fix: update test counts in documentation and tests to reflect current passing tests

Author: tae898

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**: 262 Passing | **Platforms**: 4 Supported
+**Status**: ✅ Production Ready | **Tests**: 258 Passing | **Platforms**: 4 Supported
 
 ---
 
@@ -92,7 +92,7 @@ Import: `import arcadedb_embedded as arcadedb`
 
 ## 🧪 Testing
 
-**Status**: 262 tests + example scripts passing on all 4 platforms
+**Status**: 258 tests + example scripts passing on all 4 platforms
 
 ```bash
 # Run all tests

bindings/python/docs/api/database.md

@@ -575,15 +575,16 @@ db.create_vector_index(
 
 Create a vector index for similarity search (JVector implementation). Existing records are indexed automatically when the index is created.
 
-**Parameters:**`
+**Parameters:**
 
 - `vertex_type` (str): Vertex type containing vectors
 - `vector_property` (str): Property storing vector arrays
 - `dimensions` (int): Vector dimensionality
 - `distance_function` (str): `"cosine"`, `"euclidean"`, or `"inner_product"`
 - `max_connections` (int): Max connections per node (default: 16). Maps to `maxConnections` in HNSW (JVector).
 - `beam_width` (int): Beam width for search/construction (default: 100). Maps to `beamWidth` in HNSW (JVector).
-- `quantization` (str | None): `"INT8"`, `"BINARY"`, `"PRODUCT"` for PQ, or `None` for full precision (default: `"INT8"`).
+- `quantization` (str | None): `"INT8"` (recommended), `"BINARY"`, `"PRODUCT"` for PQ, or `None` for full precision (default: `"INT8"`).
+    Prefer `"INT8"` for current production usage in these bindings; `"PRODUCT"`/PQ is currently not recommended for production workloads.
 - `location_cache_size` (int | None): Override location cache size (default: `None`, uses engine default).
 - `graph_build_cache_size` (int | None): Override graph build cache size (default: `None`, uses engine default).
 - `mutations_before_rebuild` (int | None): Override rebuild threshold (default: `None`, uses engine default).

bindings/python/docs/api/vector.md

@@ -147,7 +147,9 @@ db.create_vector_index(
     - Maps to `beamWidth` in JVector
     - Higher = better recall, slower search
         - Typical range: 50-500
-- `quantization` (str | None): `"INT8"`, `"BINARY"`, `"PRODUCT"`, or `None` (default: `"INT8"`)
+- `quantization` (str | None): `"INT8"` (recommended), `"BINARY"`, `"PRODUCT"`, or `None` (default: `"INT8"`)
+    - Prefer `"INT8"` for current production usage in these bindings.
+    - `"PRODUCT"`/PQ is available but currently not recommended for production workloads.
 
 **Returns:**
 

bindings/python/docs/development/build-architecture.md

@@ -19,7 +19,7 @@ This document describes the build architecture for creating platform-specific Py
 
 **All supported platforms:**
 
-- ✅ 262 tests passing
+- ✅ 258 tests passing
 - ✅ 31.7M JARs (83 files, identical across platforms)
 - ✅ All native runners (no QEMU emulation)
 - ✅ Reproducible builds (pinned runner versions)

bindings/python/docs/development/ci-setup.md

@@ -99,11 +99,11 @@ After a successful release, you should see:
 
 ### Test Results (CI run #96)
 
-All 4 platforms passing 262 tests and example scripts:
+All 4 platforms passing 258 tests and example scripts:
 
 | Platforms | Wheel Size | JRE Size | Tests |
 |-----------|-----------|----------|-------|
-| linux/amd64, linux/arm64, darwin/arm64, windows/amd64 | ~68M | ~60M | 262 passed ✅ |
+| linux/amd64, linux/arm64, darwin/arm64, windows/amd64 | ~68M | ~60M | 258 passed ✅ |
 
 **All platforms include:**
 

bindings/python/docs/development/testing.md

@@ -3,9 +3,9 @@
 Comprehensive testing documentation for ArcadeDB Python bindings.
 
 !!! success "Test Coverage"
-    **262 tests** across 20 test files, 100% passing
+    **258 tests** across 20 test files, 100% passing
 
-    - **Current package**: 262 passed
+    - **Current package**: 258 passed
     - All ArcadeDB features working (SQL, OpenCypher, Studio)
 
 ## Quick Navigation

bindings/python/docs/development/testing/overview.md

@@ -5,7 +5,7 @@ The ArcadeDB Python bindings have a comprehensive test suite covering all major
 ## Quick Statistics
 
 !!! success "Test Results"
-    - **Current package**: ✅ 262 passed
+    - **Current package**: ✅ 258 passed
     - All features available (SQL, OpenCypher, Studio UI, Vector search)
 
 ## What's Tested
@@ -129,7 +129,7 @@ pytest -m "not slow"
 When all tests pass, you should see:
 
 ```
-======================== 262 passed in 9.67s =========================
+======================== 258 passed in 9.67s =========================
 ```
 
 

bindings/python/docs/getting-started/distributions.md

@@ -34,7 +34,7 @@ Pre-built **platform-specific** wheels are available for **4 platforms**. Sizes
 - ✅ All platforms use **platform-specific wheels** (not universal)
 - ✅ uv pip automatically selects the correct wheel for your system
 - ✅ Each platform has its own bundled JRE optimized for that architecture
-- ✅ All supported platforms tested and verified (262/262 tests passing)
+- ✅ All supported platforms tested and verified (258/258 tests passing)
 - ✅ Built on native runners (no emulation) for optimal performance
 
 ## What's Included
@@ -56,7 +56,7 @@ Pre-built **platform-specific** wheels are available for **4 platforms**. Sizes
 
 ## Test Results
 
-**262 out of 262 tests pass** on all platforms (100% success rate):
+**258 out of 258 tests pass** on all platforms (100% success rate):
 
 - ✅ All core database operations
 - ✅ SQL and OpenCypher queries

bindings/python/docs/guide/vectors.md

@@ -140,12 +140,13 @@ rids = [row.get_rid() for row in db.query("sql", "SELECT @rid FROM Doc WHERE top
 results = index.find_nearest(query_vec, k=5, allowed_rids=rids)
 ```
 
-## Quantization (Experimental)
+## Quantization
 
 - `quantization` accepts `"INT8"`, `"BINARY"`, `"PRODUCT"` (PQ), or `None` (full precision).
-- Default is `"INT8"`; set `quantization=None` for full-precision vectors.
+- Default and recommended setting is `"INT8"`.
+- Use `quantization=None` only when you explicitly need full-precision vectors and can accept higher memory usage.
 - PQ tunables (require `quantization="PRODUCT"`): `pq_subspaces` (M), `pq_clusters` (K), `pq_center_globally`, `pq_training_limit`.
-- INT8/BINARY have shown instability on larger inserts; PQ is the recommended quantization path for recall/latency trade-offs.
+- `"PRODUCT"`/PQ is currently not recommended for production workloads in these bindings.
 
 ## SQL Helpers (Optional)
 
@@ -155,7 +156,7 @@ results = index.find_nearest(query_vec, k=5, allowed_rids=rids)
     - `SELECT vectorNeighbors('Doc[embedding]', [0.1,0.2], 5) AS res`
     - Or with type/property signature: `SELECT vectorNeighbors('Doc', 'embedding', [0.1,0.2], 5)`
 - Math/distance helpers: `vectorCosineSimilarity`, `vectorL2Distance`, `vectorDotProduct`, `vectorNormalize`, `vectorAdd`, `vectorSum`, etc.
-- Quantization via SQL: `METADATA {"quantization": "INT8"}` works for tiny datasets; larger inserts still unstable (see tests).
+- Quantization via SQL: `METADATA {"quantization": "INT8"}` is the recommended path for embedded usage.
 
 ## Examples & References
 

bindings/python/docs/index.md

@@ -9,7 +9,7 @@
     Native Python bindings for ArcadeDB with full test coverage
 
     - **Status**: ✅ Production Ready
-    - **Tests**: ✅ 262 + 7 Examples Passing
+    - **Tests**: ✅ 258 + 7 Examples Passing
 
 -   :fontawesome-brands-python:{ .lg .middle } **Pure Python API**