A managed ML platform can reduce your dev time from months to days. For example, let's build a production RAG app on top of your company's documents using Snowflake.
🤕 𝘛𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮?
Your company's documentation is scattered and hard to work with. You try to build a RAG application on top of it, but the development time, costs and complexity are too high.
🤔 𝘛𝘩𝘦 𝘴𝘰𝘭𝘶𝘵𝘪𝘰𝘯?
Use a fully managed platform, such as Snowflake, which handles all the infrastructure pain points while offering the flexibility to process your documentation as you see fit.
So...
💻 𝘛𝘩𝘪𝘴 𝘪𝘴 𝘩𝘰𝘸 𝘺𝘰𝘶 𝘪𝘮𝘱𝘭𝘦𝘮𝘦𝘯𝘵 𝘢 𝘙𝘈𝘎 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘶𝘴𝘪𝘯𝘨 𝘚𝘯𝘰𝘸𝘧𝘭𝘢𝘬𝘦 ↓
Any RAG architecture contains 3 pipelines + a Chatbot UI. Here is how it looks like implemented in Snowflake:
𝗧𝗵𝗲 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: A batch pipeline used to populate the vector DB.
1. Access your company documents stored in Snowflake (e.g., PDFs)
2. Extract the text from the PDFs, clean and chunk it using Snowpark.
3. Embed each chunk using an embedding model hosted on Snowflake Cortex AI.
4. Store the embeddings, along with their metadata, in Snowflake's vector DB.
5. Schedule the ingestion pipeline to check for new documents every 10 minutes and load them into the vector DB.
𝗧𝗵𝗲 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Queries the vector DB and retrieves relevant data to the user's input.
6. Takes user inputs from the Streamlit chatbot UI.
7. Compute the query embedding using the same model hosted on Snowflake Cortex AI.
8. Query the vector DB using the query embedding and return top K similar chunks along with their metadata.
𝗧𝗵𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲: Takes the user's input and the retrieved context, builds the prompt and passes it to an LLM.
9. Using the prompt template, the latest user input and the retrieved chunks as context, create the prompt.
10. Input the prompt to the LLM hosted on Cortex AI (e.g., llama3, mistral, arctic) and return the generated answer to the Streamlit UI.
→ All of this sits directly in Snowflake. Even the Streamlit UI.
As we leverage a fully managed platform, there are no more headaches on:
- Storing and processing your data using Snowpark
- Hosting and scaling your embedding models and LLMs on Snowflake Cortex AI
- Deploying your RAG app as a Streamlit application
All of this can reduce the development time from months to days.
What is your experience building on fully managed platforms vs. building from scratch using multiple tools?
To learn how to build the RAG app from this post using Snowflake, consider checking this article:
↳🔗https://lnkd.in/e3VaFT_Q
#sponsored #machinelearning #artificialintelligence #llm
