200 lines
7.4 KiB
Python
200 lines
7.4 KiB
Python
import os
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from langchain.chat_models import init_chat_model
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from langchain_community.tools.shell.tool import ShellTool
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from langgraph.prebuilt import create_react_agent
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from langchain_core.messages import HumanMessage
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from log_analyzer import analyze_log_file
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def create_agent():
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"""Create and return a ReAct agent with shell and log analysis capabilities."""
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# Initialize the chat model (using OpenAI GPT-4)
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# Make sure you have set your OPENAI_API_KEY environment variable
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llm = init_chat_model("openai:gpt-4o-mini")
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# Define the tools available to the agent
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shell_tool = ShellTool()
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tools = [shell_tool, analyze_log_file]
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# Create a ReAct agent with system prompt
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system_prompt = """You are a helpful assistant with access to shell commands and log analysis capabilities.
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You can:
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1. Execute shell commands using the shell tool to interact with the system
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2. Analyze log files using the analyze_log_file tool to help with debugging and system administration tasks
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The log analyzer can process files in the loghub directory with different analysis types:
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- "error_patterns": Find and categorize error messages
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- "frequency": Analyze frequency of different log patterns
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- "timeline": Show chronological patterns of events
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- "summary": Provide an overall summary of the log file
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When helping users:
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- Be thorough in your analysis
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- Explain what you're doing and why
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- Use appropriate tools based on the user's request
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- If analyzing logs, suggest which analysis type might be most helpful
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- Always be cautious with shell commands and explain what they do
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Available log files are in the loghub directory with subdirectories for different systems like:
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Android, Apache, BGL, Hadoop, HDFS, HealthApp, HPC, Linux, Mac, OpenSSH, OpenStack, Proxifier, Spark, Thunderbird, Windows, Zookeeper
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"""
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# Create the ReAct agent
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agent = create_react_agent(
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llm,
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tools,
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prompt=system_prompt
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)
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return agent
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def stream_agent_updates(agent, user_input: str):
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"""Stream agent updates for a user input."""
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# Create a human message
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message = HumanMessage(content=user_input)
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print("\nAgent: ", end="", flush=True)
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# Use the agent's stream method to get real-time updates
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final_response = ""
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tool_calls_made = False
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for event in agent.stream({"messages": [message]}, stream_mode="updates"):
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for node_name, node_output in event.items():
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if node_name == "agent" and "messages" in node_output:
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last_message = node_output["messages"][-1]
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# Check if this is a tool call
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if hasattr(last_message, 'tool_calls') and last_message.tool_calls:
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tool_calls_made = True
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for tool_call in last_message.tool_calls:
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print(f"\n🔧 Using tool: {tool_call['name']}")
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if tool_call.get('args'):
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print(f" Args: {tool_call['args']}")
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# Check if this is the final response (no tool calls)
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elif hasattr(last_message, 'content') and last_message.content and not getattr(last_message, 'tool_calls', None):
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final_response = last_message.content
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elif node_name == "tools" and "messages" in node_output:
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# Show tool results
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for msg in node_output["messages"]:
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if hasattr(msg, 'content'):
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print(f"\n📋 Tool result: {msg.content[:200]}{'...' if len(msg.content) > 200 else ''}")
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# Print the final response
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if final_response:
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if tool_calls_made:
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print(f"\n\n{final_response}")
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else:
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print(final_response)
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else:
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print("No response generated.")
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print() # Add newline
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def visualize_agent(agent):
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"""Display the agent's graph structure."""
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try:
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print("\n📊 Agent Graph Structure:")
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print("=" * 40)
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# Get the graph and display its structure
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graph = agent.get_graph()
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# Print nodes
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print("Nodes:")
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for node_id in graph.nodes:
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print(f" - {node_id}")
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# Print edges
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print("\nEdges:")
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for edge in graph.edges:
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print(f" - {edge}")
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print("=" * 40)
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print("This agent follows the ReAct (Reasoning and Acting) pattern:")
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print("1. Receives user input")
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print("2. Reasons about what tools to use")
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print("3. Executes tools when needed")
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print("4. Provides final response")
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print("=" * 40)
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except Exception as e:
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print(f"Could not visualize agent: {e}")
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def main():
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# Check if required API keys are set
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if not os.getenv("OPENAI_API_KEY"):
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print("Please set your OPENAI_API_KEY environment variable.")
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print("You can set it by running: export OPENAI_API_KEY='your-api-key-here'")
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return
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print("🤖 LangGraph Log Analysis Agent")
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print("Type 'quit', 'exit', or 'q' to exit the chat.")
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print("Type 'help' or 'h' for help and examples.")
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print("Type 'graph' to see the agent structure.")
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print("⚠️ WARNING: This agent has shell access - use with caution!")
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print("📊 Available log analysis capabilities:")
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print(" - Analyze log files in the loghub directory")
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print(" - Execute shell commands for system administration")
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print(" - Help with debugging and troubleshooting")
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print("-" * 60)
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# Create the agent
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try:
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agent = create_agent()
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print("✅ Log Analysis Agent initialized successfully!")
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print("💡 Try asking: 'Analyze the Apache logs for error patterns'")
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print("💡 Or: 'List the available log files in the loghub directory'")
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# Show agent structure
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visualize_agent(agent)
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except Exception as e:
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print(f"❌ Error initializing agent: {e}")
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return
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# Start the chat loop
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while True:
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try:
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user_input = input("\nUser: ")
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if user_input.lower() in ["quit", "exit", "q"]:
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print("👋 Goodbye!")
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break
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elif user_input.lower() in ["help", "h"]:
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print("\n🆘 Help:")
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print("Commands:")
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print(" - quit/exit/q: Exit the agent")
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print(" - help/h: Show this help")
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print(" - graph: Show agent structure")
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print("\nExample queries:")
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print(" - 'Analyze the Apache logs for error patterns'")
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print(" - 'Show me a summary of the HDFS logs'")
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print(" - 'List all available log files'")
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print(" - 'Find error patterns in Linux logs'")
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print(" - 'Check disk usage on the system'")
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continue
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elif user_input.lower() in ["graph", "structure"]:
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visualize_agent(agent)
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continue
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if user_input.strip():
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stream_agent_updates(agent, user_input)
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else:
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print("Please enter a message.")
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except KeyboardInterrupt:
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print("\n👋 Goodbye!")
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break
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except Exception as e:
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print(f"❌ Error: {e}")
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if __name__ == "__main__":
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main()
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