Introduction: Embracing the Future with OpenAI’s Updates In the ever-evolving landscape of artificial intelligence, staying updated with the latest advancements is not just a matter of curiosity, but a necessity for those looking to leverage AI for their projects. On the 11th of June, 2023, OpenAI introduced a slew of new features, marking a significant update to their Python SDK, now at version 1.0.0. In this blog, we’ll dive into these updates and explore how they can revolutionize the way we interact with AI.

OpenAI 11.06更新 在这篇博客中，将讨论 OpenAI 11.06 的一些更新，更新主要有: 聊天内容支持图片, gpt-4-vision-preview 返回内容支持json模式 引入system_fingerprint， 支持可复现性 OpenAI 多模态模型 OpenAI 引入的最令人兴奋的新功能之一是多模态模型，它可以处理文本和图像的组合。这一能力为 AI 应用打开了一个新的维度，从增强的视觉数据分析到更互动的聊天机器人。 GPT-4 Vision: gpt-4-vision-preview 示例: 分析阿里巴巴股票的K线. import openai openai.api_key = "your-api-key" response = openai.ChatCompletion.create( model="gpt-4-vision-preview", messages=[ { "role": "user", "content": [ {"type": "text", "text": "What information can you understand from the K-line of the image?"}, { "type": "image_url", "image_url": "https://mggg.cloud/img/ali.png", }, ], } ], max_tokens=300, ) print(response.choices[0].message.content) output: The image appears to show a candlestick chart for a stock, specifically ticker 'BABA' which is Alibaba Group Holding Limited.

Vector Database: Weaviate Weaviate is an innovative vector database known for its efficiency in storing and retrieving data. Utilizing vectors, Weaviate indexes data objects based on their semantic properties, offering a unique approach to data handling. It supports a variety of modules, including text2vec and OpenAI embeddings, providing flexibility in data vectorization. Getting Started with Weaviate Deploying Weaviate is straightforward with docker-compose. The OpenAI module transforms text into embeddings, enhancing semantic search capabilities.