Add assignment description in English

stream-agent/assignment_description.txt

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+# Assignment Description (English)
+
+## Goal
+
+Upgrade the agent from the previous assignment: replace the single `.invoke()` call with streaming output via `.stream()`, so that the response appears in the console token‑by‑token instead of after the entire generation.
+
+---
+
+## Background
+
+In the previous assignment the agent was invoked with `.invoke()`, which returned the result only after the agent finished all its work. For long answers or when the agent calls several tools this can look like a hang.
+
+**Stream mode** allows receiving the answer token by token in real time, just like ChatGPT or any other chat interface.
+
+---
+
+## What to do
+
+1. **Replace `.invoke()` with `.stream()`**
+   ```python
+   # before
+   answer = agent.invoke({"messages": [{"role": "human", "content": "..."}]})
+   # after
+   stream = agent.stream({"messages": [{"role": "human", "content": "..."}]}, stream_mode=["messages", "updates"])
+   ```
+   `stream_mode` is a list of modes. You can pass one or both:
+   * `'messages'` – each token of the text as it is generated.
+   * `'updates'` – events about state changes (tool calls, step finishes).
+
+2. **Iterate over the chunks**
+   ```python
+   for chunk in stream:
+       chunk_type, chunk_data = chunk
+       if chunk_type == 'messages':
+           # token stream
+       elif chunk_type == 'updates':
+           # state update
+   ```
+
+3. **Handle `'messages'` chunks**
+   ```python
+   message, meta = chunk_data
+   if meta['langgraph_step'] != step:
+       step = meta['langgraph_step']
+       print('\n---\n')
+   if message.content:
+       print(message.content, end='', flush=True)
+   ```
+
+4. **Handle `'updates'` chunks**
+   ```python
+   if chunk_type == 'updates' and chunk_data.get('model'):
+       last_message = chunk_data['model']['messages'][-1]
+       print(format_message(last_message))
+   ```
+   The helper `format_message` is the same as used for `.invoke()`.
+
+---
+
+## Final script structure
+
+```python
+# import LLM and tools
+# define the tool
+# create the agent
+# run the stream
+# iterate over chunks and print
+# finally print the full result
+```
+
+---
+
+## Expected output
+
+The console should show the text gradually, with each new agent step separated by a divider. For example:
+```
+---
+get_price({'product': 'milk', 'city': 'Kazan'})
+---
+Milk in Kazan: 89 rub.
+---
+```