For our entire journey so far, we’ve focused on the “agent”—the reasoning loop, the tools, the planner. Our framework of choice, LangChain, is a powerful “agent-first” toolkit, a general-purpose workshop for building any kind of AI application.
But what if we’ve been looking at the problem from only one angle? What if, instead of starting with the agent’s brain, we started with the data it needs to think about?
This is the world of LlamaIndex. It is a data framework for LLM applications. While it can build agents, its heart and soul are dedicated to solving one problem exceptionally well: connecting your private data to LLMs with maximum performance and precision. This is its “data-first” philosophy.
LangChain vs. LlamaIndex: A Shift in Focus
To understand LlamaIndex, it helps to contrast its core philosophy with LangChain’s.
- LangChain (Agent-First): LangChain starts with the agent as the central actor. The reasoning loop is the primary focus. Data sources, like vector databases, are treated as Tools that the agent can choose to use among many others. The key question is: “How can I build a smarter agent?”
- LlamaIndex (Data-First): LlamaIndex starts with your data as the central actor. The framework is built around optimizing the entire pipeline of ingesting, indexing, and retrieving information. The LLM and agentic logic are powerful components that sit on top of this data layer. The key question is: “How can I provide the highest quality data to the LLM?”
It’s a subtle but profound difference that leads to different strengths and a unique set of core concepts.
The Core Concepts of LlamaIndex
1. Nodes
In LangChain, we have “Documents.” In LlamaIndex, the atomic unit of data is a Node. A Node is a “chunk” of a source document, but it’s richer. It contains not only the text but also metadata (e.g., the source file name, creation date) and relationships to other Nodes (e.g., previous_node, next_node).
Analogy: A Node is like a smart index card. It doesn’t just have a snippet of text; it has the text, the book it came from, the page number, and pointers to the cards for the previous and next pages.
2. Indexes
An Index is a data structure that organizes your Nodes for efficient retrieval. While the most common type is a VectorStoreIndex (which is what we know from RAG), LlamaIndex’s data-first approach provides many different indexing strategies for different use cases, such as:
ListIndex: Optimized for querying over sequential data.KeywordTableIndex: Based on extracting keywords, excellent for specific term lookups.TreeIndex: Builds a tree hierarchy over nodes, great for summarization.
Analogy: An Index is the card catalog system in the library. A vector index organizes the cards by meaning, while a keyword index organizes them alphabetically.
3. Retrievers
A Retriever is the engine that fetches relevant Nodes from an Index based on a query. This is where LlamaIndex truly shines. It offers highly advanced retrieval strategies that go far beyond simple similarity search. For example, it can perform “fusion retrieval” (combining vector search with keyword search) or retrieve smaller, more focused chunks that link back to larger parent chunks for better context.
Analogy: A Retriever is a skilled librarian. A basic retriever just finds the book you asked for. An advanced LlamaIndex retriever might find that book, plus a related journal article, rank them by relevance, and synthesize the key points for your specific question.
4. Query Engines
A Query Engine is the high-level, end-to-end interface for asking questions of your data. It bundles a Retriever and an LLM into a seamless pipeline that takes your natural language query and returns a synthesized answer.
The Flow:
User Query -> Retriever fetches relevant Nodes -> Nodes + Query sent to LLM -> LLM generates final answer
Analogy: A Query Engine is the entire “Ask a Librarian” service. You go to the desk, ask your question, and the librarian (Retriever) finds the sources, reads them, and gives you a complete, synthesized answer (LLM).
“Hello, LlamaIndex!” – A Simple Example
Let’s see how these concepts come together. LlamaIndex excels at creating a powerful RAG pipeline with very little code.
First, install the necessary libraries:
pip install llama-index llama-index-llms-openaiNow, let’s build a Q&A system over a local text file.
# main.py
import os
from dotenv import load_dotenv
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
load_dotenv() # Make sure your OPENAI_API_KEY is in your .env file
# 1. Create a 'data' folder for your knowledge base
if not os.path.exists("data"):
os.makedirs("data")
with open("data/Sodepur_info.txt", "w", encoding="utf-8") as f:
f.write("Sodepur is a historical city and a municipality of North 24 Parganas "
"district in the Indian state of West Bengal. It is a part of the area "
"covered by Kolkata Metropolitan Development Authority (KMDA).\n")
f.write("Sodepur is known for its historical temples, including the Sodepureshwari Kali Mandir, "
"which is a prominent Kali template.")
# 2. Load the data. SimpleDirectoryReader handles loading and parsing.
documents = SimpleDirectoryReader("./data").load_data()
# 3. Create the Index. This handles chunking, embedding, and storing in-memory.
index = VectorStoreIndex.from_documents(documents)
# 4. Create the Query Engine. This bundles the retriever and LLM.
query_engine = index.as_query_engine()
# 5. Query your data! This runs the full RAG pipeline.
response = query_engine.query("What is Sodepur known for?")
print(response)Output:
Sodepur is known for its historical temples, with the Sodepureshwari Kali Mandir being a notable Kali temple.
In just a few lines of code, LlamaIndex has built a complete, high-performance RAG pipeline for us.
Conclusion: Which One Should You Use?
LangChain and LlamaIndex are not competitors; they are two powerful frameworks with different centers of gravity.
- Start with LlamaIndex if: Your application is primarily a knowledge-intensive Q&A system. If your main challenge is getting high-quality answers from your own documents, LlamaIndex’s advanced indexing and retrieval capabilities are unparalleled.
- Start with LangChain if: Your application is primarily an action-oriented agent. If your main challenge is building complex, multi-tool workflows, and RAG is just one of many tools the agent might use, LangChain’s agent-centric approach is a more natural fit.
The best part is that they are highly interoperable. You can easily wrap a LlamaIndex Query Engine and use it as a custom Tool inside a LangChain agent, getting the best of both worlds. By understanding LlamaIndex’s data-first philosophy, you’ve added a powerful new perspective and a specialized toolkit to your AI development arsenal.
Author
Experienced Cloud & DevOps Engineer with hands-on experience in AWS, GCP, Terraform, Ansible, ELK, Docker, Git, GitLab, Python, PowerShell, Shell, and theoretical knowledge on Azure, Kubernetes & Jenkins. In my free time, I write blogs on ckdbtech.com