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/coreml_embed_server.py

@@ -0,0 +1,78 @@
+"""
+Simple Flask-like HTTP server for CoreML ANE embedding inference.
+Replaces /api/embeddings endpoint that comic_embed.rs calls.
+"""
+import json, os, argparse
+from http.server import HTTPServer, BaseHTTPRequestHandler
+import numpy as np
+from transformers import AutoTokenizer
+
+# Global model
+MODEL = None
+TOKENIZER = None
+MODEL_PATH = "/Users/accusys/models/mxbai-embed-large-v1.mlpackage"
+
+class EmbeddingHandler(BaseHTTPRequestHandler):
+    def do_POST(self):
+        if self.path == "/api/embeddings":
+            length = int(self.headers.get("Content-Length", 0))
+            body = self.read(length)
+            try:
+                data = json.loads(body)
+                prompt = data.get("prompt", "")
+                # Strip search_document: or search_query: prefix
+                if prompt.startswith("search_document: "):
+                    prompt = prompt[17:]
+                elif prompt.startswith("search_query: "):
+                    prompt = prompt[14:]
+                
+                tokens = TOKENIZER(prompt, return_tensors="np", padding="max_length", truncation=True, max_length=512)
+                input_ids = tokens["input_ids"].astype(np.int32)
+                attention_mask = tokens["attention_mask"].astype(np.int32)
+                result = MODEL.predict({"input_ids": input_ids, "attention_mask": attention_mask})
+                embedding = result["embedding"][0].tolist()
+                
+                resp = json.dumps({"embedding": embedding}).encode()
+                self.send_response(200)
+                self.send_header("Content-Type", "application/json")
+                self.end_headers()
+                self.wfile.write(resp)
+            except Exception as e:
+                resp = json.dumps({"error": str(e)}).encode()
+                self.send_response(500)
+                self.send_header("Content-Type", "application/json")
+                self.end_headers()
+                self.wfile.write(resp)
+        else:
+            self.send_response(404)
+            self.end_headers()
+    
+    def read(self, length):
+        return self.rfile.read(length)
+
+def main():
+    global MODEL, TOKENIZER
+    
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int, default=11435)
+    parser.add_argument("--model", default=MODEL_PATH)
+    args = parser.parse_args()
+    
+    import coremltools as ct
+    print(f"Loading CoreML model from {args.model}...")
+    MODEL = ct.models.MLModel(args.model, compute_units=ct.ComputeUnit.ALL)
+    print(f"Model loaded (compute: {MODEL.compute_unit})")
+    
+    print("Loading tokenizer...")
+    TOKENIZER = AutoTokenizer.from_pretrained("mixedbread-ai/mxbai-embed-large-v1")
+    print("Tokenizer loaded")
+    
+    server = HTTPServer(("127.0.0.1", args.port), EmbeddingHandler)
+    print(f"ANE Embedding server running on port {args.port}")
+    print(f"API: POST http://127.0.0.1:{args.port}/api/embeddings")
+    print(f"     Body: {{\"model\": \"...\", \"prompt\": \"...\"}}")
+    print(f"     Response: {{\"embedding\": [...]}}")
+    server.serve_forever()
+
+if __name__ == "__main__":
+    main()