Incorporate RAG Into Your Workflows

Build Your First RAG Workflow: Connecting Knowledge to Intelligence 🧠

• Now for the magic moment! You've built a workflow, created a collection, and populated it with knowledge. Let's connect them together to create an AI assistant that can answer questions using your specific documents and data.

🎯 What We're Building

• A smart Q&A system that searches your collection for relevant information and uses that context to generate accurate, source-backed answers. No more generic AI responses, your assistant will know your specific knowledge!

📋 Step-by-Step: Your First RAG Workflow

Step 1: Create Your RAG Workflow

1. Navigate to Workflows in the main menu
2. Click "+ New Workflow"
3. Name it "Knowledge Base Assistant" or "Smart Q&A Bot"

Step 2: Configure Your Input

• Remember your workflow starts with a Trigger block, for this workflow we'll use an Input Block, on your workflow canvas click on "Set a trigger" and select input. Your input block will have an input field titled "Message."

Step 3: Add a Search Table Block

1. Click the "+" button to add a new block
2. Select "Search Table Block"
3. Connect it to your Input Block
4. Configure the Search Table Block:
   • Collection: Select the collection you just created
   • Table: Choose your table (likely "Untitled" unless you renamed it)
   • Search Term (Required): {{inputs.message}} (this searches for the user's question that is passed in via the input block.)
   • Limit: 5 (returns top 5 most relevant results)
   • Similarity Score: Default value is 0.35

Understanding Similarity Scores

• When configuring your Search Table Block, you'll see a "Minimum Similarity" setting. This controls how closely related the search results need to be to your question.

How It Works:

• Range: 0.0 to 1.0 (where 1.0 is a perfect match)
• Lower values (0.2-0.4): Only return very relevant, closely matching content
• Higher values (0.6-0.8): Cast a wider net, include somewhat related content
• Default: Usually around 0.35 for balanced results

When to Adjust:

• Too few results? Increase the similarity threshold (try 0.5-0.7)
• Too many irrelevant results? Decrease the threshold (try 0.2-0.3)
• Getting off-topic answers? Lower the threshold for more precise matching

Step 4: Add Your LLM Block

1. Click "+" to add another block
2. Select "LLM Block"
3. Connect it to your Query Collection Block
4. Configure for RAG:

• LLM Block Configuration:
  • Model: Here you can select from the wide range of LLM options on Scout. Default model is GPT-4o.
  • Temperature: 0.3 (lower for more factual, consistent answers)
  • Token Limit: 500 (adjust based on desired response length)

System Message:

• Set up your system message to guide the LLM. This message should clearly explain the LLM’s role and how it should respond

You are a helpful AI assistant that answers questions based on provided documentation. Use the documentation to provide accurate, detailed answers. If the documentation doesn't contain relevant information, say so clearly.

Documentation: {{collections_v2_tables_query.output}}

Remember to make sure you change out collections_v2_tables_query for the ID in your workflow, this should match the ID of your Search Table block.

This workflow will also incorporate a User Message, your user message is the input or question that the end-user sends to the AI. Scroll down to message 2 and select "User"

• For this workflow your user message will be {{inputs.message}}

Step 5: Test Your RAG Workflow

1. Click "Publish" in the top right once all changes have been made
2. Enter a question related to your uploaded content in the workflow console on the left hand side of the screen.
3. Watch the magic happen:
   • Query Block searches your collection
   • Finds relevant documents
   • LLM generates an answer using that context

🧠 What Just Happened? The RAG Process

🔍 Retrieve:

The Query Collection Block searched your documents using semantic similarity, finding content related to your question (even if exact keywords don't match).

📄 Augment:

The retrieved context was passed to the LLM Block as additional information to inform its response.

✍️ Generate:

The LLM created an answer based on both your question AND the relevant context from your knowledge base.

🎉 Congratulations!

You've just built a complete RAG workflow!