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@@ -4,6 +4,12 @@ import shutil
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from chains.local_doc_qa import LocalDocQA
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from configs.model_config import *
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+# return top-k text chunk from vector store
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+VECTOR_SEARCH_TOP_K = 6
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+
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+# LLM input history length
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+LLM_HISTORY_LEN = 3
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+
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def get_file_list():
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if not os.path.exists("content"):
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@@ -49,7 +55,8 @@ def init_model():
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try:
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local_doc_qa.init_cfg()
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return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
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- except:
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+ except Exception as e:
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+ print(e)
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return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
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@@ -60,14 +67,15 @@ def reinit_model(llm_model, embedding_model, llm_history_len, top_k, history):
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llm_history_len=llm_history_len,
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top_k=top_k)
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model_status = """模型已成功重新加载，请选择文件后点击"加载文件"按钮"""
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- except:
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+ except Exception as e:
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+ print(e)
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model_status = """模型未成功重新加载，请重新选择后点击"加载模型"按钮"""
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return history + [[None, model_status]]
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def get_vector_store(filepath, history):
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- if local_doc_qa.llm and local_doc_qa.llm:
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+ if local_doc_qa.llm and local_doc_qa.embeddings:
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vs_path = local_doc_qa.init_knowledge_vector_store(["content/" + filepath])
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if vs_path:
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file_status = "文件已成功加载，请开始提问"
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@@ -123,7 +131,7 @@ with gr.Blocks(css=block_css) as demo:
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interactive=True)
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llm_history_len = gr.Slider(0,
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10,
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- value=3,
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+ value=LLM_HISTORY_LEN,
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step=1,
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label="LLM history len",
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interactive=True)
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@@ -133,7 +141,7 @@ with gr.Blocks(css=block_css) as demo:
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interactive=True)
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top_k = gr.Slider(1,
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20,
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- value=6,
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+ value=VECTOR_SEARCH_TOP_K,
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step=1,
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label="向量匹配 top k",
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interactive=True)
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