What it is
Specialized framework for connecting LLMs to your data. Loaders for 100+ formats, multiple index types (vector, keyword, summary, knowledge graph), hybrid retrievers, evaluators.
How Vaaani uses it
- Multi-document QA with cross-document reasoning
- Building Knowledge Graph indexes alongside vector indexes
- Hierarchical summarization of long documents
- Retrieval-augmented agents with cited sources
Why it makes the cut
LangChain orchestrates; LlamaIndex retrieves. Together they handle 90% of the production RAG stack.
Sample code
from llama_index.core import KnowledgeGraphIndex, SimpleDirectoryReader docs = SimpleDirectoryReader("./pdfs").load_data() kg = KnowledgeGraphIndex.from_documents(docs, max_triplets_per_chunk=10) kg.as_query_engine().query("What did Q2 reveal?")
Related in the Vaaani stack
Have a project that needs LlamaIndex?
30-min discovery call. You describe the busywork; I map it to an AI worker and a budget.