Implementing Vector Search with LlamaIndex
Use LlamaIndex as a vector store, build a Chroma index and implement multi-step query pipelines.
A comprehensive guide to using the LlamaIndex framework to implement vector search in LLM-based applications.
LlamaIndex is an open source framework designed to simplify and optimize the connection between Large Language Models (LLMs) and your custom data. It acts as a data orchestration layer to enable vector search in your applications.
LLMs are trained on large amounts of general data but do not know your specific data. LlamaIndex bridges this gap by giving LLMs access to your custom information: local documents, websites, databases, APIs.
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What's inside
9 sections- 1 Table of Contents
- 2 Training Overview
- 3 Getting Started with the LlamaIndex Framework
- 4 Installation and Configuration
- 5 Module 1 — Using LlamaIndex as a Vector Store
- 6 Module 2 — Creating a Chroma Index
- 7 Module 3 — Implementing a Multi-step Query Pipeline
- 8 Summary and Best Practices
- 9 Quick Reference
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