Building a Specialized RAG-Based AI Agent with LangDB.ai and LlamaIndex

Introduction

In today's GenAI-driven landscape, enterprises increasingly integrate multiple AI agents to optimize decision-making, automate workflows, and enhance productivity. A key challenge is managing diverse AI models and libraries in a seamless, scalable way.

LangDB.ai simplifies this by offering an intelligent AI Gateway that integrates effortlessly with popular AI frameworks.

In this guide, we'll explore how to leverage LangDB.ai with LlamaIndex to build a powerful, enterprise-ready AI solution that can store and retrieve knowledge efficiently.

By the end of this blog, you will have a fully functional Python application that queries a Large Language Model (LLM) and retrieves structured responses, paving the way for advanced enterprise AI workflows.

Table of Contents:

• Pre-requisites

• Installation

• Building a LlamaIndex-based Knowledge Store

• Configuring LangDB.ai for Scalable AI Workflows

• Do More with LangDB.ai

Feel free to jump to any section as needed. Let's dive in!

Pre-requisites

To follow along, ensure you have:

• A LangDB.ai account (Sign up here)

• A LangDB.ai API Key

• Basic understanding of LlamaIndex

Installation

First, install the necessary dependencies:

pip install llama-index
pip install openai

Building a LlamaIndex-based Knowledge Store

Step 1: Set Up Your Data Storage

Create a data directory in your root folder and add relevant documents.

Step 2: Import Required Libraries

from llama_index.llms import openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
import uuid
import os

Step 3: Load and Index Documents

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist('storage')
query_engine = index.as_query_engine()
response = query_engine.query("What are the features of LangDB?")

Explanation:

• The SimpleDirectoryReader loads all files from the data directory.

• Documents are transformed into vector embeddings and stored persistently.

• Query Engine enables us to retrieve relevant documents using an LLM-powered AI gateway.

Now, let’s supercharge this setup with LangDB.ai, one of the best artificial intelligence software solutions!

Configuring LangDB.ai for Scalable AI Workflows

LangDB.ai acts as a high-performance AI governance system, ensuring that our AI agents have structured and persistent memory.

Step 1: Create a LangDB.ai Project

1. Log in to LangDB.ai and create a new project.

2. Navigate to Manage API Keys and generate a key.

Step 2: Load Environment Variables in Python

os.environ["OPENAI_API_KEY"] = "your-langdb-api-key"
os.environ["OPENAI_API_BASE"] = "https://api.us-east-1.langdb.ai"

Step 3: Configure LangDB.ai with LlamaIndex

Settings.llm = OpenAI(
   base_url=os.getenv("OPENAI_API_BASE"),  # fetching variables declared on step 2
   api_key=os.getenv("OPENAI_API_KEY"),  # fetching variables declared on step 2
   model="gpt-4o-mini"  # Your preferred model
)

Our connection and configuration is complete.

• Let's have a look at the whole code

import os
from llama_index.llms import openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI

langdb_api_base = os.getenv("LANGDB_URL", "https://api.us-east-1.langdb.ai") ### LangDB API base URL
LANGDB_API_KEY = os.getenv("LANGDB_API_KEY")

Settings.llm = OpenAI(
   base_url=langdb_api_base,
   api_key=LANGDB_API_KEY,
   model="gpt-4o-mini"
)

documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist('storage')
query_engine = index.as_query_engine()
response = query_engine.query("what are features of langdb?")
print(response)

Output:

For multiple projects

Use project id in your langdb_api_base url if you have multiple projects in your LangDB.ai account
Below is the example:

langdb_api_base = "https://api.us-east-1.langdb.ai/your-project-id/v1"

Do more with LangDB.ai

Checkout our Youtube Video

Now that we have a structured AI-powered Specialized RAG-Based AI Agent, here’s what we will bring next:

• Build a multi-agent system with LlamaIndex + LangDB.ai Dynamic Routing

• Integrate Langchain and build a chat application with LlamaIndex and LangDB.ai

You can also read our next guide: How to use LangChain with LangDB