Streamlit in Snowflake

1. In Snowsight for your Snowflake account, on the sidebar, click Projects > Streamlit.
2. Click + Streamlit App.
3. For App title, enter a name for your app, such as Unstructured Demo Streamlit App.
4. For App location, chose the target database and schema to store the app in.
5. For App warehouse, choose the warehouse that you want to use to run your app and execute its queries.
6. Click Create.

In this step, you add Python code to the Streamlit app that you created in the previous step.

This step explains each part of the code as you add it. If you want to skip past these explanations, add the code in the complete code example all at once, and then skip ahead to the next step, “Run the Streamlit app.”

1. Import Python dependencies that get the current connection to the Snowflake database and schema and get Streamlit functions and features.

   from snowflake.snowpark.context import get_active_session
   import streamlit as st
   

2. Get the current connection to the Snowflake database and schema.

   session = get_active_session() 
   

3. Display the title of the app in the Streamlit UI, and get the user’s search query from the Streamlit UI.

   st.title("Snowflake Cortex Search for RAG with Data from Unstructured")
   
   query = st.text_input("Enter your search query:")
   

4. Get the user’s search query and display a progress indicator in the UI.

   if query:
       with st.spinner("Embedding and retrieving..."):
   

5. Use the user’s search query to get the top result from the ELEMENTS table. The ELEMENTS table contains the data that was generated by Unstructured. The code uses the SNOWFLAKE.CORTEX.EMBED_TEXT_1024 function to generate vector embeddings for the user’s search query and the VECTOR_COSINE_SIMILARITY function to get the similarity between the vector embeddings for the user’s search query and the vector embeddings for the TEXT column for each rown in the ELEMENTS table. The code then orders the results by similarity and limits the results to the row with the greatest similarity between the search query and the target text.

           top_result_df = session.sql(f"""
               WITH query_embedding AS (
                   SELECT SNOWFLAKE.CORTEX.EMBED_TEXT_1024(
                       'snowflake-arctic-embed-l-v2.0', '{query}'
                   ) AS EMBED
               )
               SELECT 
                   e.TEXT,
                   VECTOR_COSINE_SIMILARITY(e.EMBEDDINGS, q.EMBED) AS similarity
               FROM ELEMENTS e, query_embedding q
               ORDER BY similarity DESC
               LIMIT 1
           """).to_pandas()
   

6. Get the TEXT column from the top result and use it as context for the user’s search query.

           context = top_result_df["TEXT"][0]
   

7. Use the user’s search query and the context from the top result to get a response from Snowflake Cortex Search for RAG. The code uses the SNOWFLAKE.CORTEX.COMPLETE function to generate a response to the user’s search query based on the context from the top result.

           completion_df = session.sql(f"""
               SELECT SNOWFLAKE.CORTEX.COMPLETE(
                   'snowflake-arctic',
               CONCAT('Context: ', $$ {context} $$, ' \\n\\nQuestion: {query}\\nAnswer:')
           ) AS RESPONSE
           """).to_pandas()
   

8. Display the generated response in the Streamlit UI.

           st.write("Answer:")
           st.write(completion_df["RESPONSE"][0])
   

1. In the upper right corner, click Run.
2. For Enter your search query, enter some natural-language question about the TEXT column in the table.
3. Press Enter.

Snowflake Cortex Search for RAG returns its answer to your question in natural-language, chatbot-style format.