Source: DeepHub IMBA This article is approximately 1800 words and is suggested to be read in 6 minutes. This article will introduce how to integrate and create a scalable and customizable agent RAG. Building large language model applications can be quite challenging, especially when we have to choose between different frameworks like LangChain and LlamaIndex. … Read more
Source: DeepHub IMBA This article is about 1100 words long, and it is recommended to read in 5 minutes. In this article, we will explore some issues related to Langchain and consider some alternative frameworks. Langchain has gained attention for its ability to simplify interactions with large language models (LLM). With its advanced API, it … Read more
Photographer: Product Manager Fried Crab Shell When it comes to RAG or Agent, many people immediately think of LangChain or LlamaIndex, as they seem to believe these two are standard tools for developing applications with large models. But for me, I particularly dislike these two. Because they are the typical representatives of over-encapsulation. Especially with … Read more
Source: DeepHub IMBA This article is about 3200 words long, and it is recommended to read for 6 minutes. This article explores the new integration of LlamaIndex for fine-tuning OpenAI's GPT-3.5 Turbo. OpenAI announced on August 22, 2023, that fine-tuning of GPT-3.5 Turbo is now possible. This means we can customize our own models. Subsequently, … Read more
Source: DeepHub IMBA This article is approximately 3300 words long and is recommended for a 5-minute read. In this article, I will use two frameworks to complete some basic tasks in parallel. When using large models locally, especially when building RAG applications, there are generally two mature frameworks available: LangChain: A general framework for developing … Read more
Detailed steps and code are provided for how to choose the best embedding model and reranker model. When building a Retrieval-Augmented Generation (RAG) pipeline, one of the key components is the retriever. We have various embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers available from CohereAI … Read more
Click on the top↗️「HuoShui Intelligence」, Follow + Star🌟 Author: Tomaz Bratanic Compiled by: HuoShui Intelligence Retrieval-augmented generation (RAG) has become a mainstream technology, with ample reasons supporting its widespread application. It is a powerful framework that combines advanced large language models with targeted information retrieval techniques to achieve faster access to relevant data and generate … Read more
▼Recently, there have been a lot of live broadcasts,make an appointment to ensure you gain something. The RAG (Retrieval-Augmented Generation) technology is detailed in the article “Understanding RAG: A Comprehensive Guide to Retrieval-Augmented Generation,” with a typical RAG case shown in the image below, which includes three steps: Indexing: Split the document library into shorter … Read more
In RAG, after reading the files, the main task is to split the data into smaller chunks and then embed these features to express their semantics. The location of this process in RAG is shown in the figure below. The most common chunking method is rule-based, using techniques such as fixed chunk sizes or overlapping … Read more
Introduction The pursuit of adaptability and domain-specific understanding in the field of artificial intelligence and language models has been relentless. The emergence of large language models (LLMs) has ushered in a new era in natural language processing, achieving significant advancements across various domains. However, the challenge lies in how to leverage the potential of these … Read more