Implement retrieval and evaluation for a simple RAG

## Task You are given a small “toy RAG” notebook setup with helper utilities. You have a corpus of text passages stored in a **pandas DataFrame** `docs_df` with columns: - `doc_id` (string/int) - `text` (string) You are also given: - A utility function `embed_texts(texts: List[str]) -> np.ndarray` that returns a 2D array of embeddings with shape `(n, d)`. - A DataFrame `queries_df` with columns: - `query_id` - `query_text` - `relevant_doc_ids` (a list of doc_ids that are considered relevant for that query) ### Part A — Build document embeddings Compute embeddings for every row in `docs_df` and store them so they can be used for retrieval. ### Part B — Retrieve top-k by cosine similarity For each query: 1. Compute the query embedding. 2. Compute **cosine similarity** between the query embedding and all document embeddings. 3. Return the **top 10** documents ranked by similarity. Your output per query should include: - `query_id` - a ranked list of the 10 retrieved `doc_id`s - optionally the similarity scores ### Part C — Evaluate retrieval Implement evaluation over all queries using: - **Recall@10**: fraction of queries where at least one relevant document appears in the top 10. - **MRR@10**: mean reciprocal rank of the first relevant document within the top 10 (0 if none). ## Constraints / Notes - Assume `docs_df` can be large enough that you should avoid unnecessary Python loops over documents. - Embeddings may or may not be pre-normalized; handle cosine similarity correctly. - You may use NumPy/Pandas only (no external vector databases). Describe your approach and implement the missing pieces (as if filling blanks in a notebook).