Update vectordbs doc,batching-strategy is remove (#2550)

Signed-off-by: Xwh <[email protected]>

Author: xwh1108

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc

@@ -127,7 +127,6 @@ spring:
         index-name: custom-index
         dimensions: 1536
         similarity: cosine
-        batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 ----
 
 The Spring Boot properties starting with `spring.elasticsearch.*` are used to configure the Elasticsearch client:
@@ -158,7 +157,6 @@ Properties starting with `spring.ai.vectorstore.elasticsearch.*` are used to con
 |`spring.ai.vectorstore.elasticsearch.index-name` | The name of the index to store the vectors | `spring-ai-document-index`
 |`spring.ai.vectorstore.elasticsearch.dimensions` | The number of dimensions in the vector | `1536`
 |`spring.ai.vectorstore.elasticsearch.similarity` | The similarity function to use | `cosine`
-|`spring.ai.vectorstore.elasticsearch.batching-strategy` | Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |===
 
 The following similarity functions are available:

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc

@@ -102,7 +102,6 @@ spring:
         index-name: custom-index
         dimensions: 1536
         distance-type: cosine
-        batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 ----
 
 The Spring Boot properties starting with `spring.neo4j.*` are used to configure the Neo4j client:
@@ -129,7 +128,6 @@ Properties starting with `spring.ai.vectorstore.neo4j.*` are used to configure t
 |`spring.ai.vectorstore.neo4j.distance-type` | The distance function to use | `cosine`
 |`spring.ai.vectorstore.neo4j.label` | The label used for document nodes | `Document`
 |`spring.ai.vectorstore.neo4j.embedding-property` | The property name used to store embeddings | `embedding`
-|`spring.ai.vectorstore.neo4j.batching-strategy` | Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |===
 
 The following distance functions are available:

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc

@@ -103,7 +103,6 @@ spring:
         index-name: spring-ai-document-index
         initialize-schema: true
         similarity-function: cosinesimil
-        batching-strategy: TOKEN_COUNT
         aws:  # Only for Amazon OpenSearch Service
           host: <aws opensearch host>
           service-name: <aws service name>
@@ -124,7 +123,6 @@ Properties starting with `spring.ai.vectorstore.opensearch.*` are used to config
 |`spring.ai.vectorstore.opensearch.index-name`| Name of the index to store vectors | `spring-ai-document-index`
 |`spring.ai.vectorstore.opensearch.initialize-schema`| Whether to initialize the required schema | `false`
 |`spring.ai.vectorstore.opensearch.similarity-function`| The similarity function to use | `cosinesimil`
-|`spring.ai.vectorstore.opensearch.batching-strategy`| Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |`spring.ai.vectorstore.opensearch.aws.host`| Hostname of the OpenSearch instance | -
 |`spring.ai.vectorstore.opensearch.aws.service-name`| AWS service name | -
 |`spring.ai.vectorstore.opensearch.aws.access-key`| AWS access key | -

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc

@@ -106,7 +106,6 @@ spring:
 		index-type: HNSW
 		distance-type: COSINE_DISTANCE
 		dimensions: 1536
-		batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 		max-document-batch-size: 10000 # Optional: Maximum number of documents per batch
 ----
 
@@ -153,7 +152,6 @@ You can use the following properties in your Spring Boot configuration to custom
 |`spring.ai.vectorstore.pgvector.schema-name` | Vector store schema name | `public`
 |`spring.ai.vectorstore.pgvector.table-name` | Vector store table name | `vector_store`
 |`spring.ai.vectorstore.pgvector.schema-validation` | Enables schema and table name validation to ensure they are valid and existing objects. | false
-|`spring.ai.vectorstore.pgvector.batching-strategy` | Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE`. | TOKEN_COUNT
 |`spring.ai.vectorstore.pgvector.max-document-batch-size` | Maximum number of documents to process in a single batch. | 10000
 
 |===

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc

@@ -90,7 +90,6 @@ spring:
         collection-name: <collection name>
         use-tls: false
         initialize-schema: true
-        batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 ----
 
 Properties starting with `spring.ai.vectorstore.qdrant.*` are used to configure the `QdrantVectorStore`:
@@ -105,7 +104,6 @@ Properties starting with `spring.ai.vectorstore.qdrant.*` are used to configure
 |`spring.ai.vectorstore.qdrant.collection-name`| The name of the collection to use | `vector_store`
 |`spring.ai.vectorstore.qdrant.use-tls`| Whether to use TLS(HTTPS) | `false`
 |`spring.ai.vectorstore.qdrant.initialize-schema`| Whether to initialize the schema | `false`
-|`spring.ai.vectorstore.qdrant.batching-strategy`| Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |===
 
 == Manual Configuration

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc

@@ -97,7 +97,6 @@ spring:
         initialize-schema: true
         index-name: custom-index
         prefix: custom-prefix
-        batching-strategy: TOKEN_COUNT # Optional: Controls how documents are batched for embedding
 ----
 
 Properties starting with `spring.ai.vectorstore.redis.*` are used to configure the `RedisVectorStore`:
@@ -109,7 +108,6 @@ Properties starting with `spring.ai.vectorstore.redis.*` are used to configure t
 |`spring.ai.vectorstore.redis.initialize-schema`| Whether to initialize the required schema | `false`
 |`spring.ai.vectorstore.redis.index-name` | The name of the index to store the vectors | `spring-ai-index`
 |`spring.ai.vectorstore.redis.prefix` | The prefix for Redis keys | `embedding:`
-|`spring.ai.vectorstore.redis.batching-strategy` | Strategy for batching documents when calculating embeddings. Options are `TOKEN_COUNT` or `FIXED_SIZE` | `TOKEN_COUNT`
 |===
 
 == Metadata Filtering