feat(embedding): Add Ollama-powered vector embedding pipeline

Implements the embedding module that generates vector representations
of documents using a local Ollama instance with the nomic-embed-text
model. These embeddings enable semantic (vector) search and the hybrid
search mode that fuses lexical and semantic results via RRF.

Key components:

- embedding::ollama: HTTP client for the Ollama /api/embeddings
  endpoint. Handles connection errors with actionable error messages
  (OllamaUnavailable, OllamaModelNotFound) and validates response
  dimensions.

- embedding::chunking: Splits long documents into overlapping
  paragraph-aware chunks for embedding. Uses a configurable max token
  estimate (8192 default for nomic-embed-text) with 10% overlap to
  preserve cross-chunk context.

- embedding::chunk_ids: Encodes chunk identity as
  doc_id * 1000 + chunk_index for the embeddings table rowid. This
  allows vector search to map results back to documents and
  deduplicate by doc_id efficiently.

- embedding::change_detector: Compares document content_hash against
  stored embedding hashes to skip re-embedding unchanged documents,
  making incremental embedding runs fast.

- embedding::pipeline: Orchestrates the full embedding flow: detect
  changed documents, chunk them, call Ollama in configurable
  concurrency (default 4), store results. Supports --retry-failed
  to re-attempt previously failed embeddings.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/embedding/mod.rs

@@ -0,0 +1,9 @@
+pub mod change_detector;
+pub mod chunk_ids;
+pub mod chunking;
+pub mod ollama;
+pub mod pipeline;
+
+pub use change_detector::{count_pending_documents, find_pending_documents, PendingDocument};
+pub use chunking::{split_into_chunks, CHUNK_MAX_BYTES, CHUNK_OVERLAP_CHARS};
+pub use pipeline::{embed_documents, EmbedResult};