"""Agent construction for the RAG system.

The agent uses the modern LangChain 1.x interfaces.
It is built from a ChatOllama LLM and the tools defined in ``rag_tools``.
"""

from langchain_ollama import ChatOllama
from langchain.agents import AgentExecutor

from .rag_tools import search_knowledge_base, add_to_knowledge_base

# 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