RAG • Rust AI Agent SDK
RAG (Retrieval Augmented Generation) Module
rag
Rust AI Agent SDK
RAG (Retrieval Augmented Generation) Module
This module provides RAG capabilities for agents:
RAG– Main RAG pipelineRAGConfig– Configuration for RAGRAGResult– Result with answer and citationsCitation– Source citationSmartRetriever– Intelligent document retrieval
Status
RAG::query is not implemented. It returns an error rather than a result: it previously returned a fabricated answer, a fabricated chunk with a 0.95 confidence score and a fabricated citation, which a caller could not distinguish from real retrieval. The surrounding types (chunking, context packing, citation formatting) are real and usable.
Example
use praisonai::rag::{RAG, RAGConfig};
let rag = RAG::new()
.config(RAGConfig::default())
.build()?;
let result = rag.query("What is the main finding?")?;
println!("{}", result.answer);Import
use praisonai::rag::*;Classes
A citation referencing a source document.
ContextPackA pack of context chunks for RAG.
ContextChunkA single context chunk.
RAGResultResult of a RAG query.
RAGConfigConfiguration for RAG pipeline.
RetrievalConfigUnified retrieval configuration (Agent-first).
TokenBudgetToken budget for context management.
RetrievalResultResult of a retrieval operation.
RAGMain RAG pipeline.
RAGBuilderBuilder for RAG
RetrievalStrategyRetrieval strategy for RAG.
CitationsModeCitations mode for RAG.
Functions
Get model context window size.
estimate_tokens()Estimate token count for text.
build_context()Build context string from chunks.
truncate_context()Truncate context to fit token limit.
deduplicate_chunks()Deduplicate chunks by content similarity.