elasticsearch query related

本文介绍了一个针对Elasticsearch实现的抽象基类EsBaseDaoImpl,该类提供了包括增删改查在内的多种操作方法,并支持批量操作、条件查询等功能。通过具体的代码示例,展示了如何使用此基类来简化Elasticsearch的应用开发。

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public abstract class EsBaseDaoImpl<T> implements EsBaseDao<T> {  
  
    private Logger logger = LoggerFactory.getLogger(EsBaseService.class);  
  
    @Autowired  
    private ElasticsearchTemplate elasticsearchTemplate;  
  
    @Autowired  
    private Client esClient;  
  
      
    /**  
     * 插入或等新,需要有id,id需要自己生成  
     *   
     * @param tList  
     * @return  
     */  
    public boolean insertOrUpdate(List<T> tList) {  
        List<IndexQuery> queries = new ArrayList<IndexQuery>();  
        for (T t : tList) {  
            String id = ((EsBaseBean) t).getId();  
            if (id == null) {  
                id = UuidHelper.getRandomUUID();  
                ((EsBaseBean) t).setId(id);  
            }  
            IndexQuery indexQuery = new IndexQueryBuilder().withId(id).withObject(t).build();  
            queries.add(indexQuery);  
        }  
        elasticsearchTemplate.bulkIndex(queries);  
        return true;  
    }  
  
    /**  
     * 插入或更新  
     *   
     * @param t  
     * @return  
     */  
    public boolean insertOrUpdate(T t) {  
  
        String id = ((EsBaseBean) t).getId();  
        if (id == null) {  
            id = UuidHelper.getRandomUUID();  
            ((EsBaseBean) t).setId(id);  
        }  
        try {  
            IndexQuery indexQuery = new IndexQueryBuilder().withId(id).withObject(t).build();  
            elasticsearchTemplate.index(indexQuery);  
            return true;  
        } catch (Exception e) {  
            logger.error("insert or update user info error.", e);  
            return false;  
        }  
    }  
  
    /**  
     * 删除  
     *   
     * @param id  
     * @return  
     */  
    public boolean deleteById(String id) {  
        try {  
            elasticsearchTemplate.delete(getEntityClass(), id);  
            return true;  
        } catch (Exception e) {  
            logger.error("delete " + getEntityClass() + " by id " + id  
                    + " error.", e);  
            return false;  
        }  
    }  
      
    /**  
     * 删除ids  
     * @param idList  
     * @return  
     */  
    @Override  
    public boolean deleteByIds(List<String> idList) {  
        try {  
             CriteriaQuery criteriaQuery = new CriteriaQuery(new Criteria());  
             criteriaQuery.setIds(idList);  
             elasticsearchTemplate.delete(criteriaQuery, getEntityClass());  
            return true;  
        } catch (Exception e) {  
            e.printStackTrace();  
            return false;  
        }  
    }  
  
  
    /**  
     * 根据条件查询  
     * @param filedContentMap 不能为null  
     * @return  
     */  
    public boolean deleteByQuery(Map<String,Object> filedContentMap) {  
        try {  
            DeleteQuery dq = new DeleteQuery();  
              
            BoolQueryBuilder qb=QueryBuilders. boolQuery();  
            if(filedContentMap!=null)  
                for (String key : filedContentMap.keySet()) {//字段查询  
                    qb.must(QueryBuilders.matchQuery(key,filedContentMap.get(key)));  
                }  
            dq.setQuery(qb);;  
            elasticsearchTemplate.delete(dq, getEntityClass());;  
            return true;  
        } catch (Exception e) {  
            e.printStackTrace();  
            return false;  
        }  
    }  
    /**  
     * 检查健康状态  
     *   
     * @return  
     */  
    public boolean ping() {  
        try {  
            ActionFuture<ClusterHealthResponse> health = esClient.admin()  
                    .cluster().health(new ClusterHealthRequest());  
            ClusterHealthStatus status = health.actionGet().getStatus();  
            if (status.value() == ClusterHealthStatus.RED.value()) {  
                throw new RuntimeException(  
                        "elasticsearch cluster health status is red.");  
            }  
            return true;  
        } catch (Exception e) {  
            logger.error("ping elasticsearch error.", e);  
            return false;  
        }  
    }  
  
    /**  
     * 条件查询  
     *   
     * @param searchfields  
     *            查询字段  
     * @param filedContentMap  
     *            字段和查询内容  
     * @param sortField  
     *            排序 字段  
     * @param order  
     *            排序  
     * @param from  
     * @param size  
     * @return  
     */  
    @Override  
    public BasePage<T> queryPage(Map<String,Object> filedContentMap, final List<String> heightFields, String sortField, SortOrder order, BasePage<T>basePage) {  
          
        Field[] hfields=new Field[0];  
        if(heightFields!=null)  
        {  
            hfields = new Field[heightFields.size()];  
            for (int i = 0; i < heightFields.size(); i++) {  
                hfields[i] = new HighlightBuilder.Field(heightFields.get(i)).preTags("<em style='color:red'>").postTags("</em>").fragmentSize(250);  
            }  
        }  
        NativeSearchQueryBuilder nsb = new NativeSearchQueryBuilder().withHighlightFields(hfields);//高亮字段  
        if (sortField != null && order != null)//排序  
            nsb.withSort(new FieldSortBuilder(sortField).ignoreUnmapped(true).order(order));  
        if (basePage != null)//分页  
            nsb.withPageable(new PageRequest(basePage.getPageNo(), basePage.getPageSize()));  
        BoolQueryBuilder qb=QueryBuilders. boolQuery();  
        for (String key : filedContentMap.keySet()) {//字段查询  
            qb.must(QueryBuilders.matchQuery(key,filedContentMap.get(key)));  
              
        }  
        //userKey=78e48b85e94911e0d285f4eec990d556  
        //fa6e9c5bb24a21807c59e5fd3b609e12  
        nsb.withQuery(qb);  
        SearchQuery searchQuery = nsb.build();//查询建立  
  
        Page<T> page = null;  
        if (heightFields!=null&&heightFields.size() > 0) {//如果设置高亮  
            page = elasticsearchTemplate.queryForPage(searchQuery,  
                    getEntityClass(), new SearchResultMapper() {  
                        @SuppressWarnings("unchecked")  
                        @Override  
                        public <T> Page<T> mapResults(SearchResponse response,Class<T> clazz, Pageable pageable) {  
                            List<T> chunk = new ArrayList<T>();  
                            for (SearchHit searchHit : response.getHits()) {  
                                if (response.getHits().getHits().length <= 0) {  
                                    return null;  
                                }  
  
                                Map<String, Object> entityMap = searchHit.getSource();  
                                for (String highName : heightFields) {  
                                    Text text[]=searchHit.getHighlightFields().get(highName).fragments();  
                                    if(text.length>0)  
                                    {  
                                        String highValue = searchHit.getHighlightFields().get(highName).fragments()[0].toString();  
                                        entityMap.put(highName, highValue);  
                                    }  
                                }  
                                chunk.add((T) PropertyHelper.getFansheObj(  
                                        getEntityClass(), entityMap));  
                            }  
                            if (chunk.size() > 0) {  
                                return new PageImpl<T>((List<T>) chunk);  
                            }  
                            return new PageImpl<T>(new ArrayList<T>());  
                        }  
  
                    });  
        } else//如果不设置高亮  
        {  
            logger.info("#################"+qb.toString());  
            page = elasticsearchTemplate.queryForPage(searchQuery,getEntityClass());  
        }  
          
  
    //  List<T> ts = page.getContent();  
  
        basePage.setTotalRecord(page.getTotalElements());  
        basePage.setResults(page.getContent());  
        return basePage;  
    }  
  
      
    @Override  
    public List<T> queryList(Map<String, Object> filedContentMap,final List<String> heightFields, String sortField, SortOrder order) {  
        Field[] hfields=new Field[0];  
        if(heightFields!=null)  
        {  
            hfields = new Field[heightFields.size()];  
            for (int i = 0; i < heightFields.size(); i++) {  
                //String o="{\"abc\" : \"[abc]\"}";  
                hfields[i] = new HighlightBuilder.Field(heightFields.get(i)).preTags("<em>").postTags("</em>").fragmentSize(250);  
            }  
        }  
        NativeSearchQueryBuilder nsb = new NativeSearchQueryBuilder().withHighlightFields(hfields);//高亮字段  
        if (sortField != null && order != null)//排序  
            nsb.withSort(new FieldSortBuilder(sortField).ignoreUnmapped(true).order(order));  
        BoolQueryBuilder qb=QueryBuilders. boolQuery();  
        for (String key : filedContentMap.keySet()) {//字段查询  
            qb.must(QueryBuilders.matchQuery(key,filedContentMap.get(key)));  
              
        }  
        nsb.withQuery(qb);  
        SearchQuery searchQuery = nsb.build();//查询建立  
        Page<T> page = null;  
        if (heightFields!=null&&heightFields.size() > 0) {//如果设置高亮  
            page = elasticsearchTemplate.queryForPage(searchQuery,  
                    getEntityClass(), new SearchResultMapper() {  
                        @SuppressWarnings("unchecked")  
                        @Override  
                        public <T> Page<T> mapResults(SearchResponse response,Class<T> clazz, Pageable pageable) {  
                            List<T> chunk = new ArrayList<T>();  
                            for (SearchHit searchHit : response.getHits()) {  
                                if (response.getHits().getHits().length <= 0) {  
                                    return null;  
                                }  
  
                                Map<String, Object> entityMap = searchHit.getSource();  
                                for (String highName : heightFields) {  
                                    String highValue = searchHit.getHighlightFields().get(highName).fragments()[0].toString();  
                                    entityMap.put(highName, highValue);  
                                }  
                                chunk.add((T) PropertyHelper.getFansheObj(getEntityClass(), entityMap));  
                            }  
                            if (chunk.size() > 0) {  
                                return new PageImpl<T>((List<T>) chunk);  
                            }  
                            return null;  
                        }  
  
                    });  
        } else//如果不设置高亮  
            page = elasticsearchTemplate.queryForPage(searchQuery,getEntityClass());  
          
        return page.getContent();  
    }  
    /**  
     * 本类查询  
     *   
     * @param id  
     * @return  
     */  
    public T queryById(String id) {  
        StringQuery stringQuery = new StringQuery("id=" + id);  
        T t = elasticsearchTemplate.queryForObject(stringQuery,  
                getEntityClass());  
        return t;  
  
    }  
  
      
      
    public ElasticsearchTemplate getElasticsearchTemplate() {  
        return elasticsearchTemplate;  
    }  
  
  
    public Client getEsClient() {  
        return esClient;  
    }  
  
  
  
    /**  
     * 得到类型  
     *   
     * @return  
     */  
    public abstract Class<T> getEntityClass();  
    /**  
     * 添加各自类的影射  
     */  
    public abstract void putClassMapping();  
      
  
  
      
  
}  

### Elasticsearch Retrieval-Augmented Generation Implementation and Best Practices #### Understanding the Integration of Elasticsearch with RAG Elasticsearch serves as a powerful tool within the context of implementing retrieval-augmented generation (RAG). The integration leverages Elasticsearch's capabilities in handling large volumes of data efficiently while providing fast query responses. This setup enhances the performance of language models when generating text based on retrieved information from vast datasets. For instance, one can refer to practical examples provided through GitHub repositories such as `langchain-elasticsearch-RAG`[^1], showcasing how these technologies work together seamlessly for specific applications like document summarization or question answering systems. #### Data Indexing Strategy In designing an effective RAG system utilizing Elasticsearch, careful consideration must be given to how data gets indexed. An index structure similar to that found in traditional relational databases plays a crucial role here—each record corresponds to entries within this schema-specific container[^3]. When dealing with dynamic content streams or log analysis scenarios, daily indices might prove beneficial due to their ability to manage time-series data effectively without compromising search efficiency across multiple periods simultaneously. #### Text Segmentation Techniques To optimize interactions between Elasticsearch and LLMs during the preprocessing phase before feeding into any generative model, appropriate segmentation strategies become essential. Two primary aspects influence decision-making regarding splitting documents: - **Token Limitation**: Adhering strictly to embedding models' token constraints ensures compatibility. - **Semantic Integrity**: Maintaining coherent meaning units improves overall retrieval quality significantly[^4]. Common approaches include sentence-based partitioning, paragraph-level divisions, or even custom logic tailored specifically towards domain-specific requirements ensuring both conditions above remain satisfied adequately throughout processing stages leading up until final output generation via chosen neural architectures employed post-retrieval steps. #### Code Example Demonstrating Basic Setup Below demonstrates setting up basic components necessary for integrating Elasticsearch alongside Python-based NLP pipelines supporting RAG workflows: ```python from elasticsearch import Elasticsearch import langchain.elasticsearch_rag as rag es_client = Elasticsearch() def initialize_index(): es_client.indices.create( index="product_catalog", body={ "settings": { "number_of_shards": 1, "analysis": { "analyzer": {"default": {"type": "standard"}} } }, "mappings": { "properties": { "title": {"type": "text"}, "description": {"type": "text"} } } }, ignore=400) initialize_index() ``` This snippet initializes an Elasticsearch cluster configured appropriately for storing structured metadata about products intended later use within downstream tasks involving natural language understanding processes powered by advanced machine learning techniques implemented over RESTful APIs exposed externally through web services architecture patterns common today among cloud-native deployments targeting scalable solutions capable enough meeting modern enterprise demands around big data analytics platforms built atop distributed computing frameworks optimized toward real-time insights extraction directly out-of-the-box without requiring extensive customization efforts upfront investment costs associated traditionally seen elsewhere inside IT departments managing legacy infrastructure environments not designed originally keeping current trends mind at all times moving forward strategically speaking. --related questions-- 1. How does Elasticsearch handle high-frequency updates in indexes used for RAG? 2. What are some best practices for optimizing queries in Elasticsearch for better RAG performance? 3. Can you provide more details on configuring Elasticsearch settings for optimal text retrieval? 4. Are there alternative methods besides daily indexing for improving temporal data management in Elasticsearch? 5. Which factors should be considered when choosing between different text segmentation algorithms for preparing input for RAG?
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