Delete app/search_engine.py

app/search_engine.py

@@ -1,54 +0,0 @@
-import numpy as np
-from typing import List, Tuple
-
-from .similarity import cosine_similarity
-from .vectorizer import Vectorizer
-import logging
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class PromptSearchEngine:
-    def __init__(self):
-        self.vectorizer = Vectorizer(init_pinecone=False)
-        self.vectorizer._data_loaded = True
-        self.prompts = self.vectorizer.prompts
-        self.corpus_vectors = self.vectorizer.transform(self.prompts)
-        self.index_name = self.vectorizer.pinecone_index_name
-
-    def most_similar(self, query: str, n: int = 5, use_pinecone=True) -> List[Tuple[float, str]]:
-        logger.info(f"Encoding query: {query}")
-        query_vector = self.vectorizer.transform([query])[0]
-        logger.info(f"Encoded query vector: {query_vector}")
-        if use_pinecone:
-            logger.info(f"I'm doing pinecone vector search because the use_pinecone is: {use_pinecone}")
-            try:
-                # Convert numpy array to list of native Python floats
-                query_vector_list = query_vector.tolist()
-                search_result = self.vectorizer.index.query(
-                    vector=query_vector_list,
-                    top_k=n,
-                    include_metadata=True
-                )
-                logger.info(f"Search result: {search_result}")
-
-                # Retrieve and format the results
-                results = [(match['score'], match['metadata']['text']) for match in search_result['matches'] if
-                           'text' in match['metadata']]
-            except Exception as e:
-                logger.error(f"Pinecone query failed: {e}")
-                logger.info("Falling back to cosine similarity search.")
-
-                # Fallback to cosine similarity search
-                similarities = cosine_similarity(query_vector, self.corpus_vectors)
-                top_n_indices = np.argsort(similarities)[-n:][::-1]
-                results = [(float(similarities[i]), self.prompts[i]) for i in top_n_indices]
-        else:
-            logger.info(f"I'm cosine similarity search because the use_pinecone is: {use_pinecone}")
-            logger.info("Using cosine similarity for search")
-            similarities = cosine_similarity(query_vector, self.corpus_vectors)
-            top_n_indices = np.argsort(similarities)[-n:][::-1]
-            results = [(float(similarities[i]), self.prompts[i]) for i in top_n_indices]
-        return results