Chat with your Data (RAG)

In this tutorial, you'll learn how to use chat to converse with your data using AI.

What is RAG?

RAG (Retrieval-Augmented Generation) is a technique that combines:

1. Retrieval: Finds the most relevant information in your data

2. Augmented Generation: Uses that information as context for AI to generate a response

Result: Accurate answers based on your data, without hallucinations.

Search vs Chat: Which to Use?

Scenario	Use	Reason
Need exact excerpts	Search	Returns original chunks
Need an elaborate answer	Chat	AI synthesizes the information
Need to cite exact source	Both	Chat returns sources
High performance/low latency	Search	No LLM call
Complex questions	Chat	AI interprets and responds

Prerequisites

• A valid tenant_id (See how to find it)

• Data already ingested in the Knowledge Base

Step 1: Ask a Question

curl -X POST https://api-integrations.snackprompt.com/v1/kb/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "what are the benefits of the premium plan?",
    "filters": {
      "tenant_id": "YOUR_TENANT_ID"
    }
  }'

Step 2: Understand the Response

{
  "answer": "The Premium plan offers the following benefits:\n\n1. **24/7 Priority Support** - Response within 1 hour\n2. **Unlimited Storage** - No upload limits\n3. **Advanced API** - Access to all endpoints\n4. **Custom Reports** - Customizable dashboards\n\nThe price is $99/month with a 20% discount on the annual plan.",
  "sources": [
    {
      "id": "chunk-uuid-1",
      "score": 0.91,
      "snack_item_id": "item-plans-001",
      "snack_elemental_id": "doc-pricing-123",
      "text": "Premium Plan: 24/7 priority support, unlimited storage...",
      "tag_names": ["Plans", "Pricing"]
    },
    {
      "id": "chunk-uuid-2",
      "score": 0.85,
      "snack_item_id": "item-plans-002",
      "snack_elemental_id": "doc-pricing-123",
      "text": "Prices: Premium $99/month, 20% annual discount...",
      "tag_names": ["Plans", "Pricing"]
    }
  ]
}

Response Fields

Field	Description
answer	AI-generated answer based on your data
sources	List of sources used to generate the answer
sources[].id	Chunk ID in the vector database
sources[].score	Source relevance (0-1)
sources[].text	Original excerpt used as context
sources[].snack_item_id	Source item ID
sources[].snack_elemental_id	Source elemental ID

Step 3: Use Filters for Specific Context

You can direct chat to search in specific data:

curl -X POST https://api-integrations.snackprompt.com/v1/kb/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "what are the benefits of the premium plan?",
    "filters": {
      "tenant_id": "YOUR_TENANT_ID",
      "tag_names": ["Plans", "Commercial"]
    }
  }'

This ensures the AI only uses documents with these tags as context.

Using Streaming for Real-time Chat

For real-time chat interfaces, use the streaming endpoint:

curl -X POST https://api-integrations.snackprompt.com/v1/kb/chat/stream \
  -H "Content-Type: application/json" \
  -d '{
    "query": "what are the benefits of the premium plan?",
    "filters": {
      "tenant_id": "YOUR_TENANT_ID"
    }
  }' \
  --no-buffer

The response comes as Server-Sent Events (SSE):

data: {"event":"message","data":{"content":"The Premium"}}

data: {"event":"message","data":{"content":" plan"}}

data: {"event":"message","data":{"content":" offers"}}

data: {"event":"message","data":{"content":"..."}}

data: [DONE]

To implement streaming in the frontend, see the guide Real-time Chat.

Understanding Citations (Sources)

The sources allow you to verify where each piece of information came from:

{
  "sources": [
    {
      "snack_elemental_id": "doc-pricing-123",
      "snack_item_id": "item-plans-001",
      "text": "Original excerpt...",
      "score": 0.91
    }
  ]
}

You can use these IDs to:

• Link to the original document in your interface

• Show users the source of information

• Validate response accuracy

Tips for Better Responses

1. Be Specific in Your Question

- "tell me about plans"
+ "what are the differences between the basic and premium plans?"

2. Use Filters for Context

If you know where the information is, use filters:

{
  "query": "delivery time to New York",
  "filters": {
    "tenant_id": "...",
    "tag_names": ["Logistics", "Shipping"]
  }
}

3. Comparison Questions Work Well

"compare plan A with plan B"
"what are the advantages of X over Y?"

4. List Questions Work Well

"list the required documents for..."
"what are the steps to..."

When Chat Doesn't Find Information

If the AI doesn't find relevant information, it will respond something like:

{
  "answer": "I didn't find information about this topic in the knowledge base. Could you rephrase the question or verify if the related data has been indexed?",
  "sources": []
}

What to do:

1. Check if the data was ingested correctly

2. Try rephrasing the question

3. Remove overly restrictive filters

4. Use semantic search to explore available data

Next Steps

Now that you've mastered RAG chat:

1. Real-time Chat - Implement streaming in the frontend

2. Filter by Tags - Direct context with precision

3. Error Handling - Handle errors gracefully

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Estimated time: 10 minutes ✅