chunk_attachment 返回值

状态码说明

请求消息

curl -i -X POST \
  -H 'Content-Type: application/json' \
  -H 'Authorization: HMAC-SHA256 ***' \
  https://api-knowledgebase.mlp.cn-beijing.volces.com/api/knowledge/collection/search \
  -d '{
    "name": "test_name",
    "project": ",
    "query": "introduce a new document level structure",
    "retrieve_count": 25,
    "limit": 2,
    "query_param": {
        "doc_filter": {
          "op": "must",    
          "field": "doc_id", 
          "conds": ["tos_doc_id_123", "tos_doc_id_456"]
        },
    }, 
    "rerank_switch": true,
    "dense_weight": 0.5
}'

响应消息

执行成功返回：

HTTP/1.1 200 OK
Content-Length: 43
Content-Type: application/json
 
{
    "code":0,
    "data": {
          "collection_name": "test_name",
          "count": 2
          "result_list": [
            {
                "id": "tos_doc_id_123",
                "chunk_title": "Conclusion"，
                "content": "In this paper, we discussed the task of document level structure parsing. This task is more intricate compared to the traditional page level scenario. This complexity arises because we need to consider connecting paragraphs across pages and linking paragraphs into sections. To address these challenges, we introduced a transition-based parser as a solution. Alongside this, we introduced a new dataset called DocTree to support this task.",
                "score"：0.7119365930557251,
                "recall_position": 1
                "point_id": "tos_doc_id_2_1-217-6834848478902922598",
                "process_time": 1709097567,
                "rerank_score": 0.877777
                "rerank_position": 1
                "doc_info": {        
                    "doc_id": "tos_doc_id_123",
                    "doc_name": "DLSP: A Document Level Structure Parser for Multi-Page Digital Documents.pdf",
                    "create_time": 1677561567,
                    "doc_type": "pdf",
                    "doc_meta": "[{"field_name": "author", "field_type": "string", "field_value": "Mike"}, {"field_name": "category", "field_type": "string", "field_value": "Mike"}]",
                    "source": "tos"，
                    "title"："DLSP: A Document Level Structure Parser for Multi-Page Digital Documents"
               }
            }，
            {
                "id": "tos_doc_id_456",
                "chunk_title": "Conclusion"，
                "content": "We also introduce a new document level structure parsing dataset called DocTree. It comprises 1,298 manually annotated documents with document level structural information. In contrast to previous datasets focusing on single page, the maximum page number in DocTree reaches 85 while the average is 7.2.",
                "score"：0.711473822593689
                "recall_position": 1
                "point_id": "tos_doc_id_2_1-37-3242137170643999406",
                "process_time": 1709097567,
                "rerank_score": 0.5874546
                "rerank_position": 2
                "doc_info": {        
                    "doc_id": "tos_doc_id_123",
                    "doc_name": "DLSP: A Document Level Structure Parser for Multi-Page Digital Documents.pdf",
                    "create_time": 1677561593,
                    "doc_type": "pdf",
                    "doc_meta": "[{"field_name": "author", "field_type": "string", "field_value": "Mike"}, {"field_name": "category", "field_type": "string", "field_value": "Mike"}]",
                    "source": "tos"，
                    "title"："DLSP: A Document Level Structure Parser for Multi-Page Digital Documents"
               }
            }
        ]
    },
    "message": "success",
    "request_id": "02170910041086600000000000000000000ffff0a00609d26d25e"
}

执行失败返回：

HTTP/1.1 400 OK
Content-Length: 43
Content-Type: application/json
 
{"code":1000003, "message":"invalid request：%s", "request_id": "021695029757920fd001de6666600000000000000000002569b8f"}