Add local AI and Qdrant vector backends

LOCAL-MODELS.md

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+# Local Models
+
+RA-H does not run model weights directly. You run a local OpenAI-compatible model server, then RA-H calls that server over HTTP.
+
+The supported local profile is intentionally narrow:
+
+- Utility LLM: Qwen3 4B or the closest tested Qwen3 4B runtime equivalent
+- Embeddings: Qwen3 Embedding 0.6B
+- Embedding dimensions: `1024`
+- Runtime options behind the same contract: Ollama or llama.cpp
+
+OpenAI remains the default supported path. Local mode is for users who are comfortable installing a model runtime, pulling model weights, starting local server processes, and managing rebuilds when embedding settings change.
+
+## Core Env
+
+```bash
+LLM_PROFILE=openai-compatible
+LLM_BASE_URL=http://127.0.0.1:11434/v1
+LLM_MODEL=qwen3:4b
+
+EMBEDDING_PROFILE=openai-compatible
+EMBEDDING_BASE_URL=http://127.0.0.1:11434/v1
+EMBEDDING_MODEL=qwen3-embedding:0.6b
+EMBEDDING_DIMENSIONS=1024
+```
+
+Run:
+
+```bash
+npm run doctor:local-ai
+```
+
+If you change embedding provider, model, dimensions, or vector backend after data exists, run:
+
+```bash
+npm run rebuild:embeddings
+```
+
+## Vector Storage
+
+Qwen3 creates vectors. sqlite-vec or Qdrant stores and searches those vectors.
+
+You do not need Qdrant just because models are local. Use Qdrant when sqlite-vec is unavailable or unreliable on your platform, especially Alpine/musl Docker images, Windows ARM64, or other native-extension-hostile environments.
+
+SQLite remains the source-of-truth database. Qdrant stores only derived vector indexes.