diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc index b8e475d130..add86a541f 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/elasticsearch.adoc @@ -121,7 +121,6 @@ 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: @@ -152,7 +151,6 @@ 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: diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc index a0f7cb7b91..c59c7ef842 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/neo4j.adoc @@ -96,7 +96,6 @@ 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: @@ -123,7 +122,6 @@ 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: diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc index 66d3261def..5b46d02015 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/opensearch.adoc @@ -97,7 +97,6 @@ spring: index-name: spring-ai-document-index initialize-schema: true similarity-function: cosinesimil - batching-strategy: TOKEN_COUNT aws: # Only for Amazon OpenSearch Service host: service-name: @@ -118,7 +117,6 @@ 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 | - diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc index d5d570cb52..1b41392f75 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/pgvector.adoc @@ -100,7 +100,6 @@ 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 ---- @@ -147,7 +146,6 @@ 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 |=== diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc index 8e16a975a0..efc1a4766f 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/qdrant.adoc @@ -84,7 +84,6 @@ spring: 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`: @@ -99,7 +98,6 @@ 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 diff --git a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc index a7bc657a9b..dbc33978bb 100644 --- a/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc +++ b/spring-ai-docs/src/main/antora/modules/ROOT/pages/api/vectordbs/redis.adoc @@ -91,7 +91,6 @@ 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`: @@ -103,7 +102,6 @@ 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