# TREC RAG baselines using arctic-l and arctic-m-v1.5

First, download the data including documents, queries, qrels. 


## Generate The doc and query embeddings

```sh
bash get_data.sh
```

Next, go ahead and convert the qrels format into json using the script below. 

```sh
python convert_qrels_to_json.py
```

After that, go ahead and generate the query embeddings using the command below. 

```sh
python generate_query_embeddings.py
```

After that, go ahead and generate embeddings for each shard. This will take ~ 20m per shard on a single H100. Feel free to parallelize. Make sure you have at least 600 gbs free.

```sh
python generate_doc_embeddings.py
```


## Retrieval Runs
Once you have query and doc embeddings go ahead and retrieve. Given the size of the vectors we do this in shards. First we retrieve the top_n from each shard for each queryset on each shard. Feel free to parrelize. 

```sh
python retrieve_from_shard.py <path to embeddings> <query_embedding_prefix>  <shard> <num_retrieved> <use_faiss>
```
Alternatively, you can just run retrieve.sh in the background. 

```sh
python merge_retrieved_shard.py <shard_retrieved_results> <output_filename> <top_n_docs> <qrel json> <metric to get per_query breakdown>
```
## Retrieval Scores


### NDCG@10
| NDCG @10           |        |                |          |               |               |               |                           |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset            | BM25   | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim                | N/A    |           1024 |     1024 |           768 |           256 |           128 |                       128 |
| Deep Learning 2021 | 0.5778 |   0.71928             |  0.70682 |        0.6936 |       0.69392 |       0.60578 |                    0.6962 |
| Deep Learning 2022 | 0.3576 |    0.53576            |   0.5444 |       0.55199 |       0.55608 |       0.47348 |                    0.5396 |
| Deep Learning 2023 | 0.3356 |      0.46423           |  0.47372 |       0.46963 |       0.45196 |       0.32789 |                    0.4473 |
| msmarcov2-dev      | N/A    |  0.3538              |  0.35844 |         0.346 |       0.34074 |       0.28499 | N/A                       |
| msmarcov2-dev2     | N/A    |   0.34698             |  0.35821 |       0.34518 |       0.34339 |       0.29606 | N/A                       |
| Raggy Queries      | 0.4227 |   0.56782             |  0.57759 |       0.57439 |       0.56686 |       0.47555 | N/A                       |

### Recall @100
| Recall@100         |        |                |          |               |               |               |                           |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset            | BM25   | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim                | N/A    |           1024 |     1024 |           768 |           256 |           128 |                       128 |
| Deep Learning 2021 | 0.3811 |  0.4156              |  0.41361 |          0.43 |       0.42245 |        0.3488 |                    0.3914 |
| Deep Learning 2022 |  0.233 |  0.31173              |  0.31351 |       0.32125 |        0.3165 |       0.26714 |                    0.3019 |
| Deep Learning 2023 | 0.3049 |   0.35236             |  0.34793 |       0.37622 |       0.36089 |       0.28314 |                    0.3438 |
| msmarcov2-dev      | 0.6683 |   0.85135             |  0.85131 |       0.85435 |       0.84985 |       0.76201 | N/A                       |
| msmarcov2-dev2     | 0.6771 |  0.84333              |  0.84767 |        0.8576 |        0.8526 |       0.78987 | N/A                       |
| Raggy Queries      | 0.2807 |    0.35125            |  0.36228 |       0.36915 |       0.36149 |       0.30272 | N/A                       |
### Recall @1000

| Recall@1000        |        |                |          |               |               |               |                           |
|--------------------|--------|----------------|----------|---------------|---------------|---------------|---------------------------|
| Dataset            | BM25   | GTE-Large-v1.5 | Arctic-L | Arctic-M-V1.5 | Arctic-M-V1.5 | Arctic-M-V1.5 | Cohere Embed3 - Trunc 128 |
| Dim                | N/A    |           1024 |     1024 |           768 |           256 |           128 |                       128 |
| Deep Learning 2021 | 0.7115 |   0.73185             |   0.7193 |       0.74895 |       0.73511 |       0.63253 |                    0.7188 |
| Deep Learning 2022 |  0.479 |    0.55174             |  0.54566 |       0.55413 |       0.54499 |       0.47823 |                    0.5558 |
| Deep Learning 2023 | 0.5852 |      0.6167           |  0.59577 |       0.62262 |       0.61199 |       0.49188 |                    0.6025 |
| msmarcov2-dev      | 0.8528 |   0.93549              |  0.93966 |       0.94156 |       0.94014 |       0.87705 | N/A                       |
| msmarcov2-dev2     | 0.8577 |   0.93997              |  0.93947 |       0.94277 |       0.94047 |       0.91683 | N/A                       |
| Raggy Queries      | 0.5745 |   0.63515             |  0.63092 |       0.64527 |       0.63826 |       0.55002 | N/A                       |