修改知识库范围

agentic_rag.py

@@ -130,12 +130,21 @@ def initialize_memory(model) -> AgentMemory:
 def initialize_vector_db() -> LanceDb:
     """初始化并返回配置好的LanceDb实例"""
     return LanceDb(
-        table_name="recipes",
-        uri=os.getenv("VECTOR_DB_PATH", "tmp/lancedb"),
+        table_name="knowledge",
+        uri=os.getenv("VECTOR_DB_PATH", "tmp/knowledgedb"),
         search_type=SearchType.hybrid,
         embedder=OpenAIEmbedder(id=embedding_model, base_url=embedding_baseUrl, api_key=api_key)
     )
-    
+
+def initialize_mingci_vector_db() -> LanceDb:
+    """初始化并返回配置好的LanceDb实例"""
+    return LanceDb(
+        table_name="mingci",
+        uri=os.getenv("MINGCI_VECTOR_DB_PATH", "tmp/mingcidb"),
+        search_type=SearchType.hybrid,
+        embedder=OpenAIEmbedder(id=embedding_model, base_url=embedding_baseUrl, api_key=api_key)
+    )
+
 def initialize_knowledge_base() -> AgentKnowledge:
     """初始化并返回配置好的AgentKnowledge实例"""
     return AgentKnowledge(
@@ -149,6 +158,18 @@ def initialize_knowledge_base() -> AgentKnowledge:
         reader=TextReader(),  # 默认文本读取器
     )
 
+def initialize_mingci_knowledge_base() -> AgentKnowledge:
+    """初始化并返回配置好的AgentKnowledge实例"""
+    return AgentKnowledge(
+        vector_db=initialize_mingci_vector_db(),
+        num_documents=3,  # 检索3个最相关的文档
+        chunking_strategy=DocumentChunking(
+            chunk_size=500,
+            overlap=50,
+        ),  # 固定大小分块
+        optimize_on=1000,  # 每1000条数据进行向量优化
+        reader=TextReader(),  # 默认文本读取器
+    )
 
 def get_agentic_rag_agent(
     model_id: str = "openai:gpt-4o",