**What was implemented**  
- 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 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/vector_store.py` – Chroma client and collection creation  
```python
self.client = chromadb.Client()
self.collection = self.client.get_or_create_collection(name=collection_name)
```

`src/rag_agent.py` – ingestion uses the new store  
```python
self.vector_store.add_documents(docs_with_embeddings)
```

`src/main.py` – FastAPI endpoint that calls the agent  
```python
@app.post("/ingest")
def ingest(request: IngestRequest):
    docs = [doc.dict() for doc in request.documents]
    rag_agent.ingest(docs)
```

`src/web_search.py` – still feeds results into the agent  
```python
agent.ingest(docs_to_ingest)
```

**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.