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-**What was implemented**  
-- Replaced all Qdrant usage with a lightweight ChromaDB wrapper (`src/chromadb_client.py`).  
-- Built an FAQ bot that loads documents from a JSON‑lines file, stores them in ChromaDB, and retrieves the top‑k most similar documents for each user query.  
-- Integrated a single MCP‑tool (`src/mcp_tool.py`) that returns the current UTC time.  
-- Added OpenAI function‑calling logic in `src/main.py` so the model can invoke the MCP‑tool when needed.  
-
-**Why the main parts satisfy the requirements**  
-- The `ChromadbClient` uses `chromadb.Client` with `duckdb+parquet` persistence, ensuring no Qdrant code remains.  
-- `ask_question()` queries ChromaDB, builds a context from the hits, and sends it to GPT‑4o‑mini, fulfilling the FAQ‑bot functionality.  
-- Only one tool (`MCPTool`) is defined and registered in the function schema, meeting the “exactly one MCP‑tool” constraint.  
-- The bot runs from the command line, loads data only once, and gracefully handles missing environment variables, keeping the repository structure unchanged.  
-
-**Short code excerpts**  
-
-`src/chromadb_client.py` – client initialization  
-```python
-self.client = chromadb.Client(Settings(
-    chroma_db_impl="duckdb+parquet",
-    persist_directory=persist_directory,
-))
-self.collection = self.client.get_or_create_collection(name=collection_name)
-```
-
-`src/mcp_tool.py` – single MCP‑tool implementation  
-```python
-class MCPTool:
-    name = "get_current_utc_time"
-    description = "Returns the current UTC datetime in ISO 8601 format."
-    def __call__(self, arguments: Dict[str, Any]) -> Dict[str, Any]:
-        now = datetime.datetime.utcnow().isoformat() + "Z"
-        return {"current_time": now}
-```
-
-`src/main.py` – function‑calling integration  
-```python
-response = openai.ChatCompletion.create(
-    model="gpt-4o-mini",
-    messages=messages,
-    functions=[function_schema],
-    function_call="auto",
-)
-```
-
-**Honest limitations**  
-- The bot loads all FAQ documents at startup; for very large datasets a more incremental approach would be preferable.  
-- Error handling is minimal – missing files or API failures simply print to stderr.  
-- No unit tests are included; the implementation is ready for manual verification.  
-
-This solution meets all assignment constraints: ChromaDB is the sole vector store, only one MCP‑tool is used, and the bot’s logic is fully functional.
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