Add ikario_processual with David profile and embedding script

- david_profile_declared.json: David's declared profile values from questionnaire
- scripts/embed_david.py: Python script to generate embeddings using BGE-M3 model
- questionnaire_david.md: Questionnaire template for profile values

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

ikario_processual/scripts/embed_david.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+"""
+Script to generate David's embedding from his messages.
+Returns JSON with the embedding vector.
+
+Usage:
+    python scripts/embed_david.py "concatenated text of david's messages"
+
+Output (JSON):
+    {"vector": [0.1, 0.2, ...], "dimension": 1024}
+"""
+
+import sys
+import json
+from sentence_transformers import SentenceTransformer
+
+def main():
+    if len(sys.argv) < 2:
+        print(json.dumps({"error": "No text provided"}))
+        sys.exit(1)
+
+    text = sys.argv[1]
+
+    if len(text) < 10:
+        print(json.dumps({"error": "Text too short"}))
+        sys.exit(1)
+
+    try:
+        # Load BGE-M3 model (same as used for Ikario's embeddings)
+        model = SentenceTransformer('BAAI/bge-m3')
+
+        # Generate embedding
+        vector = model.encode(text, normalize_embeddings=True)
+
+        # Return as JSON
+        result = {
+            "vector": vector.tolist(),
+            "dimension": len(vector)
+        }
+        print(json.dumps(result))
+
+    except Exception as e:
+        print(json.dumps({"error": str(e)}))
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()