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license: cc-by-nc-sa-4.0 |
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--- |
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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. |
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You can search on this dataset with just 500MB of memory using [DiskVectorIndex](https://github.com/cohere-ai/DiskVectorIndex). |
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# Installation & Usage |
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Get your free **Cohere API key** from [cohere.com](https://cohere.com). You must set this API key as an environment variable: |
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``` |
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export COHERE_API_KEY=your_api_key |
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``` |
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Install the package: |
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``` |
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pip install DiskVectorIndex |
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``` |
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You can then search via: |
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```python |
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from DiskVectorIndex import DiskVectorIndex |
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index = DiskVectorIndex("Cohere/trec-rag-2024-index") |
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while True: |
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query = input("\n\nEnter a question: ") |
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docs = index.search(query, top_k=3) |
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for doc in docs: |
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print(doc) |
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print("=========") |
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``` |
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# License |
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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. |