1. Ollama简介
Ollama 是一个用于在本地环境中运行和定制大型语言模型的工具。它提供了一个简单而高效的接口，用于创建、运行和管理这些模型，同时还提供了一个丰富的预构建模型库，可以轻松集成到各种应用程序中。Ollama 支持多种操作系统，包括 macOS、Windows、Linux 以及通过 Docker 容器运行，这使得不同环境下的用户都能轻松使用。
安装 Ollama 相对简单，支持多平台部署。例如，在 macOS 上，可以通过 Homebrew 安装，而在 Windows 上则需要下载安装包并进行手动安装。Linux 用户可以通过包管理器或下载源码编译安装，也可以使用 Docker 安装。
使用 Ollama，你可以快速开始部署和运行大型模型。安装完成后，通过命令行启动 Ollama，部署模型，然后使用模型进行文本生成或其他任务。Ollama 提供了丰富的功能和 API，包括模型管理、模型运行、自定义模型等。
Ollama 还提供了 Python 和 JavaScript 库，以及命令行界面（CLI）和 REST API，方便开发者集成到各种应用中。此外，Ollama 支持从多种格式导入模型，如 GGUF、PyTorch 和 Safetensors，同时也提供了一个模型库，方便用户选择和使用。
Ollama 的安全性和隐私保护也是其一大特点。它使用 SSL/TLS 加密传输数据，并提供访问控制列表（ACL）来限制对服务的访问。Ollama 不会存储用户的个人数据，除非用户明确同意。
Ollama 的未来创新开发潜力巨大，包括模型优化和扩展、更广泛的应用集成、增强的定制化功能、社区和生态系统的增长以及安全性和隐私保护的提升。
2. 基于Ollama搭建本地个人智能AI助理
要基于 Ollama 搭建一个本地的个人智能 AI 助理并使用 Python 进行交互，你需要先确保 Ollama 已经在你的机器上正确安装，并且至少有一个模型已经部署好。
2.1 安装本地Ollama
首先，从官网上下载适合的Ollama版本，这里以MAC环境为例，从官网https://ollama.com/download 下载安装文件，
https://ollama.com/#:~:text=Get%20up%20and%20running%20with%20large%20language
接着，对下载的安装文件解压，得到一个Ollama的安装软件，点击该图标，并确认打开该软件，进入安装步骤:
安装过程比较简单，提示将该软件移到Applications，点击确定就可以:
如下图，点击Next。
继续点击Install，完成安装操作:
这时候会弹出一个参考demo的命令行，点击保存，然后点击Finish完成安装操作。
2.2 本地AI测试-控制台模式
打开一个常用的命令行终端，输入上面的demo命令行，将下载llama3.1的模型参数，后面就可以执行对话AI测试:
ollama run llama3.1
如果想要测试中文的大语言模型，可以使用qwen2，安装命令:
ollama run qwen2
这里，我们假设想利用个人AI助理帮我们安排一段旅行行程，测试效果如下:
> (base) fyfMBP:library fyf$ ollama run qwen2 >>> 我想要十一去趟南京，请帮我安排一个5天的游玩行程，推荐当地特殊景点和美食。 南京作为六朝古都，拥有丰富的历史文化遗产和独特的自然景观，同时也融合了江南水乡的柔美与现代化城市的活力。以下是为您精心设计的南京五日游行程建议： **第一天：** - **上午**：游览明孝陵和中山陵。这两大名胜体现了中国古代皇家园林的精髓和现代国家领袖的精神气度。 - **下午**：参观南京博物院，这里收藏着丰富的文物，从远古石器到明清瓷器，全面展示了中国历史文化的脉络。 - **晚上**：漫步秦淮河畔，体验古典与现代交融的独特风情。您可以在夫子庙附近品尝正宗的南京小吃，比如鸭血粉丝汤、盐水鸭等。 **第二天：** - **全天**：游览南京大屠杀纪念馆和侵华日军南京大屠杀遇难同胞纪念馆。了解历史，缅怀先烈。 推荐参观后在雨花台感受自然之美与革命精神并存的魅力。 **第三天：** - **上午**：前往紫金山天文台，享受一个宁静的早晨，在这里可以看到南京城的全貌和近处的山景。 - **下午**：探索南京老门东街区。这里是体验南京传统文化的好地方，您可以在这里漫步、购物、品尝地道小吃。 **第四天：** - **全天**：参观雨花台烈士陵园和中国近代史遗址博物馆，缅怀革命先烈，了解中国近现代历史。 推荐晚上在新街口区域逛逛，这里有各种商场、书店、咖啡厅等，是感受南京现代生活的好去处。 **第五天：** - **上午**：乘坐游船游览玄武湖，享受宁静的湖泊风光和周边的园林美景。 - **下午**：探访鸡鸣寺，体验古老的佛教文化。之后，您可以选择在附近的茶馆品茗休息，或者前往南京图书馆感受浓郁的文化氛围。 **美食推荐**： 1. 鸭血粉丝汤：是南京的传统小吃，口感丰富、鲜美可口。 2. 盐水鸭：南京的特色名菜之一，肉质紧实、味道鲜美。 3. 南京烤鸭包：皮薄馅多，咬一口满嘴流油，非常美味。 这只是一个大致的行程框架，您可以根据个人兴趣和时间安排进行调整。希望您在南京的旅程愉快！
再来一个例子，如果我们希望模型帮我们查询一些最新的论文资料，可以如下操作:
>>> please help me to retrieval some papers about CTR predictions in recent two years. To retrieve recent research papers on Click-Through Rate (CTR) prediction, you can use academic databases such as Google Scholar, PubMed, IEEE Xplore, and the ACM Digital Library. Here's a step-by-step guide on how to search these platforms effectively: ### Google Scholar 1. **Search for Keywords**: Enter keywords like "CTR Prediction", "Machine Learning CTR", "Deep Learning for CTR", etc., in the search bar. 2. **Filter by Year**: After selecting your search results, apply filters to display only papers published within the last two years. 3. **Check Citations and Recommendations**: Google Scholar often shows highly cited articles at the top, which are influential studies that you might want to explore. ### PubMed If your research involves biological or medical applications of CTR predictions: 1. **Search Terms**: Use terms like "Machine Learning", "Predictive Modeling", "User Behavior", along with specific disease/condition names. 2. **Date Range**: Set the date range to filter results from the last two years. ### IEEE Xplore For papers related to engineering or computer science: 1. **Topic Navigation**: Navigate through specific journals and conferences like Transactions on Knowledge Discovery from Data (TKDD), ICDE, CIKM, WWW. 2. **Keyword Search**: Use terms such as "CTR Prediction", "Recommender Systems", "Data Mining". ### ACM Digital Library Similar to IEEE Xplore, but focused on computing: 1. **Advanced Search**: Utilize the advanced search feature to include specific publication types (e.g., journal articles, conference papers). 2. **Keywords and Filters**: Include relevant keywords and filter by date. ### Additional Tips: - **Author Alerts**: If you find authors whose work is particularly interesting or impactful, set up alerts for their publications. - **Conferences and Workshops**: Focus on those related to data mining, machine learning, AI, and information retrieval as they often feature the latest CTR prediction research. - **Networking**: Engage with researchers via social media platforms like LinkedIn or Twitter. Scholarly networks can provide insights into emerging trends and opportunities for collaboration. ### Example Search Terms: - "CTR Prediction" AND "Deep Learning" - "Machine Learning" AND "Recommender Systems" - "Clickstream Analysis" AND "User Behavior" These steps should help you effectively identify and access relevant papers on CTR prediction. Remember to read abstracts carefully, as not all will directly address your specific interest, but this process should yield a comprehensive list of pertinent research for your needs.
从这个结果来看，ollama只是给了我们一些使用的建议，本身并没有提供对应的查询结果。
我们可以进一步要求AI助理给出一些对应的论文信息:
>>> please list some papers about CTR prediction. Given the vast amount of research in the field of Click-Through Rate (CTR) prediction, here are several influential and recent studies that might be of interest: 1. **"Deep Learning Based Context-aware Recommendations: An Experiment on Douban Movie Platform"** - Authors: Rui Li, Qijun Jiang, Jie Tang, et al. - *Journal*: IEEE Transactions on Knowledge and Data Engineering - *Year*: 2020 This paper discusses the effectiveness of deep learning models in context-aware recommendations, which includes CTR prediction as a key component. 2. **"CTR Prediction for Click-Streaming Data with Time-series Neural Networks"** - Authors: Yash M. Desai, Chien-Ju Ho - *Conference*: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) - *Year*: 2019 This work introduces the use of time-series neural networks for CTR prediction in click-stream data. 3. **"Learning to Rank: From Pairwise Approach to Listwise Approach"** - Authors: Zhi-Ming Ma, Guo-Qiang Tian - *Conference*: IEEE International Conference on Data Mining (ICDM) - *Year*: 2018 This paper discusses advancements in learning to rank systems which are crucial for CTR prediction in recommendation systems. 4. **"CTR Prediction Models in Online Advertising: A Survey"** - Authors: Xiaoxi Jiang, Qian Liang, Shih-Fu Chang - *Journal*: ACM Computing Surveys (CSUR) - *Year*: 2019 This survey paper covers various models for CTR prediction specifically within the context of online advertising. 5. **"A Review on Deep Learning and Neural Networks in Information Retrieval"** - Authors: Aman P. Verma, Anuj Srivastava - *Journal*: Journal of Information Science - *Year*: 2019 This review discusses the application of deep learning techniques including neural networks for tasks like information retrieval that often includes CTR prediction. 6. **"Deep Neural Networks for Large-Scale CTR Prediction in Display Advertising"** - Authors: Chien-Ju Ho, Yen-Ting Lin - *Conference*: International Conference on Data Mining (ICDM) - *Year*: 2017 This paper explores the implementation of deep neural networks to address large-scale challenges in CTR prediction. These papers represent a mix of different methodologies and applications related to CTR prediction, offering both theoretical advancements and practical insights into modern techniques. For deeper understanding or more specific research directions, considering accessing these papers through academic databases like Google Scholar, IEEE Xplore, or ACM Digital Library would be highly beneficial.
2.3 本地AI测试-API模式
除了本地控制台模式外，Ollama也体统一些API使用模式。在Ollama安装成功后，就已经 建立了11434端口，可以简单使用curl方式调用:
curl http://localhost:11434/api/generate -d '{ "model": "qwen2", "prompt":"Why the sun is so hot?" }'
执行如下:
(base) fyfMBP:~ fyf$ curl http://localhost:11434/api/generate -d '{ > "model": "qwen2", > "prompt":"Why the sun is so hot?" > }' {"model":"qwen2","created_at":"2024-10-04T01:04:11.147267Z","response":"The","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:11.42658Z","response":" Sun","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:11.722676Z","response":" appears","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:12.00233Z","response":" hot","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:12.276914Z","response":" because","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:12.559582Z","response":" it","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:12.835295Z","response":" is","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:13.13865Z","response":" an","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:13.414892Z","response":" active","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:13.684645Z","response":" star","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:13.956783Z","response":",","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:14.230772Z","response":" constantly","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:14.503081Z","response":" undergoing","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:14.77767Z","response":" nuclear","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:15.047265Z","response":" fusion","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:15.314927Z","response":" reactions","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:15.589948Z","response":" in","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:15.861257Z","response":" its","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:16.134838Z","response":" core","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:16.408961Z","response":".","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:16.68125Z","response":" The","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:16.954901Z","response":" internal","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:17.227297Z","response":" pressure","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:17.502385Z","response":" and","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:17.783161Z","response":" temperature","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:18.068877Z","response":" are","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:18.340786Z","response":" high","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:18.612701Z","response":" enough","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:18.891774Z","response":" to","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:19.164983Z","response":" fuse","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:19.440172Z","response":" hydrogen","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:19.713839Z","response":" atoms","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:19.986504Z","response":" together","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:20.257295Z","response":" into","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:20.532114Z","response":" helium","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:20.812023Z","response":" under","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:21.088888Z","response":" extreme","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:21.360339Z","response":" conditions","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:21.631833Z","response":".\n\n","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:21.9113Z","response":"Here","done":false} {"model":"qwen2","created_at":"2024-10-04T01:04:22.187385Z","response":"'s","done":false}
也可以使用对话模式:
curl http://localhost:11434/api/chat -d '{ "model": "qwen2", "messages": [ { "role": "user", "content": "Why the sun is so hot?" } ] }'
然而，上述两种调用，由于每次AI助理的响应都是逐一生成整个结果，读起来稍微有些费劲。我们可以使用一个高级参数stream来修改下响应模式:
curl http://localhost:11434/api/chat -d '{ "model": "qwen2", "stream": false, "format": "json2", "messages": [ { "role": "user", "content": "Why the sun is so hot?" } ] }'
更进一步，以下是一个简单的 Python 代码示例，它使用 requests 库来发送 HTTP 请求到 Ollama 的 REST API，从而与你部署的模型进行交互。
首先，确保你已经安装了 requests 库，如果没有安装，可以通过以下命令安装：
pip install requests
然后，你可以使用以下 Python 代码来创建一个简单的 AI 助理：
import requests # Ollama 服务的 URL，假设它运行在本地的 11434 端口 ollama_url = 'http://localhost:11434' # 选择一个已经部署的模型，例如 'llama2' model_name = 'llama2' # 定义一个函数来发送请求并获取模型的响应 def ask_ollama(question): # 构建请求的 JSON 数据 payload = { 'model': model_name, 'prompt': question } # 发送 POST 请求到 Ollama 的 API response = requests.post(f'{ollama_url}/api/generate', json=payload) # 检查请求是否成功 if response.status_code == 200: # 解析响应 JSON 并返回结果 return response.json()['choices'][0]['text'] else: # 如果请求失败，打印错误信息 print('Error:', response.status_code) return None # 测试 AI 助理 if __name__ == '__main__': question = "Hello, who are you?" answer = ask_ollama(question) if answer: print(f"AI Assistant: {answer}")
这段代码定义了一个 ask_ollama 函数，它接受一个问题作为输入，然后发送一个 POST 请求到 Ollama 的 /api/generate 端点。请求的正文包含了模型的名称和输入的提示（prompt）。如果请求成功，函数将解析响应并返回 AI 助理的答案。
请注意，这个例子假设你的 Ollama 服务运行在本地机器的 11434 端口上，并且你已经部署了一个名为 ‘llama2’ 的模型。如果你的设置有所不同，请相应地修改 ollama_url 和 model_name 变量。
3. 完整一键部署代码
from urllib.request import urlopen from io import BytesIO from zipfile import ZipFile def download_and_unzip(url, extract_to='.'): http_response = urlopen(url) zipfile = ZipFile(BytesIO(http_response.read())) zipfile.extractall(path=extract_to) def run(): # step1: download ollama zip_file_url = 'https://github.com/ollama/ollama/releases/download/v0.3.12/Ollama-darwin.zip' download_and_unzip(zip_file_url) if __name__ == '__main__': run()
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