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update rag eval readme for crud ragas. #154

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update rag eval readme for crud ragas. #154

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6 changes: 6 additions & 0 deletions evals/evaluation/rag_eval/README.md
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Expand Up @@ -63,6 +63,9 @@ To setup a LLM model, we can use [tgi-gaudi](https://github.com/huggingface/tgi-
# please set your llm_port and hf_token

docker run -p {your_llm_port}:80 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HF_TOKEN={your_hf_token} --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.1 --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 --max-input-tokens 2048 --max-total-tokens 4096 --sharded true --num-shard 2

# for better performance, set `PREFILL_BATCH_BUCKET_SIZE`, `BATCH_BUCKET_SIZE`, `max-batch-total-tokens`, `max-batch-prefill-tokens`
docker run -p {your_llm_port}:80 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HF_TOKEN={your_hf_token} -e PREFILL_BATCH_BUCKET_SIZE=1 -e BATCH_BUCKET_SIZE=8 --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 --max-input-tokens 2048 --max-total-tokens 4096 --sharded true --num-shard 2 --max-batch-total-tokens 65536 --max-batch-prefill-tokens 2048
```

### Prepare Dataset
Expand Down Expand Up @@ -138,6 +141,9 @@ If you are using docker compose to deploy RAG system, you can simply run the eva
```bash
cd examples
python eval_crud.py --dataset_path ../data/split_merged.json --docs_path ../data/80000_docs --ingest_docs

# if you want to get ragas metrics
python eval_crud.py --dataset_path ../data/split_merged.json --docs_path ../data/80000_docs --contain_original_data --llm_endpoint "http://{llm_as_judge_ip}:{llm_as_judge_port}" --ragas_metrics
```

If you are using Kubernetes manifest/helm to deploy RAG system, you must specify more arguments as following:
Expand Down
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