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agent.py aktualisiert

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Till-Immer
2026-07-05 16:07:43 +00:00
parent 072e303d5f
commit 45e741ade3
1 changed files with 52 additions and 46 deletions
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agent.py
+44 -38
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@@ -1,8 +1,12 @@
from llm.bedrock import BedrockClient from llm.bedrock import BedrockClient
from jarvis_mcp.client import MCPClient # oder wo auch immer dein MCP-Client liegt from jarvis_mcp.client import MCPClient
from config import MCP_URL from config import MCP_URL
from util.logger import get_logger from util.logger import get_logger
import asyncio import asyncio
from pathlib import Path
# Import des neuen Web-Search Tools
from tools.web_search import web_search_tool, web_search
class Agent: class Agent:
@@ -14,33 +18,44 @@ class Agent:
self.tools = [] self.tools = []
# System-Prompt als erste User-Nachricht (Nova 2 Lite kompatibel) # Personality / System-Prompt (als User-Message für Nova 2 Lite)
self.system_prompt = self._load_system_prompt()
self.messages = [ self.messages = [
{ {
"role": "user", "role": "user",
"content": [ "content": [{"text": self.system_prompt}]
{ }
"text": ( ]
"Du bist N.O.R.A (Neural Operations for Residential Automation), ein freundlicher und kompetenter Smart-Home Assistent. Du bist ähnlich wie die KI Jarvis. höfflich, zielorientiert und leicht zynisch und ironisch. Du antwortest in kurzen Sätzen ausser es wird eine lange Erklärung verlangt. "
"für OpenHAB. Du steuerst Geräte und beantwortest Fragen über das Zuhause. " def _load_system_prompt(self) -> str:
"Nutze Tools, wenn sinnvoll. Antworte auf Deutsch und sei hilfreich." """Lädt Personality aus externer Datei"""
try:
prompt_path = Path("prompts/nora_system.txt")
if prompt_path.exists():
return prompt_path.read_text(encoding="utf-8").strip()
else:
self.logger.warning("System-Prompt-Datei nicht gefunden. Verwende Standard.")
return (
"Du bist N.O.R.A (Neural Operations for Residential Automation), "
"ein freundlicher, kompetenter und leicht ironischer Smart-Home Assistent. "
"Du antwortest natürlich und auf Deutsch. Nutze Tools wenn sinnvoll."
) )
} except Exception:
] return "Du bist N.O.R.A, ein hilfreicher Smart-Home Assistent."
}
]
# ------------------------- # -------------------------
# TOOL LOADING # TOOL LOADING
# ------------------------- # -------------------------
async def load_tools(self): async def load_tools(self):
try: try:
# 1. OpenHAB Tools vom MCP Server laden
res = await self.mcp.list_tools() res = await self.mcp.list_tools()
self.tools = [] self.tools = []
for t in res.tools: for t in res.tools:
schema = t.inputSchema schema = t.inputSchema
# Bedrock-kompatibles Format
# Bedrock-kompatibles Format erzwingen
if isinstance(schema, dict) and "json" not in schema: if isinstance(schema, dict) and "json" not in schema:
bedrock_schema = {"json": schema} bedrock_schema = {"json": schema}
else: else:
@@ -54,35 +69,37 @@ class Agent:
} }
}) })
self.logger.info(f"{len(self.tools)}OpenHAB Tools erfolgreich für Bedrock angepasst und geladen") self.logger.info(f"{len(self.tools)} OpenHAB-Tools erfolgreich geladen")
# Nach dem Laden der MCP-Tools:
from tools.web_search import web_search_tool, web_search
# Manuell hinzufügen # 2. Web-Search Tool manuell hinzufügen
if web_search_tool not in self.tools: if not any(t.get("toolSpec", {}).get("name") == "web_search" for t in self.tools):
self.tools.append(web_search_tool) self.tools.append(web_search_tool)
self.logger.info("Web-Search Tool hinzugefügt (DuckDuckGo)")
self.logger.info(f"Gesamt: {len(self.tools)} Tools geladen")
self.logger.info(f"{len(self.tools)} Tools geladen (inkl. Web-Suche)")
except Exception as e: except Exception as e:
self.logger.error(f"Tool Load Error: {e}") self.logger.error(f"Tool Load Error: {e}")
self.tools = [] self.tools = []
# ------------------------- # -------------------------
# TOOL EXECUTION # TOOL EXECUTION
# ------------------------- # -------------------------
async def _run_tool(self, tool): async def _run_tool(self, tool):
try: try:
# Robustere Extraktion tool_use = tool.get("toolUse", tool)
tool_use = tool.get("toolUse", tool) # falls schon extrahiert
name = tool_use.get("name") name = tool_use.get("name")
args = tool_use.get("input", {}) or {} args = tool_use.get("input", {}) or {}
self.logger.info(f"Tool Call → {name} | args={args}") self.logger.info(f"Tool Call → {name} | args={args}")
# Web-Search Tool
if name == "web_search":
return await web_search(**args)
# Normale MCP Tools (OpenHAB)
result = await self.mcp.call_tool(name, args) result = await self.mcp.call_tool(name, args)
# MCP result stabilisieren
if hasattr(result, "content"): if hasattr(result, "content"):
return result.content return result.content
return result return result
@@ -100,18 +117,16 @@ class Agent:
"content": [{"text": user_input}] "content": [{"text": user_input}]
}) })
for iteration in range(10): # etwas mehr Schleifendurchläufe erlaubt for iteration in range(12):
try: try:
response = await self.llm.chat( response = await self.llm.chat(
messages=self.messages, messages=self.messages,
tools=self.tools if self.tools else None tools=self.tools if self.tools else None
) )
except Exception as e: except Exception as e:
self.logger.error(f"LLM Error: {e}") self.logger.error(f"LLM Error: {e}")
return f"LLM Fehler: {e}" return f"LLM Fehler: {e}"
# Response-Struktur von Bedrock Converse
output = response.get("output", {}) output = response.get("output", {})
message = output.get("message", {}) message = output.get("message", {})
content = message.get("content", []) content = message.get("content", [])
@@ -119,28 +134,23 @@ class Agent:
tool_uses = [] tool_uses = []
final_text = [] final_text = []
# Verbessertes Parsing
for c in content: for c in content:
if "toolUse" in c: if "toolUse" in c:
tool_uses.append(c) tool_uses.append(c)
elif "text" in c: elif "text" in c:
final_text.append(c["text"]) final_text.append(c["text"])
# TOOL EXECUTION PATH
if tool_uses: if tool_uses:
self.logger.info(f"{len(tool_uses)} Tool(s) detected") self.logger.info(f"{len(tool_uses)} Tool(s) detected")
# Assistant-Nachricht mit Tool-Call speichern
self.messages.append({ self.messages.append({
"role": "assistant", "role": "assistant",
"content": content "content": content
}) })
tool_results = [] tool_results = []
for tool in tool_uses: for tool in tool_uses:
result = await self._run_tool(tool) result = await self._run_tool(tool)
tool_results.append({ tool_results.append({
"toolResult": { "toolResult": {
"toolUseId": tool.get("toolUse", tool).get("toolUseId"), "toolUseId": tool.get("toolUse", tool).get("toolUseId"),
@@ -148,23 +158,19 @@ class Agent:
} }
}) })
# Tool-Ergebnisse zurück an das Modell
self.messages.append({ self.messages.append({
"role": "user", "role": "user",
"content": tool_results "content": tool_results
}) })
continue
continue # nächste Runde für finale Antwort # Finale Antwort
# FINAL RESPONSE
if final_text: if final_text:
text = "\n".join(final_text) text = "\n".join(final_text)
self.messages.append({ self.messages.append({
"role": "assistant", "role": "assistant",
"content": [{"text": text}] "content": [{"text": text}]
}) })
return text return text
return "Tool Loop Limit erreicht. Bitte versuche es erneut." return "Tool Loop Limit erreicht. Bitte versuche es erneut."