**What was implemented**

- Switched the embedding provider from `OpenAIEmbeddings` to `OllamaEmbeddings` (langchain‑community).  
- Replaced the FAISS vector store with a Qdrant store.  
- Updated all imports, configuration, and helper functions to use the new stack.  
- Added the required dependencies (`langchain-community`, `qdrant-client`) to `requirements.txt`.  
- Kept the LLM (`OpenAI`), prompt templates, chain structure, and memory unchanged.  
- Provided local‑host configuration for both Ollama and Qdrant in `config.py`.

**Why the main parts satisfy the requirements**

- **Embeddings** – `embeddings.py` now returns an `OllamaEmbeddings` instance that talks to a local Ollama server (`base_url=f"{OLLAMA_HOST}:{OLLAMA_PORT}"`).  
- **Vector store** – `vector_store.py` creates a `QdrantClient`, ensures the collection exists, and returns a `Qdrant` vector store wired to the Ollama embeddings.  
- **Agent** – `agent.py` builds a `RetrievalQA` chain that uses the Qdrant retriever, the same OpenAI LLM, and a conversation buffer memory.  
- **Configuration** – `config.py` exposes host/port for both services, so the agent can connect to local instances without hard‑coding URLs.  
- **Dependencies** – `requirements.txt` now lists `langchain-community` and `qdrant-client`, satisfying the “add dependencies” requirement.

**Key code excerpts**

`embeddings.py` – Ollama embeddings

```python
from langchain_community.embeddings import OllamaEmbeddings
...
return OllamaEmbeddings(
    model=OLLAMA_EMBEDDING_MODEL,
    base_url=f"{OLLAMA_HOST}:{OLLAMA_PORT}"
)
```

`vector_store.py` – Qdrant store

```python
from qdrant_client import QdrantClient
...
return Qdrant(
    client=client,
    collection_name=QDRANT_COLLECTION_NAME,
    embeddings=embeddings
)
```

`agent.py` – RetrievalQA chain unchanged except for the retriever

```python
vector_store: Qdrant = get_vector_store()
retriever = vector_store.as_retriever(search_kwargs={"k": 5})
...
chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    memory=memory
)
```

`config.py` – local‑host settings

```python
OLLAMA_HOST: str = "http://localhost"
OLLAMA_PORT: int = 11434
QDRANT_HOST: str = "http://localhost"
QDRANT_PORT: int = 6333
```

**Honest limitations**

- The solution assumes a running local Ollama server exposing the chosen embedding model (`llama2`) and a Qdrant instance listening on the default ports.  
- The vector size is hard‑coded to 768; if the chosen Ollama model uses a different dimensionality, the collection creation will need adjustment.  
- No automated tests were executed; the changes are based on the provided project structure and should satisfy the functional requirements.