Update README

README.md

@@ -5,16 +5,20 @@
 
 Agentic RAG built on [LanceDB](https://lancedb.com/), [Pydantic AI](https://ai.pydantic.dev/), and [Docling](https://docling-project.github.io/docling/).
 
+> **New: vision and multimodal search.** Picture-aware ingestion captures embedded figure bytes; vision-capable QA models receive them alongside text. Multimodal embedders put picture vectors in the same space as text, enabling text-as-query → figure hits and image-as-query retrieval.
+
 ## Features
 
 - **Hybrid search** — Vector + full-text with Reciprocal Rank Fusion
+- **Multimodal & cross-modal search** — Multimodal embedders (vLLM) put picture vectors in the same space as text; supports text-as-query → figure hits and image-as-query
 - **Question answering** — QA agents with citations (page numbers, section headings)
+- **Vision QA** — Vision-capable models receive figure bytes alongside chunk text
 - **Reranking** — MxBAI, Cohere, Zero Entropy, or vLLM
 - **Research agents** — Multi-agent workflows via pydantic-graph: plan, search, evaluate, synthesize
 - **Analysis agent** — Complex analytical tasks via sandboxed Python code execution (aggregation, computation, multi-document analysis)
 - **Conversational RAG** — Chat TUI and web application for multi-turn conversations with session memory
 - **Document structure** — Stores full [DoclingDocument](https://docling-project.github.io/docling/concepts/docling_document/), enabling structure-aware context expansion
-- **Multiple providers** — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM. QA/Research: any model supported by Pydantic AI
+- **Multiple providers** — Embeddings: Ollama, OpenAI, VoyageAI, LM Studio, vLLM (multimodal). QA/Research: any model supported by Pydantic AI
 - **Local-first** — Embedded LanceDB, no servers required. Also supports S3, GCS, Azure, and LanceDB Cloud
 - **CLI & Python API** — Full functionality from command line or code
 - **MCP server** — Expose as tools for AI assistants (Claude Desktop, etc.)