Phase 0 foundation: canonical schema, ingest pipeline, CRM MCP server

Workstream A–C substrate for the Ten31 agentic system:
- A1: docs/crm-overview.md; CLAUDE.md conventions + guardrail #9
- A2: additive/reversible core migration (canonical_entities, entity_links,
  interaction_log, relationship_edges, soft-delete) + ledgered runner
- B1/B3: chunking + deterministic entity resolution (backend/ingest)
- B2: dense (bge-m3) + BM25 sparse ingest to Qdrant crm_chunks
- C: CRM MCP server (reads, retrieval modes, logged writes) — no outbound tools
- docs: redaction/re-hydration, Gmail enablement runbook
- synthetic test data; .env.example; housekeeping (.gitignore, untrack crm.db,
  drop legacy files + start9/0.3.5)

Verified end-to-end on synthetic data + live Sparks (hybrid > dense on entity
queries). Real backfill runs on Ten31 infra; index holds synthetic data only.
Branch snapshot also captures pre-existing working-tree changes.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/ingest/qdrant_io.py

@@ -0,0 +1,50 @@
+"""Minimal Qdrant REST client for the ingest pipeline (direct to QDRANT_URL).
+
+Creates the crm_chunks collection per EMBEDDINGS.md: a named dense vector
+(1024, Cosine) + a named sparse vector with modifier:idf, plus payload indexes.
+"""
+import config
+import http_util
+
+Q = config.QDRANT_URL
+COL = config.COLLECTION
+
+
+def _req(method, path, body=None):
+    return http_util.request(method, f"{Q}{path}", body, verify=False)
+
+
+def exists() -> bool:
+    status, _ = _req("GET", f"/collections/{COL}")
+    return status == 200
+
+
+def create_collection(recreate=False, dim=config.DENSE_DIM):
+    if exists():
+        if not recreate:
+            return "exists"
+        _req("DELETE", f"/collections/{COL}")
+    status, data = _req("PUT", f"/collections/{COL}", {
+        "vectors": {"dense": {"size": dim, "distance": "Cosine"}},
+        "sparse_vectors": {"sparse": {"modifier": "idf"}},
+    })
+    if status not in (200, 201):
+        raise RuntimeError(f"create collection -> {status}: {data}")
+    return "created"
+
+
+def ensure_indexes():
+    for field, schema in (("lp_id", "keyword"), ("doc_type", "keyword"), ("date_ts", "integer")):
+        _req("PUT", f"/collections/{COL}/index", {"field_name": field, "field_schema": schema})
+
+
+def upsert(points):
+    status, data = _req("PUT", f"/collections/{COL}/points?wait=true", {"points": points})
+    if status not in (200, 201):
+        raise RuntimeError(f"upsert -> {status}: {data}")
+    return data
+
+
+def count():
+    status, data = _req("POST", f"/collections/{COL}/points/count", {"exact": True})
+    return (data or {}).get("result", {}).get("count")