1、文本分割优化，建议 200 和40，把文档切得更碎一些方便检索命中。

2、RAG接口进一步优化

 /**
     * RAG知识库接口
     *
     * @param prompt
     * @return
     */
    @GetMapping(value = "/rag/chat", produces = MediaType.TEXT_EVENT_STREAM_VALUE)
    public Flux<ChatCompletionResponse> ragchat(String prompt) {

        List<Float> floatList = embeddingClient.embed(prompt);
        Map<String, Object> searchParams = Maps.newHashMap();
        searchParams.put("nprobe", 10);

// anythingllm_hhy  anythingllm_test01
        SearchReq searchReq = SearchReq.builder()
                .collectionName("anythingllm_test01")
                .data(Collections.singletonList(new FloatVec(floatList)))
                .metricType(IndexParam.MetricType.COSINE)
                .searchParams(searchParams)
//                metadata  text   deepseek4j_test
                .outputFields(Collections.singletonList("metadata"))
                //anything中的默认配置为4
                .topK(5)
                .build();


        // 定义相似性阈值
        double threshold = 0.8;
        SearchResp searchResp = milvusClientV2.search(searchReq);

        List<String> resultList = new ArrayList<>();
        List<List<SearchResp.SearchResult>> searchResults = searchResp.getSearchResults();
        for (List<SearchResp.SearchResult> results : searchResults) {
            for (SearchResp.SearchResult result : results) {
                System.out.println(result.getEntity() + "相似度为：" + result.getScore());
                result.getScore();
                Gson gson = new Gson();
                JsonObject jsonObject = gson.fromJson(result.getEntity().get("metadata").toString(), JsonObject.class);
                resultList.add(result.getEntity().get("metadata").toString());
            }
        }


        ChatCompletionRequest request = ChatCompletionRequest.builder()
                // 根据渠道模型名称动态修改这个参数
                .model("deepseek-r1:32b")
                .addUserMessage(String.format("你要根据用户输入的问题：%s \n \n 参考如下内容： %s  \n\n 整理处理最终结果", prompt, resultList)).build();

        return deepSeekClient.chatFluxCompletion(request);
    }