---
license: cc-by-nc-sa-4.0
---

This dataset contains the embeddings for the segmented [TREC RAG 2024 corpus](https://trec-rag.github.io/annoucements/2024-corpus-finalization/), embedded with the Cohere Embed V3 model.

You can search on this dataset with just 500MB of memory using [DiskVectorIndex](https://github.com/cohere-ai/DiskVectorIndex).

# Installation & Usage

Get your free **Cohere API key** from [cohere.com](https://cohere.com). You must set this API key as an environment variable: 
```
export COHERE_API_KEY=your_api_key
```

Install the package:
```
pip install DiskVectorIndex
```

You can then search via:
```python
from DiskVectorIndex import DiskVectorIndex

index = DiskVectorIndex("Cohere/trec-rag-2024-index")

while True:
    query = input("\n\nEnter a question: ")
    docs = index.search(query, top_k=3)
    for doc in docs:
        print(doc)
        print("=========")
```

# License

Please observe the License for the [TREC RAG 2024 Corpus](https://trec-rag.github.io/annoucements/2024-corpus-finalization/). The license displayed here is just for the embeddings.