58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
import os
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import asyncio
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from deepagents import create_deep_agent
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from deepagents.backends import FilesystemBackend, LocalShellBackend, CompositeBackend
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from tools import search_knowledge_base, add_to_knowledge_base
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load_dotenv()
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llm = ChatOpenAI(
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model="openai/gpt-oss-20b:free",
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base_url="https://openrouter.ai/api/v1",
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=0.0,
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)
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backend = CompositeBackend([
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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])
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agent = create_deep_agent(
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model=llm,
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tools=[search_knowledge_base, add_to_knowledge_base],
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backend=backend,
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system_prompt="You are a helpful knowledge assistant. Use the tools to search and add documents.",
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)
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async def interactive_loop():
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thread_id = "interactive-session"
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print("Welcome to RAG Agent. Commands: /add <title> <content>, /search <query>, /quit")
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while True:
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user_input = input(">> ")
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if user_input.strip() == "/quit":
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print("Goodbye.")
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break
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if user_input.startswith("/add"):
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try:
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_, title, content = user_input.split(" ", 2)
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except ValueError:
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print("Usage: /add <title> <content>")
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continue
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message = HumanMessage(content=f"Add document titled '{title}' with content: {content}")
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elif user_input.startswith("/search"):
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query = user_input[len("/search"):].strip()
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message = HumanMessage(content=f"Search knowledge base for: {query}")
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else:
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message = HumanMessage(content=user_input)
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result = await agent.ainvoke(
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{"messages": [message]},
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{"configurable": {"thread_id": thread_id}},
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)
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print(result["messages"][-1].content)
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if __name__ == "__main__":
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asyncio.run(interactive_loop()) |