{"repo": "9ao2hen/luca-pro", "files": {"luca-module-ai/luca-spring-boot-starter-ai/src/main/java/cn/mervyn/luca/framework/ai/core/model/deepseek/DeepSeekChatModel.java": {"c757bedb257b57d1a1f11c48a1904c149314f180": {"t": 1785746122.0487106, "n": 7441, "h": "285ece47127b4f0c", "c": "package cn.mervyn.luca.framework.ai.core.model.deepseek;\n\nimport cn.hutool.core.lang.Assert;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.ai.chat.metadata.ChatGenerationMetadata;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.chat.model.ChatResponse;\nimport org.springframework.ai.chat.model.Generation;\nimport org.springframework.ai.chat.prompt.ChatOptions;\nimport org.springframework.ai.chat.prompt.Prompt;\nimport org.springframework.ai.model.ModelOptionsUtils;\nimport org.springframework.ai.openai.OpenAiChatOptions;\nimport org.springframework.ai.openai.api.OpenAiApi;\nimport org.springframework.ai.openai.metadata.OpenAiChatResponseMetadata;\nimport org.springframework.ai.retry.RetryUtils;\nimport org.springframework.http.ResponseEntity;\nimport org.springframework.retry.support.RetryTemplate;\nimport reactor.core.publisher.Flux;\n\nimport java.util.HashMap;\nimport java.util.List;\nimport java.util.Map;\n\nimport static cn.mervyn.luca.framework.ai.core.model.deepseek.DeepSeekChatOptions.MODEL_DEFAULT;\n\n/**\n * DeepSeek {@link ChatModel} 实现类\n *\n * @author fansili\n */\n@Slf4j\npublic class DeepSeekChatModel implements ChatModel {\n\n    private static final String BASE_URL = \"https://api.deepseek.com\";\n\n    private final DeepSeekChatOptions defaultOptions;\n    private final RetryTemplate retryTemplate;\n\n    /**\n     * DeepSeek 兼容 OpenAI 的 HTTP 接口，所以复用它的实现，简化接入成本\n     *\n     * 不过要注意，DeepSeek 没有完全兼容，所以不能使用 {@link org.springframework.ai.openai.OpenAiChatModel} 调用，但是实现会参考它\n     */\n    private final OpenAiApi openAiApi;\n\n    public DeepSeekChatModel(String apiKey) {\n        this(apiKey, DeepSeekChatOptions.builder().model(MODEL_DEFAULT).temperature(0.7F).build());\n    }\n\n    public DeepSeekChatModel(String apiKey, DeepSeekChatOptions options) {\n       this(apiKey, options, RetryUtils.DEFAULT_RETRY_TEMPLATE);\n    }\n\n    public DeepSeekChatModel(String apiKey, DeepSeekChatOptions options, RetryTemplate retryTemplate) {\n        Assert.notEmpty(apiKey, \"apiKey 不能为空\");\n        Assert.notNull(options, \"options 不能为空\");\n        Assert.notNull(retryTemplate, \"retryTemplate 不能为空\");\n        this.openAiApi = new OpenAiApi(BASE_URL, apiKey);\n        this.defaultOptions = options;\n        this.retryTemplate = retryTemplate;\n    }\n\n    @Override\n    public ChatResponse call(Prompt prompt) {\n        OpenAiApi.ChatCompletionRequest request = createRequest(prompt, false);\n        return this.retryTemplate.execute(ctx -> {\n            // 1.1 发起调用\n            ResponseEntity<OpenAiApi.ChatCompletion> completionEntity = openAiApi.chatCompletionEntity(request);\n            // 1.2 校验结果\n            OpenAiApi.ChatCompletion chatCompletion = completionEntity.getBody();\n            if (chatCompletion == null) {\n                log.warn(\"No chat completion returned for prompt: {}\", prompt);\n                return new ChatResponse(List.of());\n            }\n            List<OpenAiApi.ChatCompletion.Choice> choices = chatCompletion.choices();\n            if (choices == null) {\n                log.warn(\"No choices returned for prompt: {}\", prompt);\n                return new ChatResponse(List.of());\n            }\n\n            // 2. 转换 ChatResponse 返回\n            List<Generation> generations = choices.stream().map(choice -> {\n                Generation generation = new Generation(choice.message().content(), toMap(chatCompletion.id(), choice));\n                if (choice.finishReason() != null) {\n                    generation.withGenerationMetadata(ChatGenerationMetadata.from(choice.finishReason().name(), null));\n                }\n                return generation;\n            }).toList();\n            return new ChatResponse(generations,\n                    OpenAiChatResponseMetadata.from(completionEntity.getBody()));\n        });\n    }\n\n    private Map<String, Object> toMap(String id, OpenAiApi.ChatCompletion.Choice choice) {\n        Map<String, Object> map = new HashMap<>();\n        OpenAiApi.ChatCompletionMessage message = choice.message();\n        if (message.role() != null) {\n            map.put(\"role\", message.role().name());\n        }\n        if (choice.finishReason() != null) {\n            map.put(\"finishReason\", choice.finishReason().name());\n        }\n        map.put(\"id\", id);\n        return map;\n    }\n\n    @Override\n    public Flux<ChatResponse> stream(Prompt prompt) {\n        OpenAiApi.ChatCompletionRequest request = createRequest(prompt, true);\n        return this.retryTemplate.execute(ctx -> {\n            // 1. 发起调用\n            Flux<OpenAiApi.ChatCompletionChunk> response = this.openAiApi.chatCompletionStream(request);\n            return response.map(chatCompletion -> {\n                String id = chatCompletion.id();\n                // 2. 转换 ChatResponse 返回\n                List<Generation> generations = chatCompletion.choices().stream().map(choice -> {\n                    String finish = (choice.finishReason() != null ? choice.finishReason().name() : \"\");\n                    String role = (choice.delta().role() != null ? choice.delta().role().name() : \"\");\n                    if (choice.finishReason() == OpenAiApi.ChatCompletionFinishReason.STOP) {\n                        // 兜底处理 DeepSeek 返回 STOP 时，role 为空的情况\n                        role = OpenAiApi.ChatCompletionMessage.Role.ASSISTANT.name();\n                    }\n                    Generation generation = new Generation(choice.delta().content(),\n                            Map.of(\"id\", id, \"role\", role, \"finishReason\", finish));\n                    if (choice.finishReason() != null) {\n                        generation = generation.withGenerationMetadata(\n                                ChatGenerationMetadata.from(choice.finishReason().name(), null));\n                    }\n                    return generation;\n                }).toList();\n                return new ChatResponse(generations);\n            });\n        });\n    }\n\n    OpenAiApi.ChatCompletionRequest createRequest(Prompt prompt, boolean stream) {\n        // 1. 构建 ChatCompletionMessage 对象\n        List<OpenAiApi.ChatCompletionMessage> chatCompletionMessages = prompt.getInstructions().stream().map(m ->\n                new OpenAiApi.ChatCompletionMessage(m.getContent(), OpenAiApi.ChatCompletionMessage.Role.valueOf(m.getMessageType().name()))).toList();\n        OpenAiApi.ChatCompletionRequest request = new OpenAiApi.ChatCompletionRequest(chatCompletionMessages, stream);\n\n        // 2.1 补充 prompt 内置的 options\n        if (prompt.getOptions() != null) {\n            if (prompt.getOptions() instanceof ChatOptions runtimeOptions) {\n                OpenAiChatOptions updatedRuntimeOptions = ModelOptionsUtils.copyToTarget(runtimeOptions,\n                        ChatOptions.class, OpenAiChatOptions.class);\n                request = ModelOptionsUtils.merge(updatedRuntimeOptions, request, OpenAiApi.ChatCompletionRequest.class);\n            } else {\n                throw new IllegalArgumentException(\"Prompt options are not of type ChatOptions: \"\n                        + prompt.getOptions().getClass().getSimpleName());\n            }\n        }\n        // 2.2 补充默认 options\n        if (this.defaultOptions != null) {\n            request = ModelOptionsUtils.merge(request, this.defaultOptions, OpenAiApi.ChatCompletionRequest.class);\n        }\n        return request;\n    }\n\n    @Override\n    public ChatOptions getDefaultOptions() {\n         return DeepSeekChatOptions.fromOptions(defaultOptions);\n    }\n\n}\n"}, "7b4546b1e018dfec16618d144d663c3764f74cb2": {"t": 1785760008.7278576, "n": 7441, "h": "285ece47127b4f0c", "c": "package cn.mervyn.luca.framework.ai.core.model.deepseek;\n\nimport cn.hutool.core.lang.Assert;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.ai.chat.metadata.ChatGenerationMetadata;\nimport org.springframework.ai.chat.model.ChatModel;\nimport org.springframework.ai.chat.model.ChatResponse;\nimport org.springframework.ai.chat.model.Generation;\nimport org.springframework.ai.chat.prompt.ChatOptions;\nimport org.springframework.ai.chat.prompt.Prompt;\nimport org.springframework.ai.model.ModelOptionsUtils;\nimport org.springframework.ai.openai.OpenAiChatOptions;\nimport org.springframework.ai.openai.api.OpenAiApi;\nimport org.springframework.ai.openai.metadata.OpenAiChatResponseMetadata;\nimport org.springframework.ai.retry.RetryUtils;\nimport org.springframework.http.ResponseEntity;\nimport org.springframework.retry.support.RetryTemplate;\nimport reactor.core.publisher.Flux;\n\nimport java.util.HashMap;\nimport java.util.List;\nimport java.util.Map;\n\nimport static cn.mervyn.luca.framework.ai.core.model.deepseek.DeepSeekChatOptions.MODEL_DEFAULT;\n\n/**\n * DeepSeek {@link ChatModel} 实现类\n *\n * @author fansili\n */\n@Slf4j\npublic class DeepSeekChatModel implements ChatModel {\n\n    private static final String BASE_URL = \"https://api.deepseek.com\";\n\n    private final DeepSeekChatOptions defaultOptions;\n    private final RetryTemplate retryTemplate;\n\n    /**\n     * DeepSeek 兼容 OpenAI 的 HTTP 接口，所以复用它的实现，简化接入成本\n     *\n     * 不过要注意，DeepSeek 没有完全兼容，所以不能使用 {@link org.springframework.ai.openai.OpenAiChatModel} 调用，但是实现会参考它\n     */\n    private final OpenAiApi openAiApi;\n\n    public DeepSeekChatModel(String apiKey) {\n        this(apiKey, DeepSeekChatOptions.builder().model(MODEL_DEFAULT).temperature(0.7F).build());\n    }\n\n    public DeepSeekChatModel(String apiKey, DeepSeekChatOptions options) {\n       this(apiKey, options, RetryUtils.DEFAULT_RETRY_TEMPLATE);\n    }\n\n    public DeepSeekChatModel(String apiKey, DeepSeekChatOptions options, RetryTemplate retryTemplate) {\n        Assert.notEmpty(apiKey, \"apiKey 不能为空\");\n        Assert.notNull(options, \"options 不能为空\");\n        Assert.notNull(retryTemplate, \"retryTemplate 不能为空\");\n        this.openAiApi = new OpenAiApi(BASE_URL, apiKey);\n        this.defaultOptions = options;\n        this.retryTemplate = retryTemplate;\n    }\n\n    @Override\n    public ChatResponse call(Prompt prompt) {\n        OpenAiApi.ChatCompletionRequest request = createRequest(prompt, false);\n        return this.retryTemplate.execute(ctx -> {\n            // 1.1 发起调用\n            ResponseEntity<OpenAiApi.ChatCompletion> completionEntity = openAiApi.chatCompletionEntity(request);\n            // 1.2 校验结果\n            OpenAiApi.ChatCompletion chatCompletion = completionEntity.getBody();\n            if (chatCompletion == null) {\n                log.warn(\"No chat completion returned for prompt: {}\", prompt);\n                return new ChatResponse(List.of());\n            }\n            List<OpenAiApi.ChatCompletion.Choice> choices = chatCompletion.choices();\n            if (choices == null) {\n                log.warn(\"No choices returned for prompt: {}\", prompt);\n                return new ChatResponse(List.of());\n            }\n\n            // 2. 转换 ChatResponse 返回\n            List<Generation> generations = choices.stream().map(choice -> {\n                Generation generation = new Generation(choice.message().content(), toMap(chatCompletion.id(), choice));\n                if (choice.finishReason() != null) {\n                    generation.withGenerationMetadata(ChatGenerationMetadata.from(choice.finishReason().name(), null));\n                }\n                return generation;\n            }).toList();\n            return new ChatResponse(generations,\n                    OpenAiChatResponseMetadata.from(completionEntity.getBody()));\n        });\n    }\n\n    private Map<String, Object> toMap(String id, OpenAiApi.ChatCompletion.Choice choice) {\n        Map<String, Object> map = new HashMap<>();\n        OpenAiApi.ChatCompletionMessage message = choice.message();\n        if (message.role() != null) {\n            map.put(\"role\", message.role().name());\n        }\n        if (choice.finishReason() != null) {\n            map.put(\"finishReason\", choice.finishReason().name());\n        }\n        map.put(\"id\", id);\n        return map;\n    }\n\n    @Override\n    public Flux<ChatResponse> stream(Prompt prompt) {\n        OpenAiApi.ChatCompletionRequest request = createRequest(prompt, true);\n        return this.retryTemplate.execute(ctx -> {\n            // 1. 发起调用\n            Flux<OpenAiApi.ChatCompletionChunk> response = this.openAiApi.chatCompletionStream(request);\n            return response.map(chatCompletion -> {\n                String id = chatCompletion.id();\n                // 2. 转换 ChatResponse 返回\n                List<Generation> generations = chatCompletion.choices().stream().map(choice -> {\n                    String finish = (choice.finishReason() != null ? choice.finishReason().name() : \"\");\n                    String role = (choice.delta().role() != null ? choice.delta().role().name() : \"\");\n                    if (choice.finishReason() == OpenAiApi.ChatCompletionFinishReason.STOP) {\n                        // 兜底处理 DeepSeek 返回 STOP 时，role 为空的情况\n                        role = OpenAiApi.ChatCompletionMessage.Role.ASSISTANT.name();\n                    }\n                    Generation generation = new Generation(choice.delta().content(),\n                            Map.of(\"id\", id, \"role\", role, \"finishReason\", finish));\n                    if (choice.finishReason() != null) {\n                        generation = generation.withGenerationMetadata(\n                                ChatGenerationMetadata.from(choice.finishReason().name(), null));\n                    }\n                    return generation;\n                }).toList();\n                return new ChatResponse(generations);\n            });\n        });\n    }\n\n    OpenAiApi.ChatCompletionRequest createRequest(Prompt prompt, boolean stream) {\n        // 1. 构建 ChatCompletionMessage 对象\n        List<OpenAiApi.ChatCompletionMessage> chatCompletionMessages = prompt.getInstructions().stream().map(m ->\n                new OpenAiApi.ChatCompletionMessage(m.getContent(), OpenAiApi.ChatCompletionMessage.Role.valueOf(m.getMessageType().name()))).toList();\n        OpenAiApi.ChatCompletionRequest request = new OpenAiApi.ChatCompletionRequest(chatCompletionMessages, stream);\n\n        // 2.1 补充 prompt 内置的 options\n        if (prompt.getOptions() != null) {\n            if (prompt.getOptions() instanceof ChatOptions runtimeOptions) {\n                OpenAiChatOptions updatedRuntimeOptions = ModelOptionsUtils.copyToTarget(runtimeOptions,\n                        ChatOptions.class, OpenAiChatOptions.class);\n                request = ModelOptionsUtils.merge(updatedRuntimeOptions, request, OpenAiApi.ChatCompletionRequest.class);\n            } else {\n                throw new IllegalArgumentException(\"Prompt options are not of type ChatOptions: \"\n                        + prompt.getOptions().getClass().getSimpleName());\n            }\n        }\n        // 2.2 补充默认 options\n        if (this.defaultOptions != null) {\n            request = ModelOptionsUtils.merge(request, this.defaultOptions, OpenAiApi.ChatCompletionRequest.class);\n        }\n        return request;\n    }\n\n    @Override\n    public ChatOptions getDefaultOptions() {\n         return DeepSeekChatOptions.fromOptions(defaultOptions);\n    }\n\n}\n"}}, "luca-module-ai/luca-spring-boot-starter-ai/src/test/java/cn/mervyn/luca/framework/ai/chat/DeepSeekChatModelTests.java": {"c757bedb257b57d1a1f11c48a1904c149314f180": {"t": 1785747155.8321664, "n": 1583, "h": "1066bd866a795585", "c": "package cn.mervyn.luca.framework.ai.chat;\n\nimport cn.mervyn.luca.framework.ai.core.model.deepseek.DeepSeekChatModel;\nimport org.junit.jupiter.api.Disabled;\nimport org.junit.jupiter.api.Test;\nimport org.springframework.ai.chat.messages.Message;\nimport org.springframework.ai.chat.messages.SystemMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatResponse;\nimport org.springframework.ai.chat.prompt.Prompt;\nimport reactor.core.publisher.Flux;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\n/**\n * {@link DeepSeekChatModel} 集成测试\n *\n * @author 9ao2hen\n */\npublic class DeepSeekChatModelTests {\n\n    private final DeepSeekChatModel chatModel = new DeepSeekChatModel(\"sk-e94db327cc7d457d99a8de8810fc6b12\");\n\n    @Test\n    @Disabled\n    public void testCall() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        ChatResponse response = chatModel.call(new Prompt(messages));\n        // 打印结果\n        System.out.println(response);\n    }\n\n    @Test\n    @Disabled\n    public void testStream() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        Flux<ChatResponse> flux = chatModel.stream(new Prompt(messages));\n        // 打印结果\n        flux.doOnNext(System.out::println).then().block();\n    }\n\n}\n"}, "70930cc4597d834048507eb285c7b7b7b0431e0c": {"t": 1785760009.3686285, "n": 1583, "h": "1066bd866a795585", "c": "package cn.mervyn.luca.framework.ai.chat;\n\nimport cn.mervyn.luca.framework.ai.core.model.deepseek.DeepSeekChatModel;\nimport org.junit.jupiter.api.Disabled;\nimport org.junit.jupiter.api.Test;\nimport org.springframework.ai.chat.messages.Message;\nimport org.springframework.ai.chat.messages.SystemMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatResponse;\nimport org.springframework.ai.chat.prompt.Prompt;\nimport reactor.core.publisher.Flux;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\n/**\n * {@link DeepSeekChatModel} 集成测试\n *\n * @author 9ao2hen\n */\npublic class DeepSeekChatModelTests {\n\n    private final DeepSeekChatModel chatModel = new DeepSeekChatModel(\"sk-e94db327cc7d457d99a8de8810fc6b12\");\n\n    @Test\n    @Disabled\n    public void testCall() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        ChatResponse response = chatModel.call(new Prompt(messages));\n        // 打印结果\n        System.out.println(response);\n    }\n\n    @Test\n    @Disabled\n    public void testStream() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        Flux<ChatResponse> flux = chatModel.stream(new Prompt(messages));\n        // 打印结果\n        flux.doOnNext(System.out::println).then().block();\n    }\n\n}\n"}, "7b4546b1e018dfec16618d144d663c3764f74cb2": {"t": 1785760011.0089662, "n": 1580, "h": "83b20e6c7d84c841", "c": "package cn.mervyn.luca.framework.ai.chat;\n\nimport cn.mervyn.luca.framework.ai.core.model.deepseek.DeepSeekChatModel;\nimport org.junit.jupiter.api.Disabled;\nimport org.junit.jupiter.api.Test;\nimport org.springframework.ai.chat.messages.Message;\nimport org.springframework.ai.chat.messages.SystemMessage;\nimport org.springframework.ai.chat.messages.UserMessage;\nimport org.springframework.ai.chat.model.ChatResponse;\nimport org.springframework.ai.chat.prompt.Prompt;\nimport reactor.core.publisher.Flux;\n\nimport java.util.ArrayList;\nimport java.util.List;\n\n/**\n * {@link DeepSeekChatModel} 集成测试\n *\n * @author 芋道源码\n */\npublic class DeepSeekChatModelTests {\n\n    private final DeepSeekChatModel chatModel = new DeepSeekChatModel(\"sk-e94db327cc7d457d99a8de8810fc6b12\");\n\n    @Test\n    @Disabled\n    public void testCall() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        ChatResponse response = chatModel.call(new Prompt(messages));\n        // 打印结果\n        System.out.println(response);\n    }\n\n    @Test\n    @Disabled\n    public void testStream() {\n        // 准备参数\n        List<Message> messages = new ArrayList<>();\n        messages.add(new SystemMessage(\"你是一个优质的文言文作者，用文言文描述着各城市的人文风景。\"));\n        messages.add(new UserMessage(\"1 + 1 = ？\"));\n\n        // 调用\n        Flux<ChatResponse> flux = chatModel.stream(new Prompt(messages));\n        // 打印结果\n        flux.doOnNext(System.out::println).then().block();\n    }\n\n}\n"}}}}