import streamlit as st from models.prompt_search_engine import PromptSearchEngine from models.data_reader import load_prompts_from_jsonl # Cache the prompts data to avoid reloading every time @st.cache_data def load_prompts(): prompt_path = "data/prompts_data.jsonl" return load_prompts_from_jsonl(prompt_path) # Cache the search engine initialization @st.cache_resource def get_search_engine(): search_engine = PromptSearchEngine() prompts = load_prompts() search_engine.add_prompts_to_vector_database(prompts) return search_engine # Initialize search engine only once search_engine = get_search_engine() # Streamlit App Interface st.title("Prompt Search Engine") st.write("Search for similar prompts using the local search engine.") # Input for the user's prompt query_input = st.text_input("Enter your prompt:") # Number of similar prompts to retrieve (k) k = st.number_input("Number of similar prompts to retrieve:", min_value=1, max_value=10, value=3) # Button to trigger search if st.button("Search Prompts"): if query_input: print(f'Search engine is searching the most similar prompts for query {query_input}') similar_prompts, distances = search_engine.most_similar(query_input, top_k=k) print(f'Those are: {similar_prompts}, {distances}') # Format and display search results st.write(f"Search Results: ") for i, (prompt, distance) in enumerate(zip(similar_prompts, distances)): st.write(f"{i+1}. Prompt: {prompt}, Distance: {distance}") print(f'Those are: {prompt}, {distance}') else: st.error("Please enter a prompt.") # Additional functionality for vector similarity st.write("---") st.write("### Vector Similarities") if st.button("Retrieve All Vector Similarities"): if query_input: query_embedding = search_engine.model.encode([query_input]) # Encode the prompt to a vector all_similarities = search_engine.cosine_similarity(query_embedding, search_engine.index) st.write(f"Vector Similarities: {all_similarities}") else: st.error("Please enter a prompt.")