Struct WeaviateVectorStore 

pub struct WeaviateVectorStore { /* private fields */ }

Weaviate-backed vector store.

Implements VectorStore using the Weaviate v1 REST API:

• Batch add: POST /v1/batch/objects
• Similarity search: POST /v1/graphql with nearVector
• Delete: DELETE /v1/objects/{class}/{id}

Call WeaviateVectorStore::initialize once to create the class schema before adding documents.

Implementations

impl WeaviateVectorStore

pub fn new(config: WeaviateConfig) -> Self

Create a new store with the given configuration.

pub fn with_client(config: WeaviateConfig, client: Client) -> Self

Create with a custom reqwest client.

pub fn config(&self) -> &WeaviateConfig

Return a reference to the configuration.

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

Create the Weaviate class schema for this store (idempotent).

Creates a class with content (text), metadata (text), and docId (text) properties. Uses the cosine distance metric via "vectorizer": "none" (caller supplies vectors).

Trait Implementations

impl VectorStore for WeaviateVectorStore

fn add_documents<'life0, 'life1, 'async_trait>( &'life0 self, documents: 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.