LlamaIndex
Modular Python framework to index and query data for LLMs.
LlamaIndex provides connectors, indices, and retrieval tools to augment large language models with private data, offering both a simple high‑level API and deep customization via over 300 integrations.
Overview
Overview
LlamaIndex is a Python data framework that helps developers augment large language models (LLMs) with private data sources. It offers a unified set of data connectors, indexing structures, and retrieval interfaces, making it easy for both beginners and advanced users to build knowledge‑augmented LLM applications.
Capabilities & Deployment
The library ships as a starter package (llama-index) or a core package (llama-index-core) that can be extended with any of the 300+ integration packages on LlamaHub, covering LLMs, embeddings, vector stores, and data loaders. A high‑level API can create a searchable index in five lines of code, while low‑level components allow custom retrievers, query engines, and rerankers. Indices are stored in‑memory by default but can be persisted to disk for later reuse. Installation is via pip, and the project uses Poetry for dependency management.
Highlights
Pros
- Flexible modular architecture
- Supports many LLM, embedding, and vector‑store providers
- Beginner‑friendly quick‑start workflow
- Active community and ecosystem (LlamaHub, LlamaLab)
Considerations
- Python‑only library
- Managing numerous integration packages can add complexity
- Documentation may lag behind rapid releases
- Performance depends on external LLM and vector‑store services
Fit guide
Great for
- Developers building private‑data‑augmented LLM applications
- Teams needing rapid prototyping with a simple API
- Projects requiring custom retrieval pipelines
- Enterprises integrating LLMs with existing Python services
Not ideal when
- Non‑Python environments
- Very small scripts where a full framework adds overhead
- Users seeking a ready‑made UI without coding
- Applications requiring out‑of‑the‑box cloud hosting
How teams use it
Corporate document Q&A
Employees can ask natural‑language questions and receive answers sourced from internal policies, manuals, and reports.
Product support chatbot
Customer‑facing bot retrieves relevant sections from product guides to provide accurate troubleshooting steps.
Research paper knowledge graph
Academic team builds an index of papers and citations, enabling semantic search and relationship exploration.
Contextual code assistant
IDE plugin queries a codebase index to suggest implementations and documentation snippets in real time.
Tech snapshot
Frequently asked questions
How do I install LlamaIndex?
Use `pip install llama-index` for the starter package, or `pip install llama-index-core` plus any desired integration packages from LlamaHub.
Can I persist an index to disk?
Yes, call `index.storage_context.persist()` and later reload with `StorageContext.from_defaults(persist_dir=...)`.
How many integrations are available?
Over 300 community‑maintained integration packages covering LLMs, embeddings, vector stores, and data loaders.
Does LlamaIndex work with non‑OpenAI models?
Absolutely; integrations exist for Replicate, HuggingFace, Llama‑2, and many others.
Where can I find the latest documentation?
The official docs are linked in the README and kept up‑to‑date at the project's documentation site.
Project at a glance
Active- Stars
- 46,474
- Watchers
- 46,474
- Forks
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