Phase 4: corpus and retrieval — chunks, Qdrant, FTS5, freshness audit

Satisfies docs/phase-4-corpus.md. content/ stays the single source of truth:
one pass over it writes both indexes, so the static pages and the retrieval
corpus cannot drift.

- corpus/: stdlib-only pipeline. validate -> chunk -> enrich -> embed -> upsert.
  Chunking is block-based, so a boundary cannot fall inside a fenced block, a
  table or front-matter (verified structurally, not by inspection).
- Deterministic ids: sha256(source_path + chunk_index + content_hash), rendered
  as a UUID. Re-running updates points; re-chunking deletes the old ones by
  source filter, so duplicates and orphans are both impossible.
- Incremental by content hash against a SQLite manifest. A provider, dimension
  or chunker change is refused instead of mixed: it needs a rebuild.
- Payloads per section 4.2 plus provenance, validated before any vector is
  written; a missing field fails the run and names the document and chunk.
- SQLite FTS5 over chunks (the keyword half of hybrid retrieval, decision D10)
  with area/confidence filters and a `terms` column so front-matter tech and
  tags are findable.
- Freshness audit: stale documents, due soon, index drift, contract drift;
  optional Apprise alert; exit code for Prefect.
- Embeddings behind one interface: omniroute (OpenAI-compatible, retries, key
  from the environment only) and local-hashing (offline placeholder). The
  provider decision is still Sam's; the pipeline does not depend on it.
- deploy/qdrant.yml: mem_limit = memswap_limit = 400m, loopback only.
- docs/phase-4-notes.md: operator manual, DoD evidence, measured memory.
- 76 unit tests, no container and no network.

Measured, Qdrant 1.20.0 under a 400 m cap at 1024 dimensions: 500 / 5,000 /
20,000 chunks settle at 128 / 138 / 151 MiB, peaking at 149 / 305 / 292 MiB
during ingest. Qdrant's default settings are OOM-killed between 5,000 and
20,000, so the collection is created with HNSW and payloads on disk; recorded in
reference/stack.md with the ceiling and the escalation order.

Note: the `make corpus*` targets were swept into the Phase 3 deploy commit
24590cc by concurrent work in this tree; the Makefile content is correct, and
this commit contains the pipeline they call.

Co-Authored-By: Paperclip <noreply@paperclip.ing>

corpus/qdrant.py

@@ -0,0 +1,269 @@
+"""Qdrant REST client — standard library only.
+
+No `qdrant-client` dependency: the four calls this pipeline needs are HTTP
+requests, and AGENTS.md §4.1 freezes the stack. `_request` is the single
+transport hook, which is what the tests replace to run the whole pipeline
+without a container.
+
+Qdrant is bound to `127.0.0.1:6333` on the runtime host (`.13`) and is never
+publicly reachable; Caddy is the only public listener.
+"""
+
+from __future__ import annotations
+
+import http.client
+import json
+import time
+import urllib.error
+import urllib.request
+
+DISTANCE_COSINE = "Cosine"
+
+# The runtime host (.13) is memory-bound: 2.3 GiB free and 8 GiB of swap already
+# in use, so Qdrant gets a 400 m cap and must live inside it
+# (reference/stack.md). Measured on 2026-10-26: the *default* HNSW and payload
+# settings are OOM-killed at the cap between 5,000 and 20,000 chunks of 1024
+# dimensions. Keeping the HNSW graph and the payloads on disk, and using a
+# smaller graph (m=8, ef_construct=64), fits the cap because the kernel can
+# reclaim those page-cache pages instead of killing the process. Vectors stay in
+# RAM because they are the part search reads on every query.
+#
+# The trade is build time and a little search latency; the corpus is ~50 chunks
+# today and the public site has no query burst. See docs/phase-4-notes.md §7.
+MEMORY_FRUGAL_DEFAULTS = {
+    "on_disk_payload": True,
+    "hnsw_m": 8,
+    "hnsw_ef_construct": 64,
+    "vectors_on_disk": False,
+}
+
+# Segment behaviour under a cap. `memmap_threshold` makes Qdrant mmap segments
+# this size and larger (page cache the kernel can reclaim instead of OOM), and
+# `indexing_threshold` is raised so a bulk ingest does not build the HNSW graph
+# while it is still writing segments. Measured: 20,000 chunks at 1024
+# dimensions peaked at 296 MiB during ingest and settled to 96 MiB.
+MEMORY_FRUGAL_OPTIMIZERS = {
+    "default_segment_number": 2,
+    "memmap_threshold": 5000,
+    "indexing_threshold": 20000,
+}
+
+
+class QdrantError(RuntimeError):
+    """A Qdrant call failed, or Qdrant is not reachable."""
+
+
+class Qdrant:
+    def __init__(self, url: str = "http://127.0.0.1:6333", timeout: float = 30.0, attempts: int = 3) -> None:
+        self.url = url.rstrip("/")
+        self.timeout = timeout
+        self.attempts = max(1, attempts)
+
+    # -- transport --------------------------------------------------------
+
+    def _request(self, method: str, path: str, body=None) -> dict:
+        """One HTTP call with retry/backoff. The only place that touches the network."""
+        data = None if body is None else json.dumps(body).encode("utf-8")
+        last_error = None
+        for attempt in range(self.attempts):
+            request = urllib.request.Request(
+                f"{self.url}{path}",
+                data=data,
+                method=method,
+                headers={"Content-Type": "application/json"} if data else {},
+            )
+            try:
+                with urllib.request.urlopen(request, timeout=self.timeout) as response:
+                    raw = response.read().decode("utf-8")
+                return json.loads(raw) if raw.strip() else {}
+            except urllib.error.HTTPError as exc:
+                detail = exc.read().decode("utf-8", "replace")[:400]
+                exc.close()
+                if exc.code == 404:
+                    raise QdrantNotFound(f"{method} {path}: {detail}") from None
+                if exc.code != 429 and exc.code < 500:
+                    raise QdrantError(f"{method} {path} failed: HTTP {exc.code} {detail}") from None
+                last_error = f"HTTP {exc.code} {detail}"
+            except (urllib.error.URLError, TimeoutError, http.client.HTTPException, ConnectionError) as exc:
+                # RemoteDisconnected and friends arrive here when the server dies
+                # mid-request (an OOM kill looks exactly like this).
+                last_error = f"cannot reach {self.url}: {getattr(exc, 'reason', exc)}"
+            if attempt + 1 < self.attempts:
+                time.sleep(0.5 * (2 ** attempt))
+        raise QdrantError(f"{method} {path} failed after {self.attempts} attempts: {last_error}")
+
+    # -- cluster ----------------------------------------------------------
+
+    def health(self) -> bool:
+        """True when Qdrant answers. Never raises."""
+        try:
+            self._request("GET", "/")
+            return True
+        except (QdrantError, QdrantNotFound):
+            return False
+
+    def version(self) -> str:
+        response = self._request("GET", "/")
+        return str(response.get("version", "unknown"))
+
+    # -- collections ------------------------------------------------------
+
+    def collection_exists(self, name: str) -> bool:
+        try:
+            self._request("GET", f"/collections/{name}")
+            return True
+        except QdrantNotFound:
+            return False
+
+    def create_collection(
+        self,
+        name: str,
+        dim: int,
+        distance: str = DISTANCE_COSINE,
+        on_disk_payload: bool = None,
+        hnsw_m: int = None,
+        hnsw_ef_construct: int = None,
+        vectors_on_disk: bool = None,
+    ) -> None:
+        """Create a collection sized for a memory-bound host (see MEMORY_FRUGAL_DEFAULTS)."""
+        defaults = MEMORY_FRUGAL_DEFAULTS
+        body = {
+            "vectors": {
+                "size": int(dim),
+                "distance": distance,
+                "on_disk": bool(
+                    defaults["vectors_on_disk"] if vectors_on_disk is None else vectors_on_disk
+                ),
+            },
+            "hnsw_config": {
+                "m": int(defaults["hnsw_m"] if hnsw_m is None else hnsw_m),
+                "ef_construct": int(
+                    defaults["hnsw_ef_construct"] if hnsw_ef_construct is None else hnsw_ef_construct
+                ),
+                "on_disk": True,
+            },
+            "on_disk_payload": bool(
+                defaults["on_disk_payload"] if on_disk_payload is None else on_disk_payload
+            ),
+            "optimizers_config": dict(MEMORY_FRUGAL_OPTIMIZERS),
+        }
+        self._request("PUT", f"/collections/{name}", body)
+
+    def drop_collection(self, name: str) -> None:
+        try:
+            self._request("DELETE", f"/collections/{name}")
+        except QdrantNotFound:
+            pass
+
+    def collection_info(self, name: str) -> dict:
+        response = self._request("GET", f"/collections/{name}")
+        return dict(response.get("result") or {})
+
+    def point_count(self, name: str) -> int:
+        response = self._request("POST", f"/collections/{name}/points/count", {"exact": True})
+        return int((response.get("result") or {}).get("count", 0))
+
+    # -- points -----------------------------------------------------------
+
+    def upsert(self, name: str, points: list, batch: int = 256) -> int:
+        """Upsert points in batches. Returns the number of points sent."""
+        sent = 0
+        for start in range(0, len(points), max(1, batch)):
+            chunk = points[start : start + max(1, batch)]
+            self._request(
+                "PUT",
+                f"/collections/{name}/points?wait=true",
+                {"points": chunk},
+            )
+            sent += len(chunk)
+        return sent
+
+    def delete_by_source(self, name: str, source_path: str) -> None:
+        """Delete every point that came from one document.
+
+        Re-chunking a document can produce fewer chunks than before, so the old
+        points are removed rather than overwritten. Deterministic ids make
+        duplicates impossible; this makes orphans impossible.
+        """
+        self._request(
+            "POST",
+            f"/collections/{name}/points/delete?wait=true",
+            {"filter": {"must": [{"key": "source_path", "match": {"value": source_path}}]}},
+        )
+
+    def search(
+        self,
+        name: str,
+        vector: list,
+        limit: int = 5,
+        area: str = None,
+        confidence: str = None,
+        with_payload: bool = True,
+    ) -> list:
+        """Nearest neighbours, optionally filtered *before* the search."""
+        body = {
+            "query": [float(value) for value in vector],
+            "limit": int(limit),
+            "with_payload": bool(with_payload),
+        }
+        filters = _filter_conditions(area, confidence)
+        if filters:
+            body["filter"] = {"must": filters}
+        try:
+            response = self._request("POST", f"/collections/{name}/points/query", body)
+            points = ((response.get("result") or {}).get("points")) or []
+        except QdrantNotFound:
+            # Older Qdrant exposes the same operation as /points/search.
+            response = self._request("POST", f"/collections/{name}/points/search", body)
+            points = response.get("result") or []
+        return [
+            {
+                "id": point.get("id"),
+                "score": float(point.get("score") or 0.0),
+                "payload": point.get("payload") or {},
+            }
+            for point in points
+        ]
+
+    def scroll_sources(self, name: str, limit: int = 1000) -> dict:
+        """Map source_path -> number of vectors, for consistency checks."""
+        counts = {}
+        offset = None
+        while True:
+            body = {"limit": limit, "with_payload": ["source_path"], "with_vector": False}
+            if offset is not None:
+                body["offset"] = offset
+            response = self._request("POST", f"/collections/{name}/points/scroll", body)
+            result = response.get("result") or {}
+            points = result.get("points") or []
+            for point in points:
+                source = (point.get("payload") or {}).get("source_path", "<missing>")
+                counts[source] = counts.get(source, 0) + 1
+            offset = result.get("next_page_offset")
+            if not points or offset is None:
+                return counts
+
+    def metrics(self) -> str:
+        """Prometheus text from Qdrant's /metrics route (used by the memory measurement).
+
+        Returns "" when the route is not exposed, so callers can fall back to
+        reading the container's own memory counters.
+        """
+        try:
+            with urllib.request.urlopen(f"{self.url}/metrics", timeout=self.timeout) as response:
+                return response.read().decode("utf-8", "replace")
+        except (urllib.error.URLError, urllib.error.HTTPError, TimeoutError):
+            return ""
+
+
+class QdrantNotFound(QdrantError):
+    """A 404 from Qdrant: the collection or route does not exist."""
+
+
+def _filter_conditions(area: str = None, confidence: str = None) -> list:
+    conditions = []
+    if area:
+        conditions.append({"key": "area", "match": {"value": area}})
+    if confidence:
+        conditions.append({"key": "confidence", "match": {"value": confidence}})
+    return conditions