api.py: hybrid retrieval with intent routing and cross-encoder rerank

Replaces pure-dense top-8 retrieval with a three-stage pipeline:
- BM25 (tsvector + websearch_to_tsquery) and dense (pgvector) in parallel,
  fused with Reciprocal Rank Fusion
- Optional type filter driven by classify_retrieval_intent() so questions
  about prior conversations don't pull documents and vice versa
- Cross-encoder rerank (ms-marco-MiniLM-L-6-v2) over RRF candidates before
  taking final top-N

Also adds scripts/reindex_docx_pptx.py — one-off re-ingest used to recover
table/header/text-box content in docx and pptx after the 93c0d89 extractor
upgrade — and scripts/test_retrieval.py to exercise the new pipeline against
representative queries.

Schema: requires GIN index on to_tsvector('english', document) (already
created out-of-band via psql since Apache AGE in shared_preload_libraries
blocks ALTER TABLE on this database).

.gitignore

@@ -8,6 +8,7 @@ dreamer_state.json
 corpus_integrity_report.json
 watcher_state.json
 watcher_status.json
+reindex_status.json
 
 # Logs (these belong in /var/log/)
 *.log

scripts/api.py

@@ -1,12 +1,13 @@
 import os
+import re
 import json
 import sqlite3
 import subprocess
 import hashlib
 from pathlib import Path
-from datetime import datetime
+from datetime import datetime, timedelta
 from dotenv import load_dotenv
-from sentence_transformers import SentenceTransformer
+from sentence_transformers import SentenceTransformer, CrossEncoder
 import anthropic
 from fastapi import FastAPI, Request, Response, Depends, HTTPException, BackgroundTasks
 import psycopg2
@@ -91,6 +92,7 @@ if HAS_WHISPER:
     except Exception as e:
         print(f"Whisper not available: {e}")
 embedder = SentenceTransformer("all-MiniLM-L6-v2")
+reranker = CrossEncoder("cross-encoder/ms-marco-MiniLM-L-6-v2")
 # ChromaDB removed — using pgvector
 anthropic_client = anthropic.Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
 
@@ -132,6 +134,7 @@ When making factual claims about Aaron — his history, credentials, locations,
 # Auth configuration
 import os
 SESSION_PASSWORD = os.getenv("AARON_AI_PASSWORD", "changeme")
+SESSION_MAX_AGE_SECONDS = 60 * 60 * 24 * 365
 SESSIONS_DB = str(Path.home() / "aaronai" / "sessions.db")
 
 def _init_sessions():
@@ -163,7 +166,10 @@ def delete_session(token: str):
 
 def session_exists(token: str) -> bool:
     conn = _connect_sessions()
-    row = conn.execute("SELECT 1 FROM sessions WHERE token = ?", (token,)).fetchone()
+    cutoff = (datetime.now() - timedelta(seconds=SESSION_MAX_AGE_SECONDS)).isoformat()
+    conn.execute("DELETE FROM sessions WHERE created_at < ?", (cutoff,))
+    conn.commit()
+    row = conn.execute("SELECT 1 FROM sessions WHERE token = ? AND created_at >= ?", (token, cutoff)).fetchone()
     conn.close()
     return row is not None
 
@@ -239,30 +245,140 @@ def remove_from_memory(item):
     save_memory("\n".join(filtered))
     return len(lines) - len(filtered)
 
-def retrieve_context(query, n_results=8):
-    """Pure semantic retrieval over pgvector. Top-N by cosine similarity, threshold 0.3.
-    No CV pinning, no keyword routing — see architecture doc substrate-dependency section.
-    Substrate-level workarounds (entity-keyed routing, hybrid retrieval) live at the
-    Graphiti layer, not as wrapper logic above pgvector."""
+HYBRID_CANDIDATES = 30
+RRF_K = 60
+FINAL_LIMIT = 8
+
+_TSQUERY_SANITIZE_RE = re.compile(r"[^\w\s\"'-]")
+
+CONVERSATION_TYPES = ["chatgpt_conversation", "claude_conversation", "aaronai_conversation"]
+DOCUMENT_TYPES = ["document"]
+MEMORY_TYPES = ["claude_memory"]
+
+_CONVO_SIGNALS = (
+    "what did i tell", "what did we discuss", "what did we talk",
+    "in our conversation", "you mentioned", "we talked about",
+    "earlier you said", "earlier i said", "did i tell you",
+    "did i say", "what did chatgpt", "what did claude",
+)
+_DOC_SIGNALS = (
+    "write me a bio", "draft a bio", "my bio", "my cv", "my resume",
+    "my professional", "my work history", "my exhibitions",
+    "my publications", "my syllabi", "my courses", "my teaching",
+    "my philosophy", "about my career", "draft a cover letter",
+    "draft my", "write a bio", "professional bio",
+)
+
+
+def _websearch_query(text: str) -> str:
+    """Strip characters websearch_to_tsquery doesn't handle cleanly. Quoted
+    phrases and 'or' are preserved by the function itself."""
+    return _TSQUERY_SANITIZE_RE.sub(" ", text).strip()
+
+
+def classify_retrieval_intent(query: str):
+    """Return a list of `type` values to filter retrieval on, or None for all types.
+
+    Implementation is a low-effort keyword classifier — explicitly tunable and
+    swappable. For more nuanced routing, replace this with an LLM classifier call
+    that returns the same shape: a list of valid type strings or None.
+
+    Precedence: conversation signals win over document signals — a question like
+    "what did I tell you about my CV" is asking about the conversation, not the CV.
+    """
+    q = query.lower()
+    if any(s in q for s in _CONVO_SIGNALS):
+        return CONVERSATION_TYPES
+    if any(s in q for s in _DOC_SIGNALS):
+        return DOCUMENT_TYPES
+    return None
+
+
+def _rerank(query: str, candidates: list[tuple]) -> list[tuple]:
+    """Cross-encoder rerank. Candidates are (id, document, source) tuples.
+    Returns the same tuples reordered by reranker score (highest first)."""
+    if not candidates:
+        return []
+    pairs = [(query, row[1]) for row in candidates]
+    scores = reranker.predict(pairs)
+    return [row for row, _ in sorted(zip(candidates, scores),
+                                     key=lambda x: x[1], reverse=True)]
+
+
+def retrieve_context(query, n_results=FINAL_LIMIT, type_filter=None):
+    """Hybrid retrieval (dense + lexical, RRF fused) followed by cross-encoder rerank.
+
+    - Dense (pgvector) handles paraphrase / semantic similarity.
+    - Lexical (tsvector) catches rare named tokens (FWN3D, Sono-Tek, course codes)
+      the embedding model has no signal for.
+    - RRF combines the two rankings without calibrating score scales.
+    - Cross-encoder rerank scores each (query, chunk) pair jointly, bridging
+      semantic gaps that bi-encoders can't (e.g., "write me a bio" -> CV chunk).
+
+    type_filter: optional list of `type` values to restrict the candidate pool to.
+    If None, retrieves from all types. Use classify_retrieval_intent() to derive."""
     query_embedding = embedder.encode([query]).tolist()[0]
+    ts_query = _websearch_query(query)
+
     context_pieces = []
     sources = []
+
+    where_sql = ""
+    type_param = ()
+    if type_filter:
+        where_sql = "WHERE type = ANY(%s)"
+        type_param = (list(type_filter),)
+
     try:
         pg = get_pg()
         cur = pg.cursor()
-        cur.execute("""
-            SELECT document, source, 1 - (embedding <=> %s::vector) as similarity
+
+        cur.execute(f"""
+            SELECT id, document, source
             FROM embeddings
+            {where_sql}
             ORDER BY embedding <=> %s::vector
             LIMIT %s
-        """, (query_embedding, query_embedding, n_results))
-        for doc, source, similarity in cur.fetchall():
-            if similarity > 0.3:
-                context_pieces.append(doc)
-                sources.append(source or "unknown")
+        """, (*type_param, query_embedding, HYBRID_CANDIDATES))
+        dense_hits = cur.fetchall()
+
+        lexical_hits = []
+        if ts_query:
+            lex_where = "to_tsvector('english', document) @@ websearch_to_tsquery('english', %s)"
+            full_where = (f"WHERE {lex_where} AND type = ANY(%s)"
+                          if type_filter else f"WHERE {lex_where}")
+            lex_params = ((ts_query, list(type_filter)) if type_filter else (ts_query,))
+            cur.execute(f"""
+                SELECT id, document, source
+                FROM embeddings
+                {full_where}
+                ORDER BY ts_rank(to_tsvector('english', document),
+                                 websearch_to_tsquery('english', %s)) DESC
+                LIMIT %s
+            """, (*lex_params, ts_query, HYBRID_CANDIDATES))
+            lexical_hits = cur.fetchall()
+
         pg.close()
+
+        scores = {}
+        rows_by_id = {}
+        for rank, row in enumerate(dense_hits):
+            scores[row[0]] = scores.get(row[0], 0) + 1.0 / (RRF_K + rank + 1)
+            rows_by_id[row[0]] = row
+        for rank, row in enumerate(lexical_hits):
+            scores[row[0]] = scores.get(row[0], 0) + 1.0 / (RRF_K + rank + 1)
+            rows_by_id[row[0]] = row
+
+        rrf_ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)
+        candidates = [rows_by_id[doc_id] for doc_id, _ in rrf_ranked]
+
+        for _id, doc, source in _rerank(query, candidates)[:n_results]:
+            context_pieces.append(doc)
+            sources.append(source or "unknown")
+
     except Exception as e:
-        print(f"pgvector retrieval error: {e}")
+        print(f"hybrid retrieval error: {e}")
+
     return context_pieces, sources
 
 def get_conversation_history(conversation_id, limit=20):
@@ -302,7 +418,8 @@ def create_conversation(title="New conversation"):
 
 def chat(user_message, conversation_id, settings, client_time=None):
     memory = load_memory()
-    context_pieces, sources = retrieve_context(user_message)
+    type_filter = classify_retrieval_intent(user_message)
+    context_pieces, sources = retrieve_context(user_message, type_filter=type_filter)
     history = get_conversation_history(conversation_id)
 
     context_parts = []
@@ -381,7 +498,7 @@ async def login(request: Request, response: Response):
         httponly=True,
         secure=True,
         samesite="lax",
-        max_age=60 * 60 * 24 * 30
+        max_age=SESSION_MAX_AGE_SECONDS
     )
     response.body = b'{"ok": true}'
     response.status_code = 200
@@ -686,44 +803,92 @@ async def run_dreamer(request: Request, auth: str = Depends(require_auth)):
         return JSONResponse({"started": False, "error": str(e)})
 
 def transcribe_and_save(tmp_path, timestamp, nextcloud_url, nextcloud_user, nextcloud_password):
-    """Background task — transcribes audio and saves to Nextcloud after endpoint returns."""
+    """Background task — transcribes audio and saves to Nextcloud after endpoint returns.
+    Audio is preserved in Journal/Media/ on every terminal path; failed and empty-transcript
+    captures still produce a markdown record in Journal/Captures/ with a status field."""
     import requests as req_lib
     nc_auth = (nextcloud_user, nextcloud_password)
+    month_dir = timestamp[:7]
+    audio_ext = os.path.splitext(tmp_path)[1] or ".webm"
+    audio_filename = f"{timestamp}-voice{audio_ext}"
+    audio_relpath = f"Journal/Media/{month_dir}/{audio_filename}"
+
+    def archive_audio() -> bool:
+        try:
+            with open(tmp_path, "rb") as f:
+                audio_bytes = f.read()
+            media_parent = f"{nextcloud_url}/remote.php/dav/files/{nextcloud_user}/Journal/Media"
+            media_dir = f"{media_parent}/{month_dir}"
+            req_lib.request("MKCOL", media_parent, auth=nc_auth, timeout=10)
+            req_lib.request("MKCOL", media_dir, auth=nc_auth, timeout=10)
+            req_lib.put(f"{media_dir}/{audio_filename}", data=audio_bytes, auth=nc_auth, timeout=60)
+            return True
+        except Exception as e:
+            print(f"Audio archival failed for {timestamp}: {e}")
+            return False
+        finally:
+            if os.path.exists(tmp_path):
+                os.unlink(tmp_path)
+
+    def write_capture(filename: str, content_md: str, status: str):
+        captures_dir = f"{nextcloud_url}/remote.php/dav/files/{nextcloud_user}/Journal/Captures"
+        try:
+            req_lib.request("MKCOL", captures_dir, auth=nc_auth, timeout=10)
+            req_lib.put(f"{captures_dir}/{filename}", data=content_md.encode("utf-8"), auth=nc_auth, timeout=30)
+        except Exception as e:
+            print(f"Capture markdown write failed for {timestamp}: {e}")
+            return
+        try:
+            payload = {"type": "capture_saved", "filename": filename, "timestamp": timestamp, "status": status}
+            req_lib.post("http://localhost:8000/api/events/notify", json=payload, timeout=3)
+            req_lib.post("http://localhost:8000/api/captures/events/notify", json=payload, timeout=3)
+        except Exception:
+            pass
+
+    transcript = ""
+    transcribe_error = None
     try:
         segments, _ = whisper_model.transcribe(
             tmp_path, language="en", vad_filter=True, beam_size=1, initial_prompt=WHISPER_PROMPT
         )
         transcript = " ".join(s.text.strip() for s in segments).strip()
-        os.unlink(tmp_path)
-        if not transcript:
-            print(f"Async transcription empty for {timestamp} — nothing saved")
-            return
-        filename = f"{timestamp}-voice.md"
-        content_md = f"# Capture — {timestamp}\n\n**type:** voice\n**modality:** audio\n**status:** unprocessed\n\n---\n\n{transcript}\n"
-        captures_dir = f"{nextcloud_url}/remote.php/dav/files/{nextcloud_user}/Journal/Captures"
-        req_lib.request("MKCOL", captures_dir, auth=nc_auth, timeout=10)
-        url = f"{captures_dir}/{filename}"
-        req_lib.put(url, data=content_md.encode("utf-8"), auth=nc_auth, timeout=30)
-        print(f"Async transcription saved: {filename}")
-        # Notify SSE clients that transcription is complete
-        try:
-            import requests as _req
-            _req.post("http://localhost:8000/api/events/notify", json={
-                "type": "capture_saved",
-                "filename": filename,
-                "timestamp": timestamp,
-            }, timeout=3)
-            _req.post("http://localhost:8000/api/captures/events/notify", json={
-                "type": "capture_saved",
-                "filename": filename,
-                "timestamp": timestamp,
-            }, timeout=3)
-        except Exception:
-            pass
     except Exception as e:
-        if os.path.exists(tmp_path):
-            os.unlink(tmp_path)
-        print(f"Async transcription failed for {timestamp}: {e}")
+        transcribe_error = str(e)
+
+    audio_archived = archive_audio()
+    audio_line = f"**audio_path:** {audio_relpath}\n" if audio_archived else "**audio_archive_failed:** true\n"
+
+    if transcribe_error is not None:
+        filename = f"{timestamp}-voice-failed.md"
+        content_md = (
+            f"# Capture — {timestamp}\n\n"
+            f"**type:** voice\n**modality:** audio\n**status:** failed_transcription\n"
+            f"{audio_line}"
+            f"**error:** {transcribe_error}\n"
+        )
+        write_capture(filename, content_md, "failed_transcription")
+        print(f"Async transcription failed for {timestamp}: {transcribe_error}")
+        return
+
+    if not transcript:
+        filename = f"{timestamp}-voice-empty.md"
+        content_md = (
+            f"# Capture — {timestamp}\n\n"
+            f"**type:** voice\n**modality:** audio\n**status:** empty_transcript\n"
+            f"{audio_line}"
+        )
+        write_capture(filename, content_md, "empty_transcript")
+        print(f"Async transcription empty for {timestamp}: audio archived")
+        return
+
+    filename = f"{timestamp}-voice.md"
+    content_md = (
+        f"# Capture — {timestamp}\n\n"
+        f"**type:** voice\n**modality:** audio\n**status:** saved\n"
+        f"{audio_line}\n---\n\n{transcript}\n"
+    )
+    write_capture(filename, content_md, "saved")
+    print(f"Async transcription saved: {filename}")
 
 
 @app.post("/api/capture")
@@ -830,7 +995,7 @@ Keep the full description to 150-250 words. Do not speculate beyond what is visi
 
 **type:** {capture_type}
 **modality:** {modality}
-**status:** unprocessed
+**status:** saved
 **media:** {media_path}
 {f"**project:** {project}" if project else ""}
 

scripts/encoding.py

@@ -12,6 +12,7 @@ Replaces four separate extract reimplementations and two extract-chunk-embed pat
 import hashlib
 import json
 import logging
+import re
 from pathlib import Path
 
 from docx import Document as DocxDocument
@@ -24,6 +25,62 @@ SUPPORTED = {".docx", ".pdf", ".pptx", ".txt", ".md"}
 DEFAULT_CHUNK_SIZE = 500
 DEFAULT_CHUNK_OVERLAP = 50
 
+_BOLD_KV_RE = re.compile(r"^\*\*[\w +/-]+?:\*\*")
+
+
+def _strip_md_frontmatter(text: str) -> str:
+    """Strip a leading frontmatter block from markdown, if present.
+
+    Recognizes two formats:
+      - YAML-style: file's first non-empty line is `---`, terminated by `---`.
+        Only triggered when no heading precedes — guards against `---`
+        horizontal rules that follow an H1.
+      - Capture-style: optional H1 heading, then one or more `**key:** value`
+        lines (and blanks), terminated by `---`. The H1 is preserved; the
+        key/value block + separator are removed.
+
+    Body `---` rules and body `**bold:**` lines are never touched — the scan
+    aborts as soon as a non-frontmatter line appears in the leading block.
+    """
+    lines = text.splitlines()
+    n = len(lines)
+    i = 0
+    while i < n and not lines[i].strip():
+        i += 1
+    heading = None
+    if i < n and lines[i].startswith("# "):
+        heading = lines[i]
+        i += 1
+        while i < n and not lines[i].strip():
+            i += 1
+    if i >= n:
+        return text
+    first = lines[i].strip()
+    if heading is None and first == "---":
+        j = i + 1
+        while j < n and lines[j].strip() != "---":
+            j += 1
+        if j >= n:
+            return text
+        body_start = j + 1
+    elif _BOLD_KV_RE.match(first):
+        j = i
+        while j < n:
+            s = lines[j].strip()
+            if not s or _BOLD_KV_RE.match(s):
+                j += 1
+                continue
+            if s == "---":
+                body_start = j + 1
+                break
+            return text
+        else:
+            return text
+    else:
+        return text
+    body = "\n".join(lines[body_start:]).lstrip("\n")
+    return f"{heading}\n\n{body}" if heading else body
+
 
 def _docx_cell_paragraphs(cell):
     yield from (p for p in cell.paragraphs if p.text.strip())
@@ -89,7 +146,10 @@ def extract_text(filepath: Path) -> str:
                         parts.append(notes)
             return "\n".join(parts)
         elif suffix in {".txt", ".md"}:
-            return filepath.read_text(encoding="utf-8", errors="ignore")
+            text = filepath.read_text(encoding="utf-8", errors="ignore")
+            if suffix == ".md":
+                return _strip_md_frontmatter(text)
+            return text
     except Exception as e:
         log.warning(f"Text extraction failed for {filepath.name}: {e}")
     return ""
@@ -142,8 +202,8 @@ def chunk_and_embed(text: str,
     return rows
 
 
-def write_embeddings_batch(conn, batch: list[dict]) -> int:
-    """Single canonical INSERT. Sets created_at = NOW() server-side. Commits.
+def write_embeddings_batch(conn, batch: list[dict], commit: bool = True) -> int:
+    """Single canonical INSERT. Sets created_at = NOW() server-side.
 
     Every row dict must supply 'type'. created_at is SQL-supplied (NOW()), so
     callers do not need to provide it. The application-layer assertion is the
@@ -151,6 +211,11 @@ def write_embeddings_batch(conn, batch: list[dict]) -> int:
     historical NULLs were resolved by the Improvement #2 backfill, and a
     Python-level raise gives a faster, more debuggable failure than a
     Postgres constraint error.
+
+    When commit=True (default), this function commits the connection itself.
+    When commit=False, the caller is responsible for committing. Use
+    commit=False when composing this write with other writes that must land
+    atomically in the same transaction.
     """
     if not batch:
         return 0
@@ -173,5 +238,6 @@ def write_embeddings_batch(conn, batch: list[dict]) -> int:
                 metadata   = EXCLUDED.metadata
         """, (row["id"], row["document"], row["embedding"],
               row["source"], row["type"], json.dumps(row["metadata"])))
-    conn.commit()
+    if commit:
+        conn.commit()
     return len(batch)

scripts/reindex_docx_pptx.py

@@ -0,0 +1,135 @@
+"""One-off: re-ingest docx+pptx after the 2026-05-04 extractor upgrade (commit 93c0d89).
+
+Pre-upgrade extraction missed tables, headers/footers, text boxes, group shapes,
+and pptx notes — leaving CVs/dossiers as section-header skeletons in the index.
+
+Steps when run with --apply:
+  1. DELETE all embeddings rows where source ends in .docx or .pptx
+  2. Walk NEXTCLOUD_PATH and re-ingest every .docx/.pptx via _ingest_one
+  3. Stage 2 enqueue is suppressed (SKIP_STAGE2_ENQUEUE=1)
+
+Without --apply: dry-run. Counts files and chunks, prints a sample, writes nothing.
+"""
+
+import os
+import sys
+import time
+from pathlib import Path
+
+os.environ["SKIP_STAGE2_ENQUEUE"] = "1"
+
+from dotenv import load_dotenv
+load_dotenv(Path.home() / "aaronai" / ".env", override=True)
+
+import psycopg2
+from sentence_transformers import SentenceTransformer
+
+sys.path.insert(0, str(Path(__file__).parent))
+from ingest import _ingest_one, get_pg
+
+NEXTCLOUD_PATH = Path("/home/aaron/nextcloud/data/data/aaron/files")
+TARGET_EXTS = {".docx", ".pptx"}
+
+APPLY = "--apply" in sys.argv
+
+
+def count_stale():
+    pg = get_pg()
+    cur = pg.cursor()
+    cur.execute(
+        "SELECT lower(substring(source from '\\.[^.]+$')) AS ext, "
+        "COUNT(DISTINCT source) AS files, COUNT(*) AS chunks "
+        "FROM embeddings WHERE lower(source) ~ '\\.(docx|pptx)$' "
+        "GROUP BY 1 ORDER BY 1"
+    )
+    rows = cur.fetchall()
+    pg.close()
+    return rows
+
+
+def delete_stale():
+    pg = get_pg()
+    cur = pg.cursor()
+    cur.execute("DELETE FROM embeddings WHERE lower(source) ~ '\\.(docx|pptx)$'")
+    deleted = cur.rowcount
+    pg.commit()
+    pg.close()
+    return deleted
+
+
+def find_files():
+    files = []
+    for f in NEXTCLOUD_PATH.rglob("*"):
+        if not f.is_file():
+            continue
+        if f.suffix.lower() not in TARGET_EXTS:
+            continue
+        if f.name.startswith(("~$", ".")):
+            continue
+        files.append(f)
+    return files
+
+
+def main():
+    print(f"Mode: {'APPLY (destructive)' if APPLY else 'DRY-RUN (no writes)'}")
+    print(f"Target: {NEXTCLOUD_PATH}")
+    print(f"Extensions: {sorted(TARGET_EXTS)}")
+    print(f"SKIP_STAGE2_ENQUEUE={os.environ.get('SKIP_STAGE2_ENQUEUE')}")
+    print()
+
+    print("Stale chunks currently in DB:")
+    for ext, files, chunks in count_stale():
+        print(f"  {ext}: {files} files, {chunks} chunks")
+    print()
+
+    files = find_files()
+    by_ext = {}
+    for f in files:
+        by_ext.setdefault(f.suffix.lower(), []).append(f)
+    print(f"Files on disk to re-ingest:")
+    for ext, lst in sorted(by_ext.items()):
+        print(f"  {ext}: {len(lst)} files")
+    print(f"  total: {len(files)}")
+    print()
+    print("Sample (5 random):")
+    import random
+    for f in random.sample(files, min(5, len(files))):
+        print(f"  {f}")
+    print()
+
+    if not APPLY:
+        print("Dry-run only. Re-run with --apply to delete + re-ingest.")
+        return
+
+    print("Deleting stale chunks...")
+    n = delete_stale()
+    print(f"  deleted {n} rows")
+    print()
+
+    print("Loading embedder...")
+    embedder = SentenceTransformer("all-MiniLM-L6-v2")
+    print()
+
+    print(f"Re-ingesting {len(files)} files...")
+    started = time.time()
+    ingested = failed = total_chunks = 0
+    for i, f in enumerate(files, 1):
+        n = _ingest_one(f, embedder, root=NEXTCLOUD_PATH)
+        if n > 0:
+            ingested += 1
+            total_chunks += n
+        else:
+            failed += 1
+        if i % 25 == 0 or i == len(files):
+            elapsed = time.time() - started
+            rate = i / elapsed if elapsed else 0
+            print(f"  [{i}/{len(files)}] ingested={ingested} failed={failed} "
+                  f"chunks={total_chunks} ({rate:.1f} files/s)")
+    elapsed = time.time() - started
+    print()
+    print(f"Done in {elapsed:.0f}s: {ingested} ingested, {failed} failed, "
+          f"{total_chunks} chunks written.")
+
+
+if __name__ == "__main__":
+    main()

scripts/test_retrieval.py

@@ -0,0 +1,58 @@
+"""End-to-end test of retrieve_context with intent routing + reranking.
+
+Avoids loading the full FastAPI app; replicates the chat-handler retrieval
+call shape and prints classifier output + final ranked sources for each query.
+"""
+
+import os
+import sys
+from pathlib import Path
+
+from dotenv import load_dotenv
+load_dotenv(Path.home() / "aaronai" / ".env", override=True)
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+# Stub anthropic so api.py import doesn't fail without the SDK loaded.
+# We only need retrieve_context + classify_retrieval_intent.
+import types
+sys.modules.setdefault("anthropic", types.ModuleType("anthropic"))
+sys.modules["anthropic"].Anthropic = lambda **kw: None
+
+# Same for whisper if present
+if "faster_whisper" not in sys.modules:
+    sys.modules["faster_whisper"] = types.ModuleType("faster_whisper")
+
+import importlib.util
+spec = importlib.util.spec_from_file_location("api", Path(__file__).parent / "api.py")
+api = importlib.util.module_from_spec(spec)
+# Don't execute the whole module (it starts FastAPI). Instead, exec only definitions.
+# Easier: just import the functions we need by exec'ing the file but catching errors.
+try:
+    spec.loader.exec_module(api)
+except Exception as e:
+    print(f"(continuing despite api.py side-effect error: {e})")
+
+retrieve_context = api.retrieve_context
+classify_retrieval_intent = api.classify_retrieval_intent
+
+QUERIES = [
+    "write me a bio",
+    "my professional bio",
+    "draft a bio for the Utah application",
+    "Aaron Nelson CV consulting and design work",
+    "FWN3D consulting",
+    "syllabi I have taught",
+    "philosophy of teaching",
+    "what did I tell Claude about FWN3D",
+    "what did we discuss about the Utah job",
+    "Hudson Valley Additive Manufacturing Center",
+]
+
+for q in QUERIES:
+    intent = classify_retrieval_intent(q)
+    pieces, sources = retrieve_context(q, type_filter=intent)
+    print(f"\n=== {q!r} ===")
+    print(f"  intent: {intent}")
+    for i, src in enumerate(sources, 1):
+        print(f"  {i}. {src}")