基于 java 的 调用大模型 代码， 和langhcain 有相似的功能

# LangChain for Java: Supercharge your Java application with the power of LLMs ## Project goals The goal of this project is to simplify the integration of AI/LLM capabilities into your Java application. This can be achieved thanks to: - **A simple and coherent layer of abstractions**, designed to ensure that your code does not depend on concrete implementations such as LLM providers, embedding store providers, etc. This allows for easy swapping of components. - **Numerous implementations of the above-mentioned abstractions**, providing you with a variety of LLMs and embedding stores to choose from. - **Range of in-demand features on top of LLMs, such as:** - The capability to **ingest your own data** (documentation, codebase, etc.), allowing the LLM to act and respond based on your data. - **Autonomous agents** for delegating tasks (defined on the fly) to the LLM, which will strive to complete them. - **Prompt templates** to help you achieve the highest possible quality of LLM responses. - **Memory** to provide context to the LLM for your current and past conversations. - **Structured outputs** for receiving responses from the LLM with a desired structure as Java POJOs. - **"AI Services"** for declaratively defining complex AI behavior behind a simple API. - **Chains** to reduce the need for extensive boilerplate code in common use-cases. - **Auto-moderation** to ensure that all inputs and outputs to/from the LLM are not harmful. ## Highlights You can declaratively define concise "AI Services" that are powered by LLMs: ```java interface Assistant { String chat(String userMessage); } Assistant assistant = AiServices.create(Assistant.class, model); String answer = assistant.chat("Hello"); System.out.println(answer); // Hello! How can I assist you today? ``` You can use LLM as a classifier: ```java enum Sentiment { POSITIVE, NEUTRAL, NEGATIVE } interface SentimentAnalyzer { @UserMessage("Analyze sentiment of {{it}}") Sentiment analyzeSentimentOf(String text); @UserMessage("Does {{it}} have a positive sentiment?") boolean isPositive(String text); } SentimentAnalyzer sentimentAnalyzer = AiServices.create(SentimentAnalyzer.class, model); Sentiment sentiment = sentimentAnalyzer.analyzeSentimentOf("It is good!"); // POSITIVE boolean positive = sentimentAnalyzer.isPositive("It is bad!"); // false ``` You can easily extract structured information from unstructured data: ```java class Person { private String firstName; private String lastName; private LocalDate birthDate; } interface PersonExtractor { @UserMessage("Extract information about a person from {{text}}") Person extractPersonFrom(@V("text") String text); } PersonExtractor extractor = AiServices.create(PersonExtractor.class, model); String text = "In 1968, amidst the fading echoes of Independence Day, " + "a child named John arrived under the calm evening sky. " + "This newborn, bearing the surname Doe, marked the start of a new journey."; Person person = extractor.extractPersonFrom(text); // Person { firstName = "John", lastName = "Doe", birthDate = 1968-07-04 } ``` You can provide tools that LLMs can use! Can be anything: retrieve information from DB, call APIs, etc. See example [here](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithToolsExample.java). ## Compatibility - Java: 8 or higher - Spring Boot: 2 or 3 ## Getting started 1. Add LangChain4j OpenAI dependency to your project: - Maven: ``` <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> <version>0.25.0</version> </dependency> ``` - Gradle: ``` implementation 'dev.langchain4j:langchain4j-open-ai:0.25.0' ``` 2. Import your OpenAI API key: ```java String apiKey = System.getenv("OPENAI_API_KEY"); ``` You can use the API key "demo" to test OpenAI, which we provide for free. [How to gen an API key?](https://github.com/langchain4j/langchain4j#how-to-get-an-api-key) 3. Create an instance of a model and start interacting: ```java OpenAiChatModel model = OpenAiChatModel.withApiKey(apiKey); String answer = model.generate("Hello world!"); System.out.println(answer); // Hello! How can I assist you today? ``` ## Disclaimer Please note that the library is in active development and: - Many features are still missing. We are working hard on implementing them ASAP. - API might change at any moment. At this point, we prioritize good design in the future over backward compatibility now. We hope for your understanding. - We need your input! Please [let us know](https://github.com/langchain4j/langchain4j/issues/new/choose) what features you need and your concerns about the current implementation.