From be822e03b8cbeda1b49e80850e6f538c0a54d980 Mon Sep 17 00:00:00 2001 From: wxdl2002 <122009732+isxuwl@users.noreply.github.com> Date: Fri, 11 Oct 2024 11:42:10 +0800 Subject: [PATCH] =?UTF-8?q?=E8=8B=B1=E6=96=87README=20(#9)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * README-EN * README-EN --------- Co-authored-by: xuwenlong --- README-EN.md | 400 ++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 394 insertions(+), 6 deletions(-) diff --git a/README-EN.md b/README-EN.md index 467729b..2e1e566 100644 --- a/README-EN.md +++ b/README-EN.md @@ -1,8 +1,396 @@ +![Maven Central](https://img.shields.io/maven-central/v/io.github.lnyo-cly/ai4j?color=blue) # ai4j -A JavaSDK for fast access to AI big model applications, integrating multi-platform big models, such as OpenAi, Zhipu( -ChatGLM), DeepSeek, Dark Side of the Moon Moonshot(Kimi), Tencent Hunyuan, 01 Everything (01), etc. Provides unified -input and output (aligned OpenAi) to eliminate differentiation, optimize function calls (Tool calls), optimize RAG -calls, support vector databases (Pinecone), and support JDK1.8 to provide users with the ability to quickly integrate -AI. +An Java SDK for quickly integrating AI large model applications. It integrates multiple platform large models such as OpenAI, Ollama, Zhipu (ChatGLM), DeepSeek, Moonshot (Kimi), Tencent Hunyuan, Lingyi (01), etc. It provides a unified input and output (aligned with OpenAI), eliminates differences, optimizes function calls (Tool Call), optimizes RAG calls, and supports vector databases (Pinecone). It also supports JDK 1.8, providing users with the ability to quickly integrate AI. -1 \ No newline at end of file +## Supported platforms ++ OpenAi ++ Zhipu ++ DeepSeek ++ Moonshot ++ Tencent Hunyuan ++ Lingyi AI ++ Ollama ++ To be added(Qwen Llama MiniMax...) + +## Supported services ++ Chat Completions（streaming and non-streaming） ++ Embedding ++ To be added + +## Features ++ Supports Spring and ordinary Java applications. Supports applications above Java 8. ++ Multi-platform and multi-service. ++ Unified input and output. ++ Unified error handling. ++ Supports streaming output. Supports streaming output of function call parameters. ++ Easily use Tool Calls. ++ Supports simultaneous calls of multiple functions (Zhipu does not support this). ++ Supports stream_options, and directly obtains statistical token usage through streaming output. ++ Supports RAG. Built-in vector database support: Pinecone. ++ Uses Tika to read files. ++ Token statistics`TikTokensUtil.java` + + +## Tutorial documents ++ [Quick access to Spring Boot, access to streaming and non-streaming and function calls.](http://t.csdnimg.cn/iuIAW) ++ [Quick access to open source large models such as qwen2.5 and llama3.1 on the Ollama platform in Java.](https://blog.csdn.net/qq_35650513/article/details/142408092?spm=1001.2014.3001.5501) ++ [Build a legal AI assistant in Java and quickly implement RAG applications.](https://blog.csdn.net/qq_35650513/article/details/142568177?fromshare=blogdetail&sharetype=blogdetail&sharerId=142568177&sharerefer=PC&sharesource=qq_35650513&sharefrom=from_link) + +## Other support ++ [[Low-cost transit platform] Low-cost ApiKey - Limited-time special offer 0.7:1 - Supports the latest o1 model.](https://api.trovebox.online/) + +# Quick start +## Import +### Gradle +```groovy +implementation group: 'io.github.lnyo-cly', name: 'ai4j', version: '${project.version}' +``` + +```groovy +implementation group: 'io.github.lnyo-cly', name: 'ai4j-spring-boot-stater', version: '${project.version}' +``` + + +### Maven +```xml + + + io.github.lnyo-cly + ai4j + ${project.version} + + +``` +```xml + + + io.github.lnyo-cly + ai4j-spring-boot-stater + ${project.version} + +``` + +## Obtain AI service instance + +### Obtaining without Spring +```java + public void test_init(){ + OpenAiConfig openAiConfig = new OpenAiConfig(); + + Configuration configuration = new Configuration(); + configuration.setOpenAiConfig(openAiConfig); + + HttpLoggingInterceptor httpLoggingInterceptor = new HttpLoggingInterceptor(); + httpLoggingInterceptor.setLevel(HttpLoggingInterceptor.Level.HEADERS); + + OkHttpClient okHttpClient = new OkHttpClient + .Builder() + .addInterceptor(httpLoggingInterceptor) + .addInterceptor(new ErrorInterceptor()) + .connectTimeout(300, TimeUnit.SECONDS) + .writeTimeout(300, TimeUnit.SECONDS) + .readTimeout(300, TimeUnit.SECONDS) + .proxy(new Proxy(Proxy.Type.HTTP, new InetSocketAddress("127.0.0.1",10809))) + .build(); + configuration.setOkHttpClient(okHttpClient); + + AiService aiService = new AiService(configuration); + + embeddingService = aiService.getEmbeddingService(PlatformType.OPENAI); + chatService = aiService.getChatService(PlatformType.getPlatform("OPENAI")); + + } +``` +### Obtaining with Spring +```yml +# Domestic access usually requires a proxy by default. +ai: + openai: + api-key: "api-key" + okhttp: + proxy-port: 10809 + proxy-url: "127.0.0.1" + zhipu: + api-key: "xxx" + #other... +``` + +```java +// Inject Ai service +@Autowired +private AiService aiService; + +// Obtain the required service instance +IChatService chatService = aiService.getChatService(PlatformType.OPENAI); +IEmbeddingService embeddingService = aiService.getEmbeddingService(PlatformType.OPENAI); +// ...... +``` + +## Chat service + +### Synchronous request call +```java + +public void test_chat() throws Exception { + // Obtain chat service instance + IChatService chatService = aiService.getChatService(PlatformType.OPENAI); + + // Build request parameters + ChatCompletion chatCompletion = ChatCompletion.builder() + .model("gpt-4o-mini") + .message(ChatMessage.withUser("鲁迅为什么打周树人")) + .build(); + + // Send dialogue request + ChatCompletionResponse response = chatService.chatCompletion(chatCompletion); + + System.out.println(response); +} + +``` + +### Streaming call +```java +public void test_chat_stream() throws Exception { + // Obtain chat service instance + IChatService chatService = aiService.getChatService(PlatformType.OPENAI); + + // Construct request parameters + ChatCompletion chatCompletion = ChatCompletion.builder() + .model("gpt-4o-mini") + .message(ChatMessage.withUser("查询北京明天的天气")) + .functions("queryWeather") + .build(); + + + // Construct listener + SseListener sseListener = new SseListener() { + @Override + protected void send() { + System.out.println(this.getCurrStr()); + } + }; + // Display function parameters. Default is not to display. + sseListener.setShowToolArgs(true); + + // Send SSE request + chatService.chatCompletionStream(chatCompletion, sseListener); + + System.out.println(sseListener.getOutput()); + +} +``` + +### Image recognition + +```java +public void test_chat_image() throws Exception { + // Obtain chat service instance + IChatService chatService = aiService.getChatService(PlatformType.OPENAI); + + // Build request parameters + ChatCompletion chatCompletion = ChatCompletion.builder() + .model("gpt-4o-mini") + .message(ChatMessage.withUser("图片中有什么东西", "https://cn.bing.com/images/search?view=detailV2&ccid=r0OnuYkv&id=9A07DE578F6ED50DB59DFEA5C675AC71845A6FC9&thid=OIP.r0OnuYkvsbqBrYk3kUT53AHaKX&mediaurl=https%3a%2f%2fimg.zcool.cn%2fcommunity%2f0104c15cd45b49a80121416816f1ec.jpg%401280w_1l_2o_100sh.jpg&exph=1792&expw=1280&q=%e5%b0%8f%e7%8c%ab%e5%9b%be%e7%89%87&simid=607987191780608963&FORM=IRPRST&ck=12127C1696CF374CB9D0F09AE99AFE69&selectedIndex=2&itb=0&qpvt=%e5%b0%8f%e7%8c%ab%e5%9b%be%e7%89%87")) + .build(); + + // Send dialogue request + ChatCompletionResponse response = chatService.chatCompletion(chatCompletion); + + System.out.println(response); +} +``` + +### Function call + +```java +public void test_chat_tool_call() throws Exception { + // Obtain chat service instance + IChatService chatService = aiService.getChatService(PlatformType.OPENAI); + + // Build request parameters + ChatCompletion chatCompletion = ChatCompletion.builder() + .model("gpt-4o-mini") + .message(ChatMessage.withUser("今天北京天气怎么样")) + .functions("queryWeather") + .build(); + + // Send dialogue request + ChatCompletionResponse response = chatService.chatCompletion(chatCompletion); + + System.out.println(response); +} +``` +#### Define function +```java +@FunctionCall(name = "queryWeather", description = "查询目标地点的天气预报") +public class QueryWeatherFunction implements Function { + + @Data + @FunctionRequest + public static class Request{ + @FunctionParameter(description = "需要查询天气的目标位置, 可以是城市中文名、城市拼音/英文名、省市名称组合、IP 地址、经纬度") + private String location; + @FunctionParameter(description = "需要查询未来天气的天数, 最多15日") + private int days = 15; + @FunctionParameter(description = "预报的天气类型，daily表示预报多天天气、hourly表示预测当天24天气、now为当前天气实况") + private Type type; + } + + public enum Type{ + daily, + hourly, + now + } + + @Override + public String apply(Request request) { + final String key = ""; + + String url = String.format("https://api.seniverse.com/v3/weather/%s.json?key=%s&location=%s&days=%d", + request.type.name(), + key, + request.location, + request.days); + + + OkHttpClient client = new OkHttpClient(); + + okhttp3.Request http = new okhttp3.Request.Builder() + .url(url) + .get() + .build(); + + try (Response response = client.newCall(http).execute()) { + if (response.isSuccessful()) { + // 解析响应体 + return response.body() != null ? response.body().string() : ""; + } else { + return "获取天气失败 当前天气未知"; + } + } catch (Exception e) { + // 处理异常 + e.printStackTrace(); + return "获取天气失败 当前天气未知"; + } + } + +} +``` + +## Embedding service + +```java +public void test_embed() throws Exception { + // Obtain embedding service instance + IEmbeddingService embeddingService = aiService.getEmbeddingService(PlatformType.OPENAI); + + // Build request parameters + Embedding embeddingReq = Embedding.builder().input("1+1").build(); + + // Send embedding request + EmbeddingResponse embeddingResp = embeddingService.embedding(embeddingReq); + + System.out.println(embeddingResp); +} +``` + +## RAG +### Configure vector database +```yml +ai: + vector: + pinecone: + url: "" + key: "" +``` +### Obtain instance +```java +@Autowired +private PineconeService pineconeService; +``` +### Insert into vector database +```java +public void test_insert_vector_store() throws Exception { + // Obtain embedding service instance + IEmbeddingService embeddingService = aiService.getEmbeddingService(PlatformType.OPENAI); + + // Read file content using Tika + String fileContent = TikaUtil.parseFile(new File("D:\\data\\test\\test.txt")); + + // Split text content + RecursiveCharacterTextSplitter recursiveCharacterTextSplitter = new RecursiveCharacterTextSplitter(1000, 200); + List contentList = recursiveCharacterTextSplitter.splitText(fileContent); + + // Convert to vector + Embedding build = Embedding.builder() + .input(contentList) + .model("text-embedding-3-small") + .build(); + EmbeddingResponse embedding = embeddingService.embedding(build); + List> vectors = embedding.getData().stream().map(EmbeddingObject::getEmbedding).collect(Collectors.toList()); + VertorDataEntity vertorDataEntity = new VertorDataEntity(); + vertorDataEntity.setVector(vectors); + vertorDataEntity.setContent(contentList); + + // Vector storage + Integer count = pineconeService.insert(vertorDataEntity, "userId"); + +} +``` +### Query from vector database +```java +public void test_query_vector_store() throws Exception { + // // Obtain embedding service instance + IEmbeddingService embeddingService = aiService.getEmbeddingService(PlatformType.OPENAI); + + // Build the question to be queried and convert it to a vector + Embedding build = Embedding.builder() + .input("question") + .model("text-embedding-3-small") + .build(); + EmbeddingResponse embedding = embeddingService.embedding(build); + List question = embedding.getData().get(0).getEmbedding(); + + // Build the query object for the vector database + PineconeQuery pineconeQueryReq = PineconeQuery.builder() + .namespace("userId") + .vector(question) + .build(); + + String result = pineconeService.query(pineconeQueryReq, " "); + + // Carry the result and have a conversation with the chat service. + // ...... +} +``` + +### Delete data from vector database +```java +public void test_delete_vector_store() throws Exception { + // Build parameters + PineconeDelete pineconeDelete = PineconeDelete.builder() + .deleteAll(true) + .namespace("userId") + .build(); + // Delete + Boolean res = pineconeService.delete(pineconeDelete); +} +``` + + + +# Contribute to ai4j +You are welcome to provide suggestions, report issues, or contribute code to ai4j. You can contribute to ai4j in the following ways: + +## Issue feedback +Please use the GitHub Issue page to report issues. Describe as specifically as possible how to reproduce your issue, including detailed information such as the operating system, Java version, and any relevant log traces. +## PR +1. Fork this repository and create your branch. +2. Write your code and test it. +3. Ensure that your code conforms to the existing style. +4. Write clear log information when submitting. For small changes, a single line of information is sufficient, but for larger changes, there should be a detailed description. +5. Complete the pull request form and ensure that changes are made on the `dev` branch and link to the issue that your PR addresses. + +# Support +If you find this project helpful to you, please give it a star⭐。