LlamaIndex Integration | Build Retrieval-Augmented Generation applications with Zilliz Cloud and Milvus Vector DatabaseUse this integration for Free
LlamaIndex Integration, Build Retrieval-Augmented Generation applications with Zilliz Cloud
LlamaIndex (formerly GPT Index) is a data framework tailored for Large Language Models (LLM) applications, facilitating the ingestion, structuring, and access of private or domain-specific data. At their core, LLMs act as a bridge between human language and inferred data, both structured and unstructured data. While widely accessible LLMs arrive pre-trained on extensive publicly available datasets, they often are missing critical data, which results in hallucinations or incorrect answers generated from the LLMs.
LlamaIndex integrates with vector databases:
- Internal Index Usage: LlamaIndex can function as an index using a vector store. Similar to traditional indices, this LlamaIndex-based index can store documents and effectively respond to queries.
- External Data Integration: LlamaIndex can retrieve data from vector stores, operating like a conventional data connector. Once retrieved, this data can seamlessly integrate into LlamaIndex's data structures for further processing and utilization. This is often referred to as Retrieval-Augmented Generated or RAG.
How the LlamaIndex Integration with Zilliz Cloud Works
Learn More on How To Use Llama
- Tutorial | Getting Started with LlamaIndex
- Docs | Documentation QA using Zilliz Cloud and LlamaIndex
- Video Shorts with Yujian Tang | Persistent Vector Storage with LlamaIndex
- Video with Jerry Liu, CEO LlamaIndex | Boost your LLM with Private Data using LlamaIndex
- Building a Chatbot with Zilliz Cloud, LlamaIndex and LangChain Part I
- Building LLM Apps with 100x Faster Responses and Drastic Cost Reduction Using GPTCache
Unleashing the full potential of generative AI & Zilliz Cloud by bringing external data sources to large language models (LLMs) and your AI applications.
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