Arcana: Embeddable RAG for Elixir/Phoenix
Everything I've learned building RAG systems, packaged into one Elixir library. Hybrid search, agentic pipelines, GraphRAG, all running on your existing Ecto Repo and pgvector.
Everything I've learned building RAG systems, packaged into one Elixir library. Hybrid search, agentic pipelines, GraphRAG, all running on your existing Ecto Repo and pgvector.
ColBERT-style late interaction retrieval for Elixir. Per-token embeddings and MaxSim scoring for more precise search than single-vector approaches.
Addressing the three most valid criticisms of using Elixir and the BEAM for AI agents.
Test LLM outputs by meaning, not by characters. A wave operator for semantic similarity assertions in Elixir, powered by local embeddings.
Python and JavaScript/TypeScript AI frameworks are reinventing what telecom solved in 1986. What 40 years of production-grade concurrency teaches us about building AI agents.
The classic system design interview opener: design a URL shortener. Each follow-up is supposed to be harder than the last. On the BEAM, they're boring.
My talk at the San Francisco Global Elixir Meetup on building full-stack AI solutions in Elixir: multiple chains, in-memory RAG, MCP clients, and neural networks in your supervision tree.
I joined the Elixir Mentor podcast to talk about Soothsayer, why the BEAM is the best runtime for AI workflows, and agentic commerce.