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diff --git a/dsRag/Config/__pycache__/__init__.cpython-310.pyc b/dsRag/Config/__pycache__/__init__.cpython-310.pyc
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diff --git a/dsRag/Test/T12.py b/dsRag/Test/T12.py
new file mode 100644
index 00000000..bfbae62b
--- /dev/null
+++ b/dsRag/Test/T12.py
@@ -0,0 +1,47 @@
+"""
+pip install faiss-cpu
+"""
+import re
+import faiss
+from transformers import AutoTokenizer, AutoModel
+from transformers import LlamaForCausalLM
+from Util.SplitDocxUtil import SplitDocxUtil
+
+# 加载预训练模型和分词器
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+#model = AutoModel.from_pretrained('bert-base-uncased')
+
+model = LlamaForCausalLM.from_pretrained("./path/to/local/directory", local_files_only=True)
+
+def split_into_blocks(text):
+    """使用正则表达式匹配问题和话题的标题及内容"""
+    pattern = r'(问题\d+|话题\d+)([\s\S]+?)(?=问题\d+|话题\d+|$)'
+    blocks = re.findall(pattern, text, re.DOTALL)
+    return [(i+1, title + content) for i, (title, content) in enumerate(blocks)]
+
+def encode_text(text):
+    inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding='max_length')
+    outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy()
+    return embeddings
+
+# 创建向量数据库
+dimension = 768  # BERT模型的维度
+index = faiss.IndexFlatL2(dimension)
+
+# 源文件
+input_file = '../Txt/小学数学（史校长）.docx'
+output_dir = '../Txt/processed_chunks'
+
+"""处理文档主函数"""
+text = SplitDocxUtil.read_docx(input_file)
+
+blocks=split_into_blocks(text)
+
+# 将块编码并添加到向量数据库
+for block in blocks:
+    embedding = encode_text(block)
+    index.add(embedding)
+
+# 保存向量数据库
+#faiss.write_index(index, 'math_blocks.index')
\ No newline at end of file
diff --git a/dsRag/Test/T13.py b/dsRag/Test/T13.py
new file mode 100644
index 00000000..e501d919
--- /dev/null
+++ b/dsRag/Test/T13.py
@@ -0,0 +1,26 @@
+import re
+import faiss
+from transformers import AutoTokenizer, AutoModel
+# 加载预训练模型和分词器
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+model = AutoModel.from_pretrained('bert-base-uncased')
+
+def encode_text(text):
+    inputs = tokenizer(text, return_tensors='pt', max_length=512, truncation=True, padding='max_length')
+    outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy()
+    return embeddings
+
+def search_similar_blocks(query, index, k=5):
+    query_embedding = encode_text(query)
+    D, I = index.search(query_embedding, k)
+    return I[0]
+
+# 示例：查询
+query = "小学数学中有哪些模型"
+index = faiss.read_index('math_blocks.index')
+similar_indices = search_similar_blocks(query, index)
+
+# 打印最相似的块
+for idx in similar_indices:
+    print(f"Similar Block {idx + 1}:\n{blocks[idx]}\n")
\ No newline at end of file
diff --git a/dsRag/Test/T14_DownloadHuggingFace.py b/dsRag/Test/T14_DownloadHuggingFace.py
new file mode 100644
index 00000000..9a681637
--- /dev/null
+++ b/dsRag/Test/T14_DownloadHuggingFace.py
@@ -0,0 +1,27 @@
+"""
+pip install huggingface_hub
+pip install pysocks
+pip install hf_xet
+开VPN后,使用Python下载模型
+"""
+import os
+from transformers import AutoModel, AutoTokenizer
+
+# 设置环境变量
+os.environ['HTTP_PROXY'] = 'socks5://127.0.0.1:1080'
+os.environ['HTTPS_PROXY'] = 'socks5://127.0.0.1:1080'
+
+# 配置代理
+proxies = {
+    'http': 'socks5://127.0.0.1:1080',
+    'https': 'socks5://127.0.0.1:1080'
+}
+
+# 加载模型
+model_id = "google-bert/bert-base-uncased"
+model = AutoModel.from_pretrained(model_id, proxies=proxies)
+tokenizer = AutoTokenizer.from_pretrained(model_id, proxies=proxies)
+
+# 保存模型到本地
+model.save_pretrained("d:/Model/google-bert/bert-base-uncased")
+tokenizer.save_pretrained("d:/Model/google-bert/bert-base-uncased")
diff --git a/dsRag/Tools/T4_DeepSeekRag.py b/dsRag/Tools/T4_DeepSeekRag.py
index b7372087..c47c3df6 100644
--- a/dsRag/Tools/T4_DeepSeekRag.py
+++ b/dsRag/Tools/T4_DeepSeekRag.py
@@ -100,9 +100,11 @@ def search_related_data(query):
     context = ""
     for hit in vector_results['hits']['hits']:
         context += f"向量相似度结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
+        print(context)
     
     for hit in text_results['hits']['hits']:
         context += f"文本精确匹配结果(score={hit['_score']}):\n{hit['_source']['text']}\n\n"
+        print(context)
     
     return context
 
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