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refactor: Multi channel recall, adding display of knowledge base titles and other information

This commit is contained in:
shaohuzhang1 2025-02-18 14:18:41 +08:00 committed by shaohuzhang1
parent ff3dec28d2
commit 168f822416
2 changed files with 27 additions and 5 deletions
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30
apps/application/flow/step_node/reranker_node/impl/base_reranker_node.py
View File

@ -23,9 +23,14 @@ def merge_reranker_list(reranker_list, result=None):
merge_reranker_list(document, result)
elif isinstance(document, dict):
content = document.get('title', '') + document.get('content', '')
result.append(str(document) if len(content) == 0 else content)
title = document.get("title")
dataset_name = document.get("dataset_name")
document_name = document.get('document_name')
result.append(
Document(page_content=str(document) if len(content) == 0 else content,
metadata={'title': title, 'dataset_name': dataset_name, 'document_name': document_name}))
else:
result.append(str(document))
result.append(Document(page_content=str(document), metadata={}))
return result
@ -43,6 +48,21 @@ def filter_result(document_list: List[Document], max_paragraph_char_number, top_
return result
def reset_result_list(result_list: List[Document], document_list: List[Document]):
r = []
document_list = document_list.copy()
for result in result_list:
filter_result_list = [document for document in document_list if document.page_content == result.page_content]
if len(filter_result_list) > 0:
item = filter_result_list[0]
document_list.remove(item)
r.append(Document(page_content=item.page_content,
metadata={**item.metadata, 'relevance_score': result.metadata.get('relevance_score')}))
else:
r.append(result)
return r
class BaseRerankerNode(IRerankerNode):
def save_context(self, details, workflow_manage):
self.context['document_list'] = details.get('document_list', [])
@ -55,16 +75,18 @@ class BaseRerankerNode(IRerankerNode):
**kwargs) -> NodeResult:
documents = merge_reranker_list(reranker_list)
top_n = reranker_setting.get('top_n', 3)
self.context['document_list'] = documents
self.context['document_list'] = [{'page_content': document.page_content, 'metadata': document.metadata} for
document in documents]
self.context['question'] = question
reranker_model = get_model_instance_by_model_user_id(reranker_model_id,
self.flow_params_serializer.data.get('user_id'),
top_n=top_n)
result = reranker_model.compress_documents(
[Document(page_content=document) for document in documents if document is not None and len(document) > 0],
documents,
question)
similarity = reranker_setting.get('similarity', 0.6)
max_paragraph_char_number = reranker_setting.get('max_paragraph_char_number', 5000)
result = reset_result_list(result, documents)
r = filter_result(result, max_paragraph_char_number, top_n, similarity)
return NodeResult({'result_list': r, 'result': ''.join([item.get('page_content') for item in r])}, {})

2
ui/src/components/ai-chat/ExecutionDetailDialog.vue
View File

@ -429,7 +429,7 @@
<MdPreview
ref="editorRef"
editorId="preview-only"
:modelValue="paragraph"
:modelValue="paragraph.page_content"
/>
</el-scrollbar>
</template>