Remove development artifacts from notebooks directory

Remove 70+ development files:
- Documentation: execution reports, analysis docs, planning docs
- Scripts: validation scripts, setup scripts, fix scripts
- Logs: execution logs, validation logs
- Archive directories: _archive/ and .ipynb_checkpoints/ across all sections
- Test script: test_notebook_fixes.py

Keep only:
- Essential notebooks (.ipynb)
- README.md and SETUP_GUIDE.md
- course_catalog_section2.json
- Backup notebooks (.backup, _old, _executed)

Author: nkanu17

08_vector_algorithm_benchmark.py

@@ -0,0 +1,110 @@
+"""Basic Langraph Q&A Agent demo."""
+import os
+from typing import Annotated, TypedDict
+import operator
+
+from langgraph.constants import END
+from langgraph.graph import StateGraph
+from openai import OpenAI
+
+# Initialize OpenAI client with API key from environment
+client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+
+class AgentState(TypedDict):
+    """State that is access by all nodes."""
+    messages: Annotated[list, operator.add]  # Accumulates messages
+    question: str
+    answer: str
+    iteration_count: int
+
+# 2. Define Nodes - functions that do work
+def ask_question(state: AgentState) -> AgentState:
+    """Node that processes the question"""
+    print(f"Processing question: {state['question']}")
+    return {
+        "messages": [f"Question received: {state['question']}"],
+        "iteration_count": state.get("iteration_count", 0) + 1
+    }
+
+def generate_answer(state: AgentState) -> AgentState:
+    """Node that generates an answer using OpenAI"""
+    print("Generating answer with OpenAI...")
+
+    try:
+        response = client.chat.completions.create(
+            model="gpt-3.5-turbo",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant that provides clear, concise answers."},
+                {"role": "user", "content": state['question']}
+            ],
+            max_tokens=150,
+            temperature=0.7
+        )
+
+        answer = response.choices[0].message.content.strip()
+
+    except Exception as e:
+        print(f"Error calling OpenAI: {e}")
+        answer = f"Error generating answer: {str(e)}"
+
+    return {
+        "answer": answer,
+        "messages": [f"Answer generated: {answer}"]
+    }
+
+# 3. Define conditional logic
+def should_continue(state: AgentState) -> str:
+    """Decides whether to continue or end"""
+    print(f"Checking if we should continue...{state['iteration_count']}")
+    if state["iteration_count"] > 3:
+        return "end"
+    return "continue"
+
+
+if __name__=="__main__":
+    # Check if OpenAI API key is available
+    if not os.getenv("OPENAI_API_KEY"):
+        print("⚠️  Warning: OPENAI_API_KEY not found in environment variables!")
+        print("Please set your OpenAI API key: export OPENAI_API_KEY='your-key-here'")
+        exit(1)
+
+    initial_state = {
+        "question": "What is LangGraph?",
+        "messages": [],
+        "answer": "",
+        "iteration_count": 0
+    }
+
+    # # 4. Build the Graph
+    workflow = StateGraph(AgentState)
+    #
+    # Two nodes that are doing things
+    workflow.add_node("process_question", ask_question)
+    workflow.add_node("generate_answer", generate_answer)
+    # #
+    # # # Add edges
+    workflow.set_entry_point("process_question")  # Start here
+
+    # First, always go from process_question to generate_answer
+    workflow.add_edge("process_question", "generate_answer")
+
+    # After generating answer, check if we should continue or end
+    workflow.add_conditional_edges(
+        "generate_answer",  # Check after generating answer
+        should_continue,
+        {
+            "continue": "process_question",  # If continue, loop back to process_question
+            "end": END  # If end, finish
+        }
+    )
+    #
+    # # Compile the graph
+    app = workflow.compile()
+    result = app.invoke(initial_state)
+    print("\n=== Final Result ===")
+    print(f"Question: {result['question']}")
+    print(f"Answer: {result['answer']}")
+    print(f"Messages: {result['messages']}")
+    # print(result)
+