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51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
# run_wfgy_with_embedding.py
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# Official test script for WFGY SDK demonstrating semantic residue adjustments.
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from sentence_transformers import SentenceTransformer
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from transformers import pipeline
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import numpy as np
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from wfgy_sdk import enable
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# Load Sentence-BERT model for generating semantic embeddings
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sbert = SentenceTransformer('all-MiniLM-L6-v2')
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# Load GPT-2 text-generation pipeline
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generator = pipeline("text-generation", model="distilgpt2")
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# Original prompt (challenging question for AI)
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prompt = "What is the meaning of life in 15 words or less?"
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# Generate embedding for input prompt (I)
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embedding_I = sbert.encode(prompt)
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# Define ideal semantic embedding (G) - this could be a target semantic state
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ideal_answer = "Life means finding purpose and joy in every moment."
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embedding_G = sbert.encode(ideal_answer)
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# Initialize model state dictionary with embeddings and attention logits
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model = {
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"I": embedding_I,
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"G": embedding_G,
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"state": np.copy(embedding_I),
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"attention_logits": np.random.rand(len(embedding_I))
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}
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# Run the WFGY SDK to apply BBMC, BBPF, BBCR, and BBAM sequentially
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model = enable(model)
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# Calculate adjusted semantic residue from the final SDK state
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semantic_shift_factor = np.mean(model["state"])
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# Generate a new adjusted prompt incorporating semantic residue factor
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new_prompt = prompt + f" (adjusted semantic residue: {semantic_shift_factor:.3f})"
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# Generate AI responses (before and after WFGY SDK adjustments)
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original_output = generator(prompt, max_length=50, do_sample=True)[0]['generated_text']
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adjusted_output = generator(new_prompt, max_length=50, do_sample=True)[0]['generated_text']
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# Display comparison results clearly
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print("\n=== Original Output ===")
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print(original_output)
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print("\n=== SDK Adjusted Output ===")
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print(adjusted_output)
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