**SOLUTION.md**

**What was implemented**  
- Switched from OpenAI embeddings/LLM to Ollama’s `nomic-embed-text` for vector generation.  
- Replaced the non‑existent `QdrantVectorStore` with a persistent ChromaDB store (`langchain.vectorstores.Chroma`).  
- Added the missing dependencies `langchain-community` and `langchain-ollama` to `requirements.txt`.  
- Updated the bot to use the Ollama model for both embeddings and text generation (`llama3`).  
- Kept the interactive FAQ loop and retrieval‑QA chain intact.

**Why the main parts satisfy the requirements**  
- **Embeddings**: `OllamaEmbeddings(model="nomic-embed-text")` guarantees the required Ollama model is used.  
- **Vector store**: `Chroma` is imported from `langchain.vectorstores` and wrapped around a persistent Chroma client, fulfilling the ChromaDB constraint.  
- **Dependencies**: `requirements.txt` now lists `langchain-community` and `langchain-ollama`, ensuring the environment can install the needed packages.  
- **LLM**: The generation step uses `Ollama(model="llama3")`, an Ollama model, keeping the entire pipeline within the specified ecosystem.

**Key code excerpts**

*src/main.py – embeddings and vector store*  
```python
# 1. Set up embeddings using Ollama's "nomic-embed-text" model
embeddings = OllamaEmbeddings(model="nomic-embed-text")
```

```python
def create_vectorstore(embeddings, persist_directory: str = "chroma_db") -> Chroma:
    ...
    vectorstore = Chroma(
        client=client,
        collection_name="faq",
        embedding_function=embeddings
    )
    return vectorstore
```

*src/main.py – retrieval‑QA chain*  
```python
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)
```

*requirements.txt* (excerpt)  
```
langchain-community
langchain-ollama
```

**Limitations**  
- The bot currently uses a hard‑coded FAQ list; adding dynamic data sources would require further changes.  
- Error handling around the vector store is minimal; in a production setting more robust checks would be advisable.  

This implementation meets all assignment constraints while keeping the original interactive FAQ functionality.