adding files

log_reader_agent.py

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+# python3 -m pip install --user virtualenv
+# python3 -m venv myenv
+# source myenv/bin/activate
+# pip install langgraph langchain langchain-community langchain-ollama
+
+from typing import TypedDict, List
+#from asyncio import tools
+from langgraph.graph import StateGraph, END
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+import subprocess
+import shutil
+
+try:
+    from langchain_ollama import ChatOllama
+except ImportError:
+    from fake_ChatOllama import ChatOllama  # type: ignore
+
+# ----- Shared State -----
+class AgentState(TypedDict):
+    service: str
+    log: List[str]  # A list of findings
+    response: str            # The final output
+
+def reader_node(state: AgentState):
+    service = state["service"]
+    print(f"Reader is looking up: {service} logs...")
+
+    if shutil.which('docker'):
+        cmd = ['docker', 'logs', service]
+    else:
+        print('docker not installed, using dummy log')
+        cmd = ['cat', 'docker_log_pihole.txt']
+    
+    try:
+        result = subprocess.run(cmd, capture_output=True, shell=False, check=True).stdout.decode('utf-8')
+    except Exception as e:
+        print("Error during log retrieval:", e)
+        result = f"Could not read log for service: {service}:\n {e}"
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=500,
+        chunk_overlap=80)
+        
+    try:    
+        chunks = text_splitter.split_text(result)
+        results = chunks
+    except Exception as e:
+        print("Error during log splitting:", e)
+        results = f"Error during log splitting for service: {service}:\n {e}"
+    
+    # Only return the keys you want to update
+    return {"log": state.get("log", []) + [results]}
+
+def analyzer_node(state: AgentState):
+    print("start analyzing log...")
+    
+    service = state["service"]
+    data = state["log"][-1] if state["log"] else ""
+    
+    llm = ChatOllama(model="qwen3:8b", base_url="http://localhost:11434")
+    
+    prompt = ChatPromptTemplate.from_template(
+    """You are a helpful AI assistant.
+    Provide a concise summary of the service health from {service} docker log, including any errors, warnings, or notable events.
+    You must answer based *only* on the supplied log. If the log is empty or do not contain anythin indicating service-health, say 'I could not find 
+    any information in the supplied information.'
+    Log:
+    {data}
+    """
+    )
+
+    chain = prompt | llm
+    
+    response = chain.invoke({"service": service, "data": data})
+    
+    print("Analyzing complete.")
+    return {"response": response.content}
+
+
+# ----- Build the LangGraph -----
+workflow = StateGraph(AgentState)
+
+workflow.add_node("Reader", reader_node)
+workflow.add_node("Analyzer", analyzer_node)
+
+# Flow: Start -> Reader -> Writer -> END
+workflow.set_entry_point("Reader")
+workflow.add_edge("Reader", "Analyzer")
+workflow.add_edge("Analyzer", END)
+
+app = workflow.compile()
+
+if __name__ == "__main__":
+    import sys
+    import my_tools
+    #print("Starting the Multi-Agent System...\n")
+    
+    if len(sys.argv) > 1:
+        service = sys.argv[1]
+    else:
+        exit("Please provide the service name as a command-line argument.")
+
+    inputs: AgentState = {
+        "service": service,
+        "log": [],
+        "response": "",
+    }
+    
+    result = app.invoke(inputs)
+    #print(result["response"])
+
+    my_tools.tool_send_mail('johan.p1sson@gmail.com', service + ' weekly log analysis result', result["response"])