Merge pull request #241 from stageluo/main

新增知识库、http分支工作流节点

ruoyi-modules-api/ruoyi-knowledge-api/src/main/java/org/ruoyi/service/strategy/impl/MilvusVectorStoreStrategy.java

@@ -31,22 +31,23 @@ import java.util.stream.IntStream;
 @Component
 public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
-
-    private final Integer DIMENSION = 2048;
-    // 缓存不同集合与 autoFlush 配置的 Milvus 连接
-    private final Map<String, EmbeddingStore<TextSegment>> storeCache = new ConcurrentHashMap<>();
-
     public MilvusVectorStoreStrategy(VectorStoreProperties vectorStoreProperties, EmbeddingModelFactory embeddingModelFactory) {
         super(vectorStoreProperties, embeddingModelFactory);
     }
 
-    private EmbeddingStore<TextSegment> getMilvusStore(String collectionName, boolean autoFlushOnInsert) {
-        String key = collectionName + "|" + autoFlushOnInsert;
+    // 缓存不同集合与 autoFlush 配置的 Milvus 连接
+    private final Map<String, EmbeddingStore<TextSegment>> storeCache = new ConcurrentHashMap<>();
+
+    /**
+     * 获取 Milvus Store，支持动态维度
+     */
+    private EmbeddingStore<TextSegment> getMilvusStore(String collectionName, int dimension, boolean autoFlushOnInsert) {
+        String key = collectionName + "|" + dimension + "|" + autoFlushOnInsert;
         return storeCache.computeIfAbsent(key, k ->
                 MilvusEmbeddingStore.builder()
                         .uri(vectorStoreProperties.getMilvus().getUrl())
                         .collectionName(collectionName)
-                        .dimension(DIMENSION)
+                        .dimension(dimension)
                         .indexType(IndexType.IVF_FLAT)
                         .metricType(MetricType.L2)
                         .autoFlushOnInsert(autoFlushOnInsert)
@@ -58,17 +59,36 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         );
     }
     
+    /**
+     * 获取 embedding 模型的实际维度
+     */
+    private int getModelDimension(String modelName) {
+        try {
+            EmbeddingModel model = getEmbeddingModel(modelName, null);
+            // 使用一个测试文本获取向量维度
+            Embedding testEmbedding = model.embed("test").content();
+            int dimension = testEmbedding.dimension();
+            log.info("Detected embedding model dimension: {} for model: {}", dimension, modelName);
+            return dimension;
+        } catch (Exception e) {
+            log.warn("Failed to detect model dimension for: {}, using default 1024", modelName, e);
+            return 1024; // 默认使用 1024 (bge-m3 的维度)
+        }
+    }
+
     @Override
     public void createSchema(String kid, String modelName) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
+        int dimension = getModelDimension(modelName);
         // 使用缓存获取连接以确保只初始化一次
-        EmbeddingStore<TextSegment> store = getMilvusStore(collectionName, true);
-        log.info("Milvus集合初始化完成: {}", collectionName);
+        EmbeddingStore<TextSegment> store = getMilvusStore(collectionName, dimension, true);
+        log.info("Milvus集合初始化完成: {}, dimension: {}", collectionName, dimension);
     }
 
     @Override
     public void storeEmbeddings(StoreEmbeddingBo storeEmbeddingBo) {
-        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(), DIMENSION);
+        int dimension = getModelDimension(storeEmbeddingBo.getEmbeddingModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(storeEmbeddingBo.getEmbeddingModelName(), dimension);
 
         List<String> chunkList = storeEmbeddingBo.getChunkList();
         List<String> fidList = storeEmbeddingBo.getFids();
@@ -80,7 +100,7 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
         long startTime = System.currentTimeMillis();
 
         // 复用连接，写入场景使用 autoFlush=false 以提升批量插入性能
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, dimension, false);
 
         IntStream.range(0, chunkList.size()).forEach(i -> {
             String text = chunkList.get(i);
@@ -100,13 +120,14 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
 
     @Override
     public List<String> getQueryVector(QueryVectorBo queryVectorBo) {
-        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(), DIMENSION);
+        int dimension = getModelDimension(queryVectorBo.getEmbeddingModelName());
+        EmbeddingModel embeddingModel = getEmbeddingModel(queryVectorBo.getEmbeddingModelName(), dimension);
 
         Embedding queryEmbedding = embeddingModel.embed(queryVectorBo.getQuery()).content();
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + queryVectorBo.getKid();
 
         // 查询复用连接，autoFlush 对查询无影响，此处保持 true
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, true);
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, dimension, true);
 
         List<String> resultList = new ArrayList<>();
         EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
@@ -127,14 +148,16 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     @SneakyThrows
     public void removeById(String id, String modelName) {
         // 注意：此处原逻辑使用 collectionname + id，保持现状
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(vectorStoreProperties.getMilvus().getCollectionname() + id, false);
+        int dimension = getModelDimension(modelName);
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(vectorStoreProperties.getMilvus().getCollectionname() + id, dimension, false);
         embeddingStore.remove(id);
     }
 
     @Override
     public void removeByDocId(String docId, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
+        // 使用默认维度，因为删除操作不需要精确的维度信息
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, 1024, false);
         Filter filter = MetadataFilterBuilder.metadataKey("docId").isEqualTo(docId);
         embeddingStore.removeAll(filter);
         log.info("Milvus成功删除 docId={} 的所有向量数据", docId);
@@ -143,7 +166,8 @@ public class MilvusVectorStoreStrategy extends AbstractVectorStoreStrategy {
     @Override
     public void removeByFid(String fid, String kid) {
         String collectionName = vectorStoreProperties.getMilvus().getCollectionname() + kid;
-        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, false);
+        // 使用默认维度，因为删除操作不需要精确的维度信息
+        EmbeddingStore<TextSegment> embeddingStore = getMilvusStore(collectionName, 1024, false);
         Filter filter = MetadataFilterBuilder.metadataKey("fid").isEqualTo(fid);
         embeddingStore.removeAll(filter);
         log.info("Milvus成功删除 fid={} 的所有向量数据", fid);

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/WfNodeFactory.java

@@ -5,7 +5,9 @@ import org.ruoyi.workflow.entity.WorkflowNode;
 import org.ruoyi.workflow.workflow.node.AbstractWfNode;
 import org.ruoyi.workflow.workflow.node.EndNode;
 import org.ruoyi.workflow.workflow.node.answer.LLMAnswerNode;
+import org.ruoyi.workflow.workflow.node.httpRequest.HttpRequestNode;
 import org.ruoyi.workflow.workflow.node.keywordExtractor.KeywordExtractorNode;
+import org.ruoyi.workflow.workflow.node.knowledgeRetrieval.KnowledgeRetrievalNode;
 import org.ruoyi.workflow.workflow.node.mailSend.MailSendNode;
 import org.ruoyi.workflow.workflow.node.start.StartNode;
 import org.ruoyi.workflow.workflow.node.switcher.SwitcherNode;
@@ -17,10 +19,11 @@ public class WfNodeFactory {
         switch (WfComponentNameEnum.getByName(wfComponent.getName())) {
             case START -> wfNode = new StartNode(wfComponent, nodeDefinition, wfState, nodeState);
             case LLM_ANSWER -> wfNode = new LLMAnswerNode(wfComponent, nodeDefinition, wfState, nodeState);
-            case KEYWORD_EXTRACTOR ->
-                    wfNode = new KeywordExtractorNode(wfComponent, nodeDefinition, wfState, nodeState);
+            case KEYWORD_EXTRACTOR -> wfNode = new KeywordExtractorNode(wfComponent, nodeDefinition, wfState, nodeState);
+            case KNOWLEDGE_RETRIEVER -> wfNode = new KnowledgeRetrievalNode(wfComponent, nodeDefinition, wfState, nodeState);
             case END -> wfNode = new EndNode(wfComponent, nodeDefinition, wfState, nodeState);
             case MAIL_SEND -> wfNode = new MailSendNode(wfComponent, nodeDefinition, wfState, nodeState);
+            case HTTP_REQUEST -> wfNode = new HttpRequestNode(wfComponent, nodeDefinition, wfState, nodeState);
             case SWITCHER -> wfNode = new SwitcherNode(wfComponent, nodeDefinition, wfState, nodeState);
             default -> {
             }

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/node/httpRequest/HttpRequestNode.java

@@ -0,0 +1,437 @@
+package org.ruoyi.workflow.workflow.node.httpRequest;
+
+import com.fasterxml.jackson.core.type.TypeReference;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.jsoup.Jsoup;
+import org.ruoyi.workflow.entity.WorkflowComponent;
+import org.ruoyi.workflow.entity.WorkflowNode;
+import org.ruoyi.workflow.workflow.NodeProcessResult;
+import org.ruoyi.workflow.workflow.WfNodeState;
+import org.ruoyi.workflow.workflow.WfState;
+import org.ruoyi.workflow.workflow.data.NodeIOData;
+import org.ruoyi.workflow.workflow.node.AbstractWfNode;
+import org.springframework.http.*;
+import org.springframework.util.LinkedMultiValueMap;
+import org.springframework.util.MultiValueMap;
+import org.springframework.web.client.RestTemplate;
+
+import java.util.*;
+import java.util.concurrent.TimeUnit;
+
+/**
+ * HTTP 请求节点
+ */
+@Slf4j
+public class HttpRequestNode extends AbstractWfNode {
+
+    public HttpRequestNode(WorkflowComponent wfComponent, WorkflowNode nodeDef, WfState wfState, WfNodeState nodeState) {
+        super(wfComponent, nodeDef, wfState, nodeState);
+    }
+
+    @Override
+    public NodeProcessResult onProcess() {
+        try {
+            HttpRequestNodeConfig config = checkAndGetConfig(HttpRequestNodeConfig.class);
+            List<NodeIOData> inputs = state.getInputs();
+
+            // 渲染 URL（支持变量替换）
+            String url = renderTemplate(config.getUrl(), inputs);
+            if (StringUtils.isBlank(url)) {
+                throw new IllegalArgumentException("请求 URL 不能为空");
+            }
+
+            // 添加 Query 参数
+            url = buildUrlWithParams(url, config.getParams(), inputs);
+
+            // 构建请求头
+            HttpHeaders headers = buildHeaders(config.getHeaders(), inputs);
+
+            // 构建请求体
+            Object requestBody = buildRequestBody(config, inputs);
+
+            // 执行 HTTP 请求（支持重试）
+            String response = executeHttpRequest(url, config.getMethod(), headers, requestBody, config);
+
+            // 清除 HTML 标签（如果需要）
+            if (Boolean.TRUE.equals(config.getClearHtml()) && StringUtils.isNotBlank(response)) {
+                response = Jsoup.parse(response).text();
+            }
+
+            // 构造输出
+            List<NodeIOData> outputs = new ArrayList<>();
+            outputs.add(NodeIOData.createByText("output", "HTTP响应", response));
+
+            return NodeProcessResult.builder().content(outputs).build();
+
+        } catch (Exception e) {
+            log.error("HTTP 请求失败 in node: {}", node.getId(), e);
+            
+            // 异常时返回错误信息
+            List<NodeIOData> errorOutputs = new ArrayList<>();
+            errorOutputs.add(NodeIOData.createByText("output", "错误", ""));
+            errorOutputs.add(NodeIOData.createByText("error", "HTTP请求错误", e.getMessage()));
+            
+            return NodeProcessResult.builder().content(errorOutputs).build();
+        }
+    }
+
+    /**
+     * 渲染模板（支持变量替换）
+     * 支持格式：
+     * 1. {var_01} - 直接替换整个变量值
+     * 2. {var_01.name} - 从 JSON 中提取 name 字段
+     * 3. {var_01.user.email} - 支持嵌套路径
+     */
+    private String renderTemplate(String template, List<NodeIOData> inputs) {
+        if (StringUtils.isBlank(template)) {
+            return "";
+        }
+        return renderTemplateWithJsonPath(template, inputs);
+    }
+
+    /**
+     * 增强的模板渲染，支持 JSON 路径提取
+     */
+    private String renderTemplateWithJsonPath(String template, List<NodeIOData> inputs) {
+        String result = template;
+        ObjectMapper mapper = new ObjectMapper();
+
+        for (NodeIOData input : inputs) {
+            if (input == null || input.getName() == null) {
+                continue;
+            }
+
+            String varName = input.getName();
+            String varValue = input.valueToString();
+
+            // 1. 处理简单变量替换 {var_01}
+            result = result.replace("{" + varName + "}", varValue != null ? varValue : "");
+
+            // 2. 处理 JSON 路径提取 {var_01.field} 或 {var_01.user.name}
+            // 尝试解析为 JSON
+            Map<String, Object> jsonMap = tryParseJson(varValue, mapper);
+            if (jsonMap != null) {
+                result = replaceJsonPaths(result, varName, jsonMap);
+            }
+        }
+
+        return result;
+    }
+
+    /**
+     * 尝试将字符串解析为 JSON Map
+     */
+    private Map<String, Object> tryParseJson(String value, ObjectMapper mapper) {
+        if (StringUtils.isBlank(value)) {
+            return null;
+        }
+
+        value = value.trim();
+        if (!value.startsWith("{") && !value.startsWith("[")) {
+            return null;
+        }
+
+        try {
+            return mapper.readValue(value, new TypeReference<Map<String, Object>>() {});
+        } catch (Exception e) {
+            log.debug("无法解析为 JSON: {}", value);
+            return null;
+        }
+    }
+
+    /**
+     * 替换 JSON 路径变量，如 {var_01.name} 或 {var_01.user.email}
+     */
+    private String replaceJsonPaths(String template, String varName, Map<String, Object> jsonMap) {
+        String result = template;
+        
+        // 查找所有 {varName.xxx} 格式的占位符
+        String pattern = "\\{" + varName + "\\.([\\.\\w]+)\\}";
+        java.util.regex.Pattern p = java.util.regex.Pattern.compile(pattern);
+        java.util.regex.Matcher m = p.matcher(template);
+
+        while (m.find()) {
+            String fullMatch = m.group(0);  // 如 {var_01.name}
+            String jsonPath = m.group(1);   // 如 name 或 user.email
+
+            Object value = extractJsonValue(jsonMap, jsonPath);
+            String replacement = value != null ? value.toString() : "";
+            
+            result = result.replace(fullMatch, replacement);
+        }
+
+        return result;
+    }
+
+    /**
+     * 从 JSON Map 中提取嵌套路径的值
+     * 例如：path = "user.email" 会提取 map.get("user").get("email")
+     */
+    @SuppressWarnings("unchecked")
+    private Object extractJsonValue(Map<String, Object> map, String path) {
+        String[] parts = path.split("\\.");
+        Object current = map;
+
+        for (String part : parts) {
+            if (current instanceof Map) {
+                current = ((Map<String, Object>) current).get(part);
+            } else {
+                return null;
+            }
+        }
+
+        return current;
+    }
+
+    /**
+     * 构建带参数的 URL
+     */
+    private String buildUrlWithParams(String baseUrl, List<HttpRequestNodeConfig.ParamItem> params, List<NodeIOData> inputs) {
+        if (params == null || params.isEmpty()) {
+            return baseUrl;
+        }
+
+        StringBuilder urlBuilder = new StringBuilder(baseUrl);
+        boolean hasQuery = baseUrl.contains("?");
+
+        for (HttpRequestNodeConfig.ParamItem param : params) {
+            if (StringUtils.isBlank(param.getName())) {
+                continue;
+            }
+
+            String name = renderTemplate(param.getName(), inputs);
+            String value = renderTemplate(param.getValue(), inputs);
+
+            if (hasQuery) {
+                urlBuilder.append("&");
+            } else {
+                urlBuilder.append("?");
+                hasQuery = true;
+            }
+
+            urlBuilder.append(name).append("=").append(value);
+        }
+
+        return urlBuilder.toString();
+    }
+
+    /**
+     * 构建请求头
+     */
+    private HttpHeaders buildHeaders(List<HttpRequestNodeConfig.HeaderItem> headerItems, List<NodeIOData> inputs) {
+        HttpHeaders headers = new HttpHeaders();
+
+        if (headerItems != null) {
+            for (HttpRequestNodeConfig.HeaderItem item : headerItems) {
+                if (StringUtils.isNotBlank(item.getName())) {
+                    String name = renderTemplate(item.getName(), inputs);
+                    String value = renderTemplate(item.getValue(), inputs);
+                    headers.add(name, value);
+                }
+            }
+        }
+
+        return headers;
+    }
+
+    /**
+     * 构建请求体
+     */
+    private Object buildRequestBody(HttpRequestNodeConfig config, List<NodeIOData> inputs) {
+        String method = config.getMethod();
+        if ("GET".equalsIgnoreCase(method) || "DELETE".equalsIgnoreCase(method) || "HEAD".equalsIgnoreCase(method)) {
+            return null;
+        }
+
+        String contentType = config.getContentType();
+
+        // JSON Body
+        if ("application/json".equalsIgnoreCase(contentType)) {
+            if (config.getJsonBody() != null && !config.getJsonBody().isEmpty()) {
+                return renderJsonBody(config.getJsonBody(), inputs);
+            }
+        }
+
+        // Form Data
+        if ("multipart/form-data".equalsIgnoreCase(contentType)) {
+            return buildFormData(config.getFormDataBody(), inputs);
+        }
+
+        // Form URL Encoded
+        if ("application/x-www-form-urlencoded".equalsIgnoreCase(contentType)) {
+            return buildFormUrlEncoded(config.getFormUrlencodedBody(), inputs);
+        }
+
+        // Text Body
+        if (StringUtils.isNotBlank(config.getTextBody())) {
+            return renderTemplate(config.getTextBody(), inputs);
+        }
+
+        return null;
+    }
+
+    /**
+     * 渲染 JSON 请求体
+     * 支持三种模式：
+     * 1. 普通字段替换：{"name": "{var_01.name}"}
+     * 2. 整体 JSON 合并：{"$merge": "{var_01}"} - 将整个 JSON 对象合并进来
+     * 3. 智能合并：如果值是 {var_01} 且是有效 JSON，自动展开合并
+     */
+    private Map<String, Object> renderJsonBody(Map<String, Object> jsonBody, List<NodeIOData> inputs) {
+        Map<String, Object> rendered = new HashMap<>();
+        ObjectMapper mapper = new ObjectMapper();
+
+        for (Map.Entry<String, Object> entry : jsonBody.entrySet()) {
+            String key = entry.getKey();
+            Object value = entry.getValue();
+
+            // 处理特殊的 $merge 指令
+            if ("$merge".equals(key) && value instanceof String) {
+                String varRef = (String) value;
+                Map<String, Object> mergeData = resolveVariableAsJson(varRef, inputs, mapper);
+                if (mergeData != null) {
+                    rendered.putAll(mergeData);
+                }
+                continue;
+            }
+
+            if (value instanceof String) {
+                String strValue = (String) value;
+                
+                // 检查是否是单纯的变量引用（如 {var_01}）
+                if (strValue.matches("^\\{\\w+\\}$")) {
+                    // 尝试解析为 JSON 对象
+                    Map<String, Object> jsonValue = resolveVariableAsJson(strValue, inputs, mapper);
+                    if (jsonValue != null) {
+                        // 如果是 JSON 对象，合并所有字段
+                        rendered.putAll(jsonValue);
+                    } else {
+                        // 否则作为普通字符串处理
+                        rendered.put(key, renderTemplate(strValue, inputs));
+                    }
+                } else {
+                    // 普通字符串或包含多个变量的模板
+                    rendered.put(key, renderTemplate(strValue, inputs));
+                }
+            } else if (value instanceof Map) {
+                // 递归处理嵌套的 Map
+                @SuppressWarnings("unchecked")
+                Map<String, Object> nestedMap = (Map<String, Object>) value;
+                rendered.put(key, renderJsonBody(nestedMap, inputs));
+            } else {
+                // 其他类型直接保留
+                rendered.put(key, value);
+            }
+        }
+        return rendered;
+    }
+
+    /**
+     * 解析变量引用为 JSON 对象
+     * 例如：{var_01} -> 尝试解析 var_01 的值为 JSON Map
+     */
+    private Map<String, Object> resolveVariableAsJson(String varRef, List<NodeIOData> inputs, ObjectMapper mapper) {
+        // 提取变量名（去掉 {}）
+        String varName = varRef.replaceAll("[{}]", "");
+        
+        // 查找对应的输入变量
+        for (NodeIOData input : inputs) {
+            if (input != null && varName.equals(input.getName())) {
+                String varValue = input.valueToString();
+                return tryParseJson(varValue, mapper);
+            }
+        }
+        
+        return null;
+    }
+
+    /**
+     * 构建 Form Data
+     */
+    private MultiValueMap<String, String> buildFormData(List<HttpRequestNodeConfig.FormItem> formItems, List<NodeIOData> inputs) {
+        MultiValueMap<String, String> formData = new LinkedMultiValueMap<>();
+        if (formItems != null) {
+            for (HttpRequestNodeConfig.FormItem item : formItems) {
+                if (StringUtils.isNotBlank(item.getName())) {
+                    String name = renderTemplate(item.getName(), inputs);
+                    String value = renderTemplate(item.getValue(), inputs);
+                    formData.add(name, value);
+                }
+            }
+        }
+        return formData;
+    }
+
+    /**
+     * 构建 Form URL Encoded
+     */
+    private MultiValueMap<String, String> buildFormUrlEncoded(List<HttpRequestNodeConfig.FormItem> formItems, List<NodeIOData> inputs) {
+        return buildFormData(formItems, inputs);
+    }
+
+    /**
+     * 执行 HTTP 请求（支持重试）
+     */
+    private String executeHttpRequest(String url, String method, HttpHeaders headers, Object body, HttpRequestNodeConfig config) {
+        RestTemplate restTemplate = createRestTemplate(config.getTimeout());
+        
+        int maxRetries = config.getRetryTimes() != null ? config.getRetryTimes() : 0;
+        int attempt = 0;
+        Exception lastException = null;
+
+        while (attempt <= maxRetries) {
+            try {
+                // 设置 Content-Type
+                if (StringUtils.isNotBlank(config.getContentType())) {
+                    headers.setContentType(MediaType.parseMediaType(config.getContentType()));
+                }
+
+                HttpEntity<?> requestEntity = new HttpEntity<>(body, headers);
+                HttpMethod httpMethod = HttpMethod.valueOf(method.toUpperCase());
+
+                ResponseEntity<String> response = restTemplate.exchange(url, httpMethod, requestEntity, String.class);
+                
+                if (response.getStatusCode().is2xxSuccessful()) {
+                    return response.getBody();
+                } else {
+                    throw new RuntimeException("HTTP 请求失败，状态码: " + response.getStatusCode());
+                }
+
+            } catch (Exception e) {
+                lastException = e;
+                attempt++;
+                
+                if (attempt <= maxRetries) {
+                    log.warn("HTTP 请求失败，正在重试 ({}/{}): {}", attempt, maxRetries, e.getMessage());
+                    try {
+                        TimeUnit.SECONDS.sleep(1); // 重试前等待 1 秒
+                    } catch (InterruptedException ie) {
+                        Thread.currentThread().interrupt();
+                        throw new RuntimeException("重试等待被中断", ie);
+                    }
+                }
+            }
+        }
+
+        throw new RuntimeException("HTTP 请求失败，已重试 " + maxRetries + " 次", lastException);
+    }
+
+    /**
+     * 创建 RestTemplate（设置超时）
+     */
+    private RestTemplate createRestTemplate(Integer timeoutSeconds) {
+        RestTemplate restTemplate = new RestTemplate();
+        
+        // 设置超时时间
+        int timeout = (timeoutSeconds != null ? timeoutSeconds : 10) * 1000;
+        org.springframework.http.client.SimpleClientHttpRequestFactory requestFactory = 
+            new org.springframework.http.client.SimpleClientHttpRequestFactory();
+        requestFactory.setConnectTimeout(timeout);
+        requestFactory.setReadTimeout(timeout);
+        restTemplate.setRequestFactory(requestFactory);
+        
+        return restTemplate;
+    }
+}

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/node/httpRequest/HttpRequestNodeConfig.java

@@ -0,0 +1,113 @@
+package org.ruoyi.workflow.workflow.node.httpRequest;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+import lombok.Data;
+
+import java.util.List;
+import java.util.Map;
+
+/**
+ * HTTP 请求节点配置
+ */
+@Data
+public class HttpRequestNodeConfig {
+    
+    /**
+     * HTTP 请求方法
+     */
+    private String method = "GET";
+    
+    /**
+     * 请求 URL
+     */
+    private String url;
+    
+    /**
+     * Content-Type
+     */
+    @JsonProperty("content_type")
+    private String contentType = "text/plain";
+    
+    /**
+     * 请求头列表
+     */
+    private List<HeaderItem> headers;
+    
+    /**
+     * Query 参数列表
+     */
+    private List<ParamItem> params;
+    
+    /**
+     * 纯文本请求体
+     */
+    @JsonProperty("text_body")
+    private String textBody;
+    
+    /**
+     * JSON 请求体
+     */
+    @JsonProperty("json_body")
+    private Map<String, Object> jsonBody;
+    
+    /**
+     * Form Data 请求体
+     */
+    @JsonProperty("form_data_body")
+    private List<FormItem> formDataBody;
+    
+    /**
+     * Form URL Encoded 请求体
+     */
+    @JsonProperty("form_urlencoded_body")
+    private List<FormItem> formUrlencodedBody;
+    
+    /**
+     * 请求体（通用）
+     */
+    private Map<String, Object> body;
+    
+    /**
+     * 超时时间（秒）
+     */
+    private Integer timeout = 10;
+    
+    /**
+     * 重试次数
+     */
+    @JsonProperty("retry_times")
+    private Integer retryTimes = 0;
+    
+    /**
+     * 是否清除 HTML 标签
+     */
+    @JsonProperty("clear_html")
+    private Boolean clearHtml = false;
+    
+    /**
+     * 请求头项
+     */
+    @Data
+    public static class HeaderItem {
+        private String name;
+        private String value;
+    }
+    
+    /**
+     * Query 参数项
+     */
+    @Data
+    public static class ParamItem {
+        private String name;
+        private String value;
+    }
+    
+    /**
+     * Form 表单项
+     */
+    @Data
+    public static class FormItem {
+        private String name;
+        private String value;
+    }
+}

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/node/knowledgeRetrieval/KnowledgeRetrievalNode.java

@@ -0,0 +1,274 @@
+package org.ruoyi.workflow.workflow.node.knowledgeRetrieval;
+
+import lombok.extern.slf4j.Slf4j;
+import org.apache.commons.lang3.StringUtils;
+import org.ruoyi.workflow.entity.WorkflowComponent;
+import org.ruoyi.workflow.entity.WorkflowNode;
+import org.ruoyi.workflow.util.SpringUtil;
+import org.ruoyi.workflow.workflow.NodeProcessResult;
+import org.ruoyi.workflow.workflow.WfNodeState;
+import org.ruoyi.workflow.workflow.WfState;
+import org.ruoyi.workflow.workflow.WorkflowUtil;
+import org.ruoyi.workflow.workflow.data.NodeIOData;
+import org.ruoyi.workflow.workflow.node.AbstractWfNode;
+import org.ruoyi.service.VectorStoreService;
+import org.ruoyi.service.IKnowledgeInfoService;
+import org.ruoyi.domain.bo.QueryVectorBo;
+import org.ruoyi.domain.vo.KnowledgeInfoVo;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.ruoyi.workflow.cosntant.AdiConstant.WorkflowConstant.DEFAULT_OUTPUT_PARAM_NAME;
+
+/**
+ * 【节点】知识库检索节点
+ * 从知识库中检索相关内容
+ */
+@Slf4j
+public class KnowledgeRetrievalNode extends AbstractWfNode {
+
+    public KnowledgeRetrievalNode(WorkflowComponent wfComponent, WorkflowNode nodeDef, WfState wfState, WfNodeState nodeState) {
+        super(wfComponent, nodeDef, wfState, nodeState);
+    }
+
+    /**
+     * 处理知识库检索
+     * nodeConfig 格式：
+     * {
+     *   "knowledge_id": "kb_123",
+     *   "top_k": 5,
+     *   "similarity_threshold": 0.7,
+     *   "retrieval_mode": "vector",
+     *   "embedding_model": "text-embedding-3-small",
+     *   "return_source": true,
+     *   "prompt": "额外的查询改写提示词"
+     * }
+     *
+     * @return 检索结果
+     */
+    @Override
+    public NodeProcessResult onProcess() {
+        KnowledgeRetrievalNodeConfig config = checkAndGetConfig(KnowledgeRetrievalNodeConfig.class);
+        
+        // 验证知识库ID
+        if (StringUtils.isBlank(config.getKnowledgeId())) {
+            log.error("Knowledge base ID is required but not provided");
+            List<NodeIOData> outputs = new ArrayList<>();
+            outputs.add(NodeIOData.createByText(DEFAULT_OUTPUT_PARAM_NAME, "", "错误：未配置知识库ID"));
+            return NodeProcessResult.builder().content(outputs).build();
+        }
+        
+        // 获取查询文本
+        String queryText = getFirstInputText();
+        if (StringUtils.isBlank(queryText)) {
+            log.warn("Knowledge retrieval node has no input query, node: {}", state.getUuid());
+            // 返回空结果
+            List<NodeIOData> outputs = new ArrayList<>();
+            outputs.add(NodeIOData.createByText(DEFAULT_OUTPUT_PARAM_NAME, "", ""));
+            return NodeProcessResult.builder().content(outputs).build();
+        }
+        
+        log.info("Knowledge retrieval node config: {}", config);
+        log.info("Query text: {}", queryText);
+        
+        // 如果有自定义提示词，对查询进行改写
+        String finalQuery = queryText;
+        if (StringUtils.isNotBlank(config.getPrompt())) {
+            finalQuery = rewriteQuery(config, queryText);
+            log.info("Rewritten query: {}", finalQuery);
+        }
+        
+        // 根据检索模式执行不同的检索策略
+        String retrievalResult;
+        String mode = config.getRetrievalMode() != null ? config.getRetrievalMode().toLowerCase() : "vector";
+        
+        // 目前只支持向量检索，图谱检索需要依赖graph模块
+        if ("graph".equals(mode) || "hybrid".equals(mode)) {
+            log.warn("Graph retrieval mode is not supported in workflow-api module, falling back to vector retrieval");
+        }
+        
+        retrievalResult = retrieveFromVector(config, finalQuery);
+        
+        log.info("Retrieval result length: {}", retrievalResult.length());
+        
+        // 构建输出
+        List<NodeIOData> outputs = new ArrayList<>();
+        outputs.add(NodeIOData.createByText(DEFAULT_OUTPUT_PARAM_NAME, "", retrievalResult));
+        
+        // 如果需要返回原始查询
+        outputs.add(NodeIOData.createByText("query", "", finalQuery));
+        
+        return NodeProcessResult.builder().content(outputs).build();
+    }
+    
+    /**
+     * 使用LLM改写查询
+     */
+    private String rewriteQuery(KnowledgeRetrievalNodeConfig config, String originalQuery) {
+        try {
+            // 构建改写提示词
+            String prompt = WorkflowUtil.renderTemplate(config.getPrompt(), state.getInputs());
+            prompt = prompt.replace("{query}", originalQuery);
+            
+            log.info("Query rewrite prompt: {}", prompt);
+            
+            // 调用LLM进行查询改写
+            String rewrittenQuery = invokeLLMSync(config, prompt);
+            
+            if (StringUtils.isNotBlank(rewrittenQuery)) {
+                log.info("Query rewritten from '{}' to '{}'", originalQuery, rewrittenQuery);
+                return rewrittenQuery.trim();
+            }
+            
+            // 如果改写失败，返回原查询
+            return originalQuery;
+        } catch (Exception e) {
+            log.error("Failed to rewrite query, using original query", e);
+            return originalQuery;
+        }
+    }
+    
+    /**
+     * 同步调用LLM
+     * 使用一个临时的流式处理器来收集完整响应
+     */
+    private String invokeLLMSync(KnowledgeRetrievalNodeConfig config, String prompt) {
+        try {
+            // 创建一个StringBuilder来收集LLM响应
+            StringBuilder responseBuilder = new StringBuilder();
+            Object lock = new Object();
+            boolean[] completed = {false};
+            
+            // 创建临时节点状态用于LLM调用
+            WfNodeState tempState = new WfNodeState();
+            tempState.setUuid(state.getUuid() + "_rewrite");
+            List<NodeIOData> tempInputs = new ArrayList<>();
+            tempInputs.add(NodeIOData.createByText("input", "", prompt));
+            tempState.setInputs(tempInputs);
+            
+            // 创建临时工作流节点定义
+            WorkflowNode tempNode = new WorkflowNode();
+            tempNode.setUuid(tempState.getUuid());
+            tempNode.setInputConfig(node.getInputConfig());
+            
+            // 使用WorkflowUtil调用LLM（流式）
+            WorkflowUtil workflowUtil = SpringUtil.getBean(WorkflowUtil.class);
+            List<dev.langchain4j.data.message.UserMessage> systemMessage = 
+                List.of(dev.langchain4j.data.message.UserMessage.from(prompt));
+            
+            // 调用流式LLM
+            String category = StringUtils.isNotBlank(config.getCategory()) ? config.getCategory() : "llm";
+            String modelName = StringUtils.isNotBlank(config.getModelName()) ? config.getModelName() : "deepseek-chat";
+            
+            workflowUtil.streamingInvokeLLM(
+                wfState, 
+                tempState, 
+                tempNode, 
+                category,
+                modelName,
+                systemMessage
+            );
+            
+            // 等待LLM响应完成（最多等待30秒）
+            long startTime = System.currentTimeMillis();
+            long timeout = 30000; // 30秒超时
+            
+            while (!completed[0] && (System.currentTimeMillis() - startTime) < timeout) {
+                synchronized (lock) {
+                    // 检查是否有输出
+                    if (!tempState.getOutputs().isEmpty()) {
+                        for (NodeIOData output : tempState.getOutputs()) {
+                            if ("output".equals(output.getName())) {
+                                String text = output.valueToString();
+                                if (StringUtils.isNotBlank(text)) {
+                                    responseBuilder.append(text);
+                                    completed[0] = true;
+                                    break;
+                                }
+                            }
+                        }
+                    }
+                }
+                
+                if (!completed[0]) {
+                    Thread.sleep(100); // 等待100ms后重试
+                }
+            }
+            
+            String result = responseBuilder.toString().trim();
+            if (StringUtils.isBlank(result)) {
+                log.warn("LLM sync call returned empty response");
+            }
+            
+            return result;
+        } catch (Exception e) {
+            log.error("Failed to invoke LLM synchronously", e);
+            return "";
+        }
+    }
+    
+    /**
+     * 从向量库检索
+     */
+    private String retrieveFromVector(KnowledgeRetrievalNodeConfig config, String query) {
+        try {
+            VectorStoreService vectorStoreService = SpringUtil.getBean(VectorStoreService.class);
+            IKnowledgeInfoService knowledgeInfoService = SpringUtil.getBean(IKnowledgeInfoService.class);
+            
+            // 获取知识库信息以获取embedding模型配置
+            Long knowledgeId = Long.parseLong(config.getKnowledgeId());
+            KnowledgeInfoVo knowledgeInfo = knowledgeInfoService.queryById(knowledgeId);
+            
+            if (knowledgeInfo == null) {
+                log.error("Knowledge base not found: {}", config.getKnowledgeId());
+                return "错误：知识库不存在";
+            }
+            
+            // 构建查询参数
+            QueryVectorBo queryBo = new QueryVectorBo();
+            queryBo.setKid(config.getKnowledgeId());
+            queryBo.setQuery(query);
+            queryBo.setMaxResults(config.getTopK());
+            
+            // 优先使用配置中的embedding模型，否则使用知识库的默认模型
+            String embeddingModel = StringUtils.isNotBlank(config.getEmbeddingModel()) 
+                ? config.getEmbeddingModel() 
+                : knowledgeInfo.getEmbeddingModelName();
+            
+            // 验证embedding模型配置
+            if (StringUtils.isBlank(embeddingModel)) {
+                log.error("Embedding model not configured for knowledge base: {}", config.getKnowledgeId());
+                return "错误：知识库未配置向量化模型";
+            }
+            
+            queryBo.setEmbeddingModelName(embeddingModel);
+            
+            log.info("Querying knowledge base: kid={}, query='{}', embedding model: {}, topK: {}, threshold: {}", 
+                config.getKnowledgeId(), query, embeddingModel, config.getTopK(), config.getSimilarityThreshold());
+            
+            // 执行检索
+            List<String> results = vectorStoreService.getQueryVector(queryBo);
+            
+            log.info("Vector store query completed, results count: {}", results != null ? results.size() : 0);
+            
+            if (results == null || results.isEmpty()) {
+                log.warn("No results found from vector store for knowledge: {}, query: '{}'", config.getKnowledgeId(), query);
+                return "";
+            }
+            
+            // 合并结果
+            String mergedResult = String.join("\n\n---\n\n", results);
+            log.info("Retrieved {} documents from vector store", results.size());
+            
+            return mergedResult;
+        } catch (NumberFormatException e) {
+            log.error("Invalid knowledge base ID format: {}", config.getKnowledgeId(), e);
+            return "错误：知识库ID格式无效";
+        } catch (Exception e) {
+            log.error("Failed to retrieve from vector store", e);
+            return "";
+        }
+    }
+    
+}

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/node/knowledgeRetrieval/KnowledgeRetrievalNodeConfig.java

@@ -0,0 +1,111 @@
+package org.ruoyi.workflow.workflow.node.knowledgeRetrieval;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+import jakarta.validation.constraints.Max;
+import jakarta.validation.constraints.Min;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+
+/**
+ * 知识库检索节点配置
+ */
+@EqualsAndHashCode
+@Data
+public class KnowledgeRetrievalNodeConfig {
+    
+    /**
+     * 知识库UUID（主要字段）
+     */
+    @JsonProperty("knowledge_base_uuid")
+    private String knowledgeBaseUuid;
+    
+    /**
+     * 知识库ID（兼容字段）
+     */
+    @JsonProperty("knowledge_id")
+    private String knowledgeId;
+    
+    /**
+     * 获取知识库ID（优先使用knowledgeBaseUuid）
+     */
+    public String getKnowledgeId() {
+        return knowledgeBaseUuid != null ? knowledgeBaseUuid : knowledgeId;
+    }
+    
+    /**
+     * 检索的最大结果数
+     */
+    @Min(1)
+    @Max(100)
+    @JsonProperty("top_k")
+    private Integer topK = 5;
+    
+    /**
+     * 检索的最大结果数（兼容字段，前端使用top_n）
+     */
+    @JsonProperty("top_n")
+    private Integer topN;
+    
+    /**
+     * 获取topK值（优先使用topN）
+     */
+    public Integer getTopK() {
+        return topN != null ? topN : topK;
+    }
+    
+    /**
+     * 相似度阈值（0-1之间）
+     */
+    @Min(0)
+    @Max(1)
+    @JsonProperty("similarity_threshold")
+    private Double similarityThreshold = 0.7;
+    
+    /**
+     * 相似度阈值（兼容字段，前端使用score）
+     */
+    @JsonProperty("score")
+    private Double score;
+    
+    /**
+     * 获取相似度阈值（优先使用score）
+     */
+    public Double getSimilarityThreshold() {
+        return score != null ? score : similarityThreshold;
+    }
+    
+    /**
+     * 检索模式：vector（向量检索）、graph（图谱检索）、hybrid（混合检索）
+     */
+    @JsonProperty("retrieval_mode")
+    private String retrievalMode = "vector";
+    
+    /**
+     * 模型分类（用于LLM查询改写）
+     */
+    private String category;
+    
+    /**
+     * LLM模型名称（用于查询改写）
+     */
+    @JsonProperty("model_name")
+    private String modelName;
+    
+    /**
+     * Embedding模型名称（用于向量检索）
+     */
+    @JsonProperty("embedding_model")
+    private String embeddingModel;
+    
+    /**
+     * 是否返回原文
+     */
+    @JsonProperty("return_source")
+    private Boolean returnSource = true;
+    
+    /**
+     * 自定义查询提示词（可选）
+     * 用于对查询进行预处理或改写
+     */
+    private String prompt;
+}

ruoyi-modules-api/ruoyi-workflow-api/src/main/java/org/ruoyi/workflow/workflow/node/switcher/SwitcherNode.java

@@ -234,16 +234,22 @@ public class SwitcherNode extends AbstractWfNode {
     private String getValueFromInputs(String nodeUuid, String paramName, List<NodeIOData> inputs) {
         log.debug("从节点UUID '{}' 搜索参数 '{}'", nodeUuid, paramName);
         
+        String result = null;
+
         // 首先尝试从当前输入中查找
         log.debug("检查当前输入 (数量: {})", inputs.size());
         for (NodeIOData input : inputs) {
             log.debug("  - 输入: 名称='{}', 值='{}'", input.getName(), input.valueToString());
             if (paramName.equals(input.getName())) {
-                log.info("在当前输入中找到参数 '{}': '{}'", paramName, input.valueToString());
-                return input.valueToString();
+                result = input.valueToString();
             }
         }
         
+        if (result != null) {
+            log.info("在当前输入中找到参数 '{}': '{}'", paramName, result);
+            return result;
+        }
+
         // 如果当前输入中没有，尝试从工作流状态中查找指定节点的输出
         if (StringUtils.isNotBlank(nodeUuid)) {
             List<NodeIOData> nodeOutputs = wfState.getIOByNodeUuid(nodeUuid);
@@ -251,14 +257,27 @@ public class SwitcherNode extends AbstractWfNode {
             for (NodeIOData output : nodeOutputs) {
                 log.debug("  - 输出: 名称='{}', 值='{}'", output.getName(), output.valueToString());
                 if (paramName.equals(output.getName())) {
-                    log.info("在节点 '{}' 的输出中找到参数 '{}': '{}'", nodeUuid, paramName, output.valueToString());
-                    return output.valueToString();
+                    result = output.valueToString();
                 }
             }
+            
+            if (result != null) {
+                log.info("在节点 '{}' 的输出中找到参数 '{}': '{}'", nodeUuid, paramName, result);
+                return result;
+            }
         } else {
             log.debug("节点UUID为空，跳过工作流状态搜索");
         }
 
+        // 特殊处理：如果找的是 'output' 但没找到，尝试找 'input'
+        if ("output".equals(paramName)) {
+            log.debug("未找到参数 'output'，尝试查找 'input'");
+            String inputValue = getValueFromInputs(nodeUuid, "input", inputs);
+            if (inputValue != null) {
+                return inputValue;
+            }
+        }
+
         log.warn("在输入或节点 '{}' 的输出中未找到参数 '{}'", nodeUuid, paramName);
         return null;
     }

ruoyi-modules/ruoyi-chat/src/main/java/org/ruoyi/chat/service/chat/impl/OllamaServiceImpl.java

@@ -1,5 +1,8 @@
 package org.ruoyi.chat.service.chat.impl;
 
+import dev.langchain4j.model.chat.StreamingChatModel;
+import dev.langchain4j.model.chat.response.StreamingChatResponseHandler;
+import dev.langchain4j.model.ollama.OllamaStreamingChatModel;
 import io.github.ollama4j.OllamaAPI;
 import io.github.ollama4j.models.chat.OllamaChatMessage;
 import io.github.ollama4j.models.chat.OllamaChatMessageRole;
@@ -80,6 +83,30 @@ public class OllamaServiceImpl implements IChatService {
         return emitter;
     }
 
+    /**
+     * 工作流场景：支持 langchain4j handler
+     */
+    @Override
+    public void chat(ChatRequest request, StreamingChatResponseHandler handler) {
+        log.info("workflow chat, model: {}", request.getModel());
+
+        ChatModelVo chatModelVo = chatModelService.selectModelByName(request.getModel());
+
+        StreamingChatModel model = OllamaStreamingChatModel.builder()
+                .baseUrl(chatModelVo.getApiHost() != null ? chatModelVo.getApiHost() : "http://localhost:11434")
+                .modelName(chatModelVo.getModelName())
+                .build();
+
+        try {
+            // 将 ruoyi-ai 的 ChatRequest 转换为 langchain4j 的格式
+            dev.langchain4j.model.chat.request.ChatRequest chatRequest = convertToLangchainRequest(request);
+            model.chat(chatRequest, handler);
+        } catch (Exception e) {
+            log.error("workflow ollama请求失败：{}", e.getMessage(), e);
+            throw new RuntimeException("ollama workflow chat failed: " + e.getMessage(), e);
+        }
+    }
+
     @Override
     public String getCategory() {
         return ChatModeType.OLLAMA.getCode();