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
This article will introduce the LangChain framework and explain its functions with Python code examples, utilizing the OpenAI API, along with code and results. Interested readers can follow along with the code. What is LangChain Framework LangChain is a framework for building and deploying applications based on language models (such as large language models, LLMs). … Read more
Source: DeepHub IMBA This article is about 4000 words, and it is recommended to read in 5 minutes. In this article, I will demonstrate how to create your own Document Assistant from scratch using LLaMA 7b and Langchain. In the past few months, large language models (LLMs) have gained tremendous attention, creating exciting prospects, especially … Read more
Chatbot development is a challenging and complex task that requires a combination of various technologies and tools. In this field, the combination of LLAMA, LangChain, and Python forms a powerful trio, providing excellent support for the design and implementation of chatbots. First, LLAMA is a powerful natural language processing tool with advanced semantic understanding and … 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
Source: DeepHub IMBA This article is about 2700 words long, and it is recommended to read it in 5 minutes. This article mainly introduces 3 commonly used methods in Langchain. There are various methods to enhance the capabilities of Retrieval-Augmented Generation (RAG), one of which is called query expansion. Here we mainly introduce 3 commonly … Read more
1. What is RAG? Retrieval-Augmented Generation (RAG) refers to optimizing the output of large language models to enable them to reference authoritative knowledge bases outside of the training data sources before generating responses. Large Language Models (LLMs) are trained on vast amounts of data, using billions of parameters to generate raw outputs for tasks like … Read more
The article includes a purchase link at the bottom. Developing Large Model Applications: Hands-On AI Agent with GPT and LangChain (Asynchronous Book Publishing) Related Articles:
Are you still troubled by the mixed quality of AI services in the domestic market and poor performance? Then let’s take a look at DevCat AI (3in1). This is an integrated AI assistant that combines GPT-4, Claude3, and Gemini. It covers all models of the three AI tools. Including GPT-4o and Gemini flash Now, you … 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