Struct ElasticsearchVectorStore 

pub struct ElasticsearchVectorStore { /* private fields */ }

A VectorStore implementation backed by Elasticsearch.

Uses the Elasticsearch REST API with dense_vector field type and kNN search for similarity queries. Documents are stored with:

• _id: the document ID
• content: the document text
• embedding: the vector (dense_vector type)
• metadata: an object field with arbitrary metadata

Call ensure_index to create the index with proper mappings before inserting documents.

Implementations

impl ElasticsearchVectorStore

pub fn new(config: ElasticsearchConfig) -> Self

Create a new store with the given configuration.

pub fn config(&self) -> &ElasticsearchConfig

Return a reference to the configuration.

pub async fn ensure_index(&self) -> Result<(), SynapticError>

Ensure the index exists with the correct mappings.

Creates the index if it does not exist. If the index already exists, this is a no-op. Idempotent and safe to call on every startup.

Trait Implementations

impl VectorStore for ElasticsearchVectorStore

fn add_documents<'life0, 'life1, 'async_trait>( &'life0 self, docs: Vec<Document>, embeddings: &'life1 dyn Embeddings, ) -> Pin<Box<dyn Future<Output = Result<Vec<String>, SynapticError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Add documents to the store, computing their embeddings.

fn similarity_search<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, query: &'life1 str, k: usize, embeddings: &'life2 dyn Embeddings, ) -> Pin<Box<dyn Future<Output = Result<Vec<Document>, SynapticError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Search for similar documents by query string.

fn similarity_search_with_score<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, query: &'life1 str, k: usize, embeddings: &'life2 dyn Embeddings, ) -> Pin<Box<dyn Future<Output = Result<Vec<(Document, f32)>, SynapticError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Search with similarity scores (higher = more similar).

fn similarity_search_by_vector<'life0, 'life1, 'async_trait>( &'life0 self, embedding: &'life1 [f32], k: usize, ) -> Pin<Box<dyn Future<Output = Result<Vec<Document>, SynapticError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Search by pre-computed embedding vector instead of text query.

fn delete<'life0, 'life1, 'life2, 'async_trait>( &'life0 self, ids: &'life1 [&'life2 str], ) -> Pin<Box<dyn Future<Output = Result<(), SynapticError>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait,

Delete documents by ID.