LlamaIndex¶

What it is¶

LlamaIndex is a data framework for LLM applications to ingest, structure, and access private or domain-specific data. It provides tools for building RAG applications, including data connectors, indexes, and query engines.

What problem it solves¶

Simplifies the process of connecting LLMs to private and domain-specific data by providing purpose-built abstractions for data ingestion, indexing, and retrieval.

Where it fits in the stack¶

AI & Knowledge — serves as a data-centric framework for building RAG pipelines over local or private data, complementing the Ollama-based LLM infrastructure.

Typical use cases¶

Building RAG applications over private document collections
Structured data extraction from documents such as invoices
Creating query engines that combine multiple data sources

Strengths¶

Purpose-built for data ingestion and retrieval, making RAG setup straightforward
Wide range of data connectors for different file formats and sources
Clean abstractions for indexing and querying

Limitations¶

Less suited for complex agent workflows compared to LangChain
Rapid development pace can lead to API changes between versions
Smaller ecosystem for non-RAG use cases

When to use it¶

When the primary goal is building RAG over private or domain-specific data
When you need structured data extraction from documents

When not to use it¶

When building complex multi-agent workflows (consider LangChain or LangGraph instead)
When the application does not involve data retrieval or indexing

Getting started¶

Installation¶

Install LlamaIndex:

pip install llama-index

Minimal Python Example¶

Minimal example to query a directory of documents:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Assumes you have a folder named 'data' with text files
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
# Note: Ensure you have a 'data' directory with documents before running
response = query_engine.query("What did the author do growing up?")
print(response)

API examples¶

Load Documents, Create Index, and Query¶

This example shows how to load data from a local directory, create a searchable index, and execute a query.

import os
from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage
)

# 1. Load data from a directory
# Assumes you have a folder named 'docs_folder' with text files
reader = SimpleDirectoryReader(input_dir="./docs_folder")
documents = reader.load_data()

# 2. Create an index from the documents
index = VectorStoreIndex.from_documents(documents)

# 3. Create a query engine
query_engine = index.as_query_engine()

# 4. Query the index
response = query_engine.query("Summarize the main points of the documentation.")
print(f"Response: {response}")

# 5. (Optional) Persist the index to disk
index.storage_context.persist(persist_dir="./storage")

# 6. (Optional) Load the index later
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context)

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-03-03
Confidence: medium