Building RAG Apps on Snowflake

We’ve covered Cortex Basics and Vector Search. Now, let’s combine them to build the “Hello World” of the AI era: A Retrieval-Augmented Generation (RAG) application.

Imagine a chatbot that knows your company’s internal documentation (which ChatGPT knows nothing about).

The Architecture#

1. Knowledge Base: Your internal PDFs/Docs stored in a Snowflake Stage.
2. Ingestion Pipeline: Directory tables + Document AI to extract text -> Chunking -> Embedding -> Cortex Search Service.
3. App Interface: Streamlit in Snowflake.
4. Brain: Cortex COMPLETE() function.

Step 1: The Retrieval (R)#

When a user asks “How do I reset my VPN?”, we don’t send that to the LLM yet. We send it to Cortex Search.

# Pseudo-code for Streamlit
user_query = st.text_input("Ask a question")

# Search for relevant chunks using the correct function
search_results = session.sql(f"""
    SELECT chunk_text
    FROM TABLE(
        my_db.my_schema.my_search_svc!SEARCH(
            QUERY => '{user_query}',
            COLUMNS => ARRAY_CONSTRUCT('chunk_text'),
            LIMIT => 3
        )
    )
""").collect()

Step 2: The Augmentation (A)#

We take the chunks we found (the context) and glue them to the user’s question.

context_str = "\n".join([row['CHUNK_TEXT'] for row in search_results])

prompt = f"""
You are a helpful IT assistant. Answer the user question using ONLY the context provided below.
If the answer is not in the context, say "I don't know".

Context:
{context_str}

Question:
{user_query}
"""

Step 3: The Generation (G)#

Now we send the augmented prompt to the LLM.

response = session.sql(f"SELECT SNOWFLAKE.CORTEX.COMPLETE('llama3-70b', '{prompt}')").collect()[0][0]
st.write(response)

Why this changes everything#

This snippet of code replaces what used to be a massive architecture involving LangChain, Pinecone, OpenAI APIs, and complex networking. In Snowflake, it’s 20 lines of Python, runs securely inside your VPC boundary, and respects your data governance.

Conclusion#

RAG is the bridge between the reasoning power of public LLMs and the proprietary value of your private data. Snowflake makes crossing that bridge easier than anywhere else.