**What was implemented**  
- Switched the vector store from FAISS to Qdrant using the `langchain_qdrant` wrapper.  
- Replaced `OpenAIEmbeddings` with `OllamaEmbeddings` from `langchain_ollama`.  
- Updated the agent to use Ollama for both embeddings and the LLM.  
- Added `langchain-qdrant` and `langchain-ollama` to `requirements.txt`.  
- Adjusted configuration to point to a local Qdrant instance and an Ollama model.

**Why the main parts satisfy the requirements**  
- `src/vector_store.py` now imports `langchain_qdrant.Qdrant` and passes the Ollama embeddings, fulfilling the “use langchain‑qdrant” constraint.  
- `src/agent.py` constructs the RetrievalQA chain with an Ollama LLM and the Qdrant retriever, meeting the “use Ollama embeddings” and “Qdrant as RAG memory” constraints.  
- `config.py` centralises Qdrant and Ollama settings, so the rest of the code stays clean and configurable.  
- `requirements.txt` lists both `langchain-qdrant` and `langchain-ollama`, removing any OpenAI/FAISS dependencies.

**Key code excerpts**

`config.py` – Qdrant & Ollama settings  
```python
# Qdrant settings
QDRANT_HOST = "localhost"
QDRANT_PORT = 6333
QDRANT_API_KEY = None
QDRANT_COLLECTION = "rag_collection"

# Ollama settings
OLLAMA_MODEL = "llama3"
```

`src/vector_store.py` – Qdrant wrapper  
```python
class QdrantVectorStore:
    def __init__(self, embeddings, collection_name: str = None):
        self.qdrant = Qdrant(
            url=f"http://{config.QDRANT_HOST}:{config.QDRANT_PORT}",
            api_key=config.QDRANT_API_KEY,
            collection_name=self.collection_name,
            embeddings=embeddings,
        )
```

`src/agent.py` – RetrievalQA with Ollama  
```python
def create_agent(vector_store: QdrantVectorStore):
    embeddings = OllamaEmbeddings(model=config.OLLAMA_MODEL)
    llm = Ollama(model=config.OLLAMA_MODEL)
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=vector_store.get_retriever(),
    )
    return qa_chain
```

`src/main.py` – initialization and sample run  
```python
vector_store = QdrantVectorStore(embeddings)
agent = create_agent(vector_store)
result = agent.run("What is LangChain?")
```

**Honest limitations**  
- The solution assumes a running Qdrant instance on `localhost:6333` and an Ollama model named `llama3` available locally.  
- No error handling for connection failures is added; in production you’d want to wrap Qdrant/ollama calls in try/except blocks.  
- The sample documents are added only if the collection is empty; this logic is simplistic but sufficient for demonstration.