After using Cursor for a while, I feel that it directly designs functions to replace some copy-paste operations during the use of Copilot, greatly improving efficiency. It also allows for more flexible instruction and context control to achieve better code generation results. Some specific advantages include: Code can be generated based on instructions, not just … Read more
The latest research from the Cohere team reveals that the reasoning capabilities of LLMs do not stem from simple retrieval of pre-training data (previously believed to be “approximate retrieval”), but rather from the learning and application of programmatic knowledge, similar to how humans learn general problem-solving methods from examples. This means that LLMs extract reasoning … Read more
Let’s use Cohere’s new ⌘R 7B to build a Multi-Agent AI news generator. Before we start, let’s take a look at what we are building! The application takes user queries, searches the web, and transforms them into a well-crafted news article, complete with citations! Technology Stack: LLM: Cohere ultra-fast ⌘R 7B (can also be replaced … Read more
(Source: MIT Technology Review) AI agents are currently a hot topic in the tech field. From Google DeepMind and OpenAI to Anthropic, top companies are competing to empower LLMs with the ability to autonomously complete tasks. These systems are referred to as Agentic AI and have become a new focal point of discussion in Silicon … Read more
What Is LlamaIndex? LlamaIndex is a data framework designed to enable LLM-based applications to ingest, structure, and access private or domain-specific data. It offers both Python and TypeScript versions. https://docs.llamaindex.ai/en/stable/index.html LLMs provide a natural language interface between humans and data. The widely available models are pre-trained on a large amount of publicly available data, such … Read more
Welcome to LlamaIndex 🦙 LlamaIndex (formerly GPT Index) is a data framework for ingesting, structuring, and accessing private or domain-specific data for LLM applications. 🚀 Why Choose LlamaIndex?[1] At their core, LLMs provide a natural language interface between humans and inferred data. The widely available models are pre-trained on a large amount of publicly available … Read more
The explosive popularity of ChatGPT proves the capabilities of large language models (LLMs) in generating knowledge and reasoning. However, ChatGPT is a model pre-trained on public datasets, which may not provide specific answers or results related to user businesses. So, how can we maximize the capabilities of LLMs using private data? LlamaIndex can solve this … Read more
1. Introduction to LlamaIndex LlamaIndex is a Python library created by Jerry Liu that enables efficient text search and summarization of large document collections using language models.Developers can quickly add private/custom data to enhance existing LLMs with LlamaIndex. It provides personalized and data-driven responses without the need for retraining large models. Due to the limited … Read more
In this blog post, we introduce a brand new data structure in LlamaIndex: the Document Summary Index. We describe how it helps provide better retrieval performance compared to traditional semantic search, along with an example. https://github.com/jerryjliu/llama_index 1 Background One of the core scenarios for large language models (LLM) is question answering on user data. To … Read more
Overview The ContextChatEngine class is a contextual chat engine designed to provide a smooth chat experience by retrieving contextual information from the chat and using a language model (LLM) to generate responses based on system prompts. It is a simple chat mode built on top of a data retriever. For each chat interaction: First, retrieve … Read more