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

SOLUTION.md

@@ -1,54 +1,43 @@
 **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.
+- Replaced the previous Qdrant‑based vector store with a lightweight wrapper around **ChromaDB** (`src/vector_store.py`).  
+- Updated the `RAGAgent` to work exclusively with the new `ChromaVectorStore`.  
+- Kept the FastAPI endpoints (`/ingest`, `/query`, `/websearch`) unchanged, so the public API and web‑search logic remain intact.  
+- Removed every import and reference to Qdrant, ensuring the stack now matches the assignment.
 
-**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.
+**Why the main parts satisfy the requirements**  
+- `ChromaVectorStore` creates a Chroma client and a collection, then exposes `add_documents` and `similarity_search` that match the original Qdrant interface.  
+- `RAGAgent` uses this store for ingestion and querying, and still relies on OpenAI embeddings, so the RAG workflow is preserved.  
+- The FastAPI app simply forwards requests to the agent; no Qdrant code is touched, so the vector database is now exclusively ChromaDB.  
+- Web‑search utilities (`src/web_search.py`) are untouched, so the search‑to‑ingest pipeline continues to work.
 
 **Key code excerpts**
 
-`src/index.py` – imports and vector store initialization  
+`src/vector_store.py` – Chroma client and collection creation  
 ```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
+self.client = chromadb.Client()
+self.collection = self.client.get_or_create_collection(name=collection_name)
 ```
 
-`src/index.py` – ingesting documents into Chroma  
+`src/rag_agent.py` – ingestion uses the new store  
 ```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}'.")
+self.vector_store.add_documents(docs_with_embeddings)
 ```
 
-`src/index.py` – web‑search helper  
+`src/main.py` – FastAPI endpoint that calls the agent  
 ```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}]
+@app.post("/ingest")
+def ingest(request: IngestRequest):
+    docs = [doc.dict() for doc in request.documents]
+    rag_agent.ingest(docs)
 ```
 
-**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.  
+`src/web_search.py` – still feeds results into the agent  
+```python
+agent.ingest(docs_to_ingest)
+```
 
-Overall, the implementation now adheres to the required stack and fulfills the RAG agent functionality described in the assignment.
+**Honest limitations**  
+- ChromaDB is used in its default in‑memory mode; data will not persist across server restarts unless a persistent storage path is configured.  
+- No additional error handling for Chroma connection failures has been added beyond the basic try/except in the API routes.  
+
+Overall, the project now uses only ChromaDB for vector storage, keeps all existing functionality, and respects the assignment constraints.