"""Agent implementation using LangChain 1.x.

The agent uses a simple tool‑based architecture.  The LLM is a local
`ChatOllama` model (llama3).  Two tools are available:

* ``search_local_kb`` – semantic search in Qdrant.
* ``web_search`` – web search via Tavily.

The system prompt instructs the LLM to decide which tool to use based on the
question.  The response always contains a marker indicating the source
(`chromadb`/`tavily`).  The marker is added by the LLM itself.
"""

from __future__ import annotations

from typing import List

from langchain_ollama import ChatOllama
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable
from langchain_core.tools import BaseTool

# Import tools without relative import to allow top‑level import
import tools

# ---------------------------------------------------------------------------
# Helper: create a tool list
# ---------------------------------------------------------------------------

def get_tools() -> List[BaseTool]:
    """Return the list of tools used by the agent."""
    return [tools.search_local_kb, tools.web_search]

# ---------------------------------------------------------------------------
# System prompt
# ---------------------------------------------------------------------------
SYSTEM_PROMPT = (
    "You are an AI assistant that can answer questions using two sources: "
    "1) a local knowledge base (Qdrant) and 2) the web via Tavily. "
    "If the answer can be found in the local KB, use the `search_local_kb` tool. "
    "If the answer requires up‑to‑date information, use the `web_search` tool. "
    "Return the answer followed by a source marker on a new line: "
    "`Source: chromadb` or `Source: tavily`."
)

# ---------------------------------------------------------------------------
# Agent construction
# ---------------------------------------------------------------------------

def create_agent() -> Runnable:
    """Create a runnable agent.

    The agent is a simple chain: system prompt → user message → tool calls → LLM
    response.  It uses the default tool‑calling behaviour of LangChain 1.x.
    """
    llm = ChatOllama(model="llama3", temperature=0.0)

    # AgentExecutor can accept a system message via agent_kwargs
    from langchain.agents import AgentExecutor

    agent = AgentExecutor.from_llm_and_tools(
        llm=llm,
        tools=get_tools(),
        verbose=True,
        # Provide the system prompt so the LLM knows how to behave
        agent_kwargs={"system_message": SYSTEM_PROMPT},
    )

    return agent