feat: Phase 1 handover - schema migration, correction mechanism, API fixes

Schema changes: dev.chunks->dev.chunk, remove old_chunk_id/chunk_index
Correction: asr-1.json format, generate/apply scripts
API: 37/37 endpoints fixed and tested
Docs: HANDOVER_V2.0.md for M4

scripts/story_embed.py

@@ -0,0 +1,87 @@
+#!/opt/homebrew/bin/python3.11
+"""
+Story Embedding Pipeline:
+1. Read story chunks → LLM summary (Gemma4)
+2. Embed summary (EmbeddingGemma)
+3. Store in chunks table + Qdrant
+"""
+
+import json, urllib.request, subprocess, sys, time, os
+
+UUID = "aeed71342a899fe4b4c57b7d41bcb692"
+PSQL = ["/Users/accusys/pgsql/18.3/bin/psql", "-U", "accusys", "-d", "momentry", "-t", "-A"]
+LLM_URL = "http://localhost:8082/v1/chat/completions"
+EMBED_URL = "http://localhost:11436/v1/embeddings"
+QDRANT_URL = "http://localhost:6333"
+QDRANT_COL = "momentry_dev_stories"
+
+def psql(sql):
+    r = subprocess.run(PSQL + ["-c", sql], capture_output=True, text=True, timeout=30)
+    return r.stdout.strip()
+
+def call_llm(dialogue):
+    prompt = f"Dialogue: {dialogue}\n\n50-word summary:"
+    body = json.dumps({"model": "google_gemma-4-26B-A4B-it-Q5_K_M.gguf",
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.1, "max_tokens": 100}).encode()
+    req = urllib.request.Request(LLM_URL, data=body, headers={"Content-Type": "application/json"})
+    resp = urllib.request.urlopen(req, timeout=120)
+    return json.loads(resp.read())["choices"][0]["message"]["content"].strip()
+
+def call_embed(text):
+    body = json.dumps({"input": text}).encode()
+    req = urllib.request.Request(EMBED_URL, data=body, headers={"Content-Type": "application/json"})
+    resp = urllib.request.urlopen(req, timeout=30)
+    return json.loads(resp.read())["data"][0]["embedding"]
+
+# Step 0: Ensure Qdrant collection exists (768 dims)
+subprocess.run(["curl", "-s", "-X", "PUT", f"{QDRANT_URL}/collections/{QDRANT_COL}",
+    "-H", "Content-Type: application/json",
+    "-d", '{"vectors":{"size":768,"distance":"Cosine"}}'], capture_output=True)
+
+# Step 1: Get all story chunks that need summaries
+lines = [l for l in psql(f"SELECT chunk_id, chunk_index, start_time, end_time, text_content FROM dev.chunks WHERE file_uuid='{UUID}' AND chunk_type='story' AND (summary_text IS NULL OR summary_text = '') ORDER BY chunk_index").split('\n') if l.strip() and '|' in l]
+
+print(f"Chunks to process: {len(lines)}")
+total = len(lines)
+errors = 0
+
+for i, line in enumerate(lines):
+    parts = line.split('|', 4)
+    cid, idx, st, et, dialogue = parts[0].strip(), int(parts[1]), float(parts[2]), float(parts[3]), parts[4] if len(parts) > 4 else ""
+    
+    if len(dialogue) < 10:
+        summary = "[no dialogue]"
+        embedding = [0.0] * 768
+    else:
+        try:
+            summary = call_llm(dialogue)
+            time.sleep(0.3)
+            embedding = call_embed(summary)
+        except Exception as e:
+            print(f"[{i+1}/{total}] Error: {cid} - {e}")
+            errors += 1
+            summary = "[error]"
+            embedding = [0.0] * 768
+    
+    # Update DB
+    s_esc = summary.replace("'", "''")
+    psql(f"UPDATE dev.chunks SET summary_text='{s_esc}', updated_at=CURRENT_TIMESTAMP WHERE chunk_id='{cid}'")
+    
+    # Store in Qdrant
+    point = json.dumps({"points": [{"id": idx + 1, "vector": embedding,
+        "payload": {"chunk_id": cid, "file_uuid": UUID, "start_time": st, "end_time": et,
+                     "summary": summary, "type": "story_summary"}
+    }]}).encode()
+    req = urllib.request.Request(f"{QDRANT_URL}/collections/{QDRANT_COL}/points?wait=true",
+        data=point, headers={"Content-Type": "application/json"}, method="PUT")
+    try:
+        urllib.request.urlopen(req, timeout=10)
+    except:
+        pass
+    
+    if (i+1) % 20 == 0:
+        print(f"[{i+1}/{total}] {errors} errors so far")
+
+print(f"\nDone. Processed: {total}, Errors: {errors}")
+print(f"Qdrant: {QDRANT_COL}")