feat(qdrant): provision the chunks collection as an explicit deployment step

Why:
- The chunks collection needs four named vectors (dense_nomic, dense_openai,
  sparse, late_interaction) and payload indexes defined at creation time per
  ADR-0001; sparse/multivector fields cannot be added to an existing
  collection without recreating it, so schema drift here is expensive.
- Creating it at FastAPI startup would mirror the DDL-at-boot anti-pattern
  ADR-0009 already rejects for Postgres and ADR-0012 rejects for LangGraph's
  setup(), so it is a deployment step instead.

Changes:
- src/infrastructure/qdrant/collection.py: ensure_chunks_collection(),
  idempotent and schema-verifying (raises on dimension/modifier mismatch
  rather than silently accepting a misconfigured collection).
- src/cli/qdrant_bootstrap.py: the operator entry point
  (python -m src.cli.qdrant_bootstrap).
- QdrantSettings gains collection/upsert_batch_size/upsert_concurrency.

Impact:
- Deployments must run the new bootstrap command before the first upload;
  see ADR-0001's new "Collection provisioning" section.

.env.example

@@ -44,6 +44,9 @@ INGESTION_EMBED_CONCURRENCY=4
 # Qdrant
 QDRANT_URL=http://127.0.0.1:6343
 QDRANT_API_KEY=
+QDRANT_COLLECTION=chunks
+QDRANT_UPSERT_BATCH_SIZE=128
+QDRANT_UPSERT_CONCURRENCY=4
 
 # Dense embedders (ADR-0001). Both speak an OpenAI-compatible /embeddings
 # endpoint, so one adapter serves both. Models and endpoints are the ones the