Struct MilvusVectorStore 

pub struct MilvusVectorStore { /* private fields */ }

A VectorStore implementation backed by Milvus.

Uses the Milvus REST API v2. Call initialize to create the collection before inserting documents.

Implementations

impl MilvusVectorStore

pub fn new(config: MilvusConfig) -> Self

Create a new store with the given configuration.

pub fn config(&self) -> &MilvusConfig

Return a reference to the configuration.

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

Create the Milvus collection if it does not already exist.

This is idempotent — calling it when the collection already exists is a no-op.

Trait Implementations

impl VectorStore for MilvusVectorStore

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.