Category: llamaindex

Multi-Stage Vector Querying Using Matryoshka Representation Learning (MRL) in Qdrant

vandriichuk Multi Stage Vector Querying Using Matryoshka Repr 338e5bfa 8bab 4ce2 986b ca34f436cacf 1 Multi-Stage Vector Querying Using Matryoshka Representation Learning (MRL) in Qdrant

Data retrieval is a crucial component in creating an efficient Retrieval Augmented Generation (RAG) application. The effectiveness of data retrieval directly impacts the performance, accuracy, and reliability of the application.

There are various methods of data retrieval from vector databases. Some of the most efficient ones are:

  1. Self-Query Retrieval
  2. Multi-Stage Query
  3. Auto-Merging Retrieval
  4. Hybrid Retrieval

In this article, we will explore Multi-Stage Query for data retrieval using Matryoshka Representation Learning (MRL) to increase the efficiency of fetching data from the database.

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August 27, 2024 Add Comment

Simple RAG with LlamaIndex, Qdrant & GPT-4o Mini: A Step-by-Step Guide for Beginners

19082024 Simple RAG with LlamaIndex, Qdrant & GPT-4o Mini: A Step-by-Step Guide for Beginners

Retrieval-Augmented Generation (RAG) applications are gaining significant attention in the AI world. But why?

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August 19, 2024 Add Comment

LangChain and LlamaIndex: Comparison of Frameworks for Large Language Models

160820242 LangChain and LlamaIndex: Comparison of Frameworks for Large Language Models

In the world of artificial intelligence and natural language processing (NLP), two frameworks are drawing particular attention from developers: LangChain and LlamaIndex. Both tools are designed for creating applications based on large language models (LLMs), but they have their unique features and areas of application. Let’s explore their differences and when it’s best to use each of them.

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August 16, 2024 Add Comment