> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.
# 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 pipeline
* `RAGConfig` - Configuration for RAG
* `RAGResult` - Result with answer and citations
* `Citation` - Source citation
* `SmartRetriever` - Intelligent document retrieval
# Example
```ignore theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
```rust theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
use praisonai::rag::*;
```
## Classes
A citation referencing a source document.
A pack of context chunks for RAG.
A single context chunk.
Result of a RAG query.
Configuration for RAG pipeline.
Unified retrieval configuration (Agent-first).
Token budget for context management.
Result of a retrieval operation.
Main RAG pipeline.
Builder for RAG
Retrieval strategy for RAG.
Citations mode for RAG.
## Functions
Get model context window size.
Estimate token count for text.
Build context string from chunks.
Truncate context to fit token limit.
Deduplicate chunks by content similarity.
***
## Related Documentation