akashi_rag 0.1.0

Retrieval-augmented generation for the Akashi agent framework: a provider-neutral Retriever seam, a pure-Dart in-memory vector store, document chunking, and glue to wire retrieval into an agent as a tool.

Changelog

0.1.0

• Initial release of retrieval-augmented generation for Akashi.
• Retriever — the single read-side contract agents consume; the built-in KnowledgeBase and any external/"standard" RAG service satisfy it the same way.
• Document / Chunk / EmbeddedChunk value types, plus RetrievalQuery / RetrievedChunk.
• Chunker with RecursiveChunker (boundary-aware, the default) and FixedSizeChunker (sliding window). Character-based sizing with overlap.
• VectorStore contract and InMemoryVectorStore — a pure-Dart, brute-force cosine-similarity index with metadata filtering and toJson / fromJson persistence. Runs offline with no dependencies.
• KnowledgeBase — the built-in façade pairing a core EmbeddingModel with a VectorStore: chunk → embed (batched) → upsert on ingest, embed → search on retrieve. Implements Retriever.
• retrievalTool — wraps a Retriever as an Akashi Tool (model-driven retrieval), and renderChunks for a model-friendly context block.