feat: solution for 'Повторный экзамен: FAQ-бот — ChromaDB + один MCP-tool'

data/course_meta.json

@@ -0,0 +1,27 @@
+{
+  "schedule": [
+    {
+      "date": "2026-09-01",
+      "lecture": "Lecture 1",
+      "topic": "Introduction to ML",
+      "location": "Room 101"
+    },
+    {
+      "date": "2026-09-08",
+      "lecture": "Lecture 2",
+      "topic": "Data Preprocessing",
+      "location": "Room 102"
+    },
+    {
+      "date": "2026-09-15",
+      "lecture": "Lecture 3",
+      "topic": "Linear Regression",
+      "location": "Room 103"
+    }
+  ],
+  "instructor": {
+    "name": "Dr. Jane Doe",
+    "email": "jane.doe@example.com",
+    "office": "Room 201"
+  }
+}

data/faq1.md

@@ -0,0 +1,24 @@
+# Course Overview
+
+This course covers the fundamentals of machine learning, including supervised and unsupervised learning, neural networks, and reinforcement learning. The course is divided into 12 lectures, each lasting 90 minutes.
+
+## Assignment 1
+
+The first assignment is due on **August 31, 2026**. It requires you to implement a simple linear regression model and evaluate its performance.
+
+## Lecture Schedule
+
+| Lecture | Topic |
+|---------|-------|
+| 1 | Introduction to ML |
+| 2 | Data Preprocessing |
+| 3 | Linear Regression |
+| 4 | Logistic Regression |
+| 5 | Decision Trees |
+| 6 | Random Forests |
+| 7 | Support Vector Machines |
+| 8 | Neural Networks |
+| 9 | Convolutional Neural Networks |
+| 10 | Recurrent Neural Networks |
+| 11 | Reinforcement Learning |
+| 12 | Project Presentations |

data/faq2.md

@@ -0,0 +1,13 @@
+# Frequently Asked Questions
+
+**Q: How many lectures are there in the course?**  
+A: There are 12 lectures in total.
+
+**Q: What is the deadline for Assignment 2?**  
+A: Assignment 2 is due on **September 15, 2026**.
+
+**Q: Where can I find the lecture slides?**  
+A: All lecture slides are available in the course portal under the "Resources" section.
+
+**Q: Can I submit the assignment late?**  
+A: Late submissions are accepted with a penalty of 10% per day after the deadline.

data/faq3.md

@@ -0,0 +1,12 @@
+# Course Materials
+
+- **Lecture Slides**: PDF files for each lecture.
+- **Reading List**: A list of recommended books and papers.
+- **Code Repository**: GitHub repository with starter code and solutions.
+- **Discussion Forum**: For asking questions and collaborating with peers.
+
+**Q: Where is the code repository hosted?**  
+A: The code repository is hosted on GitHub at https://github.com/example/course-ml.
+
+**Q: How do I clone the repository?**  
+A: Use `git clone https://github.com/example/course-ml.git` in your terminal.