Guides

Adapters are how your domain logic plugs into the MagicAF RAG pipeline. You implement three traits — each one controls a different stage of the pipeline — without modifying any framework code. What You Need to Implement Trait Pipeline Step Responsibility EvidenceFormatter After vector search Turn search results into an LLM-ready text block PromptBuilder Before LLM call Assemble the final prompt from query + evidence ResultParser<T> After LLM call Parse the LLM’s raw text into your domain type T You can mix custom and default implementations — use defaults for rapid prototyping, then replace them one at a time. ...

MagicAF makes it straightforward to get structured, typed responses from your RAG pipeline. Instead of working with raw text, define a struct and let the framework deserialize it automatically. Using JsonResultParser<T> The fastest way to get structured output: define a struct that derives Deserialize, then use JsonResultParser. 1. Define your result type use serde::{Deserialize, Serialize}; #[derive(Debug, Serialize, Deserialize)] pub struct QAAnswer { pub answer: String, pub confidence: f32, pub sources: Vec<usize>, } 2. Tell the LLM to output JSON Use a PromptBuilder that instructs the LLM to respond in your expected format: ...

MagicAF’s trait-based design makes testing straightforward. You can test each adapter in isolation and test complete workflows using mock services — no live infrastructure required. Mock Services The test crate provides mock implementations of all infrastructure traits: use magicaf_tests::mocks::*; Mock Trait Behavior MockEmbeddingService EmbeddingService Returns fixed-dimension zero vectors MockVectorStore VectorStore Returns configurable search results MockLlmService LlmService Returns a configurable text response Testing a Custom ResultParser The simplest unit test — verify that your parser handles valid and invalid LLM output: ...