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要建设一个网站需要什么时候开始,如何自己开网店做网店,腾讯云 wordpress建站,电商运营主要做什么推荐方案 生产环境推荐#xff1a;使用ONNX Runtime方案#xff0c;性能好#xff0c;无需Python依赖快速原型开发#xff1a;使用REST API方案#xff0c;部署简单需要完整功能#xff1a;考虑DeepSeek4j等专用Java库灵活性要求高#xff1a;使用DJL#xff0c;支持多…推荐方案生产环境推荐使用ONNX Runtime方案性能好无需Python依赖快速原型开发使用REST API方案部署简单需要完整功能考虑DeepSeek4j等专用Java库灵活性要求高使用DJL支持多种模型格式方法1使用ONNX Runtime推荐这是最直接且性能较好的Java集成方式。步骤1将BGE-M3转换为ONNX格式Python端安装依赖pip install torch transformers onnx onnxruntime加载模型、设置为评估模式并导出onnximporttorchfromFlagEmbeddingimportBGEM3FlagModel modelBGEM3FlagModel(BAAI/bge-m3,use_fp16True)model.eval()# 设置为评估模式dummy_inputtorch.tensor([[0]*model.config.hidden_size])# 需要根据模型结构调整onnx_pathbge_m3.onnxtorch.onnx.export(model,dummy_input,onnx_path,opset_version13,input_names[input_ids,attention_mask],# 根据实际输入调整output_names[output])步骤2在Java中使用ONNX RuntimeMaven依赖dependencygroupIdcom.microsoft.onnxruntime/groupIdartifactIdonnxruntime/artifactIdversion1.17.0/version/dependencyJava代码示例importai.onnxruntime.*;importjava.nio.file.Paths;importjava.util.*;publicclassBGE_M3_ONNX{privateOrtSessionsession;privateOrtEnvironmentenv;publicBGE_M3_ONNX(StringmodelPath)throwsOrtException{envOrtEnvironment.getEnvironment();OrtSession.SessionOptionsoptsnewOrtSession.SessionOptions();sessionenv.createSession(modelPath,opts);}publicfloat[]getEmbedding(Stringtext)throwsOrtException{// 文本预处理需要实现分词器// 这里简化处理实际需要将文本转换为input_ids和attention_mask// 准备输入long[]inputIdstokenize(text);// 需要实现tokenize方法long[]attentionMaskcreateAttentionMask(inputIds);// 创建输入Tensorlong[]shape{1,inputIds.length};OnnxTensorinputIdsTensorOnnxTensor.createTensor(env,inputIds,shape);OnnxTensorattentionMaskTensorOnnxTensor.createTensor(env,attentionMask,shape);// 准备输入MapMapString,OnnxTensorinputsnewHashMap();inputs.put(input_ids,inputIdsTensor);inputs.put(attention_mask,attentionMaskTensor);// 运行推理OrtSession.Resultresultssession.run(inputs);// 获取输出OnnxTensoroutputTensor(OnnxTensor)results.get(output);float[]embeddings(float[])outputTensor.getValue();returnembeddings;}privatelong[]tokenize(Stringtext){// 需要实现BGE-M3的分词逻辑// 可以使用HuggingFace的tokenizer或实现简单的分词returnnewlong[]{101,2345,6789,102};// 示例}privatelong[]createAttentionMask(long[]inputIds){long[]masknewlong[inputIds.length];Arrays.fill(mask,1L);returnmask;}publicvoidclose()throwsOrtException{if(session!null){session.close();}}}方法2使用Deep Java Library (DJL)DJL是亚马逊开发的Java深度学习库支持PyTorch、TensorFlow等模型。Maven依赖dependencygroupIdai.djl/groupIdartifactIdapi/artifactIdversion0.27.0/version/dependencydependencygroupIdai.djl.pytorch/groupIdartifactIdpytorch-engine/artifactIdversion0.27.0/version/dependencyJava代码示例importai.djl.Model;importai.djl.inference.Predictor;importai.djl.modality.Input;importai.djl.modality.Output;importai.djl.translate.TranslateException;importai.djl.translate.Translator;importjava.nio.file.Paths;publicclassBGE_M3_DJL{privateModelmodel;privatePredictorString,float[]predictor;publicvoidloadModel(StringmodelPath)throwsException{modelModel.newInstance(bge-m3);model.load(Paths.get(modelPath));// 创建Translator需要自定义实现TranslatorString,float[]translatornewBGETranslator();predictormodel.newPredictor(translator);}publicfloat[]getEmbedding(Stringtext)throwsTranslateException{returnpredictor.predict(text);}publicvoidclose(){if(predictor!null){predictor.close();}if(model!null){model.close();}}// 自定义TranslatorstaticclassBGETranslatorimplementsTranslatorString,float[]{Overridepublicfloat[]processOutput(ai.djl.ndarray.NDListlist){// 处理模型输出returnlist.get(0).toFloatArray();}Overridepublicai.djl.ndarray.NDListprocessInput(TranslatorContextctx,Stringinput){// 文本预处理和tokenization// 需要实现分词逻辑returnnewai.djl.ndarray.NDList();}}}方法3通过REST API调用如果模型部署在Python服务中可以通过HTTP调用。Python服务端FastAPIfromfastapiimportFastAPIfrompydanticimportBaseModelfromFlagEmbeddingimportBGEM3FlagModelimportnumpyasnp appFastAPI()modelBGEM3FlagModel(BAAI/bge-m3,use_fp16True)classEmbeddingRequest(BaseModel):texts:list[str]app.post(/embed)asyncdefget_embeddings(request:EmbeddingRequest):embeddingsmodel.encode(request.texts)[dense_vecs]return{embeddings:embeddings.tolist()}Java客户端importcom.fasterxml.jackson.databind.ObjectMapper;importokhttp3.*;publicclassBGE_M3_API_Client{privatestaticfinalStringAPI_URLhttp://localhost:8000/embed;privatefinalOkHttpClientclientnewOkHttpClient();privatefinalObjectMappermappernewObjectMapper();publicfloat[][]getEmbeddings(ListStringtexts)throwsException{// 构建请求体MapString,ObjectrequestBodynewHashMap();requestBody.put(texts,texts);Stringjsonmapper.writeValueAsString(requestBody);RequestBodybodyRequestBody.create(json,MediaType.parse(application/json));RequestrequestnewRequest.Builder().url(API_URL).post(body).build();try(Responseresponseclient.newCall(request).execute()){if(!response.isSuccessful()){thrownewRuntimeException(请求失败: response.code());}StringresponseBodyresponse.body().string();MapString,Objectresultmapper.readValue(responseBody,Map.class);// 解析返回的向量ListListDoubleembeddingsList(ListListDouble)result.get(embeddings);float[][]embeddingsnewfloat[embeddingsList.size()][];for(inti0;iembeddingsList.size();i){ListDoublevecembeddingsList.get(i);embeddings[i]newfloat[vec.size()];for(intj0;jvec.size();j){embeddings[i][j]vec.get(j).floatValue();}}returnembeddings;}}}方法4使用DeepSeek4j专用Java库根据搜索结果DeepSeek4j提供了BGE-M3的Java支持。代码示例importcom.deepseek4j.embedding.EmbeddingClient;importcom.deepseek4j.embedding.EmbeddingRequest;importcom.deepseek4j.embedding.EmbeddingResponse;publicclassBGE_M3_DeepSeek4j{publicstaticvoidmain(String[]args){EmbeddingClientclientnewEmbeddingClient();EmbeddingRequestrequestEmbeddingRequest.builder().model(bge-m3:latest).input(What is BGE M3?).build();try{EmbeddingResponseresponseclient.embed(request);float[]embeddingresponse.getEmbedding();System.out.println(向量维度: embedding.length);System.out.println(向量: Arrays.toString(embedding));}catch(Exceptione){e.printStackTrace();}}}注分词器实现BGE-M3使用特定的分词器需要正确处理模型大小BGE-M3模型较大需要足够内存性能优化考虑批处理、GPU加速等优化手段错误处理添加适当的异常处理和资源清理