Hire LlamaIndex RAG Architects
Connect your enterprise data to AI with the world's most powerful data framework
Why Choose LlamaIndex?
Data Connectors
Ingest data from over 160+ sources including Notion, Slack, SQL, and PDFs automatically.
Advanced Retrieval
Go beyond basic vector search with hybrid search, recursive retrieval, and metadata filters.
Structured Indices
Organize data into list, tree, and keyword indices for optimized LLM consumption.
Query Engine
Powerful conversation engine that can query multiple documents and summarize complex answers.
What You Can Build
Real-world LlamaIndex automation examples
Pricing Insights
Platform Cost
Service Price Ranges
LlamaIndex vs LangChain
| Feature | Llamaindex | Langchain |
|---|---|---|
| Data ingestion | Best-in-class | Good |
| Retrieval Strategy | Advanced (Router, Recursive) | Standard |
| Agentic Capability | Data-focused | General-purpose |
Learning Resources
Master LlamaIndex automation
LlamaIndex Docs
Comprehensive guide to indexing and retrieval.
Learn More →LlamaHub
Community data loaders and agent tools.
Learn More →DeepLearning.AI LlamaIndex Course
Building Agentic RAG with Jerry Liu.
Learn More →LlamaIndex Blog
Latest techniques in RAG and data agents.
Learn More →Frequently Asked Questions
Can I use LlamaIndex with LangChain?
Yes! They are complementary. You can build a powerful retrieval engine with LlamaIndex and use it as a 'tool' within a LangChain agent.
Does LlamaIndex support vector databases?
Yes, it supports 40+ vector stores including Pinecone, Weaviate, Chroma, and pgvector out of the box.
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