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| import streamlit as st | |
| import os | |
| from pinecone import Pinecone | |
| from sentence_transformers import SentenceTransformer | |
| # import torch | |
| # device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| model = SentenceTransformer('intfloat/e5-small') | |
| # Set up the Streamlit app | |
| st.set_page_config(page_title="Search Engine", layout="wide") | |
| # Set up the Streamlit app title and search bar | |
| st.title("Search Engine") | |
| with st.sidebar: | |
| with st.form("my_form"): | |
| st.write("Login to Search Engine") | |
| index_name = st.text_input("Enter a database name:", "") | |
| key = st.text_input("Enter a key:", "") | |
| namespace = st.text_input("Enter a table name:", "") | |
| # slider_val = st.slider("Form slider") | |
| # checkbox_val = st.checkbox("Form checkbox") | |
| # Every form must have a submit button. | |
| submitted = st.form_submit_button("Connect to My Search Engine") | |
| if submitted: | |
| # if st.button("Connect to Search Engine Database", type="primary"): | |
| # index_name = st.text_input("Enter a database name:", "") | |
| # key = st.text_input("Enter a key:", "") | |
| # namespace = st.text_input("Enter a table name:", "") | |
| # # initialize connection to pinecone (get API key at app.pinecone.io) | |
| api_key = os.environ.get('PINECONE_API_KEY') or key | |
| # configure client | |
| pc = Pinecone(api_key=api_key) | |
| from pinecone import ServerlessSpec | |
| cloud = os.environ.get('PINECONE_CLOUD') or 'aws' | |
| region = os.environ.get('PINECONE_REGION') or 'us-east-1' | |
| spec = ServerlessSpec(cloud=cloud, region=region) | |
| # connect to index | |
| index = pc.Index(index_name) | |
| st.write('Successfully connected to your Search Engine DB!') | |
| st.write('Start searching...') | |
| query = st.text_input("Enter a search query:", "") | |
| # If the user has entered a search query, search the Pinecone index with the query | |
| if query: | |
| # Upsert the embeddings for the query into the Pinecone index | |
| query_embeddings = model.encode(query).tolist() | |
| # now query | |
| xc = index.query(vector=query_embeddings, top_k=10, namespace=namespace, include_metadata=True) | |
| # Display the search results | |
| st.write(f"Search results for '{query}':") | |
| for result in xc['matches']: | |
| st.write(f"{round(result['score'], 2)}: {result['metadata']['meta_text']}") |