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feat: 添加同步获取embedding向量和生成响应的方法

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This commit is contained in:
墨梓柒
2025-07-16 11:00:16 +08:00
parent a8cbb2978b
commit c71f2b21c0
5 changed files with 41 additions and 6 deletions
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4
src/chat/knowledge/ie_process.py
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@@ -28,7 +28,7 @@ def _extract_json_from_text(text: str) -> dict:
def _entity_extract(llm_req: LLMRequest, paragraph: str) -> List[str]:
"""对段落进行实体提取，返回提取出的实体列表（JSON格式）"""
entity_extract_context = prompt_template.build_entity_extract_context(paragraph)
response, (reasoning_content, model_name) = llm_req.generate_response_async(entity_extract_context)
response, (reasoning_content, model_name) = llm_req.generate_response_sync(entity_extract_context)
entity_extract_result = _extract_json_from_text(response)
# 尝试load JSON数据
@@ -50,7 +50,7 @@ def _rdf_triple_extract(llm_req: LLMRequest, paragraph: str, entities: list) ->
rdf_extract_context = prompt_template.build_rdf_triple_extract_context(
paragraph, entities=json.dumps(entities, ensure_ascii=False)
)
response, (reasoning_content, model_name) = llm_req.generate_response_async(rdf_extract_context)
response, (reasoning_content, model_name) = llm_req.generate_response_sync(rdf_extract_context)
entity_extract_result = _extract_json_from_text(response)
# 尝试load JSON数据