Released: Feb 25, 2026

2026.2.10 での更新項目

機能

• Added model cards for Qwen 3.5 family: qwen3.5:27b (27B dense hybrid GDN model) and qwen3.5:35b-a3b (35B MoE, 3B active). Both support chat, vision, tool calling, code completion, math, and OCR with a native 262K context window. The qwen3-vl:30b model card is now deprecated in favor of qwen3.5:35b-a3b.
• Introduced RagChat (LMKit.Retrieval): A turnkey multi-turn conversational RAG class that wraps a user-managed RagEngine with an internal MultiTurnConversation. Implements IMultiTurnConversation and orchestrates query contextualization, retrieval dispatch, prompt construction, and grounded response generation in a single call. Supports all four QueryGenerationMode strategies, tools, skills, and agent memory.
• Introduced RagQueryResult (LMKit.Retrieval): Returns type for RagChat.Submit/SubmitAsync, exposing the generated TextGenerationResult alongside the IReadOnlyList<PartitionSimilarity> used as context.
• Introduced RetrievalCompletedEventArgs (LMKit.Retrieval.Events): Event arguments for the RagChat.RetrievalCompleted event, providing the retrieval query, matched partitions, requested count, and elapsed time.
• Introduced RetrievalDefaults (LMKit.Retrieval): A static class centralizing default constants for the retrieval subsystem (e.g. MinRelevanceScore), used consistently by RagChat, PdfChat, RagEngine, and VectorSearch.
• Added advanced query generation strategies for RAG (QueryGenerationMode on PdfChat and RagChat):
  • Contextual: Follow-up questions are automatically reformulated into self-contained queries before retrieval, configurable via QueryContextualizationOptions.
  • MultiQuery: Generates multiple query variants and merges results using Reciprocal Rank Fusion for improved recall, configurable via MultiQueryOptions.
  • HypotheticalAnswer: Generates a hypothetical answer and uses it as the retrieval query, bridging the gap between question and document phrasing, configurable via HydeOptions.
• Added Maximal Marginal Relevance (MMR) diversity filtering (MmrLambda on RagEngine and PdfChat): Reduces near-duplicate passages in retrieval results by balancing relevance against diversity.
• Added context window expansion for retrieval results (ContextWindow on RagEngine and PdfChat): Automatically includes neighboring partitions around each match, providing the LLM with surrounding context for more accurate answers.
• Added hybrid search with BM25 + vector fusion (IRetrievalStrategy on RagEngine and PdfChat):
  • VectorRetrievalStrategy: Semantic similarity via cosine distance on embeddings (default, unchanged behavior).
  • Bm25RetrievalStrategy: BM25+ lexical ranking with configurable term saturation (K1), length normalization (B), long-document floor (Delta), proximity boosting (ProximityWeight), language-aware stopword filtering (Language), and custom stopword support (CustomStopWords).
  • HybridRetrievalStrategy: Combines vector and BM25 strategies with weighted Reciprocal Rank Fusion, configurable via VectorWeight, KeywordWeight, and RrfK.