updating labs

Author: retroryan

Lab_4_Intro_to_Bedrock_and_Agents/basic_langgraph_agent.ipynb

@@ -254,29 +254,29 @@
   {
    "cell_type": "markdown",
    "id": "b1pdivtjrsl",
-   "source": "## 9. Query with Sample Company Data\n\nThis section demonstrates how to provide context to the agent. We load sample company data and ask the agent questions about it.\n\nThis pattern is useful for:\n- **RAG (Retrieval-Augmented Generation)**: Injecting retrieved documents into prompts\n- **In-context learning**: Providing examples or data for the agent to reference\n- **Domain-specific Q&A**: Answering questions based on proprietary information",
+   "source": "## 9. Query with Sample Manufacturing Data\n\nThis section demonstrates how to provide context to the agent. We load sample manufacturing data and ask the agent questions about it.\n\nThis pattern is useful for:\n- **RAG (Retrieval-Augmented Generation)**: Injecting retrieved documents into prompts\n- **In-context learning**: Providing examples or data for the agent to reference\n- **Domain-specific Q&A**: Answering questions based on proprietary information",
    "metadata": {}
   },
   {
    "cell_type": "code",
    "id": "o1qri9pwatb",
-   "source": "# Load sample company data\nfrom load_sample_data import load_company_data, print_info\n\ncompany_text = load_company_data()\nprint_info(company_text)",
+   "source": "# Load sample manufacturing data\nfrom load_sample_data import load_manufacturing_data, print_info\n\nmanufacturing_text = load_manufacturing_data()\nprint_info(manufacturing_text)",
    "metadata": {},
    "execution_count": null,
    "outputs": []
   },
   {
    "cell_type": "code",
    "id": "0jv7ule45vm5",
-   "source": "# Ask the agent questions about the company data\ndef ask_about_data(question: str, context: str):\n    \"\"\"Ask the agent a question with context.\"\"\"\n    prompt = f\"\"\"Based on this company information:\n\n{context}\n\nQuestion: {question}\"\"\"\n    return run_agent(prompt)\n\n# Sample questions\nask_about_data(\"What products does this company make?\", company_text)",
+   "source": "# Ask the agent questions about the manufacturing data\ndef ask_about_data(question: str, context: str):\n    \"\"\"Ask the agent a question with context.\"\"\"\n    prompt = f\"\"\"Based on this manufacturing information:\n\n{context}\n\nQuestion: {question}\"\"\"\n    return run_agent(prompt)\n\n# Sample questions\nask_about_data(\"What components are in the Electric Powertrain domain?\", manufacturing_text)",
    "metadata": {},
    "execution_count": null,
    "outputs": []
   },
   {
    "cell_type": "code",
    "id": "7u7fqe1mf8w",
-   "source": "# Another sample question\nask_about_data(\"What services does the company offer?\", company_text)",
+   "source": "# Another sample question\nask_about_data(\"What defects have been found and what are their severities?\", manufacturing_text)",
    "metadata": {},
    "execution_count": null,
    "outputs": []
@@ -295,4 +295,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

Lab_4_Intro_to_Bedrock_and_Agents/load_sample_data.py

@@ -1,12 +1,17 @@
-"""Simple loader for sample company data."""
+"""Simple loader for sample manufacturing data."""
 
-def load_company_data(filepath: str = "sample_company_data.txt") -> str:
-    """Load and return the company data text."""
+
+def load_manufacturing_data(filepath: str = "sample_manufacturing_data.txt") -> str:
+    """Load and return the manufacturing data text."""
     with open(filepath, "r") as f:
         text = f.read().strip()
     return text
 
 
+# Keep backward-compatible alias
+load_company_data = load_manufacturing_data
+
+
 def print_info(text: str) -> None:
     """Print basic info about the text."""
     lines = text.split("\n")
@@ -18,5 +23,5 @@ def print_info(text: str) -> None:
 
 
 if __name__ == "__main__":
-    data = load_company_data()
+    data = load_manufacturing_data()
     print_info(data)

Lab_4_Intro_to_Bedrock_and_Agents/sample_manufacturing_data.txt

@@ -0,0 +1,39 @@
+R2D2 is a next-generation robotic vehicle product currently under development. The product
+encompasses four technology domains: Electric Powertrain, Chassis, Body, and Infotainment.
+
+Components
+
+The Electric Powertrain domain includes the HVB_3900 High-Voltage Battery, PDU_1500 Power
+Distribution Unit, INV_2300 Inverter, and EMC_1800 E-Machine. The Chassis domain covers the
+SUS_4200 Suspension and BRK_3800 Brakes systems. The Body domain includes EXT_5600 Exterior
+and INT_6700 Interior components, along with SEA_7500 Seats.
+
+Requirements
+
+The HVB_3900 High-Voltage Battery has extensive engineering requirements covering battery cell
+and module design, housing and structural integrity, high-voltage wiring and connectors,
+thermal management, energy management, electrical safety, durability and lifecycle, crash
+safety, and electromagnetic compatibility. Each requirement includes detailed specifications
+in both German and English.
+
+Testing
+
+Requirements are validated through structured test sets containing individual test cases.
+For example, the Cell Design Tests (TS_1_1) include test cases for Voltage, Capacity,
+Energy Density, and Power Density. Test cases track status (Planned, Passed, Failed,
+In Progress), required resources, duration, schedule, and responsible engineer.
+
+Defects
+
+Testing has identified several defects including DEF001 (energy density does not meet
+specifications, High severity), DEF003 (high voltage interlock system not functioning
+correctly, Critical severity), and DEF005 (battery management system communication failure,
+High severity). Defects are tracked with severity, priority, assigned engineer, and
+resolution status.
+
+Change Proposals
+
+Change proposals address issues found during development. CP001 proposes increasing energy
+density requirements to meet Chinese standards (development cost: $500,000). CP002 proposes
+enhancing water ingress protection to IP68 (development cost: $250,000). CP003 addresses
+compliance with Chinese national standards for high-voltage wiring (development cost: $300,000).