Release v0.0.9

* Bump version to 0.0.9 for development

* refactor: rename `.query()` method to `.search()` for semantic clarity

* Delete: old test files and update numbering of remaining files

* test: expand test coverage and update for latest API changes

* 📄 docs(changelog): update for v0.0.9 release

* Add: latest uv.lock file for reproducible Python dependency management

* 📄 docs(changelog): update for v0.0.9 release

Author: doubleinfinity

CHANGELOG.md

@@ -7,6 +7,20 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ---
 
+## [0.0.9] - 2025-07-10
+
+### Added
+- `search()` is a more accurate and industry-standard term for vector similarity retrieval.
+
+### Changed
+- ⚠️ Breaking Changes - Renamed `HNSWIndex.query()` → `HNSWIndex.search()` to better reflect its role as a k-nearest neighbor (KNN) similarity search method.
+- Updated all internal references, tests, and examples to reflect the new `.search()` method name.
+
+### Removed
+- All usages of `.query()` must be replaced with `.search()`.
+
+---
+
 ## [0.0.8] - 2025-07-10
 
 ### Added

README.md

@@ -126,7 +126,7 @@ print(add_result.summary())
 query_vector = [0.1, 0.2, 0.3, 0.1, 0.4, 0.2, 0.6, 0.7]
 
 # Query with no filter (all documents)
-results = index.query(vector=query_vector, filter=None, top_k=2)
+results = index.search(vector=query_vector, filter=None, top_k=2)
 print("\n--- Query Results Output - Raw ---")
 print(results)
 
@@ -300,7 +300,7 @@ Query the index using a new vector and retrieve the top-k nearest neighbors. You
 
 ```python
 print("\n--- Query returning two most similar results ---")
-results = index.query(vector=query_vector, top_k=2)
+results = index.search(vector=query_vector, top_k=2)
 print(results)
 ```
 
@@ -318,7 +318,7 @@ This filters on the given metadata after conducting the similarity search.
 
 ```python
 print("\n--- Querying with filter: author = 'Alice' ---")
-results = index.query(vector=query_vector, filter={"author": "Alice"}, top_k=5)
+results = index.search(vector=query_vector, filter={"author": "Alice"}, top_k=5)
 print(results)
 ```
 
@@ -337,7 +337,7 @@ You can optionally return the stored embedding vectors alongside metadata and si
 
 ```python
 print("\n--- Querying with filter and returning embedding vectors ---")
-results = index.query(vector=query_vector, filter={"split": "test"}, top_k=2, return_vector=True)
+results = index.search(vector=query_vector, filter={"split": "test"}, top_k=2, return_vector=True)
 print(results)
 ```
 
@@ -429,7 +429,7 @@ print(index.list(number=5)) # Shows first 5 records
 ZeusDB allows you to remove a vector and its associated metadata from the index using the .remove_point(id) method. This performs a <u>logical deletion</u>, meaning:
 - The vector is deleted from internal storage.
 - The metadata is removed.
-- The vector ID is no longer accessible via .contains(), .get_vector(), or .query().
+- The vector ID is no longer accessible via .contains(), .get_vector(), or .search().
 
 ```python
 # Remove the point using its ID
@@ -530,31 +530,31 @@ filter = {
     "year": {"gte": 2024}
 }
 
-results = index.query(vector=query_embedding, filter=filter, top_k=5)
+results = index.search(vector=query_embedding, filter=filter, top_k=5)
 ```
 
 #### ✔️ Find documents by specific authors
 ```python
 filter = {"author": {"in": ["Alice", "Bob", "Charlie"]}}
-results = index.query(vector=query_embedding, filter=filter, top_k=5)
+results = index.search(vector=query_embedding, filter=filter, top_k=5)
 ```
 
 #### ✔️ Find AI-related content
 ```python
 filter = {"tags": {"contains": "ai"}}
-results = index.query(vector=query_embedding, filter=filter, top_k=5)
+results = index.search(vector=query_embedding, filter=filter, top_k=5)
 ```
 
 #### ✔️ Find documents in price range
 ```python
 filter = {"price": {"gte": 20.0, "lte": 40.0}}
-results = index.query(vector=query_embedding, filter=filter, top_k=5)
+results = index.search(vector=query_embedding, filter=filter, top_k=5)
 ```
 
 #### ✔️ Find documents with specific file types
 ```python
 filter = {"filename": {"endswith": ".pdf"}}
-results = index.query(vector=query_embedding, filter=filter, top_k=5)
+results = index.search(vector=query_embedding, filter=filter, top_k=5)
 ```
 
 

…marks/17.new_query_data_with_metadata.py …marks/10.new_query_data_with_metadata.pybenchmarks/17.new_query_data_with_metadata.py renamed to benchmarks/10.new_query_data_with_metadata.py benchmarks/17.new_query_data_with_metadata.py renamed to benchmarks/10.new_query_data_with_metadata.py

@@ -39,7 +39,7 @@
 
 # Query all records
 query_vector = np.random.rand(384).tolist()
-all_results = index.query(query_vector, top_k=10)
+all_results = index.search(query_vector, top_k=10)
 print("\n--- Top 10 results (raw no filter) ---")
 print(all_results)
 print(type(all_results))
@@ -48,15 +48,15 @@
     print(f"{i+1}. ID: {result['id']}, Score: {result['score']:.4f}, Batch: {result['metadata']['batch']}")
 
 # Query only "large" batch records
-large_results = index.query(query_vector, filter={"batch": "large"}, top_k=5)
+large_results = index.search(query_vector, filter={"batch": "large"}, top_k=5)
 print("\n--- Top 5 'large' batch results - raw ---")
 print(large_results)
 print("\n--- Top 5 'large' batch results ---")
 for i, result in enumerate(large_results):
     print(f"{i+1}. ID: {result['id']}, Score: {result['score']:.4f}, Batch: {result['metadata']['batch']}")
 
 # Query only "small" batch records  
-small_results = index.query(query_vector, filter={"batch": "small"}, top_k=5)
+small_results = index.search(query_vector, filter={"batch": "small"}, top_k=5)
 print("\n--- Top 5 'small' batch results ---")
 for i, result in enumerate(small_results):
     print(f"{i+1}. ID: {result['id']}, Score: {result['score']:.4f}, Batch: {result['metadata']['batch']}")

…marks/18.new_query_data_return_vector.py …marks/11.new_query_data_return_vector.pybenchmarks/18.new_query_data_return_vector.py renamed to benchmarks/11.new_query_data_return_vector.py benchmarks/18.new_query_data_return_vector.py renamed to benchmarks/11.new_query_data_return_vector.py

@@ -32,7 +32,7 @@
 
 # Query with top_k=2 and return_vector=True
 query_vector = np.random.rand(4).tolist()
-results = index.query(query_vector, top_k=2, return_vector=True)
+results = index.search(query_vector, top_k=2, return_vector=True)
 
 print("\n--- Top 2 Results (with vectors) - raw ---")
 print(results)

benchmarks/19.readme_code.py benchmarks/12.readme_code.pybenchmarks/19.readme_code.py renamed to benchmarks/12.readme_code.py

@@ -27,7 +27,7 @@
 query_vector = [0.1, 0.2, 0.3, 0.1, 0.4, 0.2, 0.6, 0.7]
 
 # Query with no filter (all documents)
-results = index.query(vector=query_vector, filter=None, top_k=2)
+results = index.search(vector=query_vector, filter=None, top_k=2)
 print("\n--- Query Results Output - Raw ---")
 print(results)
 
@@ -36,7 +36,7 @@
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
 
 print("\n--- Querying with filter: author = 'Alice' ---")
-results2 = index.query(vector=query_vector, filter={"author": "Alice"}, top_k=5)
+results2 = index.search(vector=query_vector, filter={"author": "Alice"}, top_k=5)
 print(results2)
 
 

benchmarks/20.new_add_data_with_errors.py benchmarks/13.new_add_data_with_errors.pybenchmarks/20.new_add_data_with_errors.py renamed to benchmarks/13.new_add_data_with_errors.py

@@ -20,12 +20,12 @@
 print("--- Add Results Summary ---")
 print(add_result_cos.summary())
 
-results_cos = index_cos.query(vector=query_vector, filter=None, top_k=2)
+results_cos = index_cos.search(vector=query_vector, filter=None, top_k=2)
 print("\n--- Top 2 Neighbors (No Filter) ---")
 for i, res in enumerate(results_cos, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
 
-results_cos_filtered = index_cos.query(vector=query_vector, filter={"author": "Alice"}, top_k=5)
+results_cos_filtered = index_cos.search(vector=query_vector, filter={"author": "Alice"}, top_k=5)
 print("\n--- Top 5 Neighbors (Filter: author = 'Alice') ---")
 for i, res in enumerate(results_cos_filtered, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
@@ -40,12 +40,12 @@
 print("--- Add Results Summary ---")
 print(add_result_l2.summary())
 
-results_l2 = index_l2.query(vector=query_vector, filter=None, top_k=2)
+results_l2 = index_l2.search(vector=query_vector, filter=None, top_k=2)
 print("\n--- Top 2 Neighbors (No Filter) ---")
 for i, res in enumerate(results_l2, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
 
-results_l2_filtered = index_l2.query(vector=query_vector, filter={"author": "Alice"}, top_k=5)
+results_l2_filtered = index_l2.search(vector=query_vector, filter={"author": "Alice"}, top_k=5)
 print("\n--- Top 5 Neighbors (Filter: author = 'Alice') ---")
 for i, res in enumerate(results_l2_filtered, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
@@ -60,12 +60,12 @@
 print("--- Add Results Summary ---")
 print(add_result_l1.summary())
 
-results_l1 = index_l1.query(vector=query_vector, filter=None, top_k=2)
+results_l1 = index_l1.search(vector=query_vector, filter=None, top_k=2)
 print("\n--- Top 2 Neighbors (No Filter) ---")
 for i, res in enumerate(results_l1, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")
 
-results_l1_filtered = index_l1.query(vector=query_vector, filter={"author": "Alice"}, top_k=5)
+results_l1_filtered = index_l1.search(vector=query_vector, filter={"author": "Alice"}, top_k=5)
 print("\n--- Top 5 Neighbors (Filter: author = 'Alice') ---")
 for i, res in enumerate(results_l1_filtered, 1):
     print(f"{i}. ID: {res['id']}, Score: {res['score']:.4f}, Metadata: {res['metadata']}")