Mark bbq indices as GA and add rolling upgrade integration tests

Author: benwtrent

docs/reference/mapping/types/dense-vector.asciidoc

@@ -118,7 +118,7 @@ The three following quantization strategies are supported:
 
 * `int8` - Quantizes each dimension of the vector to 1-byte integers. This reduces the memory footprint by 75% (or 4x) at the cost of some accuracy.
 * `int4` - Quantizes each dimension of the vector to half-byte integers. This reduces the memory footprint by 87% (or 8x) at the cost of accuracy.
-* `bbq` - experimental:[] Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
+* `bbq` - Better binary quantization which reduces each dimension to a single bit precision. This reduces the memory footprint by 96% (or 32x) at a larger cost of accuracy. Generally, oversampling during query time and reranking can help mitigate the accuracy loss.
 
 
 When using a quantized format, you may want to oversample and rescore the results to improve accuracy. See <<dense-vector-knn-search-rescoring, oversampling and rescoring>> for more information.
@@ -133,7 +133,7 @@ This means disk usage will increase by ~25% for `int8`, ~12.5% for `int4`, and ~
 
 NOTE: `int4` quantization requires an even number of vector dimensions.
 
-NOTE: experimental:[] `bbq` quantization only supports vector dimensions that are greater than 64.
+NOTE: `bbq` quantization only supports vector dimensions that are greater than 64.
 
 Here is an example of how to create a byte-quantized index:
 
@@ -325,15 +325,15 @@ by 4x at the cost of some accuracy. See <<dense-vector-quantization, Automatical
 * `int4_hnsw` - This utilizes the https://arxiv.org/abs/1603.09320[HNSW algorithm] in addition to automatically scalar
 quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint
 by 8x at the cost of some accuracy. See <<dense-vector-quantization, Automatically quantize vectors for kNN search>>.
-* experimental:[] `bbq_hnsw` - This utilizes the https://arxiv.org/abs/1603.09320[HNSW algorithm] in addition to automatically binary
+* `bbq_hnsw` - This utilizes the https://arxiv.org/abs/1603.09320[HNSW algorithm] in addition to automatically binary
 quantization for scalable approximate kNN search with `element_type` of `float`. This can reduce the memory footprint
 by 32x at the cost of accuracy. See <<dense-vector-quantization, Automatically quantize vectors for kNN search>>.
 * `flat` - This utilizes a brute-force search algorithm for exact kNN search. This supports all `element_type` values.
 * `int8_flat` - This utilizes a brute-force search algorithm in addition to automatically scalar quantization. Only supports
 `element_type` of `float`.
 * `int4_flat` - This utilizes a brute-force search algorithm in addition to automatically half-byte scalar quantization. Only supports
 `element_type` of `float`.
-* experimental:[] `bbq_flat` - This utilizes a brute-force search algorithm in addition to automatically binary quantization. Only supports
+* `bbq_flat` - This utilizes a brute-force search algorithm in addition to automatically binary quantization. Only supports
 `element_type` of `float`.
 --
 `m`:::

qa/rolling-upgrade/src/javaRestTest/java/org/elasticsearch/upgrades/VectorSearchIT.java

@@ -33,11 +33,14 @@ public VectorSearchIT(@Name("upgradedNodes") int upgradedNodes) {
     private static final String SCRIPT_BYTE_INDEX_NAME = "script_byte_vector_index";
     private static final String BYTE_INDEX_NAME = "byte_vector_index";
     private static final String QUANTIZED_INDEX_NAME = "quantized_vector_index";
+    private static final String BBQ_INDEX_NAME = "bbq_vector_index";
     private static final String FLAT_QUANTIZED_INDEX_NAME = "flat_quantized_vector_index";
+    private static final String FLAT_BBQ_INDEX_NAME = "flat_bbq_vector_index";
     private static final String FLOAT_VECTOR_SEARCH_VERSION = "8.4.0";
     private static final String BYTE_VECTOR_SEARCH_VERSION = "8.6.0";
     private static final String QUANTIZED_VECTOR_SEARCH_VERSION = "8.12.1";
     private static final String FLAT_QUANTIZED_VECTOR_SEARCH_VERSION = "8.13.0";
+    private static final String BBQ_VECTOR_SEARCH_VERSION = "8.18.0";
 
     public void testScriptByteVectorSearch() throws Exception {
         assumeTrue("byte vector search is not supported on this version", getOldClusterTestVersion().onOrAfter(BYTE_VECTOR_SEARCH_VERSION));
@@ -429,6 +432,182 @@ public void testFlatQuantizedVectorSearch() throws Exception {
         assertThat((double) hits.get(0).get("_score"), closeTo(0.9934857, 0.005));
     }
 
+    public void testBBQVectorSearch() throws Exception {
+        assumeTrue(
+            "Quantized vector search is not supported on this version",
+            getOldClusterTestVersion().onOrAfter(BBQ_VECTOR_SEARCH_VERSION)
+        );
+        if (isOldCluster()) {
+            String mapping = """
+                {
+                  "properties": {
+                    "vector": {
+                      "type": "dense_vector",
+                      "dims": 64,
+                      "index": true,
+                      "similarity": "cosine",
+                      "index_options": {
+                        "type": "bbq_hnsw",
+                        "ef_construction": 100,
+                        "m": 16
+                      }
+                    }
+                  }
+                }
+                """;
+            // create index and index 10 random floating point vectors
+            createIndex(BBQ_INDEX_NAME, Settings.EMPTY, mapping);
+            index64DimVectors(BBQ_INDEX_NAME);
+            // force merge the index
+            client().performRequest(new Request("POST", "/" + BBQ_INDEX_NAME + "/_forcemerge?max_num_segments=1"));
+        }
+        Request searchRequest = new Request("POST", "/" + BBQ_INDEX_NAME + "/_search");
+        searchRequest.setJsonEntity("""
+            {
+              "query": {
+                "script_score": {
+                  "query": {
+                    "exists": {
+                      "field": "vector"
+                    }
+                  },
+                  "script": {
+                   "source": "cosineSimilarity(params.query, 'vector') + 1.0",
+                    "params": {
+                      "query": [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
+                    }
+                  }
+                }
+              }
+            }
+            """);
+        Map<String, Object> response = search(searchRequest);
+        assertThat(extractValue(response, "hits.total.value"), equalTo(7));
+        List<Map<String, Object>> hits = extractValue(response, "hits.hits");
+        assertThat(hits.get(0).get("_id"), equalTo("0"));
+        assertThat((double) hits.get(0).get("_score"), closeTo(1.9869276, 0.0001));
+
+        // search with knn
+        searchRequest = new Request("POST", "/" + BBQ_INDEX_NAME + "/_search");
+        searchRequest.setJsonEntity("""
+            {
+                "knn": {
+                "field": "vector",
+                  "query_vector": [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                   5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
+                  "k": 2,
+                  "num_candidates": 5
+                }
+            }
+            """);
+        response = search(searchRequest);
+        assertThat(extractValue(response, "hits.total.value"), equalTo(2));
+        hits = extractValue(response, "hits.hits");
+        assertThat(hits.get(0).get("_id"), equalTo("0"));
+        assertThat((double) hits.get(0).get("_score"), closeTo(0.9934857, 0.005));
+    }
+
+    public void testFlatBBQVectorSearch() throws Exception {
+        assumeTrue(
+            "Quantized vector search is not supported on this version",
+            getOldClusterTestVersion().onOrAfter(BBQ_VECTOR_SEARCH_VERSION)
+        );
+        if (isOldCluster()) {
+            String mapping = """
+                {
+                  "properties": {
+                    "vector": {
+                      "type": "dense_vector",
+                      "dims": 64,
+                      "index": true,
+                      "similarity": "cosine",
+                      "index_options": {
+                        "type": "bbq_flat"
+                      }
+                    }
+                  }
+                }
+                """;
+            // create index and index 10 random floating point vectors
+            createIndex(FLAT_BBQ_INDEX_NAME, Settings.EMPTY, mapping);
+            index64DimVectors(FLAT_BBQ_INDEX_NAME);
+            // force merge the index
+            client().performRequest(new Request("POST", "/" + FLAT_BBQ_INDEX_NAME + "/_forcemerge?max_num_segments=1"));
+        }
+        Request searchRequest = new Request("POST", "/" + FLAT_BBQ_INDEX_NAME + "/_search");
+        searchRequest.setJsonEntity("""
+            {
+              "query": {
+                "script_score": {
+                  "query": {
+                    "exists": {
+                      "field": "vector"
+                    }
+                  },
+                  "script": {
+                   "source": "cosineSimilarity(params.query, 'vector') + 1.0",
+                    "params": {
+                      "query": [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                       5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6]
+                    }
+                  }
+                }
+              }
+            }
+            """);
+        Map<String, Object> response = search(searchRequest);
+        assertThat(extractValue(response, "hits.total.value"), equalTo(7));
+        List<Map<String, Object>> hits = extractValue(response, "hits.hits");
+        assertThat(hits.get(0).get("_id"), equalTo("0"));
+        assertThat((double) hits.get(0).get("_score"), closeTo(1.9869276, 0.0001));
+
+        // search with knn
+        searchRequest = new Request("POST", "/" + FLAT_BBQ_INDEX_NAME + "/_search");
+        searchRequest.setJsonEntity("""
+            {
+                "knn": {
+                "field": "vector",
+                  "query_vector": [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+                   5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6],
+                  "k": 2,
+                  "num_candidates": 5
+                }
+            }
+            """);
+        response = search(searchRequest);
+        assertThat(extractValue(response, "hits.total.value"), equalTo(2));
+        hits = extractValue(response, "hits.hits");
+        assertThat(hits.get(0).get("_id"), equalTo("0"));
+        assertThat((double) hits.get(0).get("_score"), closeTo(0.9934857, 0.005));
+    }
+
+    private void index64DimVectors(String indexName) throws Exception {
+        String[] vectors = new String[] {
+            "{\"vector\":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "
+                + "1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}",
+            "{\"vector\":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "
+                + "1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}",
+            "{\"vector\":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "
+                + "1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}",
+            "{\"vector\":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, "
+                + "2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}",
+            "{\"vector\":[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, "
+                + "3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}",
+            "{\"vector\":[2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "
+                + "1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}",
+            "{\"vector\":[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, "
+                + "1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}",
+            "{}" };
+        for (int i = 0; i < vectors.length; i++) {
+            Request indexRequest = new Request("PUT", "/" + indexName + "/_doc/" + i);
+            indexRequest.setJsonEntity(vectors[i]);
+            assertOK(client().performRequest(indexRequest));
+        }
+        // always refresh to ensure the data is visible
+        refresh(indexName);
+    }
+
     private void indexVectors(String indexName) throws Exception {
         String[] vectors = new String[] {
             "{\"vector\":[1, 1, 1]}",