38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
import os
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from langchain_community.chat_models import ChatOllama
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from langchain.tools import tool
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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 langchain_core.messages import HumanMessage
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from tools import search_local_kb, web_search
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llm = ChatOllama(
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model="llama3",
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base_url=os.getenv("OLLAMA_HOST", "http://localhost:11434"),
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)
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backend = CompositeBackend(
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[
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LocalShellBackend(workspace_dir="./workspace"),
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FilesystemBackend(),
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]
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)
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agent = create_deep_agent(
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model=llm,
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tools=[search_local_kb, web_search],
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backend=backend,
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system_prompt=(
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"You are a helpful agent. "
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"Use search_local_kb for local knowledge and web_search for up-to-date info. "
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"Prefix answers with [Local KB] or [Web Search] and indicate source."
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),
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)
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async def run_agent(query: str) -> str:
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result = await agent.ainvoke(
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{"messages": [HumanMessage(content=query)]},
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{"configurable": {"thread_id": "session-1"}},
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)
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return result["messages"][-1].content |