feat: solution for 'Практическое задание: Агент с RAG-памятью'

src/agent.py

@@ -0,0 +1,40 @@
+```python
+"""
+Agent creation with RAG integration.
+"""
+
+from langchain.llms import Ollama
+from langchain.agents import initialize_agent, AgentType
+from langchain.tools import Tool
+
+from tools import search_knowledge_base, add_to_knowledge_base
+
+
+def create_agent():
+    """
+    Create and configure the LangChain agent.
+
+    Returns:
+        AgentExecutor instance ready to run queries.
+    """
+    llm = Ollama(model="llama3")
+
+    tools = [
+        Tool.from_function(search_knowledge_base),
+        Tool.from_function(add_to_knowledge_base),
+    ]
+
+    agent = initialize_agent(
+        tools,
+        llm,
+        agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=True,
+        agent_kwargs={
+            "system_message": (
+                "You are an AI assistant that can search and add documents to a knowledge base. "
+                "Use the provided tools to answer user queries."
+            )
+        },
+    )
+    return agent
+```