LlamaIndex — Data Framework for LLM Applications

What it is

LlamaIndex is a data framework that connects your private data to large language models. It provides tools for document ingestion, indexing, retrieval-augmented generation (RAG), knowledge graph construction, and structured data extraction. You feed it documents, databases, or APIs, and it creates queryable indexes that LLMs use to answer questions grounded in your data.

LlamaIndex targets developers building AI applications that need to work with private or domain-specific data. Instead of fine-tuning a model, you use LlamaIndex to retrieve relevant context at query time and inject it into the LLM prompt.

Why it saves time or tokens

Naive RAG implementations stuff entire documents into the prompt, wasting tokens on irrelevant content. LlamaIndex's indexing and retrieval pipeline extracts only the relevant chunks, reducing per-query token consumption. Its chunking strategies, reranking, and metadata filtering ensure the LLM receives high-quality context, producing better answers with fewer tokens.

How to use

1. Install LlamaIndex: pip install llama-index
2. Load documents using one of 300+ data connectors (PDF, web, database, API)
3. Create an index and query it with natural language

Example

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load documents from a directory
documents = SimpleDirectoryReader('./data').load_data()

# Create a vector index
index = VectorStoreIndex.from_documents(documents)

# Query the index
query_engine = index.as_query_engine()
response = query_engine.query('What are the key findings?')
print(response)

Related on TokRepo

• AI tools for RAG — RAG frameworks and tools on TokRepo
• AI tools for documents — document processing and extraction tools

Common pitfalls

• Default chunking settings may split content at bad boundaries; customize chunk_size and chunk_overlap for your document type
• Using the default in-memory vector store works for prototyping but does not persist across restarts; switch to a persistent store like ChromaDB or Weaviate for production
• LlamaIndex's many abstractions can be confusing for newcomers; start with the simple VectorStoreIndex and add complexity only when needed