Refactor edge type creation to remove UNIDIRECTIONAL specification

- Updated tests to create edge types without UNIDIRECTIONAL keyword.
- Added tests for OpenCypher index creation, constraints, and DDL operations.
- Enhanced vector index tests to include custom ID properties and metadata.
- Improved error handling and assertions in document import tests.
- Added tests for vector neighbors functionality using both SQL and OpenCypher.

Co-authored-by: Copilot <copilot@github.com>

Author: tae898

.github/workflows/test-python-bindings.yml

@@ -5,12 +5,14 @@ on:
   push:
     paths:
       - 'bindings/python/**'
+      - '**/*.java'
       - '.github/workflows/test-python-bindings.yml'
 
   # Run on pull request affecting bindings/python/
   pull_request:
     paths:
       - 'bindings/python/**'
+      - '**/*.java'
       - '.github/workflows/test-python-bindings.yml'
 
   # Run after release workflow completes
@@ -50,7 +52,7 @@ jobs:
           cd bindings/python
 
           # Detect ArcadeDB version from pom.xml (Docker format: X.Y.Z-SNAPSHOT)
-          ARCADEDB_TAG=$(python3 extract_version.py --format=docker)
+          ARCADEDB_TAG=$(python3 scripts/extract_version.py --format=docker)
           echo "📌 ArcadeDB version: $ARCADEDB_TAG"
 
           # Download JARs from official Docker image
@@ -208,7 +210,7 @@ jobs:
         run: |
           cd bindings/python
           echo "🔨 Building arcadedb-embedded for ${{ matrix.platform }} with Python ${{ matrix.python-version }}..."
-          ./build.sh ${{ matrix.platform }} ${{ matrix.python-version }}
+          ./scripts/build.sh ${{ matrix.platform }} ${{ matrix.python-version }}
 
       - name: Extract wheel for additional testing
         shell: bash

.github/workflows/test-python-examples.yml

@@ -5,12 +5,14 @@ on:
   push:
     paths:
       - 'bindings/python/**'
+      - '**/*.java'
       - '.github/workflows/test-python-examples.yml'
 
   # Run on pull request affecting bindings/python/
   pull_request:
     paths:
       - 'bindings/python/**'
+      - '**/*.java'
       - '.github/workflows/test-python-examples.yml'
 
   # Run after release workflow completes
@@ -25,7 +27,7 @@ on:
         description: "Glob pattern(s) for examples to run (space-separated, relative to bindings/python/examples)."
         required: false
         type: string
-        default: "0[1-9]_*.py 1[0-6]_*.py"
+        default: "0[1-9]_*.py 1[0-9]_*.py 2[0-9]_*.py"
       build-version:
         description: "Override package version (PEP 440) for build.sh"
         required: false
@@ -37,10 +39,14 @@ on:
       examples:
         description: "Glob pattern(s) for examples to run (space-separated, relative to bindings/python/examples)."
         required: false
-        default: "0[1-9]_*.py 1[0-6]_*.py"
+        default: "0[1-9]_*.py 1[0-9]_*.py 2[0-9]_*.py"
 
 env:
-  EXAMPLES: ${{ inputs.examples || '0[1-9]_*.py 1[0-6]_*.py' }}
+  EXAMPLES: ${{ inputs.examples || '0[1-9]_*.py 1[0-9]_*.py 2[0-9]_*.py' }}
+
+# Note: Example 21 is intentionally excluded in CI.
+# It has shown repeated GitHub Actions instability across platforms despite
+# timeout increases, so the workflow skips it explicitly below.
 
 permissions:
   contents: read
@@ -63,7 +69,7 @@ jobs:
           cd bindings/python
 
           # Detect ArcadeDB version from pom.xml (Docker format: X.Y.Z-SNAPSHOT)
-          ARCADEDB_TAG=$(python3 extract_version.py --format=docker)
+          ARCADEDB_TAG=$(python3 scripts/extract_version.py --format=docker)
           echo "📌 ArcadeDB version: $ARCADEDB_TAG"
 
           # Download JARs from official Docker image
@@ -207,7 +213,7 @@ jobs:
         run: |
           cd bindings/python
           echo "🔨 Building arcadedb-embedded for ${{ matrix.platform }} with Python ${{ matrix.python-version }}..."
-          ./build.sh ${{ matrix.platform }} ${{ matrix.python-version }}
+          ./scripts/build.sh ${{ matrix.platform }} ${{ matrix.python-version }}
 
       # Note: Java is NOT required - arcadedb-embedded has bundled JRE!
 
@@ -311,6 +317,14 @@ jobs:
 
             # Set example-specific parameters and timeout
             case "$example" in
+              "21_server_mode_http_access.py")
+                # Example 21 is kept in the repo for local/manual use, but is
+                # excluded from GitHub Actions because it is CI-unstable.
+                echo "⏭️  SKIPPED: $example (excluded from GitHub Actions due to CI instability)" | tee -a $results_file
+                skipped=$((skipped + 1))
+                echo ""
+                continue
+                ;;
               "04_csv_import_documents.py")
                 example_args="--dataset movielens-small --export"
                 example_name="$example (movielens-small dataset with export)"
@@ -396,6 +410,30 @@ jobs:
                 timeout_duration=1200
                 example_jvm_args=""
                 ;;
+              "17_timeseries_end_to_end.py")
+                example_args="--hours 2 --interval-minutes 10"
+                example_name="$example (time-series end-to-end, minimal)"
+                timeout_duration=900
+                example_jvm_args=""
+                ;;
+              "18_geo_predicates_wkt.py")
+                example_args=""
+                example_name="$example (geo predicates with WKT points and polygons)"
+                timeout_duration=900
+                example_jvm_args=""
+                ;;
+              "19_hash_index_exact_match.py")
+                example_args=""
+                example_name="$example (hash index exact-match workflow)"
+                timeout_duration=900
+                example_jvm_args=""
+                ;;
+              "20_graph_algorithms_route_planning.py")
+                example_args=""
+                example_name="$example (graph algorithms route planning workflow)"
+                timeout_duration=900
+                example_jvm_args=""
+                ;;
               *)
                 example_args=""
                 example_name="$example"

bindings/python/.gitignore

@@ -45,3 +45,7 @@ notebooks/
 
 # local built jars
 local-jars/
+.runtime-cache/
+
+# test databases
+my_test_databases/

bindings/python/README.md

@@ -2,7 +2,7 @@
 
 Native Python bindings for ArcadeDB - the multi-model database that supports Graph, Document, Key/Value, Search Engine, Time Series, and Vector models.
 
-**Status**: ✅ Production Ready | **Tests**: 282 Passed | **Platforms**: 4 Supported
+**Status**: ✅ Production Ready | **Tests**: 290 Passed | **Platforms**: 4 Supported
 
 ---
 
@@ -84,15 +84,15 @@ The `arcadedb-embedded` package is platform-specific and self-contained:
 The compressed wheel size is measured from `dist/*.whl`, and the installed package size
 is measured from the extracted `site-packages/arcadedb_embedded/` directory.
 
-**Note**: Some JARs are excluded to optimize package size (e.g., gRPC wire protocol). See [`jar_exclusions.txt`](https://github.com/humemai/arcadedb-embedded-python/blob/main/bindings/python/jar_exclusions.txt) for details.
+**Note**: Some JARs are excluded to optimize package size (e.g., gRPC wire protocol). See [`scripts/jar_exclusions.txt`](https://github.com/humemai/arcadedb-embedded-python/blob/main/bindings/python/scripts/jar_exclusions.txt) for details.
 
 Import: `import arcadedb_embedded as arcadedb`
 
 ---
 
 ## 🧪 Testing
 
-**Status**: 282 passed
+**Status**: 290 passed
 
 ```bash
 # Run all tests
@@ -125,7 +125,7 @@ uv pip install build
 uv pip install -e ".[test]"
 
 # Build for your current platform (auto-detected)
-./build.sh
+./scripts/build.sh
 ```
 
 Built wheels will be in `dist/`.

bindings/python/examples/02_social_network_graph.py

@@ -131,7 +131,7 @@ def create_schema(db):
         print("  ✓ Created Person properties (including optional fields)")
 
         # Create FRIEND_OF edge type
-        db.command("sql", "CREATE EDGE TYPE FRIEND_OF UNIDIRECTIONAL")
+        db.command("sql", "CREATE EDGE TYPE FRIEND_OF")
         print("  ✓ Created FRIEND_OF edge type")
 
         # Create properties for FRIEND_OF edge

bindings/python/examples/03_vector_search.py

@@ -221,19 +221,20 @@ def create_mock_embedding(category_seed, doc_seed):
     print("      • beam_width: 256 (search quality, higher = more accurate)")
     print()
 
-    # Create vector index (JVector implementation - recommended)
-    # Using new defaults: max_connections=32, beam_width=256
-    # New options available:
-    # - quantization: "INT8" or "BINARY" (reduces memory usage)
-    # - store_vectors_in_graph: True (faster search, higher disk usage)
-    index = db.create_vector_index(
-        vertex_type="Article",
-        vector_property="embedding",
-        dimensions=EMBEDDING_DIM,
-        distance_function="cosine",
+    db.command(
+        "sql",
+        f"""
+        CREATE INDEX ON Article (embedding)
+        LSM_VECTOR
+        METADATA {{
+            "dimensions": {EMBEDDING_DIM},
+            "similarity": "COSINE"
+        }}
+        """,
     )
 
     print("   ✅ Created JVector vector index")
+    print("   ✅ Built vector index graph immediately via SQL")
     print("   💡 LSM index automatically indexes existing records upon creation.")
     print("   ✅ Indexing handled by ArcadeDB engine.")
     print(f"   ⏱️  Time: {time.time() - step_start:.3f}s")
@@ -262,29 +263,48 @@ def create_mock_embedding(category_seed, doc_seed):
 
         query_embedding = create_mock_embedding(category, f"query{query_num}")
 
-        # Get top 5 most similar (smallest distances)
-        most_similar = index.find_nearest(query_embedding, k=5)
+        qvec_literal = (
+            "[" + ", ".join(str(float(x)) for x in query_embedding.tolist()) + "]"
+        )
+        most_similar = db.query(
+            "sql",
+            (
+                "SELECT title, category, distance, (1 - distance) AS score "
+                "FROM (SELECT expand(vectorNeighbors('Article[embedding]', "
+                f"{qvec_literal}, 5))) ORDER BY distance"
+            ),
+        ).to_list()
 
         print("      Top 5 MOST similar documents (smallest distance):")
-        for i, (vertex, distance) in enumerate(most_similar, 1):
-            title = vertex.get("title")
-            doc_category = vertex.get("category")
+        for i, hit in enumerate(most_similar, 1):
+            title = hit.get("title")
+            doc_category = hit.get("category")
+            distance = hit.get("distance")
             print(f"      {i}. {title}")
             print(f"         Category: {doc_category}, Distance: {distance:.4f}")
         print()
 
-        # Get all documents to find least similar
-        # Note: For LSM, getting ALL documents might be slow or limited by k
-        k_limit = min(NUM_DOCUMENTS, 1000)
-        all_results = index.find_nearest(query_embedding, k=k_limit)
-        least_similar = list(all_results)[-5:]  # Last 5 = farthest
-
-        print("      Top 5 LEAST similar documents (largest distance):")
-        for i, (vertex, distance) in enumerate(least_similar, 1):
-            title = vertex.get("title")
-            doc_category = vertex.get("category")
+        filtered_hits = db.query(
+            "sql",
+            (
+                "SELECT title, category, distance, (1 - distance) AS score "
+                "FROM (SELECT expand(vectorNeighbors('Article[embedding]', "
+                f"{qvec_literal}, 50))) WHERE category = ? ORDER BY distance LIMIT 5"
+            ),
+            category,
+        ).to_list()
+
+        print("      Top 5 filtered documents in the same category:")
+        for i, hit in enumerate(filtered_hits, 1):
+            title = hit.get("title")
+            doc_category = hit.get("category")
+            distance = hit.get("distance")
+            score = hit.get("score")
             print(f"      {i}. {title}")
-            print(f"         Category: {doc_category}, Distance: {distance:.4f}")
+            print(
+                f"         Category: {doc_category}, Distance: {distance:.4f}, "
+                f"Score: {score:.4f}"
+            )
         print()
 
     print(f"   ⏱️  All queries time: {time.time() - step_start:.3f}s")