add agent.py

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2026-05-28 09:27:38 +00:00
parent 041b6358cd
commit a9f7cf8c7f
+14 -22
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@@ -1,39 +1,31 @@
""" """
Agent definition using LangChain create_agent. Agent creation with RAG integration.
""" """
import os import os
from typing import List
from langchain_openai import ChatOpenAI from langchain_openai import ChatOpenAI
from langchain.agents import create_agent from langchain.agents import create_agent
from langchain_core.messages import HumanMessage from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from tools import search_knowledge_base, add_to_knowledge_base from tools import search_knowledge_base, add_to_knowledge_base
# LLM Ollama via BroJS endpoint (placeholder) # LLM via Ollama (llama3)
llm = ChatOpenAI( llm = ChatOpenAI(
model="openai/gpt-oss-20b:free", model="ollama/llama3",
base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1", base_url="http://localhost:11434/v1",
api_key=os.getenv("JOURNAL_MCP_PAT"), api_key=None,
temperature=0.5, temperature=0.2,
) )
agent = create_agent( agent = create_agent(
llm=llm, llm=llm,
tools=[search_knowledge_base, add_to_knowledge_base], tools=[search_knowledge_base, add_to_knowledge_base],
system_prompt="You are a helpful assistant with access to a knowledge base.", system_prompt="You are a helpful assistant that can search and add documents to the knowledge base.",
) )
async def main(): memory = MemorySaver()
# Simple demo: add and search
await agent.ainvoke(
{"messages": [HumanMessage(content="Add sample text about Python")]},
{"configurable": {"thread_id": "demo-1"}},
)
result = await agent.ainvoke(
{"messages": [HumanMessage(content="Search for Python")]},
{"configurable": {"thread_id": "demo-1"}},
)
print(result["messages"][-1].content)
if __name__ == "__main__": # pragma: no cover async def run_agent(messages: List[HumanMessage]):
import asyncio result = await agent.ainvoke({"messages": messages}, {"configurable": {"thread_id": "session-1"}})
asyncio.run(main()) return result["messages"][-1].content