David Blanc Brioir
21f5676c7b
- 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>
48 lines
1.1 KiB
Python
48 lines
1.1 KiB
Python
#!/usr/bin/env python3
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"""
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Script to generate David's embedding from his messages.
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Returns JSON with the embedding vector.
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Usage:
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python scripts/embed_david.py "concatenated text of david's messages"
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Output (JSON):
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{"vector": [0.1, 0.2, ...], "dimension": 1024}
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"""
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import sys
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import json
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from sentence_transformers import SentenceTransformer
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def main():
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if len(sys.argv) < 2:
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print(json.dumps({"error": "No text provided"}))
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sys.exit(1)
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text = sys.argv[1]
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if len(text) < 10:
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print(json.dumps({"error": "Text too short"}))
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sys.exit(1)
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try:
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# Load BGE-M3 model (same as used for Ikario's embeddings)
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model = SentenceTransformer('BAAI/bge-m3')
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# Generate embedding
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vector = model.encode(text, normalize_embeddings=True)
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# Return as JSON
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result = {
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"vector": vector.tolist(),
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"dimension": len(vector)
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}
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print(json.dumps(result))
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except Exception as e:
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print(json.dumps({"error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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