Upload agent_py.py

agent_py.py

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+# -*- coding: utf-8 -*-
+"""Agent.py
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1aYmc6hcd6JKLK6sdFwPS-yC89WliBwhh
+"""
+
+pip install langchain langchain-openai pydantic openai
+
+pip install langchain-community
+
+"""# Library and Framework imports
+- ChatOpenAI: This is the LLM backend (OpenAI’s GPT model).
+
+- ConversationBufferMemory: This stores past messages, enabling Memory (MCP).
+
+- initialize_agent: Initializes the agent, which decides how to use the LLM and tools.
+
+- Tool: Represents a single tool the agent can call
+"""
+
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.agents import initialize_agent, AgentType
+from langchain.tools import Tool
+import os
+
+"""# Build the Memory Layer (MCP)
+
+- The memory ensures the agent doesn’t “forget” previous context, which is crucial for MCP (Memory + Chain-of-Thought).
+
+- return_messages=True means the memory will store entire message objects (input and output).
+"""
+
+def build_memory() -> ConversationBufferMemory:
+    """Create memory so the agent can remember past messages"""
+    return ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+"""# Prompt engineering:
+- SystemPrompt: Sets up the agent's persona as an experienced platform architect. This is the agent's guide.
+
+- HumanPrompt: Defines what the agent needs (modernization plan, resilience strategy).
+
+- CoT Behavior: The agent will reason step-by-step and expose only the reasoning summary at the end.
+"""
+
+from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate
+
+def build_prompt() -> ChatPromptTemplate:
+    """Create a system+human prompt that encourages explicit planning (CoT-style)."""
+    system = SystemMessagePromptTemplate.from_template(
+        "You are InfraResilience, a senior platform architect.\n"
+        "Task: given a legacy stack, propose modernization options AND a resilience test plan.\n"
+        "Be concrete, AWS-aware (Airflow/Step Functions/ECS/Lambda/S3/SQS/SNS), and security-conscious.\n"
+        "Output concise, actionable bullet points.\n"
+        "When planning, think step-by-step internally; expose only a brief reasoning summary."
+    )
+
+    human = HumanMessagePromptTemplate.from_template(
+        "Legacy stack description:\n{legacy_stack}\n\n"
+        "Outage scenario (optional): {outage_scenario}\n\n"
+        "Provide:\n"
+        "1) Modernization plan\n"
+        "2) Resilience test strategy\n"
+        "3) Short reasoning summary"
+    )
+
+    return ChatPromptTemplate.from_messages([system, human])
+
+"""# GPT-4o-Mini LLM build
+- temperature=0.2: This controls the randomness of the LLM. A lower value makes the LLM more deterministic (less random).
+
+- LLM (OpenAI's GPT) that the agent will use for reasoning.
+"""
+
+from typing import Optional
+def build_llm(model_name: Optional[str] = None, temperature: float = 0.2) -> ChatOpenAI:
+    """Instantiate the chat LLM with sane defaults."""
+    chosen = model_name or os.getenv("LLM_MODEL_NAME", "gpt-4o-mini")
+    return ChatOpenAI(model=chosen, temperature=temperature)
+
+"""So far, we have Memory, Prompts, LLM initialized. Now, let's build the agent with tools.
+
+The agent is now connected to tools (we'll define in tools.py), memory, and the prompt template. It's ready to think through problems.
+"""
+
+from typing import List
+def create_agent(tools: List[Tool], llm: Optional[ChatOpenAI] = None, memory: Optional[ConversationBufferMemory] = None):
+    llm = llm or build_llm()  # LLM backend (can override)
+    memory = memory or build_memory()  # Memory (MCP)
+    prompt = build_prompt()  # Prompt
+
+    agent = initialize_agent(
+        tools=tools,  # List of tools (what actions the agent can take)
+        llm=llm,  # Language model (the brain)
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,  # React-style agent
+        memory=memory,  # Memory
+        verbose=False,  # Toggle logs on/off
+        agent_kwargs={"extra_prompt_messages": [prompt]},  # Include custom prompt
+    )
+
+    return agent  # This returns the agent, which can be invoked later