Struct PgVectorStore 

pub struct PgVectorStore { /* private fields */ }

A VectorStore backed by PostgreSQL with the pgvector extension.

Documents are stored in a single table with columns:

• id TEXT PRIMARY KEY
• content TEXT NOT NULL
• metadata JSONB NOT NULL DEFAULT '{}'
• embedding vector(<dimensions>)

Call initialize once after construction to create the pgvector extension and the table (idempotent).

Implementations

impl PgVectorStore

pub fn new(pool: PgPool, config: PgVectorConfig) -> Self

Create a new store from an existing connection pool and config.

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

Ensure the pgvector extension and the backing table exist.

This is idempotent and safe to call on every application startup.

pub fn pool(&self) -> &PgPool

Return a reference to the underlying connection pool.

pub fn config(&self) -> &PgVectorConfig

Return a reference to the configuration.

Trait Implementations

impl VectorStore for PgVectorStore

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.