feat: Support tag in knowledge_workflow

Author: zhanweizhang7

apps/application/flow/step_node/knowledge_write_node/impl/base_knowledge_write_node.py

@@ -7,7 +7,7 @@
     @desc:
 """
 from functools import reduce
-from typing import Dict, List
+from typing import Dict, List, Any
 import uuid_utils.compat as uuid
 from django.db.models import QuerySet
 from django.db.models.aggregates import Max
@@ -18,7 +18,8 @@
 from application.flow.step_node.knowledge_write_node.i_knowledge_write_node import IKnowledgeWriteNode
 from common.chunk import text_to_chunk
 from common.utils.common import bulk_create_in_batches
-from knowledge.models import Document, KnowledgeType, Paragraph, File, FileSourceType, Problem, ProblemParagraphMapping
+from knowledge.models import Document, KnowledgeType, Paragraph, File, FileSourceType, Problem, ProblemParagraphMapping, \
+    Tag, DocumentTag
 from knowledge.serializers.common import ProblemParagraphObject, ProblemParagraphManage
 from knowledge.serializers.document import DocumentSerializers
 
@@ -33,10 +34,16 @@ class ParagraphInstanceSerializer(serializers.Serializer):
     chunks = serializers.ListField(required=False, child=serializers.CharField(required=True))
 
 
+class TagInstanceSerializer(serializers.Serializer):
+    key = serializers.CharField(required=True, max_length=64, label=_('Tag Key'))
+    value = serializers.CharField(required=True, max_length=128, label=_('Tag Value'))
+
+
 class KnowledgeWriteParamSerializer(serializers.Serializer):
     name = serializers.CharField(required=True, label=_('document name'), max_length=128, min_length=1,
                                  source=_('document name'))
     meta = serializers.DictField(required=False)
+    tags = serializers.ListField(required=False, label=_('Tags'), child=TagInstanceSerializer())
     paragraphs = ParagraphInstanceSerializer(required=False, many=True, allow_null=True)
     source_file_id = serializers.UUIDField(required=False, allow_null=True)
 
@@ -51,6 +58,7 @@ def convert_uuid_to_str(obj):
     else:
         return obj
 
+
 def link_file(source_file_id, document_id):
     if source_file_id is None:
         return
@@ -70,14 +78,15 @@ def link_file(source_file_id, document_id):
         # 保存文件内容和元数据
         new_file.save(file_content)
 
+
 def get_paragraph_problem_model(knowledge_id: str, document_id: str, instance: Dict):
     paragraph = Paragraph(
         id=uuid.uuid7(),
         document_id=document_id,
         content=instance.get("content"),
         knowledge_id=knowledge_id,
         title=instance.get("title") if 'title' in instance else '',
-        chunks = instance.get('chunks') if 'chunks' in instance else text_to_chunk(instance.get("content")),
+        chunks=instance.get('chunks') if 'chunks' in instance else text_to_chunk(instance.get("content")),
     )
 
     problem_paragraph_object_list = [ProblemParagraphObject(
@@ -136,6 +145,65 @@ def get_document_paragraph_model(knowledge_id: str, instance: Dict):
         instance.get('paragraphs') if 'paragraphs' in instance else []
     )
 
+def save_knowledge_tags(knowledge_id: str, tags: List[Dict[str,Any]]):
+
+    existed_tags_dict = {
+        (key, value): str(tag_id)
+        for key,value,tag_id in QuerySet(Tag).filter(knowledge_id=knowledge_id).values_list("key", "value", "id")
+    }
+
+    tag_model_list = []
+    new_tag_dict = {}
+    for tag in tags:
+        key = tag.get("key")
+        value = tag.get("value")
+
+        if (key,value) not in existed_tags_dict:
+            tag_model = Tag(
+                id=uuid.uuid7(),
+                knowledge_id=knowledge_id,
+                key=key,
+                value=value
+            )
+            tag_model_list.append(tag_model)
+            new_tag_dict[(key,value)] = str(tag_model.id)
+
+    if tag_model_list:
+        Tag.objects.bulk_create(tag_model_list)
+
+    all_tag_dict={**existed_tags_dict,**new_tag_dict}
+
+    return all_tag_dict, new_tag_dict
+
+def batch_add_document_tag(document_tag_map: Dict[str, List[str]]):
+    """
+    批量添加文档-标签关联
+    document_tag_map: {document_id: [tag_id1, tag_id2, ...]}
+    """
+    all_document_ids = list(document_tag_map.keys())
+    all_tag_ids = list(set(tag_id for tag_ids in document_tag_map.values() for tag_id in tag_ids))
+
+    # 查询已存在的文档-标签关联
+    existed_relations = set(
+        QuerySet(DocumentTag).filter(
+            document_id__in=all_document_ids,
+            tag_id__in=all_tag_ids
+        ).values_list('document_id', 'tag_id')
+    )
+
+    new_relations = [
+        DocumentTag(
+            id=uuid.uuid7(),
+            document_id=doc_id,
+            tag_id=tag_id,
+        )
+        for doc_id, tag_ids in document_tag_map.items()
+        for tag_id in tag_ids
+        if (doc_id,tag_id) not in existed_relations
+    ]
+
+    if new_relations:
+        QuerySet(DocumentTag).bulk_create(new_relations)
 
 class BaseKnowledgeWriteNode(IKnowledgeWriteNode):
 
@@ -153,6 +221,11 @@ def save(self, document_list):
         document_model_list = []
         paragraph_model_list = []
         problem_paragraph_object_list = []
+        # 所有标签
+        knowledge_tag_list = []
+        # 文档标签映射关系
+        document_tags_map = {}
+        knowledge_tag_dict = {}
 
         for document in document_list:
             document_paragraph_dict_model = get_document_paragraph_model(
@@ -162,10 +235,38 @@ def save(self, document_list):
             document_instance = document_paragraph_dict_model.get('document')
             link_file(document.get("source_file_id"), document_instance.id)
             document_model_list.append(document_instance)
+            # 收集标签
+            single_document_tag_list = document.get("tags", [])
+            # 去重传入的标签
+            for tag in single_document_tag_list:
+                tag_key = (tag['key'], tag['value'])
+                if tag_key not in knowledge_tag_dict:
+                    knowledge_tag_dict[tag_key]= tag
+
+            if single_document_tag_list:
+                document_tags_map[str(document_instance.id)] = single_document_tag_list
+
             for paragraph in document_paragraph_dict_model.get("paragraph_model_list"):
                 paragraph_model_list.append(paragraph)
             for problem_paragraph_object in document_paragraph_dict_model.get("problem_paragraph_object_list"):
                 problem_paragraph_object_list.append(problem_paragraph_object)
+        knowledge_tag_list = list(knowledge_tag_dict.values())
+        # 保存所有文档中含有的标签到知识库
+        if knowledge_tag_list:
+            all_tag_dict, new_tag_dict = save_knowledge_tags(knowledge_id, knowledge_tag_list)
+            # 构建文档-标签ID映射
+            document_tag_id_map = {}
+            # 为每个文档添加其对应的标签
+            for doc_id, doc_tags in document_tags_map.items():
+                doc_tag_ids = [
+                    all_tag_dict[(tag.get("key"),tag.get("value"))]
+                    for tag in doc_tags
+                    if (tag.get("key"),tag.get("value")) in all_tag_dict
+                ]
+                if doc_tag_ids:
+                    document_tag_id_map[doc_id] = doc_tag_ids
+            if document_tag_id_map:
+                batch_add_document_tag(document_tag_id_map)
 
         problem_model_list, problem_paragraph_mapping_list = (
             ProblemParagraphManage(problem_paragraph_object_list, knowledge_id).to_problem_model_list()

apps/locales/en_US/LC_MESSAGES/django.po

@@ -8810,7 +8810,7 @@ msgstr ""
 msgid "Audio file recognition - Tongyi Qwen"
 msgstr ""
 
-msgid "Audio file recognition - Fun-ASR/Paraformer/SenseVoice"
+msgid "Real-time speech recognition - Fun-ASR/Paraformer"
 msgstr ""
 
 msgid "Qwen-Omni"

apps/locales/zh_CN/LC_MESSAGES/django.po

@@ -8936,8 +8936,8 @@ msgstr "阻止不安全的重定向到内部主机"
 msgid "Audio file recognition - Tongyi Qwen"
 msgstr "录音文件识别-通义千问"
 
-msgid "Audio file recognition - Fun-ASR/Paraformer/SenseVoice"
-msgstr "录音文件识别-Fun-ASR/Paraformer/SenseVoice"
+msgid "Real-time speech recognition - Fun-ASR/Paraformer"
+msgstr "实时语音识别-Fun-ASR/Paraformer"
 
 msgid "Qwen-Omni"
 msgstr "多模态"

apps/locales/zh_Hant/LC_MESSAGES/django.po

@@ -8936,8 +8936,8 @@ msgstr "阻止不安全的重定向到內部主機"
 msgid "Audio file recognition - Tongyi Qwen"
 msgstr "錄音文件識別-通義千問"
 
-msgid "Audio file recognition - Fun-ASR/Paraformer/SenseVoice"
-msgstr "錄音文件識別-Fun-ASR/Paraformer/SenseVoice"
+msgid "Real-time speech recognition - Fun-ASR/Paraformer"
+msgstr "實時語音識別-Fun-ASR/Paraformer"
 
 msgid "Qwen-Omni"
 msgstr "多模態"

apps/models_provider/impl/aliyun_bai_lian_model_provider/credential/stt/default_stt.py

@@ -18,13 +18,13 @@
 
 
 class AliyunBaiLianDefaultSTTModelCredential(BaseForm, BaseModelCredential):
-    type = forms.Radio(_("Type"), required=True, text_field='label', default_value='qwen', provider='', method='',
+    type = forms.SingleSelect(_("API"), required=True, text_field='label', default_value='qwen', provider='', method='',
                        value_field='value', option_list=[
             {'label': _('Audio file recognition - Tongyi Qwen'),
              'value': 'qwen'},
             {'label': _('Qwen-Omni'),
              'value': 'omni'},
-            {'label': _('Audio file recognition - Fun-ASR/Paraformer/SenseVoice'),
+            {'label': _('Real-time speech recognition - Fun-ASR/Paraformer'),
              'value': 'other'}
         ])
     api_url = forms.TextInputField(_('API URL'), required=True, relation_show_field_dict={'type': ['qwen', 'omni']})

apps/models_provider/impl/aliyun_bai_lian_model_provider/model/stt/omni_stt.py

@@ -68,7 +68,7 @@ def speech_to_text(self, audio_file):
                                     "format": "mp3",
                                 },
                             },
-                            {"type": "text", "text": self.params.get('CueWord')},
+                            {"type": "text", "text": self.params.get('CueWord') or '这段音频在说什么'},
                         ],
                     },
                 ],
@@ -77,7 +77,7 @@ def speech_to_text(self, audio_file):
                 # stream 必须设置为 True，否则会报错
                 stream=True,
                 stream_options={"include_usage": True},
-                extra_body=self.params
+                extra_body = {'enable_thinking': False, **self.params},
             )
             result = []
             for chunk in completion: