Rename BHIVE to HYPERSCALE

Author: ejscribner

src/oss/javascript/integrations/vectorstores/couchbase_query.mdx

@@ -77,14 +77,14 @@ const vectorStore = await CouchbaseQueryVectorStore.initialize(embeddings, {
 
 The Query vector store supports creating vector indexes to improve search performance. There are two types of indexes available:
 
-### BHIVE Index
+### HYPERSCALE Index
 A specialized vector index optimized for vector operations using Couchbase's vector indexing capabilities:
 
 ```typescript
 import { IndexType } from "@langchain/community/vectorstores/couchbase_query";
 
 await vectorStore.createIndex({
-  indexType: IndexType.BHIVE,
+  indexType: IndexType.HYPERSCALE,
   indexDescription: "IVF,SQ8",
   indexName: "my_vector_index", // optional
   vectorDimension: 1536, // optional, auto-detected from embeddings
@@ -129,7 +129,7 @@ WITH {'dimension': 1536, 'similarity': 'dot', 'description': 'IVF1024,SQ8', 'sca
 
 ### Key Differences
 
-| Aspect | BHIVE Index | COMPOSITE Index |
+| Aspect | HYPERSCALE Index | COMPOSITE Index |
 |--------|-------------|-----------------|
 | **SQL++ Syntax** | `CREATE VECTOR INDEX` | `CREATE INDEX` |
 | **Vector Field** | `(field VECTOR)` with `INCLUDE` clause | `(field1, field2, vector_field VECTOR)` |
@@ -265,7 +265,7 @@ const results = await vectorStore.similaritySearch(
 
 ### Index Types
 
-- `IndexType.BHIVE` - Specialized vector index for optimal vector search performance
+- `IndexType.HYPERSCALE` - Specialized vector index for optimal vector search performance
 - `IndexType.COMPOSITE` - General-purpose index that can include vector and scalar fields
 
 ## Advanced Usage
@@ -321,20 +321,20 @@ await vectorStore.delete({ ids: documentIds });
 
 1. **Create Indexes**: Use `createIndex()` to create appropriate vector indexes for better performance
 2. **Choose Index Type**: 
-   - Use **BHIVE indexes** for pure vector search workloads where you primarily perform similarity searches
+   - Use **HYPERSCALE indexes** for pure vector search workloads where you primarily perform similarity searches
    - Use **COMPOSITE indexes** for mixed queries that combine vector similarity with scalar field filtering
 3. **Tune Parameters**: Adjust `indexScanNprobes` and `indexTrainlist` based on your data size and performance requirements
 4. **Filter Early**: Use WHERE clauses to reduce the search space before vector calculations
 5. **Index Strategy**:
-   - **BHIVE**: Better for high-performance vector similarity search with minimal scalar filtering
+   - **HYPERSCALE**: Better for high-performance vector similarity search with minimal scalar filtering
    - **COMPOSITE**: Better when you frequently filter by both vector similarity and scalar fields in the same query
 
 ## Error Handling
 
 ```typescript
 try {
   await vectorStore.createIndex({
-    indexType: IndexType.BHIVE,
+    indexType: IndexType.HYPERSCALE,
     indexDescription: "IVF,SQ8",
   });
 } catch (error) {
@@ -369,11 +369,11 @@ If you see errors related to insufficient training data, you may need to:
 
 ## Question: Do I need to create an index before using CouchbaseQueryVectorStore?
 
-No, unlike the Search-based CouchbaseVectorStore, the Query-based implementation can work without pre-created indexes. However, creating appropriate vector indexes (BHIVE or COMPOSITE) will significantly improve query performance.
+No, unlike the Search-based CouchbaseVectorStore, the Query-based implementation can work without pre-created indexes. However, creating appropriate vector indexes (HYPERSCALE or COMPOSITE) will significantly improve query performance.
 
-## Question: When should I use BHIVE vs COMPOSITE indexes?
+## Question: When should I use HYPERSCALE vs COMPOSITE indexes?
 
-- Use **BHIVE indexes** when you primarily perform vector similarity searches with minimal filtering on other fields
+- Use **HYPERSCALE indexes** when you primarily perform vector similarity searches with minimal filtering on other fields
 - Use **COMPOSITE indexes** when you frequently combine vector similarity with filtering on scalar fields in the same query
 
 ## Question: Can I use both CouchbaseVectorStore and CouchbaseQueryVectorStore on the same data?