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LlamaIndex
LlamaIndex is a document processing platform that parses, extracts, and indexes documents for AI applications.
LlamaIndex is a document processing platform that provides hosted APIs to get data out of documents for AI applications. It converts PDFs, scans, and Office files to clean markdown, text, or JSON through agentic OCR, pulls typed schema-shaped data with citations, and manages ingestion, embedding, and retrieval for RAG. It also classifies documents by type and splits combined files into logical sections. Developers can use SDKs for Python, TypeScript, Go, and Java to chain these capabilities or use them individually. The platform operates on credit-based pricing billed per page or minute.
LlamaIndex at a glance
- Best for: Software engineer
- Runs on: API, CLI
- Pricing: $1.25 per 1,000 credits, billed per page or minute based on service usage
- Released: November 2022
- Category: AI coding tool
- Website: developers.llamaindex.ai
Key features
- Parse PDFs, scans, and Office files to markdown, text, or JSON
- Extract typed, schema-shaped data with page citations
- Manage ingestion, embedding, and retrieval for document RAG
- Sort documents by type using natural-language rules
- Break combined files into logical sections
- Support Python, TypeScript, Go, and Java SDKs
Use it for
- Extract typed data and schema from documents
- Parse PDFs and scans into markdown or JSON
- Set up document retrieval for RAG
- Classify documents by type
Alternatives to LlamaIndex
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- LangChain4j — Integrates LLMs and vector stores directly into Java applications.
- llmware — Build private, local RAG pipelines with 300+ prepackaged models and integrated document parsing.
- LLMSchema — Provides AI coding agents with accurate database schemas without context bloat.
- WebLLM — Runs high-performance language models directly inside web browsers.
- MTPLX — It runs local models twice as fast via native MTP speculative decoding.
- CocoIndex — Maintains fresh context for AI agents with minimal incremental processing.
- oMLX — It uses paged SSD KV caching to reduce agent TTFT to under 5 seconds.
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