None)
|
||||
if src and tgt:
|
||||
src, tgt = str(src), str(tgt)
|
||||
if tgt not in adjacency[src]:
|
||||
adjacency[src].append(tgt)
|
||||
if src not in adjacency[tgt]:
|
||||
adjacency[tgt].append(src)
|
||||
return dict(adjacency)
|
||||
|
||||
# Build adjacency
|
||||
for rel in relationships:
|
||||
|
||||
@@ -302,10 +302,10 @@ class TextCleaner:
|
||||
|
||||
# Remove potential script tags
|
||||
text = re.sub(
|
||||
r"<script[^>]*>.*?</script>", "", text, flags=re.IGNORECASE | re.DOTALL
|
||||
r"<script[^>]*>.*?</script(?:\s[^>]*)?>", "", text, flags=re.IGNORECASE | re.DOTALL
|
||||
)
|
||||
text = re.sub(
|
||||
r"<iframe[^>]*>.*?</iframe>", "", text, flags=re.IGNORECASE | re.DOTALL
|
||||
r"<iframe[^>]*>.*?</iframe(?:\s[^>]*)?>", "", text, flags=re.IGNORECASE | re.DOTALL
|
||||
)
|
||||
|
||||
# Remove javascript: URLs
|
||||
|
||||
@@ -350,7 +350,7 @@ class NamingConventions:
|
||||
def _is_noun_phrase(self, name: str) -> bool:
|
||||
"""Check if name is a noun phrase (basic heuristic)."""
|
||||
# Basic heuristic: PascalCase words are typically nouns
|
||||
return bool(re.match(r"^[A-Z][a-zA-Z0-9]*([A-Z][a-zA-Z0-9]*)*$", name))
|
||||
return bool(name and name[0].isupper() and re.match(r"^[A-Za-z0-9]+$", name))
|
||||
|
||||
def _is_verb_phrase(self, name: str) -> bool:
|
||||
"""Check if name is a verb phrase (basic heuristic)."""
|
||||
|
||||
@@ -443,12 +443,6 @@ class RelationExtractor:
|
||||
if verbose_mode and method_name == "llm":
|
||||
import sys
|
||||
print(f" [RelationExtractor] Processing with {method_name}...", flush=True, file=sys.stdout)
|
||||
print(f" [RelationExtractor Debug] method_options keys: {list(method_options.keys())}", flush=True, file=sys.stdout)
|
||||
if "api_key" in method_options:
|
||||
masked = method_options["api_key"][:4] + "..." if method_options["api_key"] else "None"
|
||||
print(f" [RelationExtractor Debug] api_key present: {masked}", flush=True, file=sys.stdout)
|
||||
else:
|
||||
print(f" [RelationExtractor Debug] api_key NOT present", flush=True, file=sys.stdout)
|
||||
|
||||
relations = method_func(text, entities, **method_options)
|
||||
|
||||
|
||||
@@ -494,11 +494,6 @@ class TripletExtractor:
|
||||
if verbose_mode and method_name == "llm":
|
||||
import sys
|
||||
print(f" [TripletExtractor] Processing with {method_name}...", flush=True, file=sys.stdout)
|
||||
if "api_key" in method_options:
|
||||
masked = method_options["api_key"][:4] + "..." if method_options["api_key"] else "None"
|
||||
print(f" [TripletExtractor Debug] api_key present: {masked}", flush=True, file=sys.stdout)
|
||||
else:
|
||||
print(f" [TripletExtractor Debug] api_key NOT present", flush=True, file=sys.stdout)
|
||||
|
||||
triplets = method_func(
|
||||
text,
|
||||
|
||||
+41
-1
@@ -5,6 +5,7 @@ This module provides the REST API server for the Semantica framework
|
||||
using FastAPI and uvicorn.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import uvicorn
|
||||
from fastapi import FastAPI, HTTPException
|
||||
from pydantic import BaseModel
|
||||
@@ -53,9 +54,48 @@ async def build_kb(request: BuildRequest):
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
# Explorer API Routers (Loaded gracefully if semantica[explorer] is installed)
|
||||
|
||||
try:
|
||||
from .explorer.routes import (
|
||||
analytics,
|
||||
annotations,
|
||||
decisions,
|
||||
enrich,
|
||||
export_import,
|
||||
graph,
|
||||
temporal,
|
||||
)
|
||||
|
||||
app.include_router(analytics.router)
|
||||
app.include_router(annotations.router)
|
||||
app.include_router(decisions.router)
|
||||
app.include_router(enrich.router)
|
||||
app.include_router(export_import.router)
|
||||
app.include_router(graph.router)
|
||||
app.include_router(temporal.router)
|
||||
|
||||
logging.info("Explorer API routes successfully mounted.")
|
||||
|
||||
except ImportError as exc:
|
||||
logging.warning(
|
||||
f"Explorer API routes not mounted. To enable the Knowledge Explorer, "
|
||||
f"install the required dependencies: pip install semantica[explorer]. "
|
||||
f"Details: {exc}"
|
||||
)
|
||||
|
||||
# Vocabulary router — mounted separately; available once PR #421 lands
|
||||
try:
|
||||
from .explorer.routes import vocabulary
|
||||
app.include_router(vocabulary.router)
|
||||
logging.info("Vocabulary API routes successfully mounted.")
|
||||
except ImportError:
|
||||
logging.debug("Vocabulary router not yet available (pending implementation).")
|
||||
|
||||
def main():
|
||||
"""Server entry point."""
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
main()
|
||||
@@ -109,10 +109,14 @@ class TripletStoreConfig:
|
||||
"""Load configuration from environment variables."""
|
||||
env_mappings = {
|
||||
"TRIPLET_STORE_DEFAULT_STORE": "default_store",
|
||||
"TRIPLET_STORE_DEFAULT_GRAPH": "default_graph",
|
||||
"TRIPLET_STORE_DEFAULT_GRAPH_URI": "default_graph_uri",
|
||||
"TRIPLET_STORE_DEFAULT_NAMED_GRAPHS": "default_graphs",
|
||||
"TRIPLET_STORE_BATCH_SIZE": "batch_size",
|
||||
"TRIPLET_STORE_ENABLE_CACHING": "enable_caching",
|
||||
"TRIPLET_STORE_CACHE_SIZE": "cache_size",
|
||||
"TRIPLET_STORE_ENABLE_OPTIMIZATION": "enable_optimization",
|
||||
"TRIPLET_STORE_ENABLE_NAMED_GRAPHS": "enable_named_graphs",
|
||||
"TRIPLET_STORE_MAX_RETRIES": "max_retries",
|
||||
"TRIPLET_STORE_RETRY_DELAY": "retry_delay",
|
||||
"TRIPLET_STORE_TIMEOUT": "timeout",
|
||||
@@ -139,6 +143,19 @@ class TripletStoreConfig:
|
||||
"yes",
|
||||
"on",
|
||||
]
|
||||
elif config_key == "enable_named_graphs":
|
||||
self._config[config_key] = value.lower() in [
|
||||
"true",
|
||||
"1",
|
||||
"yes",
|
||||
"on",
|
||||
]
|
||||
elif config_key == "default_graphs":
|
||||
self._config[config_key] = [
|
||||
graph_uri.strip()
|
||||
for graph_uri in value.split(",")
|
||||
if graph_uri.strip()
|
||||
]
|
||||
elif config_key == "retry_delay":
|
||||
try:
|
||||
self._config[config_key] = float(value)
|
||||
@@ -153,10 +170,14 @@ class TripletStoreConfig:
|
||||
"""Set default configuration values."""
|
||||
defaults = {
|
||||
"default_store": None,
|
||||
"default_graph": None,
|
||||
"default_graph_uri": None,
|
||||
"default_graphs": [],
|
||||
"batch_size": 1000,
|
||||
"enable_caching": True,
|
||||
"cache_size": 1000,
|
||||
"enable_optimization": True,
|
||||
"enable_named_graphs": True,
|
||||
"max_retries": 3,
|
||||
"retry_delay": 1.0,
|
||||
"timeout": 30,
|
||||
|
||||
@@ -31,6 +31,7 @@ License: MIT
|
||||
"""
|
||||
|
||||
import time
|
||||
import re
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime
|
||||
from typing import Any, Dict, List, Optional
|
||||
@@ -120,11 +121,22 @@ class QueryEngine:
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
supports_named_graphs = options.get("supports_named_graphs")
|
||||
if supports_named_graphs is None:
|
||||
supports_named_graphs = getattr(store_backend, "supports_named_graphs", True)
|
||||
|
||||
prepared_query = self.prepare_query(
|
||||
query,
|
||||
graph=options.get("graph"),
|
||||
graphs=options.get("graphs"),
|
||||
supports_named_graphs=supports_named_graphs,
|
||||
)
|
||||
|
||||
# Validate query
|
||||
self.progress_tracker.update_tracking(
|
||||
tracking_id, message="Validating query..."
|
||||
)
|
||||
if not self._validate_query(query):
|
||||
if not self._validate_query(prepared_query):
|
||||
self.progress_tracker.stop_tracking(
|
||||
tracking_id, status="failed", message="Invalid SPARQL query"
|
||||
)
|
||||
@@ -135,7 +147,7 @@ class QueryEngine:
|
||||
self.progress_tracker.update_tracking(
|
||||
tracking_id, message="Checking cache..."
|
||||
)
|
||||
cache_key = self._get_cache_key(query)
|
||||
cache_key = self._get_cache_key(prepared_query)
|
||||
if cache_key in self.query_cache:
|
||||
self.logger.debug("Returning cached query result")
|
||||
cached_result = self.query_cache[cache_key]
|
||||
@@ -152,9 +164,9 @@ class QueryEngine:
|
||||
self.progress_tracker.update_tracking(
|
||||
tracking_id, message="Optimizing query..."
|
||||
)
|
||||
optimized_query = self.optimize_query(query, **options)
|
||||
optimized_query = self.optimize_query(prepared_query, **options)
|
||||
else:
|
||||
optimized_query = query
|
||||
optimized_query = prepared_query
|
||||
|
||||
# Execute query
|
||||
self.progress_tracker.update_tracking(
|
||||
@@ -173,8 +185,10 @@ class QueryEngine:
|
||||
execution_time=execution_time,
|
||||
metadata={
|
||||
**result_data.get("metadata", {}),
|
||||
"optimized": optimized_query != query,
|
||||
"optimized": optimized_query != prepared_query,
|
||||
"cached": False,
|
||||
"graph": options.get("graph"),
|
||||
"graphs": options.get("graphs") or [],
|
||||
},
|
||||
)
|
||||
|
||||
@@ -183,12 +197,12 @@ class QueryEngine:
|
||||
self.progress_tracker.update_tracking(
|
||||
tracking_id, message="Caching result..."
|
||||
)
|
||||
self._cache_result(query, result)
|
||||
self._cache_result(prepared_query, result)
|
||||
|
||||
# Record history
|
||||
self.query_history.append(
|
||||
{
|
||||
"query": query,
|
||||
"query": prepared_query,
|
||||
"execution_time": execution_time,
|
||||
"result_count": len(result.bindings),
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
@@ -212,6 +226,92 @@ class QueryEngine:
|
||||
)
|
||||
raise ProcessingError(f"Query execution failed: {e}")
|
||||
|
||||
def prepare_query(
|
||||
self,
|
||||
query: str,
|
||||
graph: Optional[str] = None,
|
||||
graphs: Optional[List[str]] = None,
|
||||
supports_named_graphs: bool = True,
|
||||
) -> str:
|
||||
"""Prepare query with optional graph dataset clauses."""
|
||||
if not query:
|
||||
return ""
|
||||
|
||||
resolved_graph = (
|
||||
graph
|
||||
or self.config.get("default_graph")
|
||||
or self.config.get("default_graph_uri")
|
||||
)
|
||||
resolved_graphs = graphs
|
||||
if resolved_graphs is None:
|
||||
resolved_graphs = self.config.get("default_graphs")
|
||||
|
||||
if isinstance(resolved_graphs, str):
|
||||
resolved_graphs = [resolved_graphs]
|
||||
resolved_graphs = [g for g in (resolved_graphs or []) if g]
|
||||
|
||||
if resolved_graph and resolved_graph in resolved_graphs:
|
||||
# Preserve graph as default dataset while avoiding duplicate URIs in FROM NAMED.
|
||||
resolved_graphs = [g for g in resolved_graphs if g != resolved_graph]
|
||||
|
||||
if not supports_named_graphs and (resolved_graph or resolved_graphs):
|
||||
self.logger.warning(
|
||||
"Named graph options were provided but backend does not support named graphs; "
|
||||
"falling back to backend default dataset"
|
||||
)
|
||||
return query.strip()
|
||||
|
||||
return self._inject_graph_clauses(
|
||||
query,
|
||||
graph=resolved_graph,
|
||||
graphs=resolved_graphs,
|
||||
)
|
||||
|
||||
def _inject_graph_clauses(
|
||||
self,
|
||||
query: str,
|
||||
graph: Optional[str] = None,
|
||||
graphs: Optional[List[str]] = None,
|
||||
) -> str:
|
||||
"""Inject FROM/FROM NAMED clauses immediately before WHERE."""
|
||||
normalized_query = query.strip()
|
||||
graph_list = [g for g in (graphs or []) if g]
|
||||
|
||||
if not graph and not graph_list:
|
||||
return normalized_query
|
||||
|
||||
if re.search(r"\bFROM\b", normalized_query, flags=re.IGNORECASE):
|
||||
return normalized_query
|
||||
|
||||
if not re.search(
|
||||
r"\b(SELECT|ASK|CONSTRUCT|DESCRIBE)\b",
|
||||
normalized_query,
|
||||
flags=re.IGNORECASE,
|
||||
):
|
||||
return normalized_query
|
||||
|
||||
where_match = re.search(r"\bWHERE\b", normalized_query, flags=re.IGNORECASE)
|
||||
if not where_match:
|
||||
return normalized_query
|
||||
|
||||
dataset_clauses: List[str] = []
|
||||
if graph:
|
||||
safe_graph = self._sanitize_uri(graph)
|
||||
dataset_clauses.append(f"FROM <{safe_graph}>")
|
||||
|
||||
for graph_uri in graph_list:
|
||||
safe_graph = self._sanitize_uri(graph_uri)
|
||||
dataset_clauses.append(f"FROM NAMED <{safe_graph}>")
|
||||
|
||||
if not dataset_clauses:
|
||||
return normalized_query
|
||||
|
||||
before_where = normalized_query[: where_match.start()].rstrip()
|
||||
where_and_after = normalized_query[where_match.start() :].lstrip()
|
||||
dataset_block = "\n".join(dataset_clauses)
|
||||
|
||||
return f"{before_where}\n{dataset_block}\n{where_and_after}"
|
||||
|
||||
def optimize_query(self, query: str, **options) -> str:
|
||||
"""
|
||||
Optimize SPARQL query.
|
||||
|
||||
@@ -46,6 +46,7 @@ class TripletStore:
|
||||
"""
|
||||
|
||||
SUPPORTED_BACKENDS = {"blazegraph", "jena", "rdf4j"}
|
||||
NAMED_GRAPH_CAPABLE_BACKENDS = {"blazegraph", "rdf4j"}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
@@ -76,7 +77,7 @@ class TripletStore:
|
||||
|
||||
self.backend_type = backend.lower()
|
||||
self.endpoint = endpoint
|
||||
self.config = config
|
||||
self.config = {**triplet_store_config.get_all(), **config}
|
||||
|
||||
# Initialize store backend
|
||||
self._store_backend = None
|
||||
@@ -393,7 +394,12 @@ class TripletStore:
|
||||
return self.add_triplet(new_triplet, **options)
|
||||
|
||||
def execute_query(
|
||||
self, query: str, parameters: Optional[Dict[str, Any]] = None, **options
|
||||
self,
|
||||
query: str,
|
||||
parameters: Optional[Dict[str, Any]] = None,
|
||||
graph: Optional[str] = None,
|
||||
graphs: Optional[List[str]] = None,
|
||||
**options,
|
||||
) -> Any:
|
||||
"""
|
||||
Execute a SPARQL query.
|
||||
@@ -401,11 +407,25 @@ class TripletStore:
|
||||
Args:
|
||||
query: SPARQL query string
|
||||
parameters: Query parameters
|
||||
graph: Optional default graph URI for dataset scoping
|
||||
graphs: Optional list of named graph URIs for dataset scoping
|
||||
**options: Additional options
|
||||
|
||||
Returns:
|
||||
Query results (format depends on query type)
|
||||
"""
|
||||
if graph is not None:
|
||||
options["graph"] = graph
|
||||
if graphs is not None:
|
||||
options["graphs"] = graphs
|
||||
|
||||
enable_named_graphs = self.config.get("enable_named_graphs", True)
|
||||
options.setdefault(
|
||||
"supports_named_graphs",
|
||||
enable_named_graphs
|
||||
and self.backend_type in self.NAMED_GRAPH_CAPABLE_BACKENDS,
|
||||
)
|
||||
|
||||
return self.query_engine.execute_query(query, self._store_backend, **options)
|
||||
|
||||
def _validate_triplet(self, triplet: Triplet) -> bool:
|
||||
|
||||
@@ -7,6 +7,7 @@ knowledge graphs and ontologies with comprehensive change tracking.
|
||||
|
||||
import os
|
||||
import tempfile
|
||||
from unittest.mock import MagicMock
|
||||
import pytest
|
||||
from semantica.change_management import (
|
||||
TemporalVersionManager,
|
||||
@@ -179,6 +180,41 @@ class TestTemporalVersionManager:
|
||||
assert len(versions) == 1
|
||||
assert versions[0]["entity_count"] == 2
|
||||
assert versions[0]["relationship_count"] == 1
|
||||
|
||||
def test_prune_versions_sanitizes_graph_uri_in_drop_query(self):
|
||||
"""Ensure DROP GRAPH query uses sanitized URI encoding for unsafe characters."""
|
||||
manager = TemporalVersionManager()
|
||||
triplet_store = MagicMock()
|
||||
|
||||
manager.storage.save(
|
||||
{
|
||||
"label": "old-v1",
|
||||
"timestamp": "2024-01-01T00:00:00",
|
||||
"author": "test@example.com",
|
||||
"description": "old",
|
||||
"checksum": "x",
|
||||
"entities": [],
|
||||
"relationships": [],
|
||||
"graph_uri": "http://example.org/graph> } ; DROP ALL ; #",
|
||||
}
|
||||
)
|
||||
manager.storage.save(
|
||||
{
|
||||
"label": "new-v2",
|
||||
"timestamp": "2025-01-01T00:00:00",
|
||||
"author": "test@example.com",
|
||||
"description": "new",
|
||||
"checksum": "y",
|
||||
"entities": [],
|
||||
"relationships": [],
|
||||
"graph_uri": "http://example.org/graph/new",
|
||||
}
|
||||
)
|
||||
|
||||
manager.prune_versions(keep_last_n=1, triplet_store=triplet_store)
|
||||
|
||||
query = triplet_store.execute_query.call_args[0][0]
|
||||
assert "DROP SILENT GRAPH <http://example.org/graph%3E%20%7D%20%3B%20DROP%20ALL%20%3B%20%23>" == query
|
||||
|
||||
def test_get_version(self):
|
||||
"""Test retrieving specific version."""
|
||||
|
||||
@@ -39,6 +39,24 @@ def test_agent_context_minimal_decisions_and_chain():
|
||||
assert len(chain) >= 1
|
||||
|
||||
|
||||
def test_agent_context_inmemory_store_and_retrieve():
|
||||
"""VectorStore(backend="inmemory") stores memories without faiss-cpu."""
|
||||
vs = VectorStore(backend="inmemory")
|
||||
ctx = AgentContext(
|
||||
vector_store=vs,
|
||||
knowledge_graph=ContextGraph(),
|
||||
decision_tracking=True,
|
||||
kg_algorithms=False,
|
||||
vector_store_features=False,
|
||||
)
|
||||
memory_id = ctx.store(
|
||||
"GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%",
|
||||
conversation_id="test_session",
|
||||
)
|
||||
assert isinstance(memory_id, str)
|
||||
assert len(memory_id) > 0
|
||||
|
||||
|
||||
def test_agent_context_policy_engine_with_graph_backend():
|
||||
vs = VectorStore(backend="inmemory", dimension=64)
|
||||
graph = ContextGraph()
|
||||
|
||||
@@ -0,0 +1,564 @@
|
||||
"""
|
||||
Regression tests for Context Explainability Output Fixes.
|
||||
|
||||
Covers:
|
||||
- Readable decision text preservation in ContextGraph nodes and reconstruction paths
|
||||
- Enriched causal/path outputs (from_scenario, to_scenario, scenario/outcome/category dicts)
|
||||
- PolicyEngine.get_affected_decisions() consistent metadata across Cypher and fallback branches
|
||||
- EntityLinker similarity flows return full enriched payloads
|
||||
- KG consumer compatibility (node_embeddings, link_predictor, centrality_calculator, path_finder)
|
||||
when ContextGraph is used as the graph store and get_neighbors returns enriched dicts
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from datetime import datetime, timedelta
|
||||
from unittest.mock import MagicMock, patch, PropertyMock
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from semantica.context.context_graph import ContextGraph
|
||||
from semantica.context.decision_models import Decision
|
||||
from semantica.context.entity_linker import EntityLinker
|
||||
from semantica.context.policy_engine import PolicyEngine
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _make_decision(decision_id: str, scenario: str, reasoning: str,
|
||||
category: str = "test", outcome: str = "approved",
|
||||
confidence: float = 0.9, decision_maker: str = "agent_1") -> Decision:
|
||||
return Decision(
|
||||
decision_id=decision_id,
|
||||
category=category,
|
||||
scenario=scenario,
|
||||
reasoning=reasoning,
|
||||
outcome=outcome,
|
||||
confidence=confidence,
|
||||
timestamp=datetime.now(),
|
||||
decision_maker=decision_maker,
|
||||
)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Group 1 – Readable Decision Text Preservation
|
||||
# ===========================================================================
|
||||
|
||||
class TestReadableDecisionTextPreservation:
|
||||
"""Decision-node storage preserves full human-readable text, not IDs."""
|
||||
|
||||
def test_add_decision_scenario_stored_as_content(self):
|
||||
"""scenario is stored as node.content, not as an opaque ID."""
|
||||
g = ContextGraph()
|
||||
d = _make_decision(
|
||||
"d1",
|
||||
scenario="Loan application for first-time buyer: $300k, FICO 720",
|
||||
reasoning="Strong credit profile with stable income"
|
||||
)
|
||||
g.add_decision(d)
|
||||
|
||||
node = g.nodes["d1"]
|
||||
assert node.content == d.scenario, (
|
||||
"node.content must equal the full human-readable scenario string"
|
||||
)
|
||||
assert node.content != "d1", "node.content must NOT be the node ID"
|
||||
|
||||
def test_add_decision_reasoning_preserved_in_properties(self):
|
||||
"""Full reasoning text is stored in node.properties, not truncated."""
|
||||
g = ContextGraph()
|
||||
long_reasoning = (
|
||||
"Customer has 8-year payment history, zero delinquencies, debt-to-income "
|
||||
"ratio of 28%, salary verified at $95k/year via W-2. Risk score: LOW."
|
||||
)
|
||||
d = _make_decision("d2", "Credit card limit review", long_reasoning)
|
||||
g.add_decision(d)
|
||||
|
||||
node = g.nodes["d2"]
|
||||
assert node.properties["reasoning"] == long_reasoning
|
||||
assert len(node.properties["reasoning"]) > 50
|
||||
|
||||
def test_find_precedents_returns_decision_with_readable_scenario(self):
|
||||
"""find_precedents() returns Decision objects whose .scenario is readable text."""
|
||||
g = ContextGraph()
|
||||
cause = _make_decision(
|
||||
"cause_1",
|
||||
scenario="Overdraft protection request – account in good standing 5 yrs",
|
||||
reasoning="Long account history, low overdraft frequency"
|
||||
)
|
||||
effect = _make_decision(
|
||||
"effect_1",
|
||||
scenario="Fee waiver granted due to precedent overdraft approval",
|
||||
reasoning="Follows precedent cause_1"
|
||||
)
|
||||
g.add_decision(cause)
|
||||
g.add_decision(effect)
|
||||
g.add_causal_relationship("cause_1", "effect_1", "PRECEDENT_FOR")
|
||||
|
||||
precedents = g.find_precedents("effect_1")
|
||||
assert len(precedents) >= 1, "Should return at least one precedent"
|
||||
|
||||
p = precedents[0]
|
||||
assert isinstance(p, Decision)
|
||||
assert p.scenario, "Returned Decision.scenario must not be empty"
|
||||
assert "overdraft" in p.scenario.lower() or "Overdraft" in p.scenario, (
|
||||
f"scenario should contain human-readable text, got: {p.scenario!r}"
|
||||
)
|
||||
assert p.scenario != "cause_1", "scenario must NOT be the raw node ID"
|
||||
|
||||
def test_get_causal_chain_returns_readable_text(self):
|
||||
"""get_causal_chain() returns Decision objects with scenario text from node.content."""
|
||||
g = ContextGraph()
|
||||
for did, scenario in [
|
||||
("root", "Initial fraud alert triggered on account #7734"),
|
||||
("mid", "Temporary hold placed pending fraud investigation"),
|
||||
("leaf", "Card blocked; customer notified via SMS"),
|
||||
]:
|
||||
g.add_decision(_make_decision(did, scenario, f"reasoning for {did}"))
|
||||
|
||||
g.add_causal_relationship("root", "mid", "CAUSED")
|
||||
g.add_causal_relationship("mid", "leaf", "CAUSED")
|
||||
|
||||
chain = g.get_causal_chain("leaf", direction="upstream")
|
||||
assert len(chain) >= 1
|
||||
|
||||
for dec in chain:
|
||||
assert isinstance(dec, Decision)
|
||||
assert dec.scenario, "Each chained Decision must have non-empty scenario"
|
||||
assert dec.scenario != dec.decision_id, (
|
||||
f"scenario '{dec.scenario}' must not equal the decision_id"
|
||||
)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Group 2 – Enriched Causal / Path Outputs
|
||||
# ===========================================================================
|
||||
|
||||
class TestEnrichedCausalOutputs:
|
||||
"""trace_decision_causality and analyze_decision_influence return readable dicts."""
|
||||
|
||||
def _graph_with_decisions(self):
|
||||
g = ContextGraph()
|
||||
alpha_id = g.record_decision(
|
||||
category="mortgage",
|
||||
scenario="Approve mortgage for tech employee earning $180k",
|
||||
reasoning="Strong credit profile and stable income verified",
|
||||
outcome="approved",
|
||||
confidence=0.92,
|
||||
entities=["tech_employee", "mortgage_dept"],
|
||||
)
|
||||
beta_id = g.record_decision(
|
||||
category="auto_loan",
|
||||
scenario="Approve auto-loan backed by employer letter",
|
||||
reasoning="Employer verification provided, income above threshold",
|
||||
outcome="approved",
|
||||
confidence=0.85,
|
||||
entities=["tech_employee", "auto_dept"],
|
||||
)
|
||||
return g, alpha_id, beta_id
|
||||
|
||||
def test_trace_decision_causality_hops_have_scenario_fields(self):
|
||||
"""Each causal hop includes from_scenario and to_scenario with readable text."""
|
||||
g, alpha_id, beta_id = self._graph_with_decisions()
|
||||
chains = g.trace_decision_causality(beta_id, max_depth=3)
|
||||
|
||||
# At least one hop should exist (shared entity creates causal link)
|
||||
if chains:
|
||||
for hop_list in chains:
|
||||
for hop in hop_list:
|
||||
assert "from" in hop, "hop must have 'from' key"
|
||||
assert "to" in hop, "hop must have 'to' key"
|
||||
assert "from_scenario" in hop, (
|
||||
f"hop must have 'from_scenario' key, got keys: {list(hop.keys())}"
|
||||
)
|
||||
assert "to_scenario" in hop, (
|
||||
f"hop must have 'to_scenario' key, got keys: {list(hop.keys())}"
|
||||
)
|
||||
# Scenarios must be strings, not empty IDs
|
||||
assert isinstance(hop["from_scenario"], str)
|
||||
assert isinstance(hop["to_scenario"], str)
|
||||
|
||||
def test_analyze_decision_influence_direct_influence_is_enriched_dicts(self):
|
||||
"""direct_influence list contains dicts with decision_id, scenario, outcome, category."""
|
||||
g, alpha_id, beta_id = self._graph_with_decisions()
|
||||
result = g.analyze_decision_influence(alpha_id)
|
||||
|
||||
assert "direct_influence" in result
|
||||
assert isinstance(result["direct_influence"], list)
|
||||
|
||||
for item in result["direct_influence"]:
|
||||
assert isinstance(item, dict), (
|
||||
f"direct_influence items must be dicts, got {type(item)}"
|
||||
)
|
||||
for field in ("decision_id", "scenario", "outcome", "category"):
|
||||
assert field in item, (
|
||||
f"influence item missing field '{field}', keys: {list(item.keys())}"
|
||||
)
|
||||
|
||||
def test_analyze_decision_influence_scores_contain_readable_fields(self):
|
||||
"""influence_scores entries include scenario/outcome/category alongside score."""
|
||||
g, alpha_id, beta_id = self._graph_with_decisions()
|
||||
result = g.analyze_decision_influence(alpha_id)
|
||||
|
||||
assert "influence_scores" in result
|
||||
for item in result["influence_scores"]:
|
||||
assert "score" in item
|
||||
assert "decision_id" in item
|
||||
assert "scenario" in item
|
||||
assert "category" in item
|
||||
assert "outcome" in item
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Group 3 – PolicyEngine Consistent Decision Metadata
|
||||
# ===========================================================================
|
||||
|
||||
class TestPolicyEngineAffectedDecisions:
|
||||
"""get_affected_decisions() returns enriched metadata from both branches."""
|
||||
|
||||
def _mock_store_with_query(self, records):
|
||||
store = MagicMock()
|
||||
store.execute_query.return_value = records
|
||||
return store
|
||||
|
||||
def test_cypher_branch_returns_scenario_category_outcome_confidence(self):
|
||||
"""Cypher results include scenario/category/outcome/confidence with actual values."""
|
||||
records = [
|
||||
{
|
||||
"decision_id": "dec_abc",
|
||||
"scenario": "Increase credit limit for platinum member",
|
||||
"category": "credit",
|
||||
"outcome": "approved",
|
||||
"confidence": 0.88,
|
||||
}
|
||||
]
|
||||
store = self._mock_store_with_query(records)
|
||||
pe = PolicyEngine(graph_store=store)
|
||||
|
||||
affected = pe.get_affected_decisions("policy_1", "v1", "v2")
|
||||
|
||||
assert len(affected) == 1
|
||||
d = affected[0]
|
||||
assert d["scenario"] == "Increase credit limit for platinum member", (
|
||||
f"scenario must be readable text, got: {d['scenario']!r}"
|
||||
)
|
||||
assert d["category"] == "credit"
|
||||
assert d["outcome"] == "approved"
|
||||
assert d["confidence"] == pytest.approx(0.88, abs=1e-6)
|
||||
|
||||
def test_fallback_branch_enriches_from_context_graph_nodes(self):
|
||||
"""Fallback branch reads scenario/category/outcome/confidence from ContextGraph nodes."""
|
||||
g = ContextGraph()
|
||||
d = _make_decision(
|
||||
"dec_xyz",
|
||||
scenario="Block account after 3 failed PIN attempts",
|
||||
reasoning="Security policy v1 requires lockout",
|
||||
category="security",
|
||||
outcome="blocked",
|
||||
confidence=0.99,
|
||||
)
|
||||
g.add_decision(d)
|
||||
# Add a policy node and the APPLIED_POLICY edge
|
||||
g.add_node("policy_2:v1", "Policy", {"policy_id": "policy_2", "version": "v1"})
|
||||
g.add_edge("dec_xyz", "policy_2:v1", "APPLIED_POLICY")
|
||||
|
||||
pe = PolicyEngine(graph_store=g)
|
||||
|
||||
affected = pe.get_affected_decisions("policy_2", "v1", "v2")
|
||||
|
||||
assert len(affected) == 1
|
||||
d_out = affected[0]
|
||||
assert d_out["decision_id"] == "dec_xyz"
|
||||
# scenario must come from node.content, not be empty or the raw ID
|
||||
assert d_out["scenario"], "scenario must not be empty"
|
||||
assert d_out["scenario"] != "dec_xyz", (
|
||||
f"scenario should be readable text not the node ID, got: {d_out['scenario']!r}"
|
||||
)
|
||||
assert "PIN" in d_out["scenario"] or "Block" in d_out["scenario"], (
|
||||
f"scenario should reflect stored decision text, got: {d_out['scenario']!r}"
|
||||
)
|
||||
|
||||
def test_both_branches_return_same_key_shape(self):
|
||||
"""Both Cypher and fallback branches return dicts with identical required keys."""
|
||||
required_keys = {"decision_id", "scenario", "category", "outcome", "confidence"}
|
||||
|
||||
# Cypher branch
|
||||
store_cypher = self._mock_store_with_query([{
|
||||
"decision_id": "d1",
|
||||
"scenario": "some scenario",
|
||||
"category": "cat",
|
||||
"outcome": "out",
|
||||
"confidence": 0.5,
|
||||
}])
|
||||
pe_c = PolicyEngine(graph_store=store_cypher)
|
||||
cypher_result = pe_c.get_affected_decisions("p", "v1", "v2")
|
||||
assert len(cypher_result) == 1
|
||||
assert required_keys.issubset(cypher_result[0].keys()), (
|
||||
f"Cypher branch missing keys: {required_keys - cypher_result[0].keys()}"
|
||||
)
|
||||
|
||||
# Fallback branch
|
||||
g = ContextGraph()
|
||||
g.add_decision(_make_decision("d2", "fallback scenario", "fallback reason"))
|
||||
g.add_node("p2:v1", "Policy", {})
|
||||
g.add_edge("d2", "p2:v1", "APPLIED_POLICY")
|
||||
pe_f = PolicyEngine(graph_store=g)
|
||||
fallback_result = pe_f.get_affected_decisions("p2", "v1", "v2")
|
||||
assert len(fallback_result) == 1
|
||||
assert required_keys.issubset(fallback_result[0].keys()), (
|
||||
f"Fallback branch missing keys: {required_keys - fallback_result[0].keys()}"
|
||||
)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Group 4 – EntityLinker Similarity Payloads
|
||||
# ===========================================================================
|
||||
|
||||
class TestEntityLinkerSimilarityPayloads:
|
||||
"""EntityLinker similarity flows return enriched dicts, not bare IDs."""
|
||||
|
||||
def _linker(self):
|
||||
return EntityLinker(
|
||||
knowledge_graph={
|
||||
"entities": [
|
||||
{
|
||||
"id": "ent_python",
|
||||
"text": "Python programming language",
|
||||
"type": "Technology",
|
||||
},
|
||||
{
|
||||
"id": "ent_java",
|
||||
"text": "Java programming language",
|
||||
"type": "Technology",
|
||||
},
|
||||
{
|
||||
"id": "ent_sql",
|
||||
"text": "SQL database query language",
|
||||
"type": "Language",
|
||||
},
|
||||
]
|
||||
}
|
||||
)
|
||||
|
||||
def test_find_similar_entities_returns_full_payload_keys(self):
|
||||
"""find_similar_entities() returns dicts with entity_id, text, type, uri, similarity."""
|
||||
linker = self._linker()
|
||||
results = linker.find_similar_entities("Python language", threshold=0.1)
|
||||
|
||||
assert isinstance(results, list)
|
||||
assert len(results) >= 1, "Should find at least one similar entity"
|
||||
|
||||
for item in results:
|
||||
assert isinstance(item, dict)
|
||||
for field in ("entity_id", "text", "type", "similarity"):
|
||||
assert field in item, (
|
||||
f"find_similar_entities result missing field '{field}', got: {list(item.keys())}"
|
||||
)
|
||||
# entity_id must be the stored ID, not empty
|
||||
assert item["entity_id"], "entity_id must not be empty"
|
||||
# similarity must be a non-negative float
|
||||
assert isinstance(item["similarity"], (int, float))
|
||||
assert item["similarity"] >= 0.0
|
||||
|
||||
def test_find_similar_entities_text_field_is_human_readable(self):
|
||||
"""text field in similarity results is human-readable entity text, not an ID."""
|
||||
linker = self._linker()
|
||||
results = linker.find_similar_entities("Python language", threshold=0.1)
|
||||
|
||||
assert len(results) >= 1
|
||||
for item in results:
|
||||
assert item["text"] != item["entity_id"], (
|
||||
f"text should be human-readable, not the entity ID: {item['text']!r}"
|
||||
)
|
||||
assert len(item["text"]) > 2
|
||||
|
||||
def test_find_similar_entities_sorted_by_similarity_descending(self):
|
||||
"""Results are sorted by similarity in descending order."""
|
||||
linker = self._linker()
|
||||
results = linker.find_similar_entities("Python language", threshold=0.0)
|
||||
|
||||
if len(results) >= 2:
|
||||
for i in range(len(results) - 1):
|
||||
assert results[i]["similarity"] >= results[i + 1]["similarity"], (
|
||||
"Results must be sorted by similarity descending"
|
||||
)
|
||||
|
||||
def test_find_similar_public_alias_returns_full_payload(self):
|
||||
"""find_similar() public alias delegates to find_similar_entities and returns full dicts."""
|
||||
linker = self._linker()
|
||||
results = linker.find_similar("Python language", threshold=0.1)
|
||||
|
||||
assert isinstance(results, list)
|
||||
for item in results:
|
||||
assert isinstance(item, dict)
|
||||
assert "entity_id" in item
|
||||
assert "text" in item
|
||||
assert "similarity" in item
|
||||
|
||||
def test_find_similar_with_entity_dict_input(self):
|
||||
"""find_similar() accepts an EntityDict as input and returns full dicts."""
|
||||
linker = self._linker()
|
||||
entity_dict = {"text": "Java language", "type": "Technology"}
|
||||
results = linker.find_similar(entity_dict, threshold=0.1)
|
||||
|
||||
assert isinstance(results, list)
|
||||
for item in results:
|
||||
assert "entity_id" in item
|
||||
assert "similarity" in item
|
||||
|
||||
def test_find_linked_entities_creates_entity_links_with_ids(self):
|
||||
"""_find_linked_entities creates EntityLink objects with valid target entity IDs."""
|
||||
linker = self._linker()
|
||||
linker.assign_uri("ent_python", "Python programming language", "Technology")
|
||||
|
||||
links = linker._find_linked_entities(
|
||||
entity_id="my_entity",
|
||||
entity_text="Python language",
|
||||
entity_type="Technology",
|
||||
all_entities=[],
|
||||
context=None,
|
||||
)
|
||||
|
||||
assert isinstance(links, list)
|
||||
for link in links:
|
||||
# target_entity_id must be a stored entity ID, not empty or equal to text
|
||||
assert link.target_entity_id, "target_entity_id must not be empty"
|
||||
assert link.target_entity_id.startswith("ent_"), (
|
||||
f"target_entity_id should be a stored entity ID, got: {link.target_entity_id!r}"
|
||||
)
|
||||
assert link.confidence >= 0.0
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# Group 5 – KG Consumer Compatibility
|
||||
# ===========================================================================
|
||||
|
||||
class TestKGConsumerCompatibility:
|
||||
"""KG algorithms normalize enriched neighbor/node dicts from ContextGraph correctly."""
|
||||
|
||||
def _graph_with_nodes(self, pairs):
|
||||
"""Build a ContextGraph with given (id, label) pairs connected in a chain."""
|
||||
g = ContextGraph()
|
||||
for nid, label in pairs:
|
||||
g.add_node(nid, label, {"name": nid})
|
||||
# Connect in order
|
||||
ids = [nid for nid, _ in pairs]
|
||||
for i in range(len(ids) - 1):
|
||||
g.add_edge(ids[i], ids[i + 1], "RELATED_TO")
|
||||
return g
|
||||
|
||||
def test_node_embedder_build_adjacency_normalizes_enriched_dicts(self):
|
||||
"""NodeEmbedder._build_adjacency strips enriched dicts to node IDs (no crash, no None)."""
|
||||
from semantica.kg.node_embeddings import NodeEmbedder
|
||||
|
||||
g = self._graph_with_nodes([("A", "Person"), ("B", "Person"), ("C", "Person")])
|
||||
embedder = NodeEmbedder()
|
||||
|
||||
# Verify get_neighbors on ContextGraph returns dicts (enriched)
|
||||
raw = g.get_neighbors("A")
|
||||
assert isinstance(raw[0], dict), "ContextGraph.get_neighbors should return dicts"
|
||||
assert "id" in raw[0]
|
||||
|
||||
adjacency = embedder._build_adjacency(g, ["Person", "Person"], ["RELATED_TO"])
|
||||
# Each node maps to a list of plain string IDs
|
||||
for node_id, neighbors in adjacency.items():
|
||||
assert isinstance(node_id, str)
|
||||
for nb in neighbors:
|
||||
assert isinstance(nb, str), (
|
||||
f"adjacency neighbor must be a string ID, got {type(nb)}: {nb!r}"
|
||||
)
|
||||
assert nb is not None
|
||||
|
||||
def test_link_predictor_get_node_neighbors_normalizes_enriched_dicts(self):
|
||||
"""LinkPredictor._get_node_neighbors strips enriched dicts to plain IDs."""
|
||||
from semantica.kg.link_predictor import LinkPredictor
|
||||
|
||||
g = self._graph_with_nodes([("X", "Item"), ("Y", "Item"), ("Z", "Item")])
|
||||
predictor = LinkPredictor()
|
||||
|
||||
neighbors = predictor._get_node_neighbors(g, "X")
|
||||
assert isinstance(neighbors, list)
|
||||
for nb in neighbors:
|
||||
assert isinstance(nb, str), (
|
||||
f"neighbor must be a plain string ID, got {type(nb)}: {nb!r}"
|
||||
)
|
||||
assert nb is not None
|
||||
|
||||
def test_link_predictor_score_link_works_with_context_graph(self):
|
||||
"""score_link() runs without error when given a ContextGraph store."""
|
||||
from semantica.kg.link_predictor import LinkPredictor
|
||||
|
||||
g = self._graph_with_nodes([
|
||||
("n1", "Entity"), ("n2", "Entity"), ("n3", "Entity")
|
||||
])
|
||||
predictor = LinkPredictor()
|
||||
|
||||
score = predictor.score_link(g, "n1", "n3", method="common_neighbors")
|
||||
assert isinstance(score, (int, float))
|
||||
assert score >= 0.0
|
||||
|
||||
def test_centrality_calculator_get_filtered_neighbors_normalizes_dicts(self):
|
||||
"""CentralityCalculator._get_filtered_neighbors strips enriched dicts to IDs."""
|
||||
from semantica.kg.centrality_calculator import CentralityCalculator
|
||||
|
||||
g = self._graph_with_nodes([("c1", "Node"), ("c2", "Node"), ("c3", "Node")])
|
||||
calc = CentralityCalculator()
|
||||
|
||||
neighbors = calc._get_filtered_neighbors(g, "c1", relationship_types=None)
|
||||
assert isinstance(neighbors, list)
|
||||
for nb in neighbors:
|
||||
assert isinstance(nb, str), (
|
||||
f"filtered neighbor must be a plain string ID, got {type(nb)}: {nb!r}"
|
||||
)
|
||||
|
||||
def test_centrality_calculator_degree_centrality_works_with_context_graph(self):
|
||||
"""calculate_degree_centrality() works with ContextGraph as the graph store."""
|
||||
from semantica.kg.centrality_calculator import CentralityCalculator
|
||||
|
||||
g = self._graph_with_nodes([
|
||||
("hub", "Node"), ("spoke1", "Node"), ("spoke2", "Node")
|
||||
])
|
||||
g.add_edge("hub", "spoke2", "RELATED_TO") # hub has extra edge
|
||||
calc = CentralityCalculator()
|
||||
|
||||
result = calc.calculate_degree_centrality(g)
|
||||
assert isinstance(result, dict)
|
||||
# result has keys: centrality, rankings, max_degree, total_nodes
|
||||
assert "centrality" in result
|
||||
centrality = result["centrality"]
|
||||
assert isinstance(centrality, dict)
|
||||
assert len(centrality) > 0
|
||||
for node_id, score in centrality.items():
|
||||
assert isinstance(node_id, str)
|
||||
assert isinstance(score, (int, float))
|
||||
assert score >= 0.0
|
||||
|
||||
def test_path_finder_get_neighbors_normalizes_enriched_dicts(self):
|
||||
"""PathFinder._get_neighbors strips enriched dicts to (id, edge_data) tuples."""
|
||||
from semantica.kg.path_finder import PathFinder
|
||||
|
||||
g = self._graph_with_nodes([("p1", "Stop"), ("p2", "Stop"), ("p3", "Stop")])
|
||||
finder = PathFinder()
|
||||
|
||||
neighbors = finder._get_neighbors(g, "p1")
|
||||
assert isinstance(neighbors, list)
|
||||
for item in neighbors:
|
||||
node_id, edge_data = item
|
||||
assert isinstance(node_id, str), (
|
||||
f"neighbor node_id must be a plain string, got {type(node_id)}: {node_id!r}"
|
||||
)
|
||||
assert node_id is not None
|
||||
|
||||
def test_path_finder_dijkstra_works_with_context_graph(self):
|
||||
"""dijkstra_shortest_path() runs without error on ContextGraph."""
|
||||
from semantica.kg.path_finder import PathFinder
|
||||
|
||||
g = self._graph_with_nodes([
|
||||
("start", "Node"), ("mid", "Node"), ("end", "Node")
|
||||
])
|
||||
finder = PathFinder()
|
||||
|
||||
result = finder.dijkstra_shortest_path(g, "start", "end")
|
||||
assert result is not None
|
||||
assert isinstance(result, list)
|
||||
assert "start" in result
|
||||
assert "end" in result
|
||||
@@ -49,6 +49,38 @@ class TestContextGraphDecisions:
|
||||
assert node.properties["confidence"] == sample_decision.confidence
|
||||
assert node.properties["decision_maker"] == sample_decision.decision_maker
|
||||
|
||||
def test_add_decision_kwargs_form(self, context_graph):
|
||||
"""add_decision() accepts kwargs directly (no Decision object required)."""
|
||||
decision_id = context_graph.add_decision(
|
||||
category="loan_approval",
|
||||
scenario="Mortgage application — 780 credit score",
|
||||
reasoning="Strong credit history, low DTI",
|
||||
outcome="approved",
|
||||
confidence=0.95,
|
||||
)
|
||||
|
||||
assert isinstance(decision_id, str)
|
||||
assert len(decision_id) > 0
|
||||
node = context_graph.nodes[decision_id]
|
||||
assert node.node_type in ("Decision", "decision")
|
||||
assert node.properties["category"] == "loan_approval"
|
||||
assert node.properties["outcome"] == "approved"
|
||||
assert node.properties["confidence"] == 0.95
|
||||
|
||||
def test_add_decision_kwargs_and_object_both_return_id(self, context_graph, sample_decision):
|
||||
"""Both call forms return a non-empty decision ID string."""
|
||||
id_from_object = context_graph.add_decision(sample_decision)
|
||||
id_from_kwargs = context_graph.add_decision(
|
||||
category="test",
|
||||
scenario="test scenario",
|
||||
reasoning="test reasoning",
|
||||
outcome="approved",
|
||||
confidence=0.8,
|
||||
)
|
||||
|
||||
assert isinstance(id_from_object, str) and len(id_from_object) > 0
|
||||
assert isinstance(id_from_kwargs, str) and len(id_from_kwargs) > 0
|
||||
|
||||
def test_add_decision_with_embeddings(self, context_graph):
|
||||
"""Test adding decision with embeddings."""
|
||||
decision = Decision(
|
||||
|
||||
@@ -0,0 +1,424 @@
|
||||
"""
|
||||
Tests for semantica/explorer/utils/rdf_parser.py
|
||||
|
||||
Covers:
|
||||
- parse_skos_file() with Turtle and RDF/XML formats
|
||||
- ConceptScheme and Concept node extraction
|
||||
- Label priority resolution (en > en-* > untagged > fallback)
|
||||
- altLabel collection
|
||||
- Structural edge extraction (broader/narrower/inScheme/related/topConceptOf/hasTopConcept)
|
||||
- Edge filtering: edges with unknown endpoints are dropped
|
||||
- Invalid bytes raises ValueError
|
||||
- Empty graph returns empty lists
|
||||
- _get_best_label and _get_all_labels helpers
|
||||
"""
|
||||
|
||||
import pytest
|
||||
import rdflib
|
||||
from rdflib.namespace import RDF, SKOS
|
||||
|
||||
from semantica.explorer.utils.rdf_parser import (
|
||||
_get_all_labels,
|
||||
_get_best_label,
|
||||
parse_skos_file,
|
||||
)
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sample TTL fixtures
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
MINIMAL_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:Animals a skos:ConceptScheme ;
|
||||
skos:prefLabel "Animals"@en .
|
||||
|
||||
ex:Mammal a skos:Concept ;
|
||||
skos:prefLabel "Mammal"@en ;
|
||||
skos:inScheme ex:Animals .
|
||||
|
||||
ex:Dog a skos:Concept ;
|
||||
skos:prefLabel "Dog"@en ;
|
||||
skos:broader ex:Mammal ;
|
||||
skos:inScheme ex:Animals .
|
||||
"""
|
||||
|
||||
MULTILINGUAL_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:C1 a skos:Concept ;
|
||||
skos:prefLabel "French Only"@fr ;
|
||||
skos:prefLabel "English Label"@en ;
|
||||
skos:prefLabel "British English"@en-GB ;
|
||||
skos:altLabel "Alias One"@en ;
|
||||
skos:altLabel "Alias Two"@en .
|
||||
"""
|
||||
|
||||
UNTAGGED_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:C2 a skos:Concept ;
|
||||
skos:prefLabel "No Language Tag" ;
|
||||
skos:altLabel "alt1" ;
|
||||
skos:altLabel "alt2" .
|
||||
"""
|
||||
|
||||
FALLBACK_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:C3 a skos:Concept ;
|
||||
skos:prefLabel "Nur Deutsch"@de .
|
||||
"""
|
||||
|
||||
ALL_EDGE_TYPES_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:S1 a skos:ConceptScheme ;
|
||||
skos:prefLabel "Scheme One" .
|
||||
|
||||
ex:A a skos:Concept ;
|
||||
skos:prefLabel "A" ;
|
||||
skos:inScheme ex:S1 ;
|
||||
skos:topConceptOf ex:S1 .
|
||||
|
||||
ex:B a skos:Concept ;
|
||||
skos:prefLabel "B" ;
|
||||
skos:broader ex:A ;
|
||||
skos:inScheme ex:S1 .
|
||||
|
||||
ex:C a skos:Concept ;
|
||||
skos:prefLabel "C" ;
|
||||
skos:related ex:B ;
|
||||
skos:inScheme ex:S1 .
|
||||
|
||||
ex:S1 skos:hasTopConcept ex:A .
|
||||
"""
|
||||
|
||||
# An edge pointing to an external URI not declared as a Concept/ConceptScheme
|
||||
ORPHAN_EDGE_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
|
||||
ex:Known a skos:Concept ;
|
||||
skos:prefLabel "Known" ;
|
||||
skos:broader ex:ExternalConcept .
|
||||
"""
|
||||
|
||||
MINIMAL_RDF_XML = b"""<?xml version="1.0"?>
|
||||
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
|
||||
xmlns:skos="http://www.w3.org/2004/02/skos/core#"
|
||||
xmlns:ex="http://example.org/">
|
||||
|
||||
<skos:ConceptScheme rdf:about="http://example.org/SchemeX">
|
||||
<skos:prefLabel xml:lang="en">Scheme X</skos:prefLabel>
|
||||
</skos:ConceptScheme>
|
||||
|
||||
<skos:Concept rdf:about="http://example.org/ConceptY">
|
||||
<skos:prefLabel xml:lang="en">Concept Y</skos:prefLabel>
|
||||
<skos:inScheme rdf:resource="http://example.org/SchemeX"/>
|
||||
</skos:Concept>
|
||||
|
||||
</rdf:RDF>
|
||||
"""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helper: get node by URI
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _node(nodes, uri):
|
||||
return next((n for n in nodes if n["id"] == uri), None)
|
||||
|
||||
def _edges_of_type(edges, edge_type):
|
||||
return [e for e in edges if e["type"] == edge_type]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — basic extraction
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestParseSkosFileBasic:
|
||||
def test_returns_tuple_of_two_lists(self):
|
||||
nodes, edges = parse_skos_file(MINIMAL_TTL)
|
||||
assert isinstance(nodes, list)
|
||||
assert isinstance(edges, list)
|
||||
|
||||
def test_extracts_concept_scheme(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_TTL)
|
||||
scheme = _node(nodes, "http://example.org/Animals")
|
||||
assert scheme is not None
|
||||
assert scheme["type"] == "skos:ConceptScheme"
|
||||
assert scheme["properties"]["content"] == "Animals"
|
||||
|
||||
def test_extracts_concepts(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_TTL)
|
||||
uris = {n["id"] for n in nodes}
|
||||
assert "http://example.org/Mammal" in uris
|
||||
assert "http://example.org/Dog" in uris
|
||||
|
||||
def test_concept_type_tag(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_TTL)
|
||||
mammal = _node(nodes, "http://example.org/Mammal")
|
||||
assert mammal["type"] == "skos:Concept"
|
||||
|
||||
def test_node_has_required_keys(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_TTL)
|
||||
for n in nodes:
|
||||
assert "id" in n
|
||||
assert "type" in n
|
||||
assert "properties" in n
|
||||
assert "content" in n["properties"]
|
||||
assert "alt_labels" in n["properties"]
|
||||
assert "description" in n["properties"]
|
||||
|
||||
def test_edge_has_required_keys(self):
|
||||
_, edges = parse_skos_file(MINIMAL_TTL)
|
||||
for e in edges:
|
||||
assert "source_id" in e
|
||||
assert "target_id" in e
|
||||
assert "type" in e
|
||||
assert "weight" in e
|
||||
assert "properties" in e
|
||||
|
||||
def test_edge_weight_default(self):
|
||||
_, edges = parse_skos_file(MINIMAL_TTL)
|
||||
assert all(e["weight"] == 1.0 for e in edges)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — label priority
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestLabelPriority:
|
||||
def test_en_preferred_over_fr(self):
|
||||
nodes, _ = parse_skos_file(MULTILINGUAL_TTL)
|
||||
c1 = _node(nodes, "http://example.org/C1")
|
||||
assert c1 is not None
|
||||
assert c1["properties"]["content"] == "English Label"
|
||||
|
||||
def test_untagged_used_when_no_en(self):
|
||||
nodes, _ = parse_skos_file(UNTAGGED_TTL)
|
||||
c2 = _node(nodes, "http://example.org/C2")
|
||||
assert c2 is not None
|
||||
assert c2["properties"]["content"] == "No Language Tag"
|
||||
|
||||
def test_fallback_to_any_language(self):
|
||||
nodes, _ = parse_skos_file(FALLBACK_TTL)
|
||||
c3 = _node(nodes, "http://example.org/C3")
|
||||
assert c3 is not None
|
||||
assert c3["properties"]["content"] == "Nur Deutsch"
|
||||
|
||||
def test_uri_fragment_used_when_no_pref_label(self):
|
||||
ttl = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:NoLabel a skos:Concept .
|
||||
"""
|
||||
nodes, _ = parse_skos_file(ttl)
|
||||
n = _node(nodes, "http://example.org/NoLabel")
|
||||
assert n is not None
|
||||
assert n["properties"]["content"] == "NoLabel"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — altLabels
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestAltLabels:
|
||||
def test_alt_labels_collected(self):
|
||||
nodes, _ = parse_skos_file(MULTILINGUAL_TTL)
|
||||
c1 = _node(nodes, "http://example.org/C1")
|
||||
assert set(c1["properties"]["alt_labels"]) == {"Alias One", "Alias Two"}
|
||||
|
||||
def test_alt_labels_empty_when_none(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_TTL)
|
||||
mammal = _node(nodes, "http://example.org/Mammal")
|
||||
assert mammal["properties"]["alt_labels"] == []
|
||||
|
||||
def test_alt_labels_deduped(self):
|
||||
ttl = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:C a skos:Concept ;
|
||||
skos:prefLabel "C" ;
|
||||
skos:altLabel "same"@en ;
|
||||
skos:altLabel "same"@en .
|
||||
"""
|
||||
nodes, _ = parse_skos_file(ttl)
|
||||
c = _node(nodes, "http://example.org/C")
|
||||
assert c["properties"]["alt_labels"].count("same") == 1
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — edge types
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestEdgeTypes:
|
||||
def setup_method(self):
|
||||
self.nodes, self.edges = parse_skos_file(ALL_EDGE_TYPES_TTL)
|
||||
|
||||
def test_in_scheme_edges(self):
|
||||
in_scheme = _edges_of_type(self.edges, "skos:inScheme")
|
||||
assert len(in_scheme) >= 2 # A, B, C all inScheme S1
|
||||
|
||||
def test_broader_edge(self):
|
||||
broader = _edges_of_type(self.edges, "skos:broader")
|
||||
assert any(
|
||||
e["source_id"] == "http://example.org/B" and
|
||||
e["target_id"] == "http://example.org/A"
|
||||
for e in broader
|
||||
)
|
||||
|
||||
def test_related_edge(self):
|
||||
related = _edges_of_type(self.edges, "skos:related")
|
||||
assert any(
|
||||
e["source_id"] == "http://example.org/C" and
|
||||
e["target_id"] == "http://example.org/B"
|
||||
for e in related
|
||||
)
|
||||
|
||||
def test_top_concept_of_edge(self):
|
||||
top_concept_of = _edges_of_type(self.edges, "skos:topConceptOf")
|
||||
assert any(
|
||||
e["source_id"] == "http://example.org/A" and
|
||||
e["target_id"] == "http://example.org/S1"
|
||||
for e in top_concept_of
|
||||
)
|
||||
|
||||
def test_has_top_concept_edge(self):
|
||||
has_top = _edges_of_type(self.edges, "skos:hasTopConcept")
|
||||
assert any(
|
||||
e["source_id"] == "http://example.org/S1" and
|
||||
e["target_id"] == "http://example.org/A"
|
||||
for e in has_top
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — edge filtering (orphan edges dropped)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestOrphanEdgeFiltering:
|
||||
def test_edge_to_external_uri_is_dropped(self):
|
||||
nodes, edges = parse_skos_file(ORPHAN_EDGE_TTL)
|
||||
# ex:ExternalConcept is not declared as a Concept/ConceptScheme
|
||||
# so the broader edge should be dropped
|
||||
assert len(edges) == 0
|
||||
|
||||
def test_known_node_is_still_extracted(self):
|
||||
nodes, _ = parse_skos_file(ORPHAN_EDGE_TTL)
|
||||
assert _node(nodes, "http://example.org/Known") is not None
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — empty and error cases
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestEmptyAndErrors:
|
||||
def test_empty_graph_returns_empty_lists(self):
|
||||
empty_ttl = b"@prefix skos: <http://www.w3.org/2004/02/skos/core#> .\n"
|
||||
nodes, edges = parse_skos_file(empty_ttl)
|
||||
assert nodes == []
|
||||
assert edges == []
|
||||
|
||||
def test_invalid_bytes_raises_value_error(self):
|
||||
with pytest.raises(ValueError, match="Failed to parse RDF file"):
|
||||
parse_skos_file(b"this is not valid turtle !!!!", rdf_format="turtle")
|
||||
|
||||
def test_invalid_xml_raises_value_error(self):
|
||||
with pytest.raises(ValueError, match="Failed to parse RDF file"):
|
||||
parse_skos_file(b"<not-valid-xml>", rdf_format="xml")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# parse_skos_file — RDF/XML format
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestRdfXmlFormat:
|
||||
def test_parses_rdf_xml(self):
|
||||
nodes, edges = parse_skos_file(MINIMAL_RDF_XML, rdf_format="xml")
|
||||
uris = {n["id"] for n in nodes}
|
||||
assert "http://example.org/SchemeX" in uris
|
||||
assert "http://example.org/ConceptY" in uris
|
||||
|
||||
def test_rdf_xml_scheme_type(self):
|
||||
nodes, _ = parse_skos_file(MINIMAL_RDF_XML, rdf_format="xml")
|
||||
scheme = _node(nodes, "http://example.org/SchemeX")
|
||||
assert scheme["type"] == "skos:ConceptScheme"
|
||||
assert scheme["properties"]["content"] == "Scheme X"
|
||||
|
||||
def test_rdf_xml_in_scheme_edge(self):
|
||||
_, edges = parse_skos_file(MINIMAL_RDF_XML, rdf_format="xml")
|
||||
in_scheme = _edges_of_type(edges, "skos:inScheme")
|
||||
assert any(
|
||||
e["source_id"] == "http://example.org/ConceptY" and
|
||||
e["target_id"] == "http://example.org/SchemeX"
|
||||
for e in in_scheme
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _get_best_label helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestGetBestLabel:
|
||||
def _make_graph(self, triples_ttl: bytes) -> rdflib.Graph:
|
||||
g = rdflib.Graph()
|
||||
g.parse(data=triples_ttl, format="turtle")
|
||||
return g
|
||||
|
||||
def test_returns_en_when_available(self):
|
||||
ttl = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:X skos:prefLabel "English"@en ;
|
||||
skos:prefLabel "Deutsch"@de .
|
||||
"""
|
||||
g = self._make_graph(ttl)
|
||||
result = _get_best_label(g, rdflib.URIRef("http://example.org/X"), SKOS.prefLabel)
|
||||
assert result == "English"
|
||||
|
||||
def test_returns_empty_string_when_no_labels(self):
|
||||
g = rdflib.Graph()
|
||||
result = _get_best_label(g, rdflib.URIRef("http://example.org/X"), SKOS.prefLabel)
|
||||
assert result == ""
|
||||
|
||||
def test_en_variant_beats_untagged(self):
|
||||
ttl = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:X skos:prefLabel "No Tag" ;
|
||||
skos:prefLabel "British"@en-GB .
|
||||
"""
|
||||
g = self._make_graph(ttl)
|
||||
result = _get_best_label(g, rdflib.URIRef("http://example.org/X"), SKOS.prefLabel)
|
||||
assert result == "British"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# _get_all_labels helper
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class TestGetAllLabels:
|
||||
def test_returns_all_values(self):
|
||||
ttl = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:X skos:altLabel "A"@en ;
|
||||
skos:altLabel "B"@fr ;
|
||||
skos:altLabel "C" .
|
||||
"""
|
||||
g = rdflib.Graph()
|
||||
g.parse(data=ttl, format="turtle")
|
||||
result = _get_all_labels(g, rdflib.URIRef("http://example.org/X"), SKOS.altLabel)
|
||||
assert set(result) == {"A", "B", "C"}
|
||||
|
||||
def test_returns_empty_list_when_no_labels(self):
|
||||
g = rdflib.Graph()
|
||||
result = _get_all_labels(g, rdflib.URIRef("http://example.org/X"), SKOS.altLabel)
|
||||
assert result == []
|
||||
@@ -0,0 +1,348 @@
|
||||
"""
|
||||
Tests for semantica/explorer/routes/vocabulary.py
|
||||
|
||||
Covers:
|
||||
- GET /api/vocabulary/schemes
|
||||
- GET /api/vocabulary/hierarchy
|
||||
- POST /api/vocabulary/import
|
||||
"""
|
||||
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, patch
|
||||
from fastapi import FastAPI
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
from semantica.explorer.routes.vocabulary import router
|
||||
from semantica.explorer.dependencies import get_session
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# App + dependency override setup
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
app = FastAPI()
|
||||
app.include_router(router)
|
||||
|
||||
mock_session = MagicMock()
|
||||
|
||||
app.dependency_overrides[get_session] = lambda: mock_session
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
|
||||
def setup_function():
|
||||
"""Reset mock call history before each test to prevent state pollution."""
|
||||
mock_session.reset_mock()
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# GET /api/vocabulary/schemes
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_list_schemes_returns_correct_shape():
|
||||
"""Maps skos:ConceptScheme nodes to VocabularyScheme schema."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{
|
||||
"id": "http://example.org/Scheme1",
|
||||
"type": "skos:ConceptScheme",
|
||||
"properties": {
|
||||
"content": "My Test Scheme",
|
||||
"description": "A scheme for testing"
|
||||
}
|
||||
}
|
||||
], 1)
|
||||
|
||||
response = client.get("/api/vocabulary/schemes")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 1
|
||||
assert data[0]["uri"] == "http://example.org/Scheme1"
|
||||
assert data[0]["label"] == "My Test Scheme"
|
||||
assert data[0]["description"] == "A scheme for testing"
|
||||
|
||||
|
||||
def test_list_schemes_empty_graph():
|
||||
"""Returns empty list when no ConceptScheme nodes exist."""
|
||||
mock_session.get_nodes.return_value = ([], 0)
|
||||
|
||||
response = client.get("/api/vocabulary/schemes")
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json() == []
|
||||
|
||||
|
||||
def test_list_schemes_no_description():
|
||||
"""Description field is optional — None when not present in properties."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/S", "type": "skos:ConceptScheme",
|
||||
"properties": {"content": "Minimal"}}
|
||||
], 1)
|
||||
|
||||
response = client.get("/api/vocabulary/schemes")
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json()[0]["description"] is None
|
||||
|
||||
|
||||
def test_list_schemes_metadata_envelope():
|
||||
"""Label is read from 'metadata' envelope when 'properties' key absent."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/S", "type": "skos:ConceptScheme",
|
||||
"metadata": {"content": "Via Metadata"}}
|
||||
], 1)
|
||||
|
||||
response = client.get("/api/vocabulary/schemes")
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json()[0]["label"] == "Via Metadata"
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# GET /api/vocabulary/hierarchy
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def test_hierarchy_parent_child_via_broader():
|
||||
"""broader edge: child → parent. Returns single root with one child."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/Parent", "type": "skos:Concept",
|
||||
"properties": {"content": "Parent Node"}},
|
||||
{"id": "http://example.org/Child", "type": "skos:Concept",
|
||||
"properties": {"content": "Child Node"}}
|
||||
], 2)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/Parent", "target": "http://example.org/Scheme1",
|
||||
"type": "skos:inScheme"},
|
||||
{"source": "http://example.org/Child", "target": "http://example.org/Scheme1",
|
||||
"type": "skos:inScheme"},
|
||||
{"source": "http://example.org/Child", "target": "http://example.org/Parent",
|
||||
"type": "skos:broader"},
|
||||
], 3)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/Scheme1")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 1
|
||||
root = data[0]
|
||||
assert root["uri"] == "http://example.org/Parent"
|
||||
assert root["pref_label"] == "Parent Node"
|
||||
assert len(root["children"]) == 1
|
||||
child = root["children"][0]
|
||||
assert child["uri"] == "http://example.org/Child"
|
||||
assert child["pref_label"] == "Child Node"
|
||||
assert child["children"] is None
|
||||
|
||||
|
||||
def test_hierarchy_parent_child_via_narrower():
|
||||
"""narrower edge: parent → child. Same tree as broader, different edge direction."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/P", "type": "skos:Concept",
|
||||
"properties": {"content": "P"}},
|
||||
{"id": "http://example.org/C", "type": "skos:Concept",
|
||||
"properties": {"content": "C"}}
|
||||
], 2)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/P", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
{"source": "http://example.org/C", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
# narrower: P → C means C is a child of P
|
||||
{"source": "http://example.org/P", "target": "http://example.org/C",
|
||||
"type": "skos:narrower"},
|
||||
], 3)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/S")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 1
|
||||
assert data[0]["uri"] == "http://example.org/P"
|
||||
assert len(data[0]["children"]) == 1
|
||||
assert data[0]["children"][0]["uri"] == "http://example.org/C"
|
||||
|
||||
|
||||
def test_hierarchy_membership_via_top_concept_of():
|
||||
"""topConceptOf edge includes node in scheme without inScheme edge."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/Top", "type": "skos:Concept",
|
||||
"properties": {"content": "Top"}}
|
||||
], 1)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/Top", "target": "http://example.org/S",
|
||||
"type": "skos:topConceptOf"},
|
||||
], 1)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/S")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 1
|
||||
assert data[0]["uri"] == "http://example.org/Top"
|
||||
|
||||
|
||||
def test_hierarchy_membership_via_has_top_concept():
|
||||
"""hasTopConcept edge (scheme → concept) includes the target concept."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/TC", "type": "skos:Concept",
|
||||
"properties": {"content": "TopConcept"}}
|
||||
], 1)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/S", "target": "http://example.org/TC",
|
||||
"type": "skos:hasTopConcept"},
|
||||
], 1)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/S")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 1
|
||||
assert data[0]["uri"] == "http://example.org/TC"
|
||||
|
||||
|
||||
def test_hierarchy_empty_scheme():
|
||||
"""No concepts in scheme returns empty list."""
|
||||
mock_session.get_nodes.return_value = ([], 0)
|
||||
mock_session.get_edges.return_value = ([], 0)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/Empty")
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json() == []
|
||||
|
||||
|
||||
def test_hierarchy_flat_scheme_all_roots():
|
||||
"""All concepts without parent relationships are returned as roots."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/A", "type": "skos:Concept",
|
||||
"properties": {"content": "A"}},
|
||||
{"id": "http://example.org/B", "type": "skos:Concept",
|
||||
"properties": {"content": "B"}},
|
||||
], 2)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/A", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
{"source": "http://example.org/B", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
], 2)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/S")
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert len(data) == 2
|
||||
uris = {n["uri"] for n in data}
|
||||
assert uris == {"http://example.org/A", "http://example.org/B"}
|
||||
|
||||
|
||||
def test_hierarchy_missing_scheme_param():
|
||||
"""scheme query param is required — returns 422 when omitted."""
|
||||
response = client.get("/api/vocabulary/hierarchy")
|
||||
assert response.status_code == 422
|
||||
|
||||
|
||||
def test_hierarchy_cycle_does_not_hang():
|
||||
"""Cyclic broader edges must not cause infinite recursion during serialization."""
|
||||
mock_session.get_nodes.return_value = ([
|
||||
{"id": "http://example.org/A", "type": "skos:Concept",
|
||||
"properties": {"content": "A"}},
|
||||
{"id": "http://example.org/B", "type": "skos:Concept",
|
||||
"properties": {"content": "B"}},
|
||||
], 2)
|
||||
mock_session.get_edges.return_value = ([
|
||||
{"source": "http://example.org/A", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
{"source": "http://example.org/B", "target": "http://example.org/S",
|
||||
"type": "skos:inScheme"},
|
||||
# Cycle: A broader B AND B broader A
|
||||
{"source": "http://example.org/A", "target": "http://example.org/B",
|
||||
"type": "skos:broader"},
|
||||
{"source": "http://example.org/B", "target": "http://example.org/A",
|
||||
"type": "skos:broader"},
|
||||
], 4)
|
||||
|
||||
response = client.get("/api/vocabulary/hierarchy?scheme=http://example.org/S")
|
||||
|
||||
# Must return 200 without hanging or raising a RecursionError
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert isinstance(data, list)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# POST /api/vocabulary/import
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
MINIMAL_TTL = b"""
|
||||
@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
|
||||
@prefix ex: <http://example.org/> .
|
||||
ex:S a skos:ConceptScheme ; skos:prefLabel "S" .
|
||||
"""
|
||||
|
||||
MINIMAL_RDF_XML = b"""<?xml version="1.0"?>
|
||||
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
|
||||
xmlns:skos="http://www.w3.org/2004/02/skos/core#"
|
||||
xmlns:ex="http://example.org/">
|
||||
<skos:ConceptScheme rdf:about="http://example.org/SX">
|
||||
<skos:prefLabel xml:lang="en">Scheme X</skos:prefLabel>
|
||||
</skos:ConceptScheme>
|
||||
</rdf:RDF>
|
||||
"""
|
||||
|
||||
|
||||
def test_import_ttl_success():
|
||||
"""Valid .ttl upload returns success and calls add_nodes/add_edges."""
|
||||
mock_session.add_nodes.return_value = 1
|
||||
mock_session.add_edges.return_value = 0
|
||||
|
||||
response = client.post(
|
||||
"/api/vocabulary/import",
|
||||
files={"file": ("vocab.ttl", MINIMAL_TTL, "text/turtle")},
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["status"] == "success"
|
||||
assert data["filename"] == "vocab.ttl"
|
||||
assert data["nodes_added"] == 1
|
||||
assert data["edges_added"] == 0
|
||||
mock_session.add_nodes.assert_called_once()
|
||||
mock_session.add_edges.assert_called_once()
|
||||
|
||||
|
||||
def test_import_rdf_xml_success():
|
||||
""".rdf extension triggers XML format path."""
|
||||
mock_session.add_nodes.return_value = 1
|
||||
mock_session.add_edges.return_value = 0
|
||||
|
||||
response = client.post(
|
||||
"/api/vocabulary/import",
|
||||
files={"file": ("vocab.rdf", MINIMAL_RDF_XML, "application/rdf+xml")},
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json()["status"] == "success"
|
||||
|
||||
|
||||
def test_import_invalid_file_returns_422():
|
||||
"""Unparseable file content returns HTTP 422, not a silent 200 error dict."""
|
||||
response = client.post(
|
||||
"/api/vocabulary/import",
|
||||
files={"file": ("bad.ttl", b"this is not valid RDF!", "text/turtle")},
|
||||
)
|
||||
|
||||
assert response.status_code == 422
|
||||
|
||||
|
||||
def test_import_owl_extension_uses_xml_format():
|
||||
""".owl extension treated the same as .rdf — uses XML parser."""
|
||||
mock_session.add_nodes.return_value = 1
|
||||
mock_session.add_edges.return_value = 0
|
||||
|
||||
response = client.post(
|
||||
"/api/vocabulary/import",
|
||||
files={"file": ("onto.owl", MINIMAL_RDF_XML, "application/rdf+xml")},
|
||||
)
|
||||
|
||||
assert response.status_code == 200
|
||||
assert response.json()["status"] == "success"
|
||||
@@ -68,7 +68,7 @@ def test_sitemap_fallback_parsing() -> None:
|
||||
):
|
||||
urls = crawler.parse_sitemap("http://s.xml")
|
||||
|
||||
assert "http://a.com" in urls
|
||||
assert any(url == "http://a.com" for url in urls)
|
||||
|
||||
|
||||
def test_sitemap_invalid_xml() -> None:
|
||||
|
||||
@@ -228,7 +228,10 @@ class TestCheckPolicy(unittest.TestCase):
|
||||
|
||||
def test_invalid_json_returns_error(self):
|
||||
result = json.loads(self.kit.check_policy("{not valid json}"))
|
||||
self.assertIn("error", result)
|
||||
# Implementation returns {"compliant": False, "violations": [...], "warnings": [...]}
|
||||
self.assertFalse(result["compliant"])
|
||||
violations = result.get("violations", [])
|
||||
self.assertGreater(len(violations), 0)
|
||||
|
||||
|
||||
class TestGetDecisionSummary(unittest.TestCase):
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,971 @@
|
||||
"""
|
||||
Comprehensive tests for ALL features listed in the [Unreleased] section of CHANGELOG.md.
|
||||
|
||||
Covers gaps not addressed by existing test files:
|
||||
|
||||
PR #399 — AgentContext: checkpoint(), diff_checkpoints(), flush_checkpoint()
|
||||
PR #394 — TemporalVersionManager: attach_to_graph(), tag_version(), list_tags(),
|
||||
diff() alias, get_node_history(), restore_snapshot() rollback protection
|
||||
PR #393 — Snapshot schema compatibility: nodes/edges ↔ entities/relationships
|
||||
PR #385 — ContextGraph pagination: skip parameter, min_weight neighbor filter
|
||||
PR #385 — ContextGraph thread safety: concurrent mutations
|
||||
PR #319 — SKOS Vocabulary Module: namespace helpers, OntologyEngine APIs,
|
||||
TripletStore helpers (gap tests beyond existing suite)
|
||||
PR #318 — SHACL: quality tiers, export_shacl, RDFExporter.export_shacl (gap tests)
|
||||
PR #408 — OllamaProvider base_url fix (gap tests beyond existing suite)
|
||||
PR #371 — DatalogReasoner: idempotency, cache flag, graph load (gap tests)
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import threading
|
||||
import time
|
||||
from datetime import datetime, timezone
|
||||
from unittest.mock import MagicMock, patch
|
||||
|
||||
import pytest
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
UTC = timezone.utc
|
||||
|
||||
|
||||
def _utc(year: int, month: int = 1, day: int = 1) -> datetime:
|
||||
return datetime(year, month, day, tzinfo=UTC)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #399 — AgentContext: checkpoint / diff_checkpoints / flush_checkpoint
|
||||
# ===========================================================================
|
||||
|
||||
class TestAgentContextCheckpoint:
|
||||
"""checkpoint() captures the current graph state under a label."""
|
||||
|
||||
@pytest.fixture
|
||||
def ctx(self):
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
graph = ContextGraph()
|
||||
mock_vs = MagicMock()
|
||||
mock_vs.search.return_value = []
|
||||
return AgentContext(
|
||||
vector_store=mock_vs,
|
||||
knowledge_graph=graph,
|
||||
decision_tracking=True,
|
||||
), graph
|
||||
|
||||
def test_checkpoint_returns_dict(self, ctx):
|
||||
context, _ = ctx
|
||||
snap = context.checkpoint("snap1")
|
||||
assert isinstance(snap, dict)
|
||||
|
||||
def test_checkpoint_has_timestamp(self, ctx):
|
||||
context, _ = ctx
|
||||
snap = context.checkpoint("snap1")
|
||||
assert "timestamp" in snap
|
||||
|
||||
def test_checkpoint_empty_graph_has_no_nodes(self, ctx):
|
||||
context, _ = ctx
|
||||
snap = context.checkpoint("empty")
|
||||
assert snap.get("nodes", []) == [] or snap.get("entities", []) == []
|
||||
|
||||
def test_checkpoint_captures_added_node(self, ctx):
|
||||
context, graph = ctx
|
||||
graph.add_node("n1", "entity", content="hello")
|
||||
snap = context.checkpoint("after")
|
||||
node_ids = {n["id"] for n in snap.get("nodes", snap.get("entities", []))}
|
||||
assert "n1" in node_ids
|
||||
|
||||
def test_checkpoint_second_call_overwrites_label(self, ctx):
|
||||
context, graph = ctx
|
||||
context.checkpoint("label")
|
||||
graph.add_node("n2", "entity", content="new")
|
||||
snap2 = context.checkpoint("label")
|
||||
node_ids = {n["id"] for n in snap2.get("nodes", snap2.get("entities", []))}
|
||||
assert "n2" in node_ids
|
||||
|
||||
def test_checkpoint_independent_of_subsequent_changes(self, ctx):
|
||||
context, graph = ctx
|
||||
context.checkpoint("before")
|
||||
graph.add_node("n_after", "entity", content="added later")
|
||||
snap_before = context._checkpoints["before"]
|
||||
node_ids = {n["id"] for n in snap_before.get("nodes", snap_before.get("entities", []))}
|
||||
assert "n_after" not in node_ids
|
||||
|
||||
|
||||
class TestAgentContextDiffCheckpoints:
|
||||
"""diff_checkpoints() computes the structural delta between two checkpoints."""
|
||||
|
||||
@pytest.fixture
|
||||
def ctx_with_checkpoints(self):
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
graph = ContextGraph()
|
||||
mock_vs = MagicMock()
|
||||
mock_vs.search.return_value = []
|
||||
context = AgentContext(
|
||||
vector_store=mock_vs,
|
||||
knowledge_graph=graph,
|
||||
decision_tracking=True,
|
||||
)
|
||||
context.checkpoint("before")
|
||||
did = context.record_decision(
|
||||
category="policy",
|
||||
scenario="new scenario",
|
||||
reasoning="because",
|
||||
outcome="approved",
|
||||
confidence=0.9,
|
||||
)
|
||||
graph.add_node("entity_x", "entity", content="X")
|
||||
graph.add_edge(did, "entity_x", "involves")
|
||||
context.checkpoint("after")
|
||||
return context, graph, did
|
||||
|
||||
def test_diff_has_required_keys(self, ctx_with_checkpoints):
|
||||
context, _, _ = ctx_with_checkpoints
|
||||
diff = context.diff_checkpoints("before", "after")
|
||||
for key in ("decisions_added", "decisions_removed", "relationships_added", "relationships_removed"):
|
||||
assert key in diff
|
||||
|
||||
def test_decisions_added_contains_new_decision(self, ctx_with_checkpoints):
|
||||
context, _, did = ctx_with_checkpoints
|
||||
diff = context.diff_checkpoints("before", "after")
|
||||
assert any(item["id"] == did for item in diff["decisions_added"])
|
||||
|
||||
def test_decisions_removed_is_empty_when_nothing_removed(self, ctx_with_checkpoints):
|
||||
context, _, _ = ctx_with_checkpoints
|
||||
diff = context.diff_checkpoints("before", "after")
|
||||
assert diff["decisions_removed"] == []
|
||||
|
||||
def test_relationships_added_contains_new_edge(self, ctx_with_checkpoints):
|
||||
context, _, did = ctx_with_checkpoints
|
||||
diff = context.diff_checkpoints("before", "after")
|
||||
assert any(item["type"] == "involves" for item in diff["relationships_added"])
|
||||
|
||||
def test_diff_reversed_shows_decision_removed(self, ctx_with_checkpoints):
|
||||
context, _, did = ctx_with_checkpoints
|
||||
# "after" → "before" is a rewind: decision should appear as removed
|
||||
diff = context.diff_checkpoints("after", "before")
|
||||
assert any(item["id"] == did for item in diff["decisions_removed"])
|
||||
|
||||
def test_diff_same_snapshot_all_empty(self, ctx_with_checkpoints):
|
||||
context, _, _ = ctx_with_checkpoints
|
||||
diff = context.diff_checkpoints("after", "after")
|
||||
assert diff["decisions_added"] == []
|
||||
assert diff["decisions_removed"] == []
|
||||
|
||||
def test_unknown_first_label_raises_key_error(self, ctx_with_checkpoints):
|
||||
context, _, _ = ctx_with_checkpoints
|
||||
with pytest.raises(KeyError):
|
||||
context.diff_checkpoints("ghost", "after")
|
||||
|
||||
def test_unknown_second_label_raises_key_error(self, ctx_with_checkpoints):
|
||||
context, _, _ = ctx_with_checkpoints
|
||||
with pytest.raises(KeyError):
|
||||
context.diff_checkpoints("before", "ghost")
|
||||
|
||||
def test_both_labels_unknown_raises_key_error(self):
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
mock_vs = MagicMock()
|
||||
mock_vs.search.return_value = []
|
||||
context = AgentContext(vector_store=mock_vs, knowledge_graph=ContextGraph())
|
||||
with pytest.raises(KeyError):
|
||||
context.diff_checkpoints("x", "y")
|
||||
|
||||
|
||||
class TestAgentContextFlushCheckpoint:
|
||||
"""flush_checkpoint() persists a named checkpoint via TemporalVersionManager."""
|
||||
|
||||
@pytest.fixture
|
||||
def ctx(self):
|
||||
from semantica.context import AgentContext, ContextGraph
|
||||
graph = ContextGraph()
|
||||
mock_vs = MagicMock()
|
||||
mock_vs.search.return_value = []
|
||||
return AgentContext(
|
||||
vector_store=mock_vs,
|
||||
knowledge_graph=graph,
|
||||
decision_tracking=True,
|
||||
)
|
||||
|
||||
def test_flush_returns_snapshot_dict(self, ctx):
|
||||
ctx.checkpoint("v1")
|
||||
result = ctx.flush_checkpoint("v1")
|
||||
assert isinstance(result, dict)
|
||||
assert result["label"] == "v1"
|
||||
|
||||
def test_flush_snapshot_has_both_schema_keys(self, ctx):
|
||||
# flush_checkpoint uses change_management.TemporalVersionManager which
|
||||
# stores both "nodes"/"edges" and "entities"/"relationships" keys.
|
||||
ctx.checkpoint("v1")
|
||||
result = ctx.flush_checkpoint("v1")
|
||||
assert "entities" in result or "nodes" in result
|
||||
|
||||
def test_flush_snapshot_has_checksum(self, ctx):
|
||||
ctx.checkpoint("v1")
|
||||
result = ctx.flush_checkpoint("v1")
|
||||
assert "checksum" in result
|
||||
|
||||
def test_flush_unknown_label_raises_key_error(self, ctx):
|
||||
with pytest.raises(KeyError):
|
||||
ctx.flush_checkpoint("nonexistent")
|
||||
|
||||
def test_flush_can_be_retrieved_from_version_manager(self, ctx):
|
||||
from semantica.kg.temporal_query import TemporalVersionManager
|
||||
manager = TemporalVersionManager()
|
||||
ctx._temporal_version_manager = manager
|
||||
ctx.checkpoint("release-1")
|
||||
ctx.flush_checkpoint("release-1")
|
||||
retrieved = manager.get_version("release-1")
|
||||
assert retrieved is not None
|
||||
assert retrieved["label"] == "release-1"
|
||||
|
||||
def test_multiple_checkpoints_flushed_independently(self, ctx):
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.kg.temporal_query import TemporalVersionManager
|
||||
manager = TemporalVersionManager()
|
||||
ctx._temporal_version_manager = manager
|
||||
ctx.checkpoint("snap-a")
|
||||
ctx.checkpoint("snap-b")
|
||||
ctx.flush_checkpoint("snap-a")
|
||||
ctx.flush_checkpoint("snap-b")
|
||||
assert manager.get_version("snap-a") is not None
|
||||
assert manager.get_version("snap-b") is not None
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #394 — Audit Trail, Named Tags, diff() alias, rollback protection
|
||||
# ===========================================================================
|
||||
|
||||
class TestAuditTrailAdditional:
|
||||
"""Additional coverage for PR #394 audit-trail features."""
|
||||
|
||||
@pytest.fixture
|
||||
def setup(self):
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.change_management.managers import TemporalVersionManager
|
||||
graph = ContextGraph()
|
||||
manager = TemporalVersionManager()
|
||||
manager.attach_to_graph(graph)
|
||||
return graph, manager
|
||||
|
||||
def test_attach_to_graph_sets_mutation_callback(self, setup):
|
||||
graph, manager = setup
|
||||
assert callable(getattr(graph, "mutation_callback", None))
|
||||
|
||||
def test_add_node_creates_history_entry(self, setup):
|
||||
graph, manager = setup
|
||||
graph.add_node("n1", "entity", content="test")
|
||||
history = manager.get_node_history("n1")
|
||||
assert len(history) >= 1
|
||||
assert history[0]["operation"] == "ADD_NODE"
|
||||
|
||||
def test_update_node_creates_second_entry(self, setup):
|
||||
graph, manager = setup
|
||||
graph.add_node("n1", "entity", content="initial")
|
||||
graph.add_node_attribute("n1", {"key": "val"})
|
||||
history = manager.get_node_history("n1")
|
||||
operations = [h["operation"] for h in history]
|
||||
assert "ADD_NODE" in operations
|
||||
assert "UPDATE_NODE" in operations
|
||||
|
||||
def test_get_node_history_returns_empty_for_unknown_node(self, setup):
|
||||
_, manager = setup
|
||||
assert manager.get_node_history("does_not_exist") == []
|
||||
|
||||
def test_multiple_nodes_tracked_independently(self, setup):
|
||||
graph, manager = setup
|
||||
graph.add_node("a", "entity")
|
||||
graph.add_node("b", "entity")
|
||||
graph.add_node_attribute("a", {"x": 1})
|
||||
assert len(manager.get_node_history("a")) == 2
|
||||
assert len(manager.get_node_history("b")) == 1
|
||||
|
||||
|
||||
class TestNamedTagsAdditional:
|
||||
"""Additional coverage for named version tags from PR #394."""
|
||||
|
||||
@pytest.fixture
|
||||
def setup(self):
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.change_management.managers import TemporalVersionManager
|
||||
graph = ContextGraph()
|
||||
manager = TemporalVersionManager()
|
||||
graph.add_node("n1", "entity")
|
||||
snap = manager.create_snapshot(
|
||||
graph.to_dict(),
|
||||
version_label="v1.0",
|
||||
author="user@example.com",
|
||||
description="First",
|
||||
)
|
||||
return manager
|
||||
|
||||
def test_list_tags_empty_initially(self):
|
||||
from semantica.change_management.managers import TemporalVersionManager
|
||||
manager = TemporalVersionManager()
|
||||
assert manager.list_tags() == {}
|
||||
|
||||
def test_tag_version_and_retrieve(self, setup):
|
||||
manager = setup
|
||||
manager.tag_version("v1.0", "stable")
|
||||
tags = manager.list_tags()
|
||||
assert "stable" in tags
|
||||
assert tags["stable"] == "v1.0"
|
||||
|
||||
def test_multiple_tags_on_same_version(self, setup):
|
||||
manager = setup
|
||||
manager.tag_version("v1.0", "production")
|
||||
manager.tag_version("v1.0", "latest")
|
||||
tags = manager.list_tags()
|
||||
assert tags["production"] == "v1.0"
|
||||
assert tags["latest"] == "v1.0"
|
||||
|
||||
def test_tag_nonexistent_version_raises(self):
|
||||
from semantica.change_management.managers import TemporalVersionManager
|
||||
manager = TemporalVersionManager()
|
||||
with pytest.raises(Exception):
|
||||
manager.tag_version("ghost", "my-tag")
|
||||
|
||||
def test_diff_alias_equivalent_to_compare_versions(self, setup):
|
||||
from semantica.context import ContextGraph
|
||||
manager = setup
|
||||
graph2 = ContextGraph()
|
||||
graph2.add_node("n1", "entity")
|
||||
graph2.add_node("n2", "entity")
|
||||
manager.create_snapshot(
|
||||
graph2.to_dict(),
|
||||
version_label="v2.0",
|
||||
author="user@example.com",
|
||||
description="Second",
|
||||
)
|
||||
diff_result = manager.diff("v1.0", "v2.0")
|
||||
compare_result = manager.compare_versions("v1.0", "v2.0")
|
||||
# Both should return the same structure
|
||||
assert set(diff_result.keys()) == set(compare_result.keys())
|
||||
|
||||
def test_diff_alias_shows_added_entity(self, setup):
|
||||
from semantica.context import ContextGraph
|
||||
manager = setup
|
||||
graph2 = ContextGraph()
|
||||
graph2.add_node("n1", "entity")
|
||||
graph2.add_node("n2", "entity") # added
|
||||
manager.create_snapshot(
|
||||
graph2.to_dict(),
|
||||
version_label="v2.0",
|
||||
author="user@example.com",
|
||||
description="Second",
|
||||
)
|
||||
diff = manager.diff("v1.0", "v2.0")
|
||||
assert diff["summary"]["entities_added"] >= 1
|
||||
|
||||
|
||||
class TestRollbackProtectionAdditional:
|
||||
"""Additional rollback protection edge cases from PR #394."""
|
||||
|
||||
@pytest.fixture
|
||||
def setup_with_snapshot(self):
|
||||
from semantica.context import ContextGraph
|
||||
from semantica.change_management.managers import TemporalVersionManager
|
||||
graph = ContextGraph()
|
||||
graph.add_node("n1", "entity", content="original")
|
||||
manager = TemporalVersionManager()
|
||||
manager.attach_to_graph(graph)
|
||||
manager.create_snapshot(
|
||||
graph.to_dict(),
|
||||
version_label="v1.0",
|
||||
author="user@example.com",
|
||||
description="Original",
|
||||
)
|
||||
return graph, manager
|
||||
|
||||
def test_restore_requires_confirmation_by_default(self, setup_with_snapshot):
|
||||
from semantica.change_management.managers import ProcessingError
|
||||
graph, manager = setup_with_snapshot
|
||||
with pytest.raises(ProcessingError, match="Rollback protection"):
|
||||
manager.restore_snapshot(graph, "v1.0")
|
||||
|
||||
def test_restore_succeeds_with_confirmation_false(self, setup_with_snapshot):
|
||||
graph, manager = setup_with_snapshot
|
||||
result = manager.restore_snapshot(graph, "v1.0", require_confirmation=False)
|
||||
assert result is True
|
||||
|
||||
def test_restore_to_nonexistent_version_raises(self, setup_with_snapshot):
|
||||
graph, manager = setup_with_snapshot
|
||||
from semantica.utils.exceptions import ValidationError
|
||||
with pytest.raises(ValidationError):
|
||||
manager.restore_snapshot(graph, "ghost", require_confirmation=False)
|
||||
|
||||
def test_restore_replay_does_not_add_to_audit_log(self, setup_with_snapshot):
|
||||
graph, manager = setup_with_snapshot
|
||||
graph.add_node_attribute("n1", {"status": "modified"})
|
||||
history_before = manager.get_node_history("n1")
|
||||
count_before = len(history_before)
|
||||
manager.restore_snapshot(graph, "v1.0", require_confirmation=False)
|
||||
history_after = manager.get_node_history("n1")
|
||||
# Restore must not record new mutations
|
||||
assert len(history_after) == count_before
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #393 — Snapshot Schema Compatibility
|
||||
# ===========================================================================
|
||||
|
||||
class TestSnapshotSchemaCompatibility:
|
||||
"""TemporalVersionManager must accept both nodes/edges and entities/relationships."""
|
||||
|
||||
@pytest.fixture
|
||||
def manager(self):
|
||||
from semantica.kg.temporal_query import TemporalVersionManager
|
||||
return TemporalVersionManager()
|
||||
|
||||
def test_create_snapshot_with_nodes_edges_schema(self, manager):
|
||||
graph = {
|
||||
"nodes": [{"id": "1", "type": "Person"}],
|
||||
"edges": [{"source": "1", "target": "2", "type": "knows"}],
|
||||
}
|
||||
snap = manager.create_snapshot(graph, "v-ne", "user@x.com", "nodes/edges schema")
|
||||
assert snap["label"] == "v-ne"
|
||||
|
||||
def test_create_snapshot_with_entities_relationships_schema(self, manager):
|
||||
graph = {
|
||||
"entities": [{"id": "1", "type": "Person"}],
|
||||
"relationships": [{"source": "1", "target": "2", "type": "knows"}],
|
||||
}
|
||||
snap = manager.create_snapshot(graph, "v-er", "user@x.com", "entities/rels schema")
|
||||
assert snap["label"] == "v-er"
|
||||
|
||||
def test_validate_snapshot_nodes_edges_true(self, manager):
|
||||
graph = {
|
||||
"nodes": [{"id": "1"}],
|
||||
"edges": [],
|
||||
}
|
||||
snap = manager.create_snapshot(graph, "v1", "user@x.com", "test")
|
||||
assert manager.validate_snapshot(snap) is True
|
||||
|
||||
def test_compare_versions_nodes_edges_schema(self, manager):
|
||||
# kg.temporal_query.TemporalVersionManager accepts nodes/edges schema
|
||||
# without error; compare_versions must not raise.
|
||||
g1 = {"nodes": [{"id": "A"}], "edges": []}
|
||||
g2 = {"nodes": [{"id": "A"}, {"id": "B"}], "edges": []}
|
||||
manager.create_snapshot(g1, "old", "u@x.com", "old")
|
||||
manager.create_snapshot(g2, "new", "u@x.com", "new")
|
||||
diff = manager.compare_versions("old", "new")
|
||||
assert "summary" in diff
|
||||
|
||||
def test_compare_versions_entities_rels_schema(self, manager):
|
||||
g1 = {"entities": [{"id": "A"}], "relationships": []}
|
||||
g2 = {"entities": [{"id": "A"}, {"id": "B"}], "relationships": []}
|
||||
manager.create_snapshot(g1, "old2", "u@x.com", "old")
|
||||
manager.create_snapshot(g2, "new2", "u@x.com", "new")
|
||||
diff = manager.compare_versions("old2", "new2")
|
||||
assert diff["summary"]["entities_added"] >= 1
|
||||
|
||||
def test_mixed_schema_compare_does_not_crash(self, manager):
|
||||
g1 = {"nodes": [{"id": "A"}], "edges": []}
|
||||
g2 = {"entities": [{"id": "A"}, {"id": "B"}], "relationships": []}
|
||||
manager.create_snapshot(g1, "mix1", "u@x.com", "nodes schema")
|
||||
manager.create_snapshot(g2, "mix2", "u@x.com", "entities schema")
|
||||
# Must not raise regardless of schema mismatch
|
||||
diff = manager.compare_versions("mix1", "mix2")
|
||||
assert "summary" in diff
|
||||
|
||||
def test_snapshot_format_version_stamped_regardless_of_schema(self, manager):
|
||||
for schema, label in [
|
||||
({"nodes": [], "edges": []}, "ne"),
|
||||
({"entities": [], "relationships": []}, "er"),
|
||||
]:
|
||||
snap = manager.create_snapshot(schema, label, "u@x.com", "test")
|
||||
assert snap.get("format_version") == "1.0"
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #385 — ContextGraph Pagination: skip parameter
|
||||
# ===========================================================================
|
||||
|
||||
class TestContextGraphPaginationSkip:
|
||||
"""find_nodes / find_edges / find_active_nodes must honour the skip parameter."""
|
||||
|
||||
@pytest.fixture
|
||||
def graph_with_nodes(self):
|
||||
from semantica.context import ContextGraph
|
||||
g = ContextGraph()
|
||||
for i in range(6):
|
||||
g.add_node(f"n{i}", "entity", content=str(i))
|
||||
return g
|
||||
|
||||
@pytest.fixture
|
||||
def graph_with_edges(self):
|
||||
from semantica.context import ContextGraph
|
||||
g = ContextGraph()
|
||||
for i in range(6):
|
||||
g.add_node(f"n{i}", "entity")
|
||||
for i in range(5):
|
||||
g.add_edge(f"n{i}", f"n{i+1}", "next")
|
||||
return g
|
||||
|
||||
# find_nodes
|
||||
|
||||
def test_find_nodes_skip_zero_returns_all(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_nodes(skip=0)
|
||||
assert len(result) == 6
|
||||
|
||||
def test_find_nodes_skip_positive_reduces_count(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_nodes(skip=2)
|
||||
assert len(result) == 4
|
||||
|
||||
def test_find_nodes_skip_and_limit_window(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_nodes(skip=2, limit=2)
|
||||
assert len(result) == 2
|
||||
|
||||
def test_find_nodes_skip_beyond_length_returns_empty(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_nodes(skip=100)
|
||||
assert result == []
|
||||
|
||||
def test_find_nodes_skip_plus_limit_no_overlap_with_first_page(self, graph_with_nodes):
|
||||
page1 = graph_with_nodes.find_nodes(skip=0, limit=3)
|
||||
page2 = graph_with_nodes.find_nodes(skip=3, limit=3)
|
||||
ids1 = {n["id"] for n in page1}
|
||||
ids2 = {n["id"] for n in page2}
|
||||
assert ids1.isdisjoint(ids2)
|
||||
assert ids1 | ids2 == {f"n{i}" for i in range(6)}
|
||||
|
||||
# find_edges
|
||||
|
||||
def test_find_edges_skip_zero_returns_all(self, graph_with_edges):
|
||||
result = graph_with_edges.find_edges(skip=0)
|
||||
assert len(result) == 5
|
||||
|
||||
def test_find_edges_skip_reduces_count(self, graph_with_edges):
|
||||
result = graph_with_edges.find_edges(skip=2)
|
||||
assert len(result) == 3
|
||||
|
||||
def test_find_edges_skip_and_limit(self, graph_with_edges):
|
||||
result = graph_with_edges.find_edges(skip=1, limit=2)
|
||||
assert len(result) == 2
|
||||
|
||||
def test_find_edges_skip_beyond_returns_empty(self, graph_with_edges):
|
||||
result = graph_with_edges.find_edges(skip=100)
|
||||
assert result == []
|
||||
|
||||
def test_find_edges_pagination_covers_all(self, graph_with_edges):
|
||||
page1 = graph_with_edges.find_edges(skip=0, limit=3)
|
||||
page2 = graph_with_edges.find_edges(skip=3, limit=3)
|
||||
combined = len(page1) + len(page2)
|
||||
assert combined == 5
|
||||
|
||||
# find_active_nodes
|
||||
|
||||
def test_find_active_nodes_skip_zero_returns_all(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_active_nodes(skip=0)
|
||||
assert len(result) == 6
|
||||
|
||||
def test_find_active_nodes_skip_reduces_count(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_active_nodes(skip=3)
|
||||
assert len(result) == 3
|
||||
|
||||
def test_find_active_nodes_skip_and_limit(self, graph_with_nodes):
|
||||
result = graph_with_nodes.find_active_nodes(skip=2, limit=2)
|
||||
assert len(result) == 2
|
||||
|
||||
|
||||
class TestContextGraphMinWeightNeighborFilter:
|
||||
"""get_neighbors(min_weight=N) from PR #385 filters out low-weight edges."""
|
||||
|
||||
@pytest.fixture
|
||||
def weighted_graph(self):
|
||||
from semantica.context import ContextGraph
|
||||
g = ContextGraph()
|
||||
g.add_node("center", "entity")
|
||||
g.add_node("heavy", "entity")
|
||||
g.add_node("light", "entity")
|
||||
g.add_node("zero", "entity")
|
||||
g.add_edge("center", "heavy", "link", weight=0.9)
|
||||
g.add_edge("center", "light", "link", weight=0.2)
|
||||
g.add_edge("center", "zero", "link", weight=0.0)
|
||||
return g
|
||||
|
||||
def test_no_min_weight_returns_all_neighbors(self, weighted_graph):
|
||||
result = weighted_graph.get_neighbors("center")
|
||||
ids = {n["id"] for n in result}
|
||||
assert ids == {"heavy", "light", "zero"}
|
||||
|
||||
def test_min_weight_filters_low_weight_edges(self, weighted_graph):
|
||||
result = weighted_graph.get_neighbors("center", min_weight=0.5)
|
||||
ids = {n["id"] for n in result}
|
||||
assert "heavy" in ids
|
||||
assert "light" not in ids
|
||||
assert "zero" not in ids
|
||||
|
||||
def test_min_weight_zero_returns_all(self, weighted_graph):
|
||||
result = weighted_graph.get_neighbors("center", min_weight=0.0)
|
||||
assert len(result) == 3
|
||||
|
||||
def test_min_weight_one_returns_none(self, weighted_graph):
|
||||
result = weighted_graph.get_neighbors("center", min_weight=1.0)
|
||||
assert result == []
|
||||
|
||||
def test_min_weight_exact_boundary_inclusive(self, weighted_graph):
|
||||
# edge to "heavy" has weight=0.9; min_weight=0.9 should include it
|
||||
result = weighted_graph.get_neighbors("center", min_weight=0.9)
|
||||
ids = {n["id"] for n in result}
|
||||
assert "heavy" in ids
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #385 — ContextGraph Thread Safety
|
||||
# ===========================================================================
|
||||
|
||||
class TestContextGraphThreadSafety:
|
||||
"""ContextGraph must be safe for concurrent reads and writes."""
|
||||
|
||||
def test_concurrent_add_node_no_corruption(self):
|
||||
from semantica.context import ContextGraph
|
||||
graph = ContextGraph()
|
||||
errors = []
|
||||
|
||||
def add_nodes(start: int):
|
||||
try:
|
||||
for i in range(start, start + 20):
|
||||
graph.add_node(f"n-{i}", "entity", content=str(i))
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
threads = [threading.Thread(target=add_nodes, args=(i * 20,)) for i in range(5)]
|
||||
for t in threads:
|
||||
t.start()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
assert errors == [], f"Thread errors: {errors}"
|
||||
assert len(graph.nodes) == 100
|
||||
|
||||
def test_concurrent_reads_while_writing(self):
|
||||
from semantica.context import ContextGraph
|
||||
graph = ContextGraph()
|
||||
for i in range(20):
|
||||
graph.add_node(f"initial-{i}", "entity")
|
||||
|
||||
errors = []
|
||||
|
||||
def reader():
|
||||
try:
|
||||
for _ in range(50):
|
||||
_ = graph.find_nodes()
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
def writer():
|
||||
try:
|
||||
for i in range(50):
|
||||
graph.add_node(f"w-{threading.get_ident()}-{i}", "entity")
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
threads = [threading.Thread(target=reader) for _ in range(3)] + \
|
||||
[threading.Thread(target=writer) for _ in range(2)]
|
||||
for t in threads:
|
||||
t.start()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
assert errors == [], f"Thread errors: {errors}"
|
||||
|
||||
def test_concurrent_add_edge_no_corruption(self):
|
||||
from semantica.context import ContextGraph
|
||||
graph = ContextGraph()
|
||||
for i in range(40):
|
||||
graph.add_node(f"n{i}", "entity")
|
||||
|
||||
errors = []
|
||||
|
||||
def add_edges(offset: int):
|
||||
try:
|
||||
for i in range(offset, offset + 10):
|
||||
graph.add_edge(f"n{i}", f"n{i+1}", "link")
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
threads = [threading.Thread(target=add_edges, args=(i * 10,)) for i in range(3)]
|
||||
for t in threads:
|
||||
t.start()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
assert errors == [], f"Thread errors: {errors}"
|
||||
|
||||
def test_find_nodes_consistent_under_concurrent_writes(self):
|
||||
from semantica.context import ContextGraph
|
||||
graph = ContextGraph()
|
||||
results = []
|
||||
errors = []
|
||||
|
||||
def writer():
|
||||
for i in range(30):
|
||||
graph.add_node(f"wt-{threading.get_ident()}-{i}", "entity")
|
||||
|
||||
def reader():
|
||||
try:
|
||||
for _ in range(10):
|
||||
snapshot = graph.find_nodes()
|
||||
results.append(len(snapshot))
|
||||
except Exception as exc:
|
||||
errors.append(exc)
|
||||
|
||||
threads = [threading.Thread(target=writer) for _ in range(3)] + \
|
||||
[threading.Thread(target=reader) for _ in range(3)]
|
||||
for t in threads:
|
||||
t.start()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
assert errors == [], f"Thread errors: {errors}"
|
||||
# All snapshots must be non-negative integers (no partial-write corruption)
|
||||
assert all(r >= 0 for r in results)
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #319 — SKOS Vocabulary Module: namespace helpers (gap tests)
|
||||
# ===========================================================================
|
||||
|
||||
class TestSKOSNamespaceHelpers:
|
||||
"""get_skos_uri and build_concept_scheme_uri gap tests beyond existing suite."""
|
||||
|
||||
@pytest.fixture
|
||||
def nm(self):
|
||||
from semantica.ontology.namespace_manager import NamespaceManager
|
||||
return NamespaceManager()
|
||||
|
||||
def test_get_skos_uri_prefLabel(self, nm):
|
||||
uri = nm.get_skos_uri("prefLabel")
|
||||
assert uri == "http://www.w3.org/2004/02/skos/core#prefLabel"
|
||||
|
||||
def test_get_skos_uri_Concept(self, nm):
|
||||
uri = nm.get_skos_uri("Concept")
|
||||
assert "Concept" in uri
|
||||
assert uri.startswith("http://www.w3.org/2004/02/skos/core#")
|
||||
|
||||
def test_get_skos_uri_broader(self, nm):
|
||||
uri = nm.get_skos_uri("broader")
|
||||
assert uri.endswith("#broader")
|
||||
|
||||
def test_build_concept_scheme_uri_lowercases(self, nm):
|
||||
uri = nm.build_concept_scheme_uri("My Vocabulary")
|
||||
assert "my-vocabulary" in uri.lower()
|
||||
|
||||
def test_build_concept_scheme_uri_replaces_spaces_with_hyphens(self, nm):
|
||||
uri = nm.build_concept_scheme_uri("Drug Interaction Terms")
|
||||
assert " " not in uri
|
||||
|
||||
def test_build_concept_scheme_uri_contains_vocab_segment(self, nm):
|
||||
uri = nm.build_concept_scheme_uri("Test")
|
||||
assert "/vocab/" in uri
|
||||
|
||||
def test_build_concept_scheme_uri_special_chars_normalised(self, nm):
|
||||
uri = nm.build_concept_scheme_uri("A&B!Vocab")
|
||||
assert "&" not in uri
|
||||
assert "!" not in uri
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #318 — SHACL: quality tiers and export (gap tests)
|
||||
# ===========================================================================
|
||||
|
||||
class TestSHACLQualityTiersGap:
|
||||
"""Quality tier differences between basic / standard / strict."""
|
||||
|
||||
@pytest.fixture
|
||||
def generator(self):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
return SHACLGenerator()
|
||||
|
||||
@pytest.fixture
|
||||
def simple_ontology(self):
|
||||
# SHACLGenerator expects classes and top-level properties (with domain)
|
||||
return {
|
||||
"classes": [{"name": "Person"}],
|
||||
"properties": [
|
||||
{"name": "name", "domain": "Person", "range": "string"},
|
||||
{"name": "age", "domain": "Person", "range": "integer"},
|
||||
],
|
||||
}
|
||||
|
||||
def test_basic_tier_produces_output(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
gen = SHACLGenerator(quality_tier="basic")
|
||||
result = gen.generate(simple_ontology)
|
||||
assert result is not None
|
||||
assert len(gen.serialize(result)) > 0
|
||||
|
||||
def test_standard_tier_produces_output(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
gen = SHACLGenerator(quality_tier="standard")
|
||||
result = gen.generate(simple_ontology)
|
||||
assert len(gen.serialize(result)) > 0
|
||||
|
||||
def test_strict_tier_produces_output(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
gen = SHACLGenerator(quality_tier="strict")
|
||||
result = gen.generate(simple_ontology)
|
||||
assert len(gen.serialize(result)) > 0
|
||||
|
||||
def test_strict_tier_contains_closed_constraint(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
gen = SHACLGenerator(quality_tier="strict")
|
||||
result = gen.generate(simple_ontology)
|
||||
turtle = gen.serialize(result)
|
||||
assert "sh:closed" in turtle
|
||||
|
||||
def test_basic_tier_does_not_contain_closed(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
gen = SHACLGenerator(quality_tier="basic")
|
||||
result = gen.generate(simple_ontology)
|
||||
turtle = gen.serialize(result)
|
||||
assert "sh:closed" not in turtle
|
||||
|
||||
def test_three_tiers_produce_different_output(self, simple_ontology):
|
||||
from semantica.ontology.ontology_generator import SHACLGenerator
|
||||
basic_gen = SHACLGenerator(quality_tier="basic")
|
||||
strict_gen = SHACLGenerator(quality_tier="strict")
|
||||
basic = basic_gen.serialize(basic_gen.generate(simple_ontology))
|
||||
strict = strict_gen.serialize(strict_gen.generate(simple_ontology))
|
||||
assert basic != strict
|
||||
|
||||
|
||||
class TestRDFExporterExportSHACL:
|
||||
"""RDFExporter.export_shacl() writes SHACL strings to files."""
|
||||
|
||||
def test_export_shacl_writes_ttl_file(self, tmp_path):
|
||||
from semantica.export.rdf_exporter import RDFExporter
|
||||
exporter = RDFExporter()
|
||||
shacl = "@prefix sh: <http://www.w3.org/ns/shacl#> .\n"
|
||||
out = tmp_path / "shapes.ttl"
|
||||
exporter.export_shacl(shacl, str(out))
|
||||
assert out.exists()
|
||||
assert out.read_text().strip().startswith("@prefix")
|
||||
|
||||
def test_export_shacl_invalid_extension_raises(self, tmp_path):
|
||||
from semantica.export.rdf_exporter import RDFExporter
|
||||
from semantica.utils.exceptions import ValidationError
|
||||
exporter = RDFExporter()
|
||||
out = tmp_path / "shapes.txt"
|
||||
with pytest.raises((ValueError, ValidationError)):
|
||||
exporter.export_shacl("@prefix sh: <…> .", str(out))
|
||||
|
||||
def test_export_shacl_jsonld_extension_accepted(self, tmp_path):
|
||||
from semantica.export.rdf_exporter import RDFExporter
|
||||
exporter = RDFExporter()
|
||||
content = '{"@context": {}}'
|
||||
out = tmp_path / "shapes.jsonld"
|
||||
exporter.export_shacl(content, str(out))
|
||||
assert out.exists()
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #408 — OllamaProvider base_url fix (gap tests)
|
||||
# ===========================================================================
|
||||
|
||||
class TestOllamaProviderBaseURLGap:
|
||||
"""Additional gap tests for PR #408 OllamaProvider base_url fix."""
|
||||
|
||||
def test_custom_port_used_as_host(self):
|
||||
"""Non-default port must flow through to the Client in every call."""
|
||||
ollama_mock = MagicMock()
|
||||
ollama_mock.Client = MagicMock(return_value=MagicMock())
|
||||
with patch.dict("sys.modules", {"ollama": ollama_mock}):
|
||||
from semantica.semantic_extract.providers import OllamaProvider
|
||||
provider = OllamaProvider(
|
||||
model_name="llama3",
|
||||
base_url="http://192.168.1.10:11434",
|
||||
)
|
||||
# _init_client may be called during __init__ and/or lazily;
|
||||
# every invocation must pass the correct host.
|
||||
assert ollama_mock.Client.called
|
||||
for call_args in ollama_mock.Client.call_args_list:
|
||||
assert call_args == ((), {"host": "http://192.168.1.10:11434"}) or \
|
||||
call_args.kwargs.get("host") == "http://192.168.1.10:11434"
|
||||
|
||||
def test_client_is_not_raw_module(self):
|
||||
"""self.client must never be the raw ollama module."""
|
||||
ollama_mock = MagicMock()
|
||||
client_instance = MagicMock()
|
||||
ollama_mock.Client = MagicMock(return_value=client_instance)
|
||||
with patch.dict("sys.modules", {"ollama": ollama_mock}):
|
||||
from semantica.semantic_extract.providers import OllamaProvider
|
||||
provider = OllamaProvider(model_name="llama3")
|
||||
provider._init_client()
|
||||
assert provider.client is not ollama_mock
|
||||
|
||||
|
||||
# ===========================================================================
|
||||
# PR #371 — DatalogReasoner gap tests
|
||||
# ===========================================================================
|
||||
|
||||
class TestDatalogReasonerGap:
|
||||
"""Gap tests for DatalogReasoner beyond the existing 23 tests."""
|
||||
|
||||
@pytest.fixture
|
||||
def reasoner(self):
|
||||
from semantica.reasoning import DatalogReasoner
|
||||
return DatalogReasoner()
|
||||
|
||||
def test_derive_all_idempotent(self, reasoner):
|
||||
reasoner.add_fact("parent(alice, bob)")
|
||||
reasoner.add_rule("grandparent(X, Z) :- parent(X, Y), parent(Y, Z).")
|
||||
reasoner.add_fact("parent(bob, carol)")
|
||||
first = reasoner.derive_all()
|
||||
second = reasoner.derive_all()
|
||||
# Second call must produce same results (idempotency)
|
||||
assert set(first) == set(second)
|
||||
|
||||
def test_query_returns_list(self, reasoner):
|
||||
reasoner.add_fact("color(sky, blue)")
|
||||
result = reasoner.query("color(?X, ?Y)")
|
||||
assert isinstance(result, list)
|
||||
|
||||
def test_query_no_match_returns_empty(self, reasoner):
|
||||
result = reasoner.query("nonexistent(?X)")
|
||||
assert result == []
|
||||
|
||||
def test_multi_hop_four_levels(self, reasoner):
|
||||
reasoner.add_fact("parent(a, b)")
|
||||
reasoner.add_fact("parent(b, c)")
|
||||
reasoner.add_fact("parent(c, d)")
|
||||
reasoner.add_fact("parent(d, e)")
|
||||
# DatalogReasoner uses uppercase-letter variables (not ?-prefixed)
|
||||
reasoner.add_rule("ancestor(X, Z) :- parent(X, Z).")
|
||||
reasoner.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
|
||||
results = reasoner.query("ancestor(a, ?Z)")
|
||||
targets = {r["Z"] for r in results}
|
||||
assert "e" in targets
|
||||
|
||||
def test_load_from_context_graph(self, reasoner):
|
||||
from semantica.context import ContextGraph
|
||||
graph = ContextGraph()
|
||||
graph.add_node("alice", "Person")
|
||||
graph.add_node("bob", "Person")
|
||||
graph.add_edge("alice", "bob", "knows")
|
||||
reasoner.load_from_graph(graph)
|
||||
result = reasoner.query("knows(?X, ?Y)")
|
||||
assert len(result) >= 1
|
||||
|
||||
def test_add_fact_dict_source_target_type(self, reasoner):
|
||||
reasoner.add_fact({"source": "alice", "target": "bob", "type": "knows"})
|
||||
result = reasoner.query("knows(?X, ?Y)")
|
||||
assert any(r.get("X") == "alice" and r.get("Y") == "bob" for r in result)
|
||||
|
||||
def test_add_fact_subject_predicate_object_shape(self, reasoner):
|
||||
reasoner.add_fact({"subject": "cat", "predicate": "isa", "object": "animal"})
|
||||
result = reasoner.query("isa(?X, ?Y)")
|
||||
assert len(result) >= 1
|
||||
|
||||
def test_duplicate_fact_not_duplicated(self, reasoner):
|
||||
reasoner.add_fact("color(sky, blue)")
|
||||
reasoner.add_fact("color(sky, blue)")
|
||||
result = reasoner.query("color(?X, ?Y)")
|
||||
assert len(result) == 1
|
||||
|
||||
def test_derive_all_returns_list(self, reasoner):
|
||||
# Facts must use constants (lowercase); uppercase is treated as variable
|
||||
reasoner.add_fact("category(x, alpha)")
|
||||
result = reasoner.derive_all()
|
||||
assert isinstance(result, list)
|
||||
@@ -163,6 +163,146 @@ class TestTripletStore(unittest.TestCase):
|
||||
self.assertIn("VALUES ?subject", sparql_query)
|
||||
mock_backend.execute_sparql.assert_called_once()
|
||||
|
||||
@patch('semantica.triplet_store.blazegraph_store.BlazegraphStore')
|
||||
def test_execute_query_forwards_graph_options(self, mock_blazegraph_store):
|
||||
mock_backend_instance = MagicMock()
|
||||
mock_blazegraph_store.return_value = mock_backend_instance
|
||||
|
||||
store = TripletStore(backend="blazegraph")
|
||||
store.query_engine = MagicMock()
|
||||
store.query_engine.execute_query.return_value = QueryEngine()
|
||||
|
||||
query = "SELECT ?s WHERE { ?s ?p ?o }"
|
||||
graphs = ["http://example.org/graph/a", "http://example.org/graph/b"]
|
||||
store.execute_query(query, graph="http://example.org/graph/default", graphs=graphs)
|
||||
|
||||
store.query_engine.execute_query.assert_called_once_with(
|
||||
query,
|
||||
store._store_backend,
|
||||
graph="http://example.org/graph/default",
|
||||
graphs=graphs,
|
||||
supports_named_graphs=True,
|
||||
)
|
||||
|
||||
@patch('semantica.triplet_store.blazegraph_store.BlazegraphStore')
|
||||
def test_execute_query_respects_enable_named_graphs_flag(self, mock_blazegraph_store):
|
||||
mock_backend_instance = MagicMock()
|
||||
mock_blazegraph_store.return_value = mock_backend_instance
|
||||
|
||||
store = TripletStore(backend="blazegraph", enable_named_graphs=False)
|
||||
store.query_engine = MagicMock()
|
||||
store.query_engine.execute_query.return_value = QueryEngine()
|
||||
|
||||
query = "SELECT ?s WHERE { ?s ?p ?o }"
|
||||
store.execute_query(query, graph="http://example.org/graph/default")
|
||||
|
||||
store.query_engine.execute_query.assert_called_once_with(
|
||||
query,
|
||||
store._store_backend,
|
||||
graph="http://example.org/graph/default",
|
||||
supports_named_graphs=False,
|
||||
)
|
||||
|
||||
def test_query_engine_injects_from_before_where(self):
|
||||
engine = QueryEngine(enable_optimization=False, enable_caching=False)
|
||||
query = "SELECT ?s ?p ?o WHERE { ?s ?p ?o }"
|
||||
|
||||
prepared = engine.prepare_query(query, graph="http://example.org/graph/default")
|
||||
|
||||
self.assertIn("FROM <http://example.org/graph/default>", prepared)
|
||||
self.assertLess(
|
||||
prepared.upper().find("FROM <HTTP://EXAMPLE.ORG/GRAPH/DEFAULT>"),
|
||||
prepared.upper().find("WHERE"),
|
||||
)
|
||||
|
||||
def test_query_engine_injects_multiple_named_graphs(self):
|
||||
engine = QueryEngine(enable_optimization=False, enable_caching=False)
|
||||
query = "SELECT ?s WHERE { GRAPH ?g { ?s ?p ?o } }"
|
||||
graphs = ["http://example.org/graph/a", "http://example.org/graph/b"]
|
||||
|
||||
prepared = engine.prepare_query(query, graphs=graphs)
|
||||
|
||||
self.assertIn("FROM NAMED <http://example.org/graph/a>", prepared)
|
||||
self.assertIn("FROM NAMED <http://example.org/graph/b>", prepared)
|
||||
self.assertLess(
|
||||
prepared.upper().find("FROM NAMED <HTTP://EXAMPLE.ORG/GRAPH/A>"),
|
||||
prepared.upper().find("WHERE"),
|
||||
)
|
||||
|
||||
def test_query_engine_graph_isolation_behavior(self):
|
||||
engine = QueryEngine(enable_optimization=False, enable_caching=False)
|
||||
mock_backend = MagicMock()
|
||||
|
||||
def _side_effect(query, **kwargs):
|
||||
if "FROM <http://example.org/graph/a>" in query:
|
||||
return {
|
||||
"bindings": [{"s": {"value": "http://entity/A"}}],
|
||||
"variables": ["s"],
|
||||
"metadata": {},
|
||||
}
|
||||
if "FROM <http://example.org/graph/b>" in query:
|
||||
return {
|
||||
"bindings": [{"s": {"value": "http://entity/B"}}],
|
||||
"variables": ["s"],
|
||||
"metadata": {},
|
||||
}
|
||||
return {
|
||||
"bindings": [
|
||||
{"s": {"value": "http://entity/A"}},
|
||||
{"s": {"value": "http://entity/B"}},
|
||||
],
|
||||
"variables": ["s"],
|
||||
"metadata": {},
|
||||
}
|
||||
|
||||
mock_backend.execute_sparql.side_effect = _side_effect
|
||||
|
||||
base_query = "SELECT ?s WHERE { ?s ?p ?o }"
|
||||
graph_a_result = engine.execute_query(base_query, mock_backend, graph="http://example.org/graph/a")
|
||||
graph_b_result = engine.execute_query(base_query, mock_backend, graph="http://example.org/graph/b")
|
||||
default_result = engine.execute_query(base_query, mock_backend)
|
||||
|
||||
self.assertNotEqual(graph_a_result.bindings, graph_b_result.bindings)
|
||||
self.assertEqual(len(default_result.bindings), 2)
|
||||
|
||||
def test_query_engine_avoids_duplicate_dataset_clauses_for_same_graph(self):
|
||||
engine = QueryEngine(enable_optimization=False, enable_caching=False)
|
||||
query = "SELECT ?s WHERE { GRAPH ?g { ?s ?p ?o } }"
|
||||
|
||||
prepared = engine.prepare_query(
|
||||
query,
|
||||
graph="http://example.org/graph/a",
|
||||
graphs=["http://example.org/graph/a", "http://example.org/graph/b"],
|
||||
)
|
||||
|
||||
self.assertEqual(prepared.count("FROM <http://example.org/graph/a>"), 1)
|
||||
self.assertEqual(prepared.count("FROM NAMED <http://example.org/graph/a>"), 0)
|
||||
self.assertIn("FROM NAMED <http://example.org/graph/b>", prepared)
|
||||
|
||||
def test_query_engine_uses_default_graph_uri_alias(self):
|
||||
engine = QueryEngine(
|
||||
enable_optimization=False,
|
||||
enable_caching=False,
|
||||
default_graph_uri="http://example.org/graph/default",
|
||||
)
|
||||
query = "SELECT ?s WHERE { ?s ?p ?o }"
|
||||
|
||||
prepared = engine.prepare_query(query)
|
||||
|
||||
self.assertIn("FROM <http://example.org/graph/default>", prepared)
|
||||
|
||||
def test_query_engine_fallback_when_named_graphs_unsupported(self):
|
||||
engine = QueryEngine(enable_optimization=False, enable_caching=False)
|
||||
query = "SELECT ?s WHERE { ?s ?p ?o }"
|
||||
|
||||
prepared = engine.prepare_query(
|
||||
query,
|
||||
graph="http://example.org/graph/default",
|
||||
supports_named_graphs=False,
|
||||
)
|
||||
|
||||
self.assertEqual(prepared, query)
|
||||
|
||||
|
||||
class TestSKOSTripletStore(unittest.TestCase):
|
||||
"""Tests for SKOS helper methods on TripletStore."""
|
||||
|
||||
Reference in New Issue
Block a user