Struct QdrantVectorStore 

pub struct QdrantVectorStore<M: EmbeddingModel> { /* private fields */ }

Represents a vector store implementation using Qdrant - https://qdrant.tech/ as the backend.

Implementations

impl<M> QdrantVectorStore<M>

where M: EmbeddingModel,

pub fn new(client: Qdrant, model: M, query_params: QueryPoints) -> Self

Creates a new instance of QdrantVectorStore.

Arguments

• client - Qdrant client instance
• model - Embedding model instance
• query_params - Search parameters for vector queries Reference: https://api.qdrant.tech/v-1-12-x/api-reference/search/query-points

pub fn client(&self) -> &Qdrant

Trait Implementations

impl<Model> InsertDocuments for QdrantVectorStore<Model>

where Model: EmbeddingModel + Send + Sync,

async fn insert_documents<Doc: Serialize + Embed + Send>( &self, documents: Vec<(Doc, OneOrMany<Embedding>)>, ) -> Result<(), VectorStoreError>

Insert documents into the vector store.

impl<M> VectorStoreIndex for QdrantVectorStore<M>

where M: EmbeddingModel + Sync + Send,

async fn top_n<T: for<'a> Deserialize<'a> + Send>( &self, req: VectorSearchRequest, ) -> Result<Vec<(f64, String, T)>, VectorStoreError>

Search for the top n nearest neighbors to the given query within the Qdrant vector store. Returns a vector of tuples containing the score, ID, and payload of the nearest neighbors.

async fn top_n_ids( &self, req: VectorSearchRequest, ) -> Result<Vec<(f64, String)>, VectorStoreError>

Search for the top n nearest neighbors to the given query within the Qdrant vector store. Returns a vector of tuples containing the score and ID of the nearest neighbors.