Finalize GenAI exam: simplify qabilah+q2, add q5 LangChain agent

q5-llm-agent/agent.py

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+"""
+Q5 starter — a minimal LangChain agent on Ghaymah's GenAI services.
+
+1. Get base_url + API key + model ID from https://deploy.ghaymah.systems
+2. pip install -r requirements.txt
+3. python agent.py "what is 12 * 7 plus 4?"
+"""
+import os
+import sys
+
+from langchain_openai import ChatOpenAI
+from langchain.agents import AgentExecutor, create_tool_calling_agent
+from langchain.tools import tool
+from langchain_core.prompts import ChatPromptTemplate
+
+# --- Ghaymah GenAI config (fill these in / use env vars) ---
+BASE_URL = os.getenv("GHAYMAH_BASE_URL", "https://<ghaymah-llm-endpoint>/v1")
+API_KEY = os.getenv("GHAYMAH_API_KEY", "<redacted>")
+MODEL = os.getenv("GHAYMAH_MODEL", "<model-id-from-dashboard>")
+
+llm = ChatOpenAI(base_url=BASE_URL, api_key=API_KEY, model=MODEL, temperature=0)
+
+
+@tool
+def calculator(expression: str) -> str:
+    """Evaluate a simple arithmetic expression and return the result."""
+    try:
+        return str(eval(expression, {"__builtins__": {}}, {}))
+    except Exception as e:  # noqa: BLE001
+        return f"error: {e}"
+
+
+tools = [calculator]
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", "You are a helpful Arabic/English assistant. Use the calculator tool when you need arithmetic."),
+        ("human", "{input}"),
+        ("placeholder", "{agent_scratchpad}"),
+    ]
+)
+
+agent = create_tool_calling_agent(llm, tools, prompt)
+executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+if __name__ == "__main__":
+    q = sys.argv[1] if len(sys.argv) > 1 else "ما حاصل 12 × 7 + 4؟"
+    print(executor.invoke({"input": q})["output"])

q5-llm-agent/llm-agent.md

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+# Q5 — Build a simple LLM agent with LangChain (on Ghaymah's GenAI services)
+
+⏱ **Budget: ~9 minutes** · Weight: 20%
+
+## The task
+
+Build a small **LLM agent** using **LangChain**, and point it at **Ghaymah's GenAI services** (the ready-to-use LLM models you saw in the dashboard).
+
+Requirements:
+
+1. Use LangChain (Python) with an **OpenAI-compatible** client pointed at Ghaymah's inference endpoint. Get the **base URL**, **API key**, and a **model ID** from https://deploy.ghaymah.systems (see the AI / models section).
+2. Give the agent **at least one tool** — e.g. a calculator, a "save note" memory tool, or a tiny retrieval tool.
+3. (Optional, +bonus) Wire it to the static site from **Q4** — the page calls your agent's endpoint and shows the reply.
+
+There's a starter `agent.py` + `requirements.txt` in this folder you may extend or replace.
+
+## Deliverable
+
+Fill in the template below and commit your code in this folder.
+
+---
+
+## ANSWER — LangChain agent
+
+### 1. What my agent does
+
+> _one or two lines_ 
+
+### 2. Architecture (LangChain components)
+
+| Component | What I used |
+|---|---|
+| Model (LLM) | |
+| Tool(s) | |
+| Prompt | |
+| Executor | |
+
+### 3. How it uses Ghaymah's GenAI services
+
+> _base URL pattern, model ID, how you got the key (do not paste the key)_ 
+
+### 4. Code
+
+> _commit your code in this folder (agent.py etc.) and reference it here_ 
+
+### 5. How to run / deploy it
+
+```bash
+# commands
+```

q5-llm-agent/requirements.txt

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+langchain
+langchain-openai