130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
# agent.py — Agent loop dan tool execution.
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# submit() adalah entry point: membaca input buffer, mengirim ke LLM,
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# memproses tool calls, dan menampilkan hasil di log.
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import json
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from datetime import datetime
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from .render import draw
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from scripts import ntro
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def log(app, role, text):
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# Simpan item ke app.log untuk di-render oleh render.py
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app.log.append({
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"role": role,
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"text": text,
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"time": datetime.now().strftime("%H:%M"),
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})
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def submit(app, stdscr):
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# Kirim query dari input buffer ke LLM.
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# Loop sampai LLM mengembalikan final answer (tanpa tool_calls)
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# atau mencapai max_iterations.
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query = "\n".join(app.input_buffer).strip()
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if not query:
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return
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log(app, "user", query)
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# Reset input buffer
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app.input_buffer = [""]
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app.input_line = 0
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app.input_col = 0
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app.scroll = 999999 # scroll ke paling bawah
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app.processing = True
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draw(app, stdscr)
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stdscr.refresh()
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app.messages.append({"role": "user", "content": query})
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stamp = ntro.start()
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for step in range(app.agent_max_iterations):
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stamp_step = ntro.start()
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log(app, "system", f" step {step + 1} \u2014 LLM...")
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app.scroll = 999999
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draw(app, stdscr)
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stdscr.refresh()
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response = app.llm.chat(app.messages, tools=app.TOOLS)
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# Hapus "step N — LLM..." log, ganti dengan hasil aktual
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app.log.pop()
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if response.tool_calls:
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# LLM meminta menjalankan tool(s)
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amsg = {
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"role": "assistant",
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"content": response.content,
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"tool_calls": response.tool_calls,
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}
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app.messages.append(amsg)
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if response.content and response.content.strip():
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log(app, "ai", response.content)
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app.scroll = 999999
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draw(app, stdscr)
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stdscr.refresh()
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for tc in response.tool_calls:
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tname = tc["function"]["name"]
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log(app, "system", f" \u2192 {tname}")
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app.scroll = 999999
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draw(app, stdscr)
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stdscr.refresh()
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execute_tool(app, tc)
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else:
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# Final answer — tidak ada tool_calls
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if response.content:
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app.messages.append({
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"role": "assistant",
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"content": response.content,
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})
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log(app, "ai", response.content)
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log(app, "sep", "")
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app.processing = False
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app.scroll = 999999
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draw(app, stdscr)
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stdscr.refresh()
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ntro.end(stamp)
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return
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ntro.end(stamp_step)
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# Timeout — max iterations tercapai tanpa final answer
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log(app, "error", "Max iterations reached without final answer.")
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app.messages.append({"role": "assistant",
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"content": "Max iterations reached without final answer."})
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app.processing = False
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ntro.end(stamp)
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def execute_tool(app, tool_call):
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# Dispatch tool_call ke handler yang terdaftar di TOOL_HANDLERS.
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# search_code dan git_operation butuh penanganan argumen khusus.
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tname = tool_call["function"]["name"]
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targs = json.loads(tool_call["function"]["arguments"])
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handler = app.TOOL_HANDLERS.get(tname)
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if not handler:
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result = f"Tool {tname} not found"
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else:
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try:
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if tname == "search_code":
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result = handler(
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pattern=targs["pattern"],
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search_type=targs["search_type"],
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path=targs.get("path", "."),
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)
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elif tname == "git_operation":
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result = handler(args=targs["args"])
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else:
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result = handler(**targs)
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except Exception as e:
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result = f"Error executing tool: {str(e)}"
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# Hasil tool disimpan ke messages agar bisa dikirim balik ke LLM
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app.messages.append({
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"role": "tool",
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"tool_call_id": tool_call["id"],
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"content": str(result),
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})
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