feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'

SOLUTION.md

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+**What was implemented**  
+The script `src/index.py` now uses **ChromaDB** as the persistent vector store instead of Qdrant.  
+It loads documents from a folder, splits them into chunks, embeds them with OpenAI embeddings, and stores the vectors in a Chroma collection.  
+A Retrieval‑QA chain is built with LangChain’s `RetrievalQA` and OpenAI’s GPT model, and a lightweight web‑search tool (`DuckDuckGoSearchRun`) is kept for quick queries.
+
+**Why the main parts satisfy the assignment**  
+* The vector database is explicitly ChromaDB – the `initialize_vectorstore()` function creates a `chromadb.PersistentClient` and wraps it with LangChain’s `Chroma` wrapper.  
+* All required stack components are present: `chromadb`, `langchain`, `openai`, and `python-dotenv`.  
+* The agent can ingest, query, and perform web search, matching the functional requirements of the exam task.
+
+**Key code excerpts**
+
+`src/index.py` – imports and vector store initialization  
+```python
+import chromadb
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+...
+def initialize_vectorstore() -> Chroma:
+    client = chromadb.PersistentClient(path=CHROMA_DB_PATH)
+    client.get_or_create_collection(name=COLLECTION_NAME)
+    vectorstore = Chroma(
+        client=client,
+        collection_name=COLLECTION_NAME,
+        embedding_function=OpenAIEmbeddings(model=EMBEDDING_MODEL),
+    )
+    return vectorstore
+```
+
+`src/index.py` – ingesting documents into Chroma  
+```python
+def ingest_documents(folder_path: str, vectorstore: Chroma) -> None:
+    raw_texts = load_documents_from_folder(folder_path)
+    chunks = split_text(raw_texts)
+    vectorstore.add_texts(chunks)
+    print(f"Ingested {len(chunks)} chunks into collection '{COLLECTION_NAME}'.")
+```
+
+`src/index.py` – web‑search helper  
+```python
+def perform_web_search(query: str) -> List[Dict[str, str]]:
+    search_tool = DuckDuckGoSearchRun()
+    results = search_tool.run(query)
+    if isinstance(results, list):
+        return results
+    return [{"title": "Search Result", "url": "", "body": results}]
+```
+
+**Limitations**  
+* No unit tests are included.  
+* Error handling is minimal (e.g., missing environment variables or empty folders).  
+* The script is single‑threaded and may not scale for very large corpora without further optimization.  
+
+Overall, the implementation now adheres to the required stack and fulfills the RAG agent functionality described in the assignment.