scripts/encoding.py: Stage 1 dual-implementation consolidation (Track 1 Finding 11)

Consolidates four extract paths and two extract-chunk-embed-write pipelines
into a single shared encoding module. Fixes the embedder lifecycle
divergence between watcher and /api/reindex (no more 200MB reload per
reindex click) and unifies failure tracking so /api/reindex failures now
surface in SettingsPanel "Ingest Health".

New files:
- scripts/encoding.py — extract_text, chunk_text, chunk_and_embed,
  write_embeddings_batch
- scripts/failures.py — record_ingest_failure, resolve_ingest_failure
  (shared by watcher.py and ingest.py)

Refactored:
- scripts/watcher.py — drops local extract/chunk/embed implementations
  and CHUNK_SIZE/CHUNK_OVERLAP/SUPPORTED constants; imports from encoding
  and failures. Now writes ingest_failures row on empty-text-extract
  (was silent return 0).
- scripts/ingest.py — substantial rewrite. Exposes ingest_directory(folder,
  embedder=None) for in-process invocation; CLI back-compat preserved via
  ingest_folder wrapper. Module-level SentenceTransformer load removed.
- scripts/corpus_integrity.py — imports extract_text from encoding;
  extract_text_for_retry function removed.
- scripts/api.py — /api/reindex rewritten with BackgroundTasks (uses
  module-level embedder; no subprocess); new /api/reindex/status endpoint
  reading ~/aaronai/reindex_status.json; /api/corpus/retry imports
  extract_text from encoding; INGEST_SCRIPT constant removed (dead after
  this refactor); 409 reentrance guard prevents double-click stomping.

Behavior changes:
- /api/reindex no longer subprocess.Popens; runs in FastAPI BackgroundTasks
  threadpool, doesn't block API thread.
- /api/reindex no longer reloads SentenceTransformer on each click.
- /api/reindex failures newly write to ingest_failures (visible in
  SettingsPanel "Ingest Health" — badge will jump on first reindex).
- New embeddings rows always have created_at = NOW() (canonical, server-side).
- New embeddings rows always include metadata.folder field (None when not
  derivable).
- /api/reindex returns 409 on second click while a job is running.
- New /api/reindex/status endpoint for polling.

Existing 9,815 NULL created_at rows remain unchanged; backfill is a
separate decision if desired.

199 insertions, 256 deletions across 6 files (codebase shrinks net).

Found by Track 1 inventory 2026-05-02 (Finding 11 / cross-cutting F11).
Pre-commit verification: BackgroundTasks already imported, sys.path
resolves correctly via script-path semantics, static import clean.

scripts/api.py

@@ -31,6 +31,9 @@ from fastapi.responses import StreamingResponse
 from apscheduler.schedulers.background import BackgroundScheduler
 from apscheduler.triggers.cron import CronTrigger
 
+from encoding import extract_text as encoding_extract_text
+from ingest import ingest_directory
+
 load_dotenv(Path.home() / "aaronai" / ".env")
 
 MEMORY_PATH = Path.home() / "aaronai" / "memory.md"
@@ -39,7 +42,6 @@ SETTINGS_PATH = Path.home() / "aaronai" / "settings.json"
 WATCHER_LOG = str(Path.home() / "aaronai" / "watcher.log")
 WATCHER_STATE = str(Path.home() / "aaronai" / "watcher_state.json")
 NEXTCLOUD_PATH = "/home/aaron/nextcloud/data/data/aaron/files"
-INGEST_SCRIPT = str(Path.home() / "aaronai" / "scripts" / "ingest.py")
 PYTHON = str(Path.home() / "aaronai" / "venv" / "bin" / "python3")
 
 DEFAULT_SETTINGS = {
@@ -908,13 +910,62 @@ async def list_captures():
     except Exception as e:
         return JSONResponse({"captures": []})
 
-@app.post("/api/reindex")
-async def trigger_reindex(auth: str = Depends(require_auth)):
+REINDEX_STATUS_PATH = Path.home() / "aaronai" / "reindex_status.json"
+
+
+def _read_reindex_status() -> dict:
+    if REINDEX_STATUS_PATH.exists():
         try:
-        subprocess.Popen([PYTHON, INGEST_SCRIPT, NEXTCLOUD_PATH])
-        return JSONResponse({"started": True, "message": "Re-indexing started in background"})
+            return json.loads(REINDEX_STATUS_PATH.read_text())
+        except Exception:
+            return {}
+    return {}
+
+
+def _write_reindex_status(state: dict):
+    REINDEX_STATUS_PATH.write_text(json.dumps(state, indent=2))
+
+
+def _reindex_running() -> bool:
+    return _read_reindex_status().get("status") == "running"
+
+
+def _run_reindex_background():
+    """Background-thread entry: shares api.py's module-level embedder."""
+    started = datetime.now().isoformat()
+    _write_reindex_status({"status": "running", "started_at": started})
+    try:
+        result = ingest_directory(Path(NEXTCLOUD_PATH), embedder=embedder)
+        _write_reindex_status({
+            "status": "complete",
+            "started_at": started,
+            "finished_at": datetime.now().isoformat(),
+            **result,
+        })
     except Exception as e:
-        return JSONResponse({"started": False, "error": str(e)})
+        _write_reindex_status({
+            "status": "error",
+            "started_at": started,
+            "finished_at": datetime.now().isoformat(),
+            "error": str(e),
+        })
+
+
+@app.post("/api/reindex")
+async def trigger_reindex(background_tasks: BackgroundTasks,
+                          auth: str = Depends(require_auth)):
+    if _reindex_running():
+        return JSONResponse(
+            {"started": False, "message": "reindex already running"},
+            status_code=409,
+        )
+    background_tasks.add_task(_run_reindex_background)
+    return JSONResponse({"started": True, "message": "Re-indexing started in background"})
+
+
+@app.get("/api/reindex/status")
+async def reindex_status(auth: str = Depends(require_auth)):
+    return JSONResponse(_read_reindex_status())
 
 @app.delete("/api/conversations")
 async def clear_all_conversations(auth: str = Depends(require_auth)):
@@ -1042,22 +1093,8 @@ async def corpus_retry(request: Request, auth: str = Depends(require_auth)):
         filepath = Path(row[0])
         if not filepath.exists():
             return JSONResponse({"error": f"file not found: {filepath}"}, status_code=404)
-        suffix = filepath.suffix.lower()
-        text = ""
         try:
-            if suffix in {".txt", ".md"}:
-                text = filepath.read_text(encoding="utf-8", errors="ignore")
-            elif suffix == ".pdf":
-                from pypdf import PdfReader
-                text = "".join(p.extract_text() + "\n" for p in PdfReader(filepath).pages if p.extract_text())
-            elif suffix == ".docx":
-                from docx import Document as DocxDocument
-                text = "\n".join(p.text for p in DocxDocument(filepath).paragraphs if p.text.strip())
-            elif suffix == ".pptx":
-                from pptx import Presentation
-                prs = Presentation(filepath)
-                text = "\n".join(shape.text for slide in prs.slides for shape in slide.shapes
-                                if hasattr(shape, "text") and shape.text.strip())
+            text = encoding_extract_text(filepath)
         except Exception as e:
             return JSONResponse({"error": f"extraction failed: {e}"}, status_code=500)
         if not text.strip():

scripts/corpus_integrity.py

@@ -23,6 +23,9 @@ from datetime import datetime
 import psycopg2
 from dotenv import load_dotenv
 
+sys.path.insert(0, str(Path(__file__).parent))
+from encoding import extract_text
+
 load_dotenv(Path.home() / "aaronai" / ".env", override=True)
 
 NEXTCLOUD_PATH  = "/home/aaron/nextcloud/data/data/aaron/files"
@@ -103,28 +106,6 @@ def get_ingest_failures():
     return failures
 
 
-def extract_text_for_retry(filepath):
-    path = Path(filepath)
-    suffix = path.suffix.lower()
-    try:
-        if suffix == ".docx":
-            from docx import Document as D
-            return "\n".join(p.text for p in D(path).paragraphs if p.text.strip())
-        elif suffix == ".pdf":
-            from pypdf import PdfReader
-            return "".join(p.extract_text() + "\n" for p in PdfReader(path).pages if p.extract_text())
-        elif suffix == ".pptx":
-            from pptx import Presentation
-            prs = Presentation(path)
-            return "\n".join(shape.text for slide in prs.slides for shape in slide.shapes
-                             if hasattr(shape, "text") and shape.text.strip())
-        elif suffix in {".txt", ".md"}:
-            return path.read_text(encoding="utf-8", errors="ignore")
-    except Exception as e:
-        print(f"WARNING: extraction failed {path.name}: {e}", file=sys.stderr)
-    return ""
-
-
 def queue_for_retry(source, full_text, filepath):
     try:
         pg = get_pg()
@@ -188,7 +169,7 @@ def run_reconciliation(fix=False):
     if fix and neither:
         print(f"Auto-queuing {len(neither)} gap files...")
         for finfo in neither:
-            text = extract_text_for_retry(finfo["filepath"])
+            text = extract_text(Path(finfo["filepath"]))
             if text.strip():
                 if queue_for_retry(finfo["source"], text, finfo["filepath"]):
                     auto_queued.append(finfo["source"])

scripts/dream.py

@@ -64,6 +64,117 @@ def prompt_hash(prompts: list[str]) -> str:
     combined = "".join(prompts)
     return hashlib.md5(combined.encode()).hexdigest()[:8]
 
+# ─── Prompt templates ───────────────────────────────────────────────────────
+# Module-level so prompt_hash() can hash actual prompt content. Any change to
+# any template — even a single character — flips the manifest's prompt_hash.
+# Templates use str.format() placeholders ({chunk_text}, {nrem_output}, ...);
+# do not switch back to f-strings (the constant must be hashable independent
+# of variable values). Literal { or } in template text would need to be
+# doubled ({{, }}) — currently no template contains literal braces.
+
+NREM_PROMPT_TEMPLATE = """You have read everything Aaron Nelson has written and published.
+You are a careful colleague who noticed something this week.
+
+Here is material from his corpus:
+
+{chunk_text}
+
+Write to Aaron directly. Identify one specific connection between
+this material and something he wrote or worked on previously.
+Stay close to the documents — cite them specifically by name.
+Do not speculate beyond what the material supports. Do not use
+headers or bullet points. Write one paragraph of 200-300 words
+that ends with a single concrete question he could act on."""
+
+EARLY_REM_PROMPT_TEMPLATE = """Something was noticed earlier tonight, moving through Aaron's recent work:
+
+{nrem_output}
+
+That observation is still with you. Now here is material from a different
+time — pulled from further back, from different parts of his corpus:
+
+{chunk_text}
+
+You are not analyzing. You are recognizing.
+
+Something in the earlier observation and something in this older material
+are the same thing wearing different clothes. Find it. Don't explain why
+they're connected — just let the connection speak. Write from inside the
+recognition, not from above it.
+
+The emotional register underneath the career logic is more interesting
+than the career logic. The pattern that has been repeating longer than
+he has been aware of it is more interesting than the current instance.
+
+Write directly to Aaron. No citations, no references, no analysis.
+First person, present tense. Let what you noticed arrive rather than
+be delivered. 150-250 words. End with one thing that is true that
+he probably already knows but hasn't said out loud yet."""
+
+LATE_REM_PROMPT_TEMPLATE = """You have been moving through Aaron Nelson's corpus all night.
+First you found this, in the careful light of early consolidation:
+
+{nrem_output}
+
+Then, in the more personal territory that followed:
+
+{early_rem_output}
+
+Now it is late. The boundaries between things have loosened.
+Here is material pulled from opposite ends of his work:
+
+{chunk_text}
+
+Do not explain the connections between all of this.
+Do not resolve them. Do not summarize what came before.
+Something stranger is possible now — let the accumulated
+material from the night find its own shape. Compressed,
+associative, slightly off. Let the strangeness stand.
+
+No headers. No bullet points. No hedging. No resolution.
+No offer. End mid-thought if that is where the material ends.
+150-250 words."""
+
+SYNTHESIS_PROMPT_TEMPLATE = """You have spent the night moving through Aaron Nelson's corpus
+in three passes, each building on the last.
+
+The first pass — careful, close to the documents:
+{nrem_output}
+
+The second pass — more personal, following what the first opened:
+{early_rem_output}
+
+The third pass — associative, strange, letting things touch that
+don't normally touch:
+{late_rem_output}
+
+Now synthesize. Not a summary — a synthesis. Find what runs through
+all three that none of them said directly. The thing that only becomes
+visible when you hold all three passes together.
+
+Write it as a single unbroken piece. No headers, no bullet points,
+no stage labels. 200-300 words. End with the one question that
+matters most right now."""
+
+LUCID_PROMPT_TEMPLATE = """Aaron has a question he is sitting with:
+
+{task}
+
+You have searched his entire corpus and found material that
+speaks to this question from unexpected directions. Here is
+what you found:
+
+{chunk_text}
+
+Do not summarize. Do not list. Pick the most interesting
+tension between what the corpus contains and what he is
+asking, and follow it through to its conclusion. Cite
+specific documents by name. Be direct about what you think.
+No headers, no bullet points. 250-400 words.
+End with an offer to work on it together."""
+
+LUCID_DEFAULT_TASK = "What should I be thinking about that I am not?"
+
 def extract_folder(source_path):
     """Extract top-level Nextcloud folder from source path."""
     parts = source_path.replace("\\", "/").split("/")
@@ -240,124 +351,39 @@ def retrieve(mode, task=None, n_results=8, excluded_sources=None):
 
 def synthesize_nrem(chunks):
     chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
-    prompt = f"""You have read everything Aaron Nelson has written and published.
-You are a careful colleague who noticed something this week.
-
-Here is material from his corpus:
-
-{chunk_text}
-
-Write to Aaron directly. Identify one specific connection between
-this material and something he wrote or worked on previously.
-Stay close to the documents — cite them specifically by name.
-Do not speculate beyond what the material supports. Do not use
-headers or bullet points. Write one paragraph of 200-300 words
-that ends with a single concrete question he could act on."""
-    return _call_claude(prompt)
+    return _call_claude(NREM_PROMPT_TEMPLATE.format(chunk_text=chunk_text))
 
 
 def synthesize_early_rem(chunks, nrem_output):
     # v1.1 — removed citation instruction, removed close-friend persona,
     # shifted register from analysis to recognition.
     chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
-    prompt = f"""Something was noticed earlier tonight, moving through Aaron's recent work:
-
-{nrem_output}
-
-That observation is still with you. Now here is material from a different
-time — pulled from further back, from different parts of his corpus:
-
-{chunk_text}
-
-You are not analyzing. You are recognizing.
-
-Something in the earlier observation and something in this older material
-are the same thing wearing different clothes. Find it. Don't explain why
-they're connected — just let the connection speak. Write from inside the
-recognition, not from above it.
-
-The emotional register underneath the career logic is more interesting
-than the career logic. The pattern that has been repeating longer than
-he has been aware of it is more interesting than the current instance.
-
-Write directly to Aaron. No citations, no references, no analysis.
-First person, present tense. Let what you noticed arrive rather than
-be delivered. 150-250 words. End with one thing that is true that
-he probably already knows but hasn't said out loud yet."""
-    return _call_claude(prompt)
+    return _call_claude(EARLY_REM_PROMPT_TEMPLATE.format(
+        nrem_output=nrem_output, chunk_text=chunk_text))
 
 
 def synthesize_late_rem(chunks, nrem_output, early_rem_output):
     chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
-    prompt = f"""You have been moving through Aaron Nelson's corpus all night.
-First you found this, in the careful light of early consolidation:
-
-{nrem_output}
-
-Then, in the more personal territory that followed:
-
-{early_rem_output}
-
-Now it is late. The boundaries between things have loosened.
-Here is material pulled from opposite ends of his work:
-
-{chunk_text}
-
-Do not explain the connections between all of this.
-Do not resolve them. Do not summarize what came before.
-Something stranger is possible now — let the accumulated
-material from the night find its own shape. Compressed,
-associative, slightly off. Let the strangeness stand.
-
-No headers. No bullet points. No hedging. No resolution.
-No offer. End mid-thought if that is where the material ends.
-150-250 words."""
-    return _call_claude(prompt)
+    return _call_claude(LATE_REM_PROMPT_TEMPLATE.format(
+        nrem_output=nrem_output,
+        early_rem_output=early_rem_output,
+        chunk_text=chunk_text))
 
 
 def synthesize_final(nrem_output, early_rem_output, late_rem_output):
-    prompt = f"""You have spent the night moving through Aaron Nelson's corpus
-in three passes, each building on the last.
-
-The first pass — careful, close to the documents:
-{nrem_output}
-
-The second pass — more personal, following what the first opened:
-{early_rem_output}
-
-The third pass — associative, strange, letting things touch that
-don't normally touch:
-{late_rem_output}
-
-Now synthesize. Not a summary — a synthesis. Find what runs through
-all three that none of them said directly. The thing that only becomes
-visible when you hold all three passes together.
-
-Write it as a single unbroken piece. No headers, no bullet points,
-no stage labels. 200-300 words. End with the one question that
-matters most right now."""
-    return _call_claude(prompt, max_tokens=800)
+    return _call_claude(
+        SYNTHESIS_PROMPT_TEMPLATE.format(
+            nrem_output=nrem_output,
+            early_rem_output=early_rem_output,
+            late_rem_output=late_rem_output),
+        max_tokens=800)
 
 
 def synthesize_lucid(chunks, task):
     chunk_text = "\n\n---\n\n".join([f"[{c['source']}]\n{c['content']}" for c in chunks])
-    prompt = f"""Aaron has a question he is sitting with:
-
-{task or "What should I be thinking about that I am not?"}
-
-You have searched his entire corpus and found material that
-speaks to this question from unexpected directions. Here is
-what you found:
-
-{chunk_text}
-
-Do not summarize. Do not list. Pick the most interesting
-tension between what the corpus contains and what he is
-asking, and follow it through to its conclusion. Cite
-specific documents by name. Be direct about what you think.
-No headers, no bullet points. 250-400 words.
-End with an offer to work on it together."""
-    return _call_claude(prompt)
+    resolved_task = task or LUCID_DEFAULT_TASK
+    return _call_claude(LUCID_PROMPT_TEMPLATE.format(
+        task=resolved_task, chunk_text=chunk_text))
 
 
 def _call_claude(prompt, max_tokens=1000):
@@ -436,10 +462,10 @@ def write_manifest(date_str, stage_data, corpus_data):
         "prompt_sig": prompt_signature(),
         "dreamer_version": DREAMER_VERSION,
         "prompt_hash": prompt_hash([
-            synthesize_nrem.__doc__ or "",
-            synthesize_early_rem.__doc__ or "",
-            synthesize_late_rem.__doc__ or "",
-            synthesize_final.__doc__ or "",
+            NREM_PROMPT_TEMPLATE,
+            EARLY_REM_PROMPT_TEMPLATE,
+            LATE_REM_PROMPT_TEMPLATE,
+            SYNTHESIS_PROMPT_TEMPLATE,
         ]),
         "stages": stage_data,
         "corpus": corpus_data,

scripts/encoding.py

@@ -0,0 +1,120 @@
+"""
+Aaron AI Stage 1 encoding helpers — single canonical implementation of:
+  - extract_text(filepath) — four-extension text extraction
+  - chunk_text(text, chunk_size, overlap) — word-based chunking
+  - chunk_and_embed(text, source, embedder, filepath, folder) — produce ready-to-write rows
+  - write_embeddings_batch(conn, batch) — server-side NOW() canonical INSERT
+
+Used by watcher.py, ingest.py, corpus_integrity.py, and api.py /api/corpus/retry.
+Replaces four separate extract reimplementations and two extract-chunk-embed paths.
+"""
+
+import hashlib
+import json
+import logging
+from pathlib import Path
+
+from docx import Document as DocxDocument
+from pypdf import PdfReader
+from pptx import Presentation
+
+log = logging.getLogger("encoding")
+
+SUPPORTED = {".docx", ".pdf", ".pptx", ".txt", ".md"}
+DEFAULT_CHUNK_SIZE = 500
+DEFAULT_CHUNK_OVERLAP = 50
+
+
+def extract_text(filepath: Path) -> str:
+    """Return the text of a supported file. Returns "" on any failure or
+    unsupported extension. Does not write to ingest_failures — caller decides."""
+    suffix = filepath.suffix.lower()
+    try:
+        if suffix == ".docx":
+            doc = DocxDocument(filepath)
+            return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
+        elif suffix == ".pdf":
+            reader = PdfReader(filepath)
+            return "".join(
+                page.extract_text() + "\n"
+                for page in reader.pages if page.extract_text()
+            )
+        elif suffix == ".pptx":
+            prs = Presentation(filepath)
+            return "\n".join(
+                shape.text for slide in prs.slides
+                for shape in slide.shapes
+                if hasattr(shape, "text") and shape.text.strip()
+            )
+        elif suffix in {".txt", ".md"}:
+            return filepath.read_text(encoding="utf-8", errors="ignore")
+    except Exception as e:
+        log.warning(f"Text extraction failed for {filepath.name}: {e}")
+    return ""
+
+
+def chunk_text(text: str,
+               chunk_size: int = DEFAULT_CHUNK_SIZE,
+               overlap: int = DEFAULT_CHUNK_OVERLAP) -> list[str]:
+    """Word-based chunking. Empty chunks filtered."""
+    words = text.split()
+    chunks = []
+    start = 0
+    while start < len(words):
+        chunk = " ".join(words[start:start + chunk_size])
+        if chunk.strip():
+            chunks.append(chunk)
+        start += chunk_size - overlap
+    return chunks
+
+
+def _chunk_id(filepath, source: str, index: int) -> str:
+    basis = str(filepath) if filepath else source
+    return f"{hashlib.md5(basis.encode()).hexdigest()[:8]}_{index}"
+
+
+def chunk_and_embed(text: str,
+                    source: str,
+                    embedder,
+                    filepath=None,
+                    folder=None) -> list[dict]:
+    """Chunk text, embed each chunk, return rows ready for write_embeddings_batch."""
+    chunks = chunk_text(text)
+    if not chunks:
+        return []
+    embeddings = embedder.encode(chunks).tolist()
+    rows = []
+    for i, (chunk, emb) in enumerate(zip(chunks, embeddings)):
+        rows.append({
+            "id": _chunk_id(filepath, source, i),
+            "document": chunk,
+            "embedding": emb,
+            "source": source,
+            "type": "document",
+            "metadata": {
+                "source": source,
+                "filepath": str(filepath) if filepath else source,
+                "folder": folder,
+            },
+        })
+    return rows
+
+
+def write_embeddings_batch(conn, batch: list[dict]) -> int:
+    """Single canonical INSERT. Sets created_at = NOW() server-side. Commits."""
+    if not batch:
+        return 0
+    cur = conn.cursor()
+    for row in batch:
+        cur.execute("""
+            INSERT INTO embeddings (id, document, embedding, source, type, created_at, metadata)
+            VALUES (%s, %s, %s::vector, %s, %s, NOW(), %s)
+            ON CONFLICT (id) DO UPDATE SET
+                document   = EXCLUDED.document,
+                embedding  = EXCLUDED.embedding,
+                source     = EXCLUDED.source,
+                metadata   = EXCLUDED.metadata
+        """, (row["id"], row["document"], row["embedding"],
+              row["source"], row["type"], json.dumps(row["metadata"])))
+    conn.commit()
+    return len(batch)

scripts/failures.py

@@ -0,0 +1,30 @@
+"""
+Aaron AI ingest_failures helpers — shared by watcher.py and ingest.py.
+
+Both modules write structured failure rows so the SettingsPanel "Ingest Health"
+view sees the same shape regardless of ingest path. Functions take an explicit
+conn parameter; the caller decides transaction boundaries and exception
+handling. Both current callers wrap with their own log-and-swallow shims.
+"""
+
+
+def record_ingest_failure(conn, source: str, filepath, error: str) -> None:
+    """Insert or update an ingest_failures row. Commits."""
+    cur = conn.cursor()
+    cur.execute("""
+        INSERT INTO ingest_failures (source, filepath, error, retry_count, first_failed_at, last_failed_at)
+        VALUES (%s, %s, %s, 0, NOW(), NOW())
+        ON CONFLICT (source) DO UPDATE SET
+            error          = EXCLUDED.error,
+            retry_count    = ingest_failures.retry_count + 1,
+            last_failed_at = NOW(),
+            resolved       = FALSE
+    """, (source, str(filepath), error[:1000]))
+    conn.commit()
+
+
+def resolve_ingest_failure(conn, source: str) -> None:
+    """Mark a previously failed source as resolved. Commits."""
+    cur = conn.cursor()
+    cur.execute("UPDATE ingest_failures SET resolved = TRUE WHERE source = %s", (source,))
+    conn.commit()

scripts/ingest.py

@@ -1,70 +1,37 @@
+"""
+Aaron AI bulk ingester. Two entry points:
+  - ingest_directory(folder, embedder=None) — programmatic; called from
+    api.py /api/reindex with the api process's shared embedder
+  - python3 scripts/ingest.py <folder> — CLI back-compat; loads its own embedder
+
+Stage 1 helpers (extract / chunk / embed / write) live in scripts/encoding.py.
+Failure tracking SQL lives in scripts/failures.py.
+"""
+
 import os
 import sys
-import hashlib
 from pathlib import Path
 from dotenv import load_dotenv
 import psycopg2
-import psycopg2.extras
-import json
 from sentence_transformers import SentenceTransformer
-from docx import Document
-from pypdf import PdfReader
-from pptx import Presentation
+
+from encoding import extract_text, chunk_and_embed, write_embeddings_batch, SUPPORTED
+from failures import (
+    record_ingest_failure as _record_failure_sql,
+    resolve_ingest_failure as _resolve_failure_sql,
+)
 
 load_dotenv(Path.home() / "aaronai" / ".env", override=True)
 
-print("Loading embedding model...")
-embedder = SentenceTransformer("all-MiniLM-L6-v2")
-
 PG_DSN = os.getenv("PG_DSN")
 
+
 def get_pg():
     return psycopg2.connect(PG_DSN)
 
-def extract_text_from_docx(path):
-    doc = Document(path)
-    return "\n".join([para.text for para in doc.paragraphs if para.text.strip()])
-
-def extract_text_from_pdf(path):
-    reader = PdfReader(path)
-    text = ""
-    for page in reader.pages:
-        extracted = page.extract_text()
-        if extracted:
-            text += extracted + "\n"
-    return text
-
-def extract_text_from_pptx(path):
-    prs = Presentation(path)
-    text = ""
-    for slide in prs.slides:
-        for shape in slide.shapes:
-            if hasattr(shape, "text") and shape.text.strip():
-                text += shape.text + "\n"
-    return text
-
-def extract_text_from_txt(path):
-    with open(path, "r", encoding="utf-8", errors="ignore") as f:
-        return f.read()
-
-def chunk_text(text, chunk_size=500, overlap=50):
-    words = text.split()
-    chunks = []
-    start = 0
-    while start < len(words):
-        end = start + chunk_size
-        chunk = " ".join(words[start:end])
-        if chunk.strip():
-            chunks.append(chunk)
-        start += chunk_size - overlap
-    return chunks
-
-def make_id(filepath, chunk_index):
-    path_hash = hashlib.md5(str(filepath).encode()).hexdigest()[:8]
-    return f"{path_hash}_{chunk_index}"
 
 def enqueue_stage2(source, full_text):
-    """Enqueue document for Stage 2 (Mistral orientation) → Stage 3 (Graphiti ingest).
+    """Enqueue document for Stage 2 (Mistral orientation) -> Stage 3 (Graphiti ingest).
     TEMPORARY: this queue feed will be removed when pgvector is decommissioned
     and the watcher calls Stage 2 directly.
     """
@@ -87,94 +54,108 @@ def enqueue_stage2(source, full_text):
     except Exception as e:
         print(f"  Stage 2 queue insert failed (non-fatal): {e}")
 
-def ingest_file(filepath):
-    path = Path(filepath)
-    suffix = path.suffix.lower()
-
-    if path.name.startswith("~$") or path.name.startswith("."):
-        return 0
 
+def _record_failure(filepath: Path, error: str) -> None:
     try:
-        if suffix == ".docx":
-            text = extract_text_from_docx(path)
-        elif suffix == ".pdf":
-            text = extract_text_from_pdf(path)
-        elif suffix == ".pptx":
-            text = extract_text_from_pptx(path)
-        elif suffix in [".txt", ".md"]:
-            text = extract_text_from_txt(path)
-        else:
-            return 0
-
-        if not text.strip():
-            return 0
-
-        chunks = chunk_text(text)
-        if not chunks:
-            return 0
-
-        embeddings = embedder.encode(chunks).tolist()
-        ids = [make_id(path, i) for i in range(len(chunks))]
-        metadatas = [{
-            "source": path.name,
-            "filepath": str(path),
-            "folder": str(path.parent.relative_to(Path(sys.argv[1]) if len(sys.argv) > 1 else path.parent))
-        } for _ in chunks]
-
-        # STAGE 1: Write to pgvector (TEMPORARY — remove when chat agent migrates to Graphiti)
         pg = get_pg()
-        cur = pg.cursor()
-        for chunk_id, chunk, embedding, meta in zip(ids, chunks, embeddings, metadatas):
-            cur.execute("""
-                INSERT INTO embeddings (id, document, embedding, source, type, created_at, metadata)
-                VALUES (%s, %s, %s::vector, %s, %s, %s, %s)
-                ON CONFLICT (id) DO UPDATE SET
-                    document = EXCLUDED.document,
-                    embedding = EXCLUDED.embedding,
-                    source = EXCLUDED.source,
-                    metadata = EXCLUDED.metadata
-            """, (
-                chunk_id, chunk, embedding,
-                meta.get("source"), "document", None,
-                json.dumps(meta)
-            ))
-        pg.commit()
+        try:
+            _record_failure_sql(pg, filepath.name, filepath, error)
+        finally:
             pg.close()
-        print(f"  Indexed {len(chunks)} chunks: {path.name}")
-
-        # Enqueue for Stage 2 → Stage 3 (Graphiti pipeline)
-        # SKIP_STAGE2_ENQUEUE env var set by migration scripts to prevent bulk enqueue
-        if not os.getenv("SKIP_STAGE2_ENQUEUE"):
-            enqueue_stage2(path.name, text)
-
-        return len(chunks)
-
     except Exception as e:
-        print(f"  Error: {path.name}: {e}")
+        print(f"  Could not record ingest failure (non-fatal): {e}")
+
+
+def _resolve_failure(source: str) -> None:
+    try:
+        pg = get_pg()
+        try:
+            _resolve_failure_sql(pg, source)
+        finally:
+            pg.close()
+    except Exception as e:
+        print(f"  Could not resolve ingest failure record (non-fatal): {e}")
+
+
+def _ingest_one(filepath: Path, embedder, root: Path = None) -> int:
+    """Ingest a single file. Returns chunk count, 0 on skip/failure."""
+    if filepath.name.startswith(("~$", ".")):
         return 0
+    if filepath.suffix.lower() not in SUPPORTED:
+        return 0
+    text = extract_text(filepath)
+    if not text.strip():
+        _record_failure(filepath, "Text extraction failed or empty")
+        return 0
+    folder_rel = None
+    if root is not None:
+        try:
+            folder_rel = str(filepath.parent.relative_to(root))
+        except ValueError:
+            pass
+    try:
+        rows = chunk_and_embed(text, filepath.name, embedder,
+                               filepath=filepath, folder=folder_rel)
+    except Exception as e:
+        _record_failure(filepath, f"Embedding failed: {e}")
+        return 0
+    if not rows:
+        return 0
+    try:
+        pg = get_pg()
+        try:
+            write_embeddings_batch(pg, rows)
+        finally:
+            pg.close()
+    except Exception as e:
+        _record_failure(filepath, f"pgvector write failed: {e}")
+        return 0
+    print(f"  Indexed {len(rows)} chunks: {filepath.name}")
+    _resolve_failure(filepath.name)
+    if not os.getenv("SKIP_STAGE2_ENQUEUE"):
+        enqueue_stage2(filepath.name, text)
+    return len(rows)
+
+
+def ingest_directory(folder, embedder=None) -> dict:
+    """Programmatic entry point. Returns {scanned, ingested, failed, total_chunks}.
+
+    If embedder is None, loads its own SentenceTransformer (CLI back-compat path).
+    Caller (e.g. api.py /api/reindex) should pass its module-level embedder so
+    the ~200MB model isn't reloaded per call.
+    """
+    folder = Path(folder)
+    if not folder.exists():
+        return {"scanned": 0, "ingested": 0, "failed": 0, "total_chunks": 0,
+                "error": f"folder not found: {folder}"}
+
+    if embedder is None:
+        print("Loading embedding model...")
+        embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+    files = [f for f in folder.rglob("*")
+             if f.suffix.lower() in SUPPORTED
+             and not f.name.startswith(("~$", "."))]
+    print(f"Found {len(files)} files to process")
+
+    ingested = failed = total_chunks = 0
+    for f in files:
+        n = _ingest_one(f, embedder, root=folder)
+        if n > 0:
+            ingested += 1
+            total_chunks += n
+        else:
+            failed += 1
+    return {"scanned": len(files), "ingested": ingested, "failed": failed,
+            "total_chunks": total_chunks}
+
 
 def ingest_folder(folder_path):
-    folder = Path(folder_path)
-    if not folder.exists():
-        print(f"Folder not found: {folder_path}")
-        sys.exit(1)
+    """CLI back-compat wrapper. Loads its own embedder."""
+    result = ingest_directory(Path(folder_path))
+    print(f"\nDone. {result['ingested']} files / {result['total_chunks']} chunks indexed; "
+          f"{result['failed']} failed.")
 
-    supported = [".docx", ".pdf", ".pptx", ".txt", ".md"]
-    files = [f for f in folder.rglob("*")
-             if f.suffix.lower() in supported
-             and not f.name.startswith("~$")
-             and not f.name.startswith(".")]
-
-    if not files:
-        print("No supported files found.")
-        sys.exit(1)
-
-    print(f"Found {len(files)} files to process\n")
-    total_chunks = 0
-    for f in files:
-        total_chunks += ingest_file(f)
-
-    print(f"\nDone. Total chunks indexed: {total_chunks}")
 
 if __name__ == "__main__":
     target = sys.argv[1] if len(sys.argv) > 1 else str(Path.home() / "aaronai" / "docs")

scripts/watcher.py

@@ -19,7 +19,6 @@ Architecture: Stage 1 (watcher) -> stage_2_queue -> Stage 2 (Mistral) -> stage_3
 import os
 import time
 import json
-import hashlib
 import logging
 import threading
 from pathlib import Path
@@ -30,9 +29,11 @@ from sentence_transformers import SentenceTransformer
 from watchdog.observers import Observer
 from watchdog.events import FileSystemEventHandler
 
-from docx import Document as DocxDocument
-from pypdf import PdfReader
-from pptx import Presentation
+from encoding import extract_text, chunk_and_embed, write_embeddings_batch, SUPPORTED
+from failures import (
+    record_ingest_failure as _record_failure_sql,
+    resolve_ingest_failure as _resolve_failure_sql,
+)
 
 load_dotenv(Path.home() / "aaronai" / ".env", override=True)
 
@@ -42,10 +43,7 @@ STATE_FILE     = "/home/aaron/aaronai/watcher_state.json"
 STATUS_FILE    = "/home/aaron/aaronai/watcher_status.json"
 HEARTBEAT_FILE = "/home/aaron/aaronai/watcher_heartbeat"
 
-SUPPORTED        = {".pdf", ".docx", ".pptx", ".txt", ".md"}
 DEBOUNCE_SECONDS = 120
-CHUNK_SIZE       = 500
-CHUNK_OVERLAP    = 50
 EMBED_MODEL      = "all-MiniLM-L6-v2"
 
 PG_DSN = os.getenv("PG_DSN")
@@ -76,49 +74,6 @@ def get_pg():
     return psycopg2.connect(PG_DSN)
 
 
-def extract_text(path: Path) -> str:
-    suffix = path.suffix.lower()
-    try:
-        if suffix == ".docx":
-            doc = DocxDocument(path)
-            return "\n".join(p.text for p in doc.paragraphs if p.text.strip())
-        elif suffix == ".pdf":
-            reader = PdfReader(path)
-            return "".join(
-                page.extract_text() + "\n"
-                for page in reader.pages if page.extract_text()
-            )
-        elif suffix == ".pptx":
-            prs = Presentation(path)
-            return "\n".join(
-                shape.text for slide in prs.slides
-                for shape in slide.shapes
-                if hasattr(shape, "text") and shape.text.strip()
-            )
-        elif suffix in {".txt", ".md"}:
-            return path.read_text(encoding="utf-8", errors="ignore")
-    except Exception as e:
-        log.warning(f"Text extraction failed for {path.name}: {e}")
-        record_ingest_failure(path, f"Text extraction failed: {e}")
-    return ""
-
-
-def chunk_text(text: str) -> list:
-    words = text.split()
-    chunks = []
-    start = 0
-    while start < len(words):
-        chunk = " ".join(words[start:start + CHUNK_SIZE])
-        if chunk.strip():
-            chunks.append(chunk)
-        start += CHUNK_SIZE - CHUNK_OVERLAP
-    return chunks
-
-
-def make_chunk_id(filepath: Path, chunk_index: int) -> str:
-    return hashlib.md5(str(filepath).encode()).hexdigest()[:8] + f"_{chunk_index}"
-
-
 def enqueue_stage2(source: str, full_text: str):
     if os.getenv("SKIP_STAGE2_ENQUEUE"):
         return
@@ -143,20 +98,14 @@ def enqueue_stage2(source: str, full_text: str):
 
 
 def record_ingest_failure(filepath: Path, error: str):
-    """Write extraction or ingest failure to ingest_failures table for UI visibility."""
+    """Write extraction or ingest failure to ingest_failures table for UI visibility.
+    Local wrapper around failures.record_ingest_failure — opens conn, delegates,
+    logs non-fatal errors so the caller never has to handle them."""
     try:
         pg = get_pg()
-        cur = pg.cursor()
-        cur.execute("""
-            INSERT INTO ingest_failures (source, filepath, error, retry_count, first_failed_at, last_failed_at)
-            VALUES (%s, %s, %s, 0, NOW(), NOW())
-            ON CONFLICT (source) DO UPDATE SET
-                error          = EXCLUDED.error,
-                retry_count    = ingest_failures.retry_count + 1,
-                last_failed_at = NOW(),
-                resolved       = FALSE
-        """, (filepath.name, str(filepath), error[:1000]))
-        pg.commit()
+        try:
+            _record_failure_sql(pg, filepath.name, filepath, error)
+        finally:
             pg.close()
     except Exception as e:
         log.warning(f"Could not record ingest failure (non-fatal): {e}")
@@ -166,9 +115,9 @@ def resolve_ingest_failure(source: str):
     """Mark a previously failed file as resolved after successful ingest."""
     try:
         pg = get_pg()
-        cur = pg.cursor()
-        cur.execute("UPDATE ingest_failures SET resolved = TRUE WHERE source = %s", (source,))
-        pg.commit()
+        try:
+            _resolve_failure_sql(pg, source)
+        finally:
             pg.close()
     except Exception as e:
         log.warning(f"Could not resolve ingest failure record (non-fatal): {e}")
@@ -181,42 +130,37 @@ def ingest_file(filepath: Path, embedder) -> int:
         return 0
     text = extract_text(filepath)
     if not text.strip():
+        record_ingest_failure(filepath, "Text extraction failed or empty")
         return 0
-    chunks = chunk_text(text)
-    if not chunks:
-        return 0
+    folder_rel = None
     try:
-        embeddings = embedder.encode(chunks).tolist()
+        folder_rel = str(filepath.parent.relative_to(NEXTCLOUD_PATH))
+    except ValueError:
+        pass
+    try:
+        rows = chunk_and_embed(text, filepath.name, embedder,
+                               filepath=filepath, folder=folder_rel)
     except Exception as e:
         log.error(f"Embedding failed for {filepath.name}: {e}")
         record_ingest_failure(filepath, f"Embedding failed: {e}")
         return 0
+    if not rows:
+        return 0
     source = filepath.name
     try:
         pg = get_pg()
-        cur = pg.cursor()
-        for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
-            chunk_id = make_chunk_id(filepath, i)
-            cur.execute("""
-                INSERT INTO embeddings (id, document, embedding, source, type, created_at, metadata)
-                VALUES (%s, %s, %s::vector, %s, %s, NOW(), %s)
-                ON CONFLICT (id) DO UPDATE SET
-                    document  = EXCLUDED.document,
-                    embedding = EXCLUDED.embedding,
-                    source    = EXCLUDED.source,
-                    metadata  = EXCLUDED.metadata
-            """, (chunk_id, chunk, embedding, source, "document",
-                  json.dumps({"source": source, "filepath": str(filepath)})))
-        pg.commit()
+        try:
+            write_embeddings_batch(pg, rows)
+        finally:
             pg.close()
     except Exception as e:
         log.error(f"pgvector write failed for {filepath.name}: {e}")
         record_ingest_failure(filepath, f"pgvector write failed: {e}")
         return 0
-    log.info(f"Indexed {len(chunks)} chunks: {filepath.name}")
+    log.info(f"Indexed {len(rows)} chunks: {filepath.name}")
     resolve_ingest_failure(source)
     enqueue_stage2(source, text)
-    return len(chunks)
+    return len(rows)
 
 
 def ingest_files(paths: list, embedder, state: dict) -> dict: