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Build an agentic RAG

This tutorial will guide you through creating, indexing, searching, and retrieving structured data. You will learn how to:

  • Create an index and upsert and manage records
  • Perform advanced search queries
  • Deploy a fully functional agentic RAG

This is useful for applications such as knowledge retrieval, content search, and structured data management.

Step 1: Set Up an Index​

Indexes provide the foundation for structured data storage and retrieval.

Define a Synthetic Dataset​

Define your data to create your index.

synthetic_data = [
    {"id": "doc1", "text": "Artificial intelligence is transforming industries worldwide.", "category": "technology"},
    {"id": "doc2", "text": "The Mona Lisa, painted by Leonardo da Vinci, is one of the most famous artworks.", "category": "art"},
    {"id": "doc3", "text": "Machine learning algorithms predict patient outcomes in hospitals.", "category": "healthcare"},
    {"id": "doc4", "text": "The Earth orbits the Sun once every 365.25 days.", "category": "science"},
]

Create an Index​

Create an index utility tool to add data.

index = IndexFactory.create("Knowledge Base Index", "Index for general knowledge retrieval")

Upsert Records​

Upsert your records into the index for fast and efficient searches:

from aixplain.modules.model.record import Record

records = [
    Record(value=item["text"], value_type="text", id=item["id"], attributes={"category": item["category"]})
    for item in synthetic_data
]

index.upsert(records)

Index ID​

Get the ID of your index model.

index.id

Refer to the Indexing and Search Guide for advanced indexing techniques and record management.

Step 2: Search with Model​

Run the code below to search the utility model with your data. This helps you understand how the search works.

import requests
import json

def search_data(text: str, category: str):
    """
    Search the index based on the given category filter.
    """
    headers = {
        "x-api-key": "your_api_key_here",
        "Content-Type": "application/json"
    }

    payload = {
        "action": "search",
        "data": text,
        "payload": {"filters": {"field": "meta.attributes.category", "operator": "==", "value": category}}
    }

    response = requests.post("https://models.aixplain.com/api/v1/execute/<model_id>", headers=headers, json=payload)
    return response.json()


result = search_data("What is the impact of AI?", "technology")
print(result)

Step 3: Build an AI Agent​

To automate queries and interactions, we create an agentic RAG.

Define the Agent​

from aixplain.factories import AgentFactory

agent = AgentFactory.create(
    name="AI Search Assistant",
    description="Searches and retrieves relevant data from indexed knowledge sources.",
    tools=[
        AgentFactory.create_model_tool(model="<model_id>"),  
    ]
)

Run the Agent​

response = agent.run(data={"query": "What are the applications of quantum computing?"})
print(response.data.get("output"))

Step 4: Deploy the AI Agent​

Once tested, deploy the agentic RAG!

agent.deploy()

You have successfully built an AI-powered search and indexing system. This setup can be extended for various applications, including chatbots, document retrieval, and intelligent search assistants. 🚀