Examples

This example demonstrates the bare minimum needed to build a working RAG pipeline. It uses all default adapters and returns raw text output. Difficulty: ★☆☆ Beginner Custom adapters: None Output type: String What This Example Does Connects to local embedding, vector store, and LLM services Indexes a handful of sample documents Runs a RAG query and prints the answer Full Code use magicaf_core::adapters::{ DefaultEvidenceFormatter, DefaultPromptBuilder, RawResultParser, }; use magicaf_core::config::{EmbeddingConfig, LlmConfig, VectorStoreConfig}; use magicaf_core::embeddings::LocalEmbeddingService; use magicaf_core::rag::RAGWorkflow; use magicaf_local_llm::LocalLlmService; use magicaf_qdrant::QdrantVectorStore; #[tokio::main] async fn main() -> anyhow::Result<()> { // ── Structured logging ───────────────────────────────────────── tracing_subscriber::fmt() .with_env_filter( tracing_subscriber::EnvFilter::try_from_default_env() .unwrap_or_else(|_| "info,magicaf=debug".parse().unwrap()), ) .json() .init(); println!("=== MagicAF · Minimal RAG Example ===\n"); // ── Configuration ────────────────────────────────────────────── let embedding_config = EmbeddingConfig { base_url: std::env::var("EMBEDDING_URL") .unwrap_or_else(|_| "http://localhost:8080".into()), model_name: std::env::var("EMBEDDING_MODEL") .unwrap_or_else(|_| "bge-large-en-v1.5".into()), batch_size: 32, timeout_secs: 30, api_key: None, }; let vector_config = VectorStoreConfig { base_url: std::env::var("QDRANT_URL") .unwrap_or_else(|_| "http://localhost:6333".into()), api_key: None, timeout_secs: 30, }; let llm_config = LlmConfig { base_url: std::env::var("LLM_URL") .unwrap_or_else(|_| "http://localhost:8000/v1".into()), model_name: std::env::var("LLM_MODEL") .unwrap_or_else(|_| "mistral-7b".into()), api_key: None, timeout_secs: 120, }; // ── Service construction ─────────────────────────────────────── let embedder = LocalEmbeddingService::new(embedding_config)?; let store = QdrantVectorStore::new(vector_config).await?; let llm = LocalLlmService::new(llm_config)?; // ── Index sample documents ───────────────────────────────────── let collection = "example_docs"; store.ensure_collection(collection, 1024).await.ok(); let docs = vec![ "Rust is a systems programming language focused on safety.", "MagicAF provides embeddings, vector search, and LLM orchestration.", "RAG combines retrieval with language model generation.", ]; let doc_texts: Vec<String> = docs.iter().map(|s| s.to_string()).collect(); use magicaf_core::embeddings::EmbeddingService; let embeddings = embedder.embed(&doc_texts).await?; let payloads: Vec<serde_json::Value> = docs.iter().enumerate() .map(|(i, text)| serde_json::json!({ "content": text, "doc_index": i, })) .collect(); use magicaf_core::vector_store::VectorStore; store.index(collection, embeddings, payloads).await?; println!("Indexed {} documents into '{collection}'\n", docs.len()); // ── Build and run RAG workflow ───────────────────────────────── let workflow = RAGWorkflow::builder() .embedding_service(embedder) .vector_store(store) .llm_service(llm) .evidence_formatter(DefaultEvidenceFormatter) .prompt_builder(DefaultPromptBuilder::new().with_system( "You are a helpful technical assistant. \ Answer using only the provided evidence.", )) .result_parser(RawResultParser) .collection(collection) .top_k(3) .build()?; let result = workflow.run("What is MagicAF?", None).await?; println!("── Answer ──────────────────────────"); println!("{}", result.result); println!("────────────────────────────────────"); println!( "Evidence items: {} | Tokens: {:?}", result.evidence_count, result.usage.map(|u| u.total_tokens) ); Ok(()) } Key Points DefaultEvidenceFormatter — pretty-prints each search result’s JSON payload DefaultPromptBuilder::new().with_system(...) — wraps evidence in <context> tags with an optional system instruction RawResultParser — returns the LLM’s output as a plain String top_k(3) — retrieve the 3 most similar documents Running cargo run -p example-minimal-rag Next Steps → Document Q&A — add custom adapters and structured output ...

This example builds a document Q&A system with custom adapters that produce structured JSON answers. Difficulty: ★★☆ Intermediate Custom adapters: EvidenceFormatter, PromptBuilder Output type: QAAnswer (JSON struct) What This Example Does Implements a custom EvidenceFormatter that includes source indices Implements a custom PromptBuilder that requests JSON output Uses JsonResultParser<QAAnswer> to get typed results Domain Result Type use serde::{Deserialize, Serialize}; #[derive(Debug, Serialize, Deserialize)] struct QAAnswer { answer: String, confidence: f32, source_indices: Vec<usize>, } Custom Evidence Formatter Formats results with source indices so the LLM can cite them: ...

This example demonstrates a multi-source analysis pipeline with custom implementations of all three adapter traits and a complex output schema. Difficulty: ★★★ Advanced Custom adapters: EvidenceFormatter, PromptBuilder, ResultParser Output type: IntelSummary (complex nested JSON) What This Example Does Implements all three adapter traits with domain-specific logic Uses a complex nested output type Configures custom GenerationConfig for analytical precision Handles potentially malformed LLM output gracefully Domain Result Type use serde::{Deserialize, Serialize}; #[derive(Debug, Serialize, Deserialize)] struct IntelSummary { key_findings: Vec<String>, confidence_assessment: String, information_gaps: Vec<String>, recommended_actions: Vec<String>, } Custom Evidence Formatter Formats evidence with source metadata, dates, and classification levels: ...