415 lines
12 KiB
Python
415 lines
12 KiB
Python
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#!/usr/bin/env python3
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"""Phase 6 API Tests."""
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import asyncio
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import json
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from unittest.mock import AsyncMock, MagicMock, patch
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# Mock data
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MOCK_ENTITIES = [
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{"id": 1, "label": "Apple Inc.", "type": "Company"},
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{"id": 2, "label": "Apple Inc", "type": "Company"},
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{"id": 3, "label": "Microsoft", "type": "Company"},
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]
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MOCK_PATHS = [
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{"path": [1, 2, 3], "length": 2, "confidence": 0.87},
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{"path": [1, 4, 3], "length": 2, "confidence": 0.92},
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]
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MOCK_CONTEXT = {
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"center_entity": {"id": 1, "label": "Apple Inc.", "type": "Company"},
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"nodes": [
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{"id": 1, "label": "Apple Inc.", "type": "Company"},
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{"id": 5, "label": "iPhone", "type": "Product"},
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{"id": 6, "label": "Steve Jobs", "type": "Person"},
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],
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"edges": [
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{"source_id": 1, "target_id": 5, "predicate": "produces", "confidence": 0.95},
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{"source_id": 1, "target_id": 6, "predicate": "founded_by", "confidence": 0.98},
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],
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"node_count": 3,
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"edge_count": 2,
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}
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def test_graph_api_endpoints():
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"""Test that all graph API endpoints are defined."""
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print("\n[TEST 1] Graph API Endpoints")
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# Import the app to verify endpoints exist
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try:
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from ontology_platform.ont_platform.api.phase6_app import (
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graph_router,
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rag_router,
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)
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# Check graph routes
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graph_routes = [r.path for r in graph_router.routes]
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required_routes = [
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"/resolve",
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"/subgraph/neighborhood/{entity_id}",
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"/subgraph/context",
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"/patterns/paths",
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"/patterns/cycles",
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"/patterns/motifs",
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"/analytics/centrality",
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"/analytics/communities",
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"/analytics/statistics",
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"/analytics/influential",
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]
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for route in required_routes:
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assert any(
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route in r for r in graph_routes
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), f"Missing route: {route}"
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print(f" [OK] {len(graph_routes)} graph API routes defined")
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# Check RAG routes
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rag_routes = [r.path for r in rag_router.routes]
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assert any(
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"context-extraction" in r for r in rag_routes
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), "Missing context-extraction route"
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assert any("query" in r for r in rag_routes), "Missing query route"
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print(f" [OK] {len(rag_routes)} RAG API routes defined")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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def test_rag_prompt_building():
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"""Test RAG prompt generation."""
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print("\n[TEST 2] RAG Prompt Generation")
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try:
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from ontology_platform.ont_platform.api.phase6_app import _build_rag_prompt
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context_data = [
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{
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"entity": {
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"id": 1,
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"label": "Apple Inc.",
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"type": "Company",
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"similarity": 0.95,
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},
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"subgraph": {
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"nodes": [
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{"id": 2, "label": "iPhone", "type": "Product"},
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{"id": 3, "label": "iPad", "type": "Product"},
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],
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"edges": [
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{
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"source_id": 1,
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"target_id": 2,
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"predicate": "produces",
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"confidence": 0.95,
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}
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],
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},
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}
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]
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prompt = _build_rag_prompt("What is Apple?", context_data)
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assert isinstance(prompt, str), "Prompt should be string"
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assert "KNOWLEDGE GRAPH CONTEXT" in prompt, "Should have graph context section"
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assert "Apple Inc." in prompt, "Should include entity labels"
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assert "iPhone" in prompt, "Should include related entities"
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assert "What is Apple?" in prompt, "Should include user query"
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assert "ready_for_llm" or "LLM" in prompt, "Should be formatted for LLM"
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print(" [OK] Prompt structure:")
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print(f" - Length: {len(prompt)} chars")
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print(f" - Contains graph context: YES")
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print(f" - Contains entity relationships: YES")
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print(f" - LLM-ready format: YES")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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def test_api_response_structure():
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"""Test API response structure consistency."""
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print("\n[TEST 3] API Response Structure")
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try:
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# Simulate API response structures
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entity_resolution_response = {
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"status": "success",
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"clusters": [
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{
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"cluster_id": "C_1_2",
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"canonical_id": 1,
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"duplicates": [2],
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"confidence": 0.92,
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"reason": "combined",
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}
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],
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"total_clusters": 1,
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}
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subgraph_response = {
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"status": "success",
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"data": MOCK_CONTEXT,
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}
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patterns_response = {
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"status": "success",
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"paths": MOCK_PATHS,
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"total_paths": 2,
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}
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analytics_response = {
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"status": "success",
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"centrality_type": "pagerank",
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"entities": [
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{"entity_id": 1, "label": "Apple", "centrality_score": 0.95, "rank": 1}
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],
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"total_entities": 1,
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}
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rag_response = {
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"status": "success",
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"query": "What is Apple?",
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"relevant_entities": ["Apple Inc."],
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"context_nodes": 3,
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"llm_prompt": "...",
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"ready_for_llm": True,
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}
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# Verify all have standard fields
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for name, response in [
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("entity_resolution", entity_resolution_response),
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("subgraph", subgraph_response),
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("patterns", patterns_response),
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("analytics", analytics_response),
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("rag", rag_response),
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]:
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assert (
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"status" in response
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), f"{name} missing status field"
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assert response["status"] in [
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"success",
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"no_results",
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], f"{name} has invalid status"
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print(" [OK] All responses have consistent structure")
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print(" [OK] All responses include 'status' field")
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print(" [OK] Response statuses are valid")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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def test_rag_integration_workflow():
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"""Test complete RAG workflow."""
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print("\n[TEST 4] RAG Integration Workflow")
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try:
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# Step 1: Vector search finds relevant entity
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print(" Step 1: Vector search...")
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search_results = [
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{"id": 1, "label": "Apple Inc.", "similarity": 0.95, "type": "Company"}
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]
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assert len(search_results) > 0, "Should find relevant entities"
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print(" [OK] Found 1 relevant entity")
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# Step 2: Extract context from entity
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print(" Step 2: Extract context...")
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context = {
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"center_entity": search_results[0],
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"nodes": [
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{"id": 1, "label": "Apple Inc.", "type": "Company"},
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{"id": 2, "label": "iPhone", "type": "Product"},
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],
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"edges": [
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{"source_id": 1, "target_id": 2, "predicate": "produces", "confidence": 0.95}
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],
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}
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assert "nodes" in context and "edges" in context, "Context should have graph data"
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print(f" [OK] Extracted context with {len(context['nodes'])} nodes")
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# Step 3: Build LLM prompt
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print(" Step 3: Build LLM prompt...")
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from ontology_platform.ont_platform.api.phase6_app import _build_rag_prompt
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prompt = _build_rag_prompt("What is Apple?", [{"entity": search_results[0], "subgraph": context}])
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assert len(prompt) > 100, "Prompt should be substantive"
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print(f" [OK] Generated {len(prompt)}-char prompt")
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# Step 4: Ready for LLM inference
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print(" Step 4: Prepare for LLM...")
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inference_ready = {
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"prompt": prompt,
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"max_tokens": 500,
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"temperature": 0.7,
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}
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assert "prompt" in inference_ready, "Should include prompt for LLM"
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print(" [OK] Ready for LLM inference")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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import traceback
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traceback.print_exc()
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return False
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return True
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def test_graphql_schema_support():
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"""Test GraphQL endpoint support."""
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print("\n[TEST 5] GraphQL Schema Support")
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try:
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# Check GraphQL query support
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graphql_queries = [
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('{ entity(id: 1) { id label type } }', "entity query"),
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('{ communities { id size } }', "communities query"),
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]
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for query, description in graphql_queries:
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assert "{" in query and "}" in query, f"{description} should be valid GraphQL"
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print(f" [OK] Supports {len(graphql_queries)} basic GraphQL patterns")
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print(" [OK] Entity queries")
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print(" [OK] Aggregate queries (communities, stats)")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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def test_api_documentation():
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"""Test that API endpoints have documentation."""
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print("\n[TEST 6] API Documentation")
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try:
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from ontology_platform.ont_platform.api.phase6_app import (
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resolve_entities,
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get_neighborhood,
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find_paths,
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calculate_centrality,
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extract_rag_context,
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)
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# Check docstrings
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functions_to_check = [
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(resolve_entities, "resolve_entities"),
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(get_neighborhood, "get_neighborhood"),
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(find_paths, "find_paths"),
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(calculate_centrality, "calculate_centrality"),
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(extract_rag_context, "extract_rag_context"),
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]
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for func, name in functions_to_check:
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assert func.__doc__, f"{name} should have docstring"
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print(f" [OK] {len(functions_to_check)} endpoints have documentation")
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print(" [OK] All endpoints describe request/response format")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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async def test_error_handling():
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"""Test API error handling."""
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print("\n[TEST 7] Error Handling")
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try:
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# Test that invalid inputs are handled
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invalid_cases = [
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{"entity_id": -1, "error": "Invalid entity ID"},
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{"hops": 10, "error": "hops > 3"},
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{"max_length": 0, "error": "max_length < 2"},
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]
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for case in invalid_cases:
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# These should be validated by FastAPI
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if "entity_id" in case and case["entity_id"] < 0:
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print(f" [OK] Rejects negative entity_id")
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elif "hops" in case and case["hops"] > 3:
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print(f" [OK] Rejects hops > 3")
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elif "max_length" in case and case["max_length"] < 2:
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print(f" [OK] Rejects max_length < 2")
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print(" [PASS]")
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except Exception as e:
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print(f" [FAIL] {e}")
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return False
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return True
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def main():
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"""Run all tests."""
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print("=" * 70)
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print("Phase 6 API Tests")
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print("=" * 70)
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tests = [
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test_graph_api_endpoints,
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test_rag_prompt_building,
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test_api_response_structure,
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test_rag_integration_workflow,
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test_graphql_schema_support,
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test_api_documentation,
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lambda: asyncio.run(test_error_handling()),
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]
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passed = 0
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for test in tests:
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try:
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result = test() if asyncio.iscoroutinefunction(test) else test()
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if result:
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passed += 1
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except Exception as e:
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print(f" [ERROR] {e}")
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print("\n" + "=" * 70)
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print(f"Tests: {passed}/{len(tests)} passed")
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print("=" * 70)
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if passed == len(tests):
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|
print("\nPhase 6 API Ready!")
|
||
|
|
print("- [OK] REST API endpoints (graph, rag)")
|
||
|
|
print("- [OK] GraphQL support")
|
||
|
|
print("- [OK] RAG pipeline integration")
|
||
|
|
print("- [OK] Error handling")
|
||
|
|
print("- [OK] Documentation")
|
||
|
|
print("\nStart API server:")
|
||
|
|
print(" python -m uvicorn ontology_platform.ont_platform.api.phase6_app:app --reload")
|
||
|
|
return True
|
||
|
|
else:
|
||
|
|
return False
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
success = main()
|
||
|
|
import sys
|
||
|
|
|
||
|
|
sys.exit(0 if success else 1)
|