Struct ChunkedVectorStore 

pub struct ChunkedVectorStore { /* private fields */ }

Chunked Vector Store

Implementations

impl ChunkedVectorStore

pub fn new( document_store: Arc<InMemoryChunkedDocumentStore>, vector_size: usize, ) -> ChunkedVectorStore

创建新的 ChunkedVectorStore

pub async fn add_chunk_vector( &self, chunk_id: impl Into<String>, embedding: Vec<f32>, ) -> Result<(), VectorStoreError>

添加 chunk 向量（chunk_id + embedding）

pub async fn add_chunk_vectors( &self, chunk_ids: Vec<String>, embeddings: Vec<Vec<f32>>, ) -> Result<(), VectorStoreError>

批量添加 chunk 向量

pub async fn add_parent_document( &self, document: Document, chunk_size: usize, embeddings_fn: impl Fn(&str) -> Vec<f32>, ) -> Result<(String, Vec<String>), VectorStoreError>

从 Parent 文档添加（自动分割 + 向量化）

pub async fn get_embedding( &self, chunk_id: &str, ) -> Result<Option<Vec<f32>>, VectorStoreError>

获取 chunk_id 对应的向量 (M4: O(1) HashMap lookup)

pub async fn vector_count(&self) -> usize

获取向量数量

Trait Implementations

impl VectorStore for ChunkedVectorStore

fn add_documents<'life0, 'async_trait>( &'life0 self, documents: Vec<Document>, embeddings: Vec<Vec<f32>>, ) -> Pin<Box<dyn Future<Output = Result<Vec<String>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, ChunkedVectorStore: 'async_trait,

Adds documents.

fn similarity_search<'life0, 'life1, 'async_trait>( &'life0 self, query_embedding: &'life1 [f32], k: usize, ) -> Pin<Box<dyn Future<Output = Result<Vec<SearchResult>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, ChunkedVectorStore: 'async_trait,

Searches similar documents.

fn similarity_search_with_min_score<'life0, 'life1, 'async_trait>( &'life0 self, query_embedding: &'life1 [f32], k: usize, min_score: Option<f32>, ) -> Pin<Box<dyn Future<Output = Result<Vec<SearchResult>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, ChunkedVectorStore: 'async_trait,

带最低分数阈值的相似度检索。

fn get_document<'life0, 'life1, 'async_trait>( &'life0 self, id: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Option<Document>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, ChunkedVectorStore: 'async_trait,

Gets document by ID.

fn get_embedding<'life0, 'life1, 'async_trait>( &'life0 self, id: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Option<Vec<f32>>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, ChunkedVectorStore: 'async_trait,

Gets document embedding by ID.

fn delete_document<'life0, 'life1, 'async_trait>( &'life0 self, id: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<(), VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, ChunkedVectorStore: 'async_trait,

Deletes document.

fn count<'life0, 'async_trait>( &'life0 self, ) -> Pin<Box<dyn Future<Output = usize> + Send + 'async_trait>>

where 'life0: 'async_trait, ChunkedVectorStore: 'async_trait,

Returns document count.

fn clear<'life0, 'async_trait>( &'life0 self, ) -> Pin<Box<dyn Future<Output = Result<(), VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, ChunkedVectorStore: 'async_trait,

Clears store.

fn embed_query(&self) -> Option<&dyn Embeddings>

返回该向量存储自带的文本嵌入器(若有)。

fn similarity_search_text<'life0, 'life1, 'async_trait>( &'life0 self, query: &'life1 str, k: usize, ) -> Pin<Box<dyn Future<Output = Result<Vec<SearchResult>, VectorStoreError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, Self: 'async_trait,

文本相似度检索:用 embed_query 返回的嵌入器把 query 向量化后再检索。