Features
- Advanced RAG with multiple retrieval strategies
- Data connectors for 160+ data sources
- Query engines for structured and unstructured data
- Agentic RAG with tool-augmented retrieval
Pros
- Best-in-class RAG framework with advanced strategies
- Widest range of data source connectors
- Strong focus on data quality and retrieval accuracy
Cons
- Primarily Python-focused, JS version less mature
- Can be complex for simple RAG use cases
- Heavy dependency tree
Overview
LlamaIndex is a data framework designed specifically for building RAG (Retrieval-Augmented Generation) applications. While LangChain provides a general-purpose LLM framework, LlamaIndex focuses deeply on the data connection problem: how to ingest, structure, index, and retrieve data from various sources for LLM consumption.
LlamaIndex provides data connectors for 160+ sources (databases, APIs, file formats, websites), multiple indexing strategies (vector, keyword, knowledge graph), and advanced retrieval methods (hybrid search, re-ranking, recursive retrieval). This makes it the most specialized tool for building applications that need to answer questions over custom data.
The framework supports agentic RAG patterns where an LLM agent decides which data sources to query and how to combine results, going beyond simple vector similarity search.
When to Use
Choose LlamaIndex when building RAG applications where retrieval quality is paramount. It excels at complex data ingestion pipelines and advanced retrieval strategies like hybrid search, re-ranking, and multi-source retrieval.
Getting Started
pip install llama-indexfrom llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is this document about?")