Transform data into easy-to-understand visuals effortlessly with our AI-powered graph generator, designed for impactful data communication.
RAG模型的关键优势在于,它能够结合静态模型的学习能力与动态的外部知识,尤其适合处理需要最新信息的任务。典型的RAG架构包括一个检索器(Retriever)和一个生成器(Generator)。检索器从大规模数据库中挑选出与当前问题最相关的文档片段,而生成器则基于这些片段和问题生成最终的答案。这种架构增强了模型的知识覆盖面和灵活性。
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources! - GitHub - graph4ai/graph4nlp: Graph4nlp is the library for the easy
工作方式:识别在语义上与查询相关的文档,并通过相似度度量(通常采用向量间的余弦相似度)计算相关性。 2)生成器(Generator): 定义:通常是一个大型语言模型。 输入:检索到的相关信息和原始查询。 输出:基于输入生成响应。 3)知识库(Knowledge Base): 用途:作为检索器查找文档或信息的数据源。 2、RAG 的工作流程 ...
Graph generation was updated for readibility (now done inGenerator.py), as well as some bugs related to how implicit Hs and chirality were handled on the GPU (not used before, despite being available for preprocessing/training). Data analysis code was updated for readibility (now done inAna...
The generator learns to directly output the graph’s representation. While the standard GAN loss forces the generator to generate molecules following a particular prior distribution, the authors add a reinforcement learning (RL) objective to generate molecules with optimized properties. The generation of...
"""A text-embedding generator LLM using Dashscope's API.""" def __init__(self, llm_config: dict = None): log.info(f"llm_config: {llm_config}") self.llm_config = llm_config or {} self.api_key = self.llm_config.get("api_key", "") ...
5) AI Image Generator 6) PDF to Deck Import 7) AI Assistant 8) AI created Q&A Session Notes 9) Rehearsal Mode 10) Presentation Notes 11) 100+ templatesMaking a bar graph on Decktopus is easier than you think! All you have to do is to choose the bar chart template. Afterward, enter...
▲图1|Graph RAG示意©️【深蓝AI】编译 本文将深入解析GitHub上备受瞩目的LLM开源项目,详细指导如何运用Graph RAG(Retriever-Augmented Generator with Graph)技术,结合Langchain框架与GPT-4o(或类似LLM模型)的能力,构建一个能够提供精准、...
The Adam optimizer is applied to the graph encoder with a learning rate ranging from 1 × 10−4 to 1 × 10−3 for all datasets, and the learning rate of the prompt generator is five times that of the graph encoder. We train the model on the training set and search hyper...