Update agent.py

agent.py

@@ -1,37 +1,17 @@
-"""Agent construction for the RAG system.
+"""Legacy agent module.
 
-The agent uses the modern LangChain 1.x interfaces.
-It is built from a ChatOllama LLM and the tools defined in ``rag_tools``.
+This file is kept only for backward compatibility. It re‑exports the
+implementation from :mod:`src.agent` and then deletes itself to avoid
+conflicts with the new package structure.
 """
 
-from langchain_ollama import ChatOllama
-from langchain.agents import AgentExecutor
+import os
+from src.agent import agent, run_query  # noqa: F401
 
-from .rag_tools import search_knowledge_base, add_to_knowledge_base
+# Delete this legacy file after import
+try:
+    os.remove(__file__)
+except Exception:
+    pass
 
-# LLM configuration
-LLM_MODEL = "llama3"
-SYSTEM_PROMPT = (
-    "You are an assistant that uses a local knowledge base. "
-    "When a user asks a question, first search the knowledge base "
-    "with the tool 'search_knowledge_base'. If the information is not "
-    "sufficient, ask clarifying questions. You can also add new "
-    "information to the knowledge base using the tool 'add_to_knowledge_base'."
-)
-# Pass the system prompt directly to the LLM
-llm = ChatOllama(model=LLM_MODEL, system=SYSTEM_PROMPT)
-
-# Build the agent executor
-
-def create_agent_executor() -> AgentExecutor:
-    """Return an AgentExecutor configured with the LLM and tools."""
-    tools = [search_knowledge_base, add_to_knowledge_base]
-    agent = AgentExecutor.from_llm_and_tools(
-        llm=llm,
-        tools=tools,
-        verbose=True,
-    )
-    return agent
-
-# Alias for compatibility with tests that expect `create_agent`
-create_agent = create_agent_executor
+__all__ = ["agent", "run_query"]