import os
import asyncio
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.messages import HumanMessage
from langchain.tools import tool
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend

# LLM configuration – always OpenRouter
llm = ChatOpenAI(
    model="openai/gpt-oss-20b:free",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
    temperature=0.0,
)

# Embeddings and vector store for RAG
embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small",
    base_url="https://openrouter.ai/api/v1",
    api_key=os.getenv("OPENAI_API_KEY"),
)
vector_store = Chroma(collection_name="knowledge", embedding_function=embeddings)

# Backend for file operations – virtual mode so no real files are created
backend = CompositeBackend(
    default=LocalShellBackend(root_dir="./workspace", virtual_mode=True, inherit_env=True),
    routes={},
)

# Tool that performs a vector search and returns the top 3 snippets
@tool
def rag_search(query: str) -> str:
    """Search the vector store for relevant documents and return a concise summary."""
    docs = vector_store.similarity_search(query, k=3)
    if not docs:
        return "No relevant information found."
    snippets = "\n---\n".join(doc.page_content for doc in docs)
    return f"Top matches:\n{snippets}"

# Create the deep agent with the RAG tool
agent = create_deep_agent(
    model=llm,
    tools=[rag_search],
    backend=backend,
    system_prompt="You are a helpful assistant that uses a knowledge base to answer questions. Use the rag_search tool when you need external information.",
)

async def main():
    # Example user query
    user_query = "What are the main causes of climate change?"
    result = await agent.ainvoke(
        {"messages": [HumanMessage(content=user_query)]},
        {"configurable": {"thread_id": "session-1"}},
    )
    # Print the final assistant message
    print(result["messages"][-1].content)

if __name__ == "__main__":
    asyncio.run(main())