162 lines
5.1 KiB
Python
162 lines
5.1 KiB
Python
from llm.bedrock import BedrockClient
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from jarvis_mcp.client import MCPClient # oder wo auch immer dein MCP-Client liegt
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from config import MCP_URL
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from util.logger import get_logger
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import asyncio
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import personality
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class Agent:
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def __init__(self):
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self.logger = get_logger("NORA")
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self.llm = BedrockClient()
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self.mcp = MCPClient(MCP_URL)
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self.tools = []
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# System-Prompt als erste User-Nachricht (Nova 2 Lite kompatibel)
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self.messages = [
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{
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"role": "user",
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"content": [
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{
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"text": (
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personality +
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"für OpenHAB. Du steuerst Geräte und beantwortest Fragen über das Zuhause. "
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"Nutze Tools, wenn sinnvoll. Antworte auf Deutsch und sei hilfreich."
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)
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}
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]
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}
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]
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# -------------------------
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# TOOL LOADING
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# -------------------------
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async def load_tools(self):
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try:
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res = await self.mcp.list_tools()
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self.tools = []
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for t in res.tools:
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schema = t.inputSchema
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# Bedrock-kompatibles Format erzwingen
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if isinstance(schema, dict) and "json" not in schema:
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bedrock_schema = {"json": schema}
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else:
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bedrock_schema = schema
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self.tools.append({
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"toolSpec": {
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"name": t.name,
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"description": t.description,
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"inputSchema": bedrock_schema
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}
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})
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self.logger.info(f"{len(self.tools)} Tools erfolgreich für Bedrock angepasst und geladen")
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except Exception as e:
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self.logger.error(f"Tool Load Error: {e}")
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self.tools = []
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# -------------------------
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# TOOL EXECUTION
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# -------------------------
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async def _run_tool(self, tool):
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try:
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# Robustere Extraktion
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tool_use = tool.get("toolUse", tool) # falls schon extrahiert
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name = tool_use.get("name")
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args = tool_use.get("input", {}) or {}
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self.logger.info(f"Tool Call → {name} | args={args}")
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result = await self.mcp.call_tool(name, args)
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# MCP result stabilisieren
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if hasattr(result, "content"):
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return result.content
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return result
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except Exception as e:
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self.logger.error(f"Tool Error ({name}): {e}")
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return f"ERROR: {e}"
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# -------------------------
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# MAIN LOOP
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# -------------------------
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async def run(self, user_input: str):
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self.messages.append({
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"role": "user",
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"content": [{"text": user_input}]
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})
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for iteration in range(10): # etwas mehr Schleifendurchläufe erlaubt
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try:
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response = await self.llm.chat(
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messages=self.messages,
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tools=self.tools if self.tools else None
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)
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except Exception as e:
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self.logger.error(f"LLM Error: {e}")
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return f"LLM Fehler: {e}"
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# Response-Struktur von Bedrock Converse
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output = response.get("output", {})
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message = output.get("message", {})
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content = message.get("content", [])
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tool_uses = []
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final_text = []
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# Verbessertes Parsing
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for c in content:
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if "toolUse" in c:
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tool_uses.append(c)
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elif "text" in c:
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final_text.append(c["text"])
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# TOOL EXECUTION PATH
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if tool_uses:
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self.logger.info(f"{len(tool_uses)} Tool(s) detected")
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# Assistant-Nachricht mit Tool-Call speichern
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self.messages.append({
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"role": "assistant",
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"content": content
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})
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tool_results = []
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for tool in tool_uses:
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result = await self._run_tool(tool)
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tool_results.append({
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"toolResult": {
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"toolUseId": tool.get("toolUse", tool).get("toolUseId"),
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"content": [{"text": str(result)}]
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}
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})
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# Tool-Ergebnisse zurück an das Modell
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self.messages.append({
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"role": "user",
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"content": tool_results
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})
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continue # nächste Runde für finale Antwort
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# FINAL RESPONSE
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if final_text:
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text = "\n".join(final_text)
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self.messages.append({
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"role": "assistant",
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"content": [{"text": text}]
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})
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return text
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return "Tool Loop Limit erreicht. Bitte versuche es erneut." |