Get up and running with MagicAF in three steps: install prerequisites, add the crates, and run your first query.
What you need before you start — Rust toolchain and local AI services.
Add MagicAF crates to your project and configure environment variables.
Build and run a complete RAG pipeline in under 50 lines of code.
MagicAF requires three local services and a Rust toolchain. All services communicate over HTTP — no cloud accounts, no vendor SDKs. Rust Toolchain MagicAF requires Rust 1.75+ (2024 edition). Install via rustup: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh Verify your installation: rustc --version # 1.75.0 or later cargo --version Local Services MagicAF connects to three local HTTP services. You can use any compatible software. Service Default Port Purpose Recommended Software Embedding server 8080 Produce dense vector embeddings llama.cpp (--embedding), text-embeddings-inference, vLLM Vector database 6333 Store and search embeddings Qdrant LLM server 8000 Chat completion / text generation vLLM, llama.cpp, TGI, Ollama Quick Setup with Docker The fastest way to get all services running: ...
Add Dependencies Add the MagicAF crates to your Cargo.toml: [dependencies] magicaf-core = { path = "path/to/magicaf/magicaf-core" } magicaf-qdrant = { path = "path/to/magicaf/magicaf-qdrant" } magicaf-local-llm = { path = "path/to/magicaf/magicaf-local-llm" } tokio = { version = "1", features = ["full"] } Note: MagicAF is distributed as source. Replace the path values with the actual location of the MagicAF workspace on your system. Optional Dependencies These are commonly used alongside MagicAF: [dependencies] anyhow = "1" # Ergonomic error handling serde = { version = "1", features = ["derive"] } # For custom result types serde_json = "1" # JSON payloads tracing = "0.1" # Structured logging tracing-subscriber = { version = "0.3", features = ["json", "env-filter"] } async-trait = "0.1" # For implementing adapter traits Environment Variables Configure service endpoints via environment variables: ...
This guide walks you through building a full Retrieval-Augmented Generation pipeline: embed documents, store them in a vector database, and answer questions using an LLM. The Complete Pipeline use magicaf_core::prelude::*; use magicaf_core::embeddings::LocalEmbeddingService; use magicaf_local_llm::LocalLlmService; use magicaf_qdrant::QdrantVectorStore; #[tokio::main] async fn main() -> anyhow::Result<()> { // 1. Configure services let embedder = LocalEmbeddingService::new(EmbeddingConfig { base_url: "http://localhost:8080".into(), model_name: "bge-large-en-v1.5".into(), batch_size: 32, timeout_secs: 30, api_key: None, })?; let store = QdrantVectorStore::new(VectorStoreConfig { base_url: "http://localhost:6333".into(), api_key: None, timeout_secs: 30, }).await?; let llm = LocalLlmService::new(LlmConfig { base_url: "http://localhost:8000/v1".into(), model_name: "mistral-7b".into(), api_key: None, timeout_secs: 120, })?; // 2. Build the RAG workflow let workflow = RAGWorkflow::builder() .embedding_service(embedder) .vector_store(store) .llm_service(llm) .evidence_formatter(DefaultEvidenceFormatter) .prompt_builder(DefaultPromptBuilder::new()) .result_parser(RawResultParser) .collection("my_docs") .top_k(5) .build()?; // 3. Run a query let result = workflow.run("What is MagicAF?", None).await?; println!("{}", result.result); println!("Evidence items: {}", result.evidence_count); Ok(()) } What Just Happened The RAGWorkflow executes a six-step pipeline: ...