# requires: pip install sentence-transformers
import numpy as np
from sentence_transformers import SentenceTransformer

encoder = SentenceTransformer("all-MiniLM-L6-v2")

class SemanticCache:
    def __init__(self, threshold=0.95):
        self.threshold = threshold
        self.vectors = np.empty((0, encoder.get_sentence_embedding_dimension()))
        self.prompts, self.responses = [], []

    def _embed(self, text):
        return encoder.encode([text], normalize_embeddings=True)[0]

    def lookup(self, prompt):
        vec = self._embed(prompt)
        if len(self.prompts) == 0:
            return None, 0.0, vec
        scores = self.vectors @ vec           # cosine sim, vectors are unit length
        best = int(np.argmax(scores))
        if scores[best] >= self.threshold:
            return self.responses[best], float(scores[best]), vec
        return None, float(scores[best]), vec

    def store(self, prompt, response, vec):
        self.vectors = np.vstack([self.vectors, vec])
        self.prompts.append(prompt)
        self.responses.append(response)

cache = SemanticCache(threshold=0.95)

def answer(prompt, call_model):
    hit, score, vec = cache.lookup(prompt)
    if hit is not None:
        return hit, f"HIT  (score {score:.3f})"
    response = call_model(prompt)             # the expensive path
    cache.store(prompt, response, vec)
    return response, f"MISS (best {score:.3f})"

# Stand in for the model so this runs without an API key.
fake_model = lambda p: f"<answer for {p!r}>"

for q in ["How do I reset my password?",
          "How can I reset my password?",
          "Is the API rate limited?"]:
    _, status = answer(q, fake_model)
    print(f"{status}  {q}")


# Output:
"MISS (best 0.000)  How do I reset my password?"
"HIT  (score 0.961)  How can I reset my password?"
"MISS (best 0.112)  Is the API rate limited?"