Struct InMemoryVectorStore 

pub struct InMemoryVectorStore { /* private fields */ }

In-memory vector store using cosine similarity.

Implementations

impl InMemoryVectorStore

pub fn new() -> Self

pub async fn from_texts( texts: Vec<(&str, &str)>, embeddings: &dyn Embeddings, ) -> Result<Self, SynapticError>

Create a new store pre-populated with texts.

pub async fn from_documents( documents: Vec<Document>, embeddings: &dyn Embeddings, ) -> Result<Self, SynapticError>

Create a new store pre-populated with documents.

pub async fn max_marginal_relevance_search( &self, query: &str, k: usize, fetch_k: usize, lambda_mult: f32, embeddings: &dyn Embeddings, ) -> Result<Vec<Document>, SynapticError>

Maximum Marginal Relevance search for diverse results.

lambda_mult controls the trade-off between relevance and diversity:

• 1.0 = pure relevance (equivalent to standard similarity search)
• 0.0 = maximum diversity
• 0.5 = balanced (typical default)

fetch_k is the number of initial candidates to fetch before MMR filtering.

Trait Implementations

impl Default for InMemoryVectorStore

fn default() -> Self

Returns the “default value” for a type.

impl VectorStore for InMemoryVectorStore

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.