Struct RAGPipeline 

pub struct RAGPipeline<S: VectorStore, E: Embedder> { /* private fields */ }

RAG (Retrieval-Augmented Generation) Pipeline

Implementations

impl<S: VectorStore, E: Embedder> RAGPipeline<S, E>

pub fn new(store: S, embedder: E) -> Self

Create a new RAG pipeline

pub fn with_chunking(self, strategy: ChunkingStrategy) -> Self

Set chunking strategy

pub fn with_top_k(self, top_k: usize) -> Self

Set default top_k for retrieval

pub async fn index_document( &self, doc_id: &str, content: &str, metadata: Option<HashMap<String, Value>>, ) -> Result<usize, MemoryError>

Index a document (with automatic chunking)

pub async fn retrieve( &self, query: &str, top_k: Option<usize>, ) -> Result<Vec<SearchResult>, MemoryError>

Retrieve relevant context for a query

pub async fn retrieve_context( &self, query: &str, top_k: Option<usize>, ) -> Result<String, MemoryError>

Retrieve and format context as a string for LLM prompt

pub async fn augment_prompt( &self, query: &str, top_k: Option<usize>, ) -> Result<String, MemoryError>

Build an augmented prompt with retrieved context

pub async fn delete_document(&self, doc_id: &str) -> Result<usize, MemoryError>

Delete a document and all its chunks

pub async fn document_count(&self) -> Result<usize, MemoryError>

Get document count

pub async fn clear(&self) -> Result<(), MemoryError>

Clear all documents