rag_kit 0.5.0

Pure-Dart RAG (retrieval-augmented generation): chunking, embeddings, vector search, and context building. Zero dependencies, bring your own embedding model.

0.5.0

• Fix a hazard that 0.4.0 introduced. Giving Chunk value equality made hashCode read the metadata map, but the constructor still stored the caller's map by reference, so a chunk could fall out of a Set that already held it: put a chunk in a set, mutate the map you passed to its constructor, and set.contains(chunk) is false for that very instance, with no error. This is the same aliasing class that 0.3.1 fixed inside the store, arriving through a different door. Chunk now copies metadata into an unmodifiable map, so mutating the map you passed in cannot reach the chunk, and mutating the map you get back throws instead of corrupting a hash. An empty map still costs nothing.
• Mark the five leaf classes final: Chunk, Document, ScoredChunk, InMemoryVectorStore and Retriever. Chunker and VectorStore stay open, because those are the documented extension points. Adding final cannot be done after 1.0.0 without a major version, while removing it later is free, and leaving a type with == open lets a subclass compare equal to its base asymmetrically.
• Stop promising, on the VectorStore interface, what only the in-memory implementation can deliver. search's documentation said it returns the documents "most similar" to the query and that where "runs before scoring, so filtered documents cost no similarity computation". Both are true of InMemoryVectorStore and neither can be guaranteed by an approximate index, which is the first item on this package's own roadmap. The interface now describes what an implementation must do, and names InMemoryVectorStore where the stronger guarantee actually holds.

0.4.0

Settles how the public types compare, which is the last thing that has to be decided before a 1.0.0: adding or removing value equality afterwards silently changes how sets and maps behave for anyone already using them.

• Chunk now has == and hashCode, over its text, its range, and its metadata. Until now two chunks covering exactly the same span of the same source were different objects, so chunks.toSet() never collapsed the duplicates that overlapping windows produce, and a test could not compare a chunker's output against the chunks it expected. Metadata is compared entry by entry and each value with its own ==, so a List or Map stored as a metadata value compares by identity; the hash is order-independent, so two equal maps built in a different order still land in the same bucket.
• Document and ScoredChunk deliberately keep identity equality, and now say so in their documentation. A store keeps embeddings as float32, so a document read back has slightly different components than the one that was written while being the same document: value equality would report those two as different and would be wrong more often than it was useful. A document is identified by its id, which is what the store already deduplicates on.
• Name every export explicitly. The library re-exported whole source files, so anything that became public inside one would have joined the API by accident, which matters much more once the API is frozen. The exported set is unchanged: Chunk, Chunker, Document, Embedder, InMemoryVectorStore, Retriever, ScoredChunk, VectorStore.

0.3.1

• Fix InMemoryVectorStore aliasing a document's metadata map instead of copying it. Unlike embedding, which was already defensively copied, metadata was stored as the exact map object handed to upsert, so mutating that map afterwards, or mutating a document handed back by search or retrieve, silently rewrote data already in the index. This was reachable through Retriever.addText too, since it builds each document's metadata before handing it to the store. metadata is now copied into an unmodifiable map at insert time, so mutating a returned document's metadata throws instead of silently corrupting the store.

0.3.0

• Add a label callback to buildContext. Until now it joined only the raw chunk texts, so the model got the passages with no way to say which document each came from. Pass label to prefix every chunk with a source marker, for example label: (c) => '[${c.document.metadata['sourceId']}]'; it counts against maxChars like the rest of the chunk. Left off, the output is byte-for-byte what it was before.

0.2.2

• Shorten the screenshot description. pub.dev accepts up to 200 characters but scores only those under 160, so the previous release published cleanly and quietly gave up the documentation points it was meant to earn.

0.2.1

• Declare the diagram in pubspec.yaml so pub.dev renders it on the package page. It was already in the repository and the README, but pub.dev shows only what the screenshots: field points at, so the page opened with prose where the picture should have been.

0.2.0

• Add Retriever.retrieveDiverse, maximal marginal relevance over a larger candidate pool. Similarity alone returns near-duplicates when a source repeats itself, so the context window pays for one fact several times while the one that answers the question falls below the cut; this picks each next chunk for its relevance minus how much it repeats what is already picked. lambda runs from pure relevance (1.0, identical to retrieve) to pure diversity (0.0), fetchK sets the candidate pool, and results keep their query-similarity score. buildContext takes diverse: true to select the same way.

0.1.4

• Docs: sharpen the pub.dev description to lead with the value and the terms people search.

0.1.3

• Retriever.retrieve now takes a where predicate, and buildContext now takes both minScore and where, forwarded to the store. The metadata filter and score threshold were already implemented in the store but could not be reached through the retriever's public API.

0.1.2

• Docs: tightened the README wording and visuals.

0.1.1

• Expand the package description to name what the package does in the words people search for. No code changes.

Changelog

0.1.0

Initial release.

• Chunker.fixed, Chunker.paragraphs, and Chunker.sentences, all reporting exact source offsets.
• VectorStore interface and InMemoryVectorStore: cosine similarity over float32 vectors with precomputed norms, top-k via a bounded min-heap, minScore and metadata where filters.
• Binary serialization (toBytes/fromBytes) and, on the VM, file persistence via package:rag_kit/io.dart.
• Retriever: chunk, batch-embed, upsert, retrieve, and buildContext for assembling LLM prompt context.