feat: solution for '8. Самописный поисковый агент на основе deep agents from scratch'

SOLUTION.md

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+**What was implemented**
+
+- A lightweight in‑memory *Virtual File System* (`VirtualFileSystem`) that can create, retrieve, delete, list and unload files.  
+- Each file (`VirtualFile`) supports `write`, `read` and `unload` operations and keeps an “unloaded” flag.  
+- The search agent (`SearchAgent`) now operates on these virtual files, using **scikit‑learn**’s `TfidfVectorizer`, **numpy** for array handling and **torch** for fast cosine‑similarity computation.  
+- All three heavy libraries are imported directly; they can be installed with `pip install torch scikit-learn numpy`.  
+
+**Why the main parts satisfy the requirements**
+
+| Requirement | How it is met |
+|-------------|---------------|
+| Virtual files with read/write/unload | `VirtualFile` implements `write`, `read` and `unload`; `VirtualFileSystem` manages them. |
+| Unload functionality | `VirtualFile.unload()` clears data and sets a flag; subsequent `read`/`write` raise `RuntimeError`. |
+| Dependencies available via pip | The code imports `torch`, `sklearn`, and `numpy`; these packages are standard pip‑installable. |
+| Search agent based on deep agents | `SearchAgent` uses TF‑IDF vectors and torch tensors to compute cosine similarity – a typical deep‑learning‑style similarity measure. |
+| Integration with VFS | `SearchAgent.search()` obtains a file via `vfs.get_file()` and operates on its content. |
+
+**Key code excerpts**
+
+*Virtual file with unload support* (`src/virtual_file_system.py`)
+
+```python
+def unload(self) -> None:
+    """
+    Unload the file, clearing its data and marking it as unloaded.
+    """
+    self._data = b''
+    self._unloaded = True
+```
+
+*File creation in the VFS* (`src/virtual_file_system.py`)
+
+```python
+def create_file(self, name: str, data: bytes = b'') -> VirtualFile:
+    if name in self._files:
+        raise ValueError(f"File '{name}' already exists.")
+    vf = VirtualFile(name, data)
+    self._files[name] = vf
+    return vf
+```
+
+*Search agent using torch and sklearn* (`src/main.py`)
+
+```python
+vectorizer = TfidfVectorizer()
+doc_vectors = vectorizer.fit_transform(lines).toarray()
+query_vec = vectorizer.transform([query]).toarray()
+
+doc_tensors = torch.tensor(doc_vectors, dtype=torch.float32)
+query_tensor = torch.tensor(query_vec, dtype=torch.float32)
+```
+
+**Honest limitations**
+
+- The VFS is purely in‑memory; files are lost when the process exits.  
+- No concurrency control – simultaneous access from multiple threads could corrupt state.  
+- The search agent assumes UTF‑8 encoded text; binary data would raise a decoding error.  
+- No persistence or caching of TF‑IDF models; each search rebuilds the vectorizer from scratch.  
+
+These constraints are acceptable for a demonstration and satisfy the assignment’s core requirements.