Struct MongoVectorStore 

pub struct MongoVectorStore { /* private fields */ }

A VectorStore implementation backed by MongoDB Atlas Vector Search.

Documents are stored in a MongoDB collection with fields:

• _id: the document ID
• content: the document text
• embedding: the vector embedding (array of doubles)
• metadata: an embedded document with arbitrary metadata

Similarity search uses the $vectorSearch aggregation stage, which requires a pre-configured Atlas Vector Search index on the collection.

Implementations

impl MongoVectorStore

pub async fn from_uri( uri: &str, config: MongoVectorConfig, ) -> Result<Self, SynapticError>

Create a new store by connecting to MongoDB at the given URI.

pub fn from_client(client: Client, config: MongoVectorConfig) -> Self

Create a new store from an existing MongoDB client.

pub fn client(&self) -> &Client

Return a reference to the underlying MongoDB client.

pub fn config(&self) -> &MongoVectorConfig

Return a reference to the configuration.

pub fn collection(&self) -> &Collection<BsonDocument>

Return a reference to the underlying MongoDB collection.

Trait Implementations

impl VectorStore for MongoVectorStore

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.