update llm-as-judge doc. (#114)

* update llm-as-judge doc.

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evals/evaluation/rag_eval/README.md

@@ -59,7 +59,7 @@ 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 1024 --max-total-tokens 2048 --sharded true --num-shard 2
+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
 ```
 
 ### Prepare Dataset
@@ -71,7 +71,7 @@ git clone https://github.com/yixuantt/MultiHop-RAG.git
 
 ### Evaluation
 
-Use below command to run the evaluation, please note that for the first run, argument `--ingest_docs` should be added in the command to ingest the documents into the vector database, while for the subsequent run, this argument should be omitted. Set `--retrieval_metrics` to get retrieval related metrics (MRR@10/MAP@10/Hits@10/Hits@4). Set `--ragas_metrics` and `--llm_endpoint` to get end-to-end rag pipeline metrics (faithfulness/answer_relevancy/...), which are judged by LLMs. 
+Use below command to run the evaluation, please note that for the first run, argument `--ingest_docs` should be added in the command to ingest the documents into the vector database, while for the subsequent run, this argument should be omitted. Set `--retrieval_metrics` to get retrieval related metrics (MRR@10/MAP@10/Hits@10/Hits@4). Set `--ragas_metrics` and `--llm_endpoint` to get end-to-end rag pipeline metrics (faithfulness/answer_relevancy/...), which are judged by LLMs. We set `--limits` is 100 as default, which means only 100 examples are evaluated by llm-as-judge as it is very time consuming.
 
 ```bash
 python eval_multihop.py --docs_path MultiHop-RAG/dataset/corpus.json  --dataset_path MultiHop-RAG/dataset/MultiHopRAG.json --ingest_docs --retrieval_metrics --ragas_metrics --llm_endpoint http://{your_ip}:{your_llm_port}/generate

evals/evaluation/rag_eval/examples/eval_multihop.py

@@ -139,7 +139,7 @@ def evaluate(self, all_queries, arguments):
     def get_ragas_metrics(self, all_queries, arguments):
         from langchain_huggingface import HuggingFaceEndpointEmbeddings
 
-        embeddings = HuggingFaceEndpointEmbeddings(model=arguments.embedding_endpoint)
+        embeddings = HuggingFaceEndpointEmbeddings(model=arguments.tei_embedding_endpoint)
 
         metric = RagasMetric(threshold=0.5, model=arguments.llm_endpoint, embeddings=embeddings)
         all_answer_relevancy = 0
@@ -163,6 +163,9 @@ def get_ragas_metrics(self, all_queries, arguments):
             ragas_inputs["ground_truth"].append(data["answer"])
             ragas_inputs["contexts"].append(retrieved_documents[:3])
 
+            if len(ragas_inputs["question"]) >= arguments.limits:
+                break
+
         ragas_metrics = metric.measure(ragas_inputs)
         return ragas_metrics
 
@@ -208,12 +211,19 @@ def args_parser():
     parser.add_argument("--ingest_docs", action="store_true", help="Whether to ingest documents to vector database")
     parser.add_argument("--retrieval_metrics", action="store_true", help="Whether to compute retrieval metrics.")
     parser.add_argument("--ragas_metrics", action="store_true", help="Whether to compute ragas metrics.")
+    parser.add_argument("--limits", type=int, default=100, help="Number of examples to be evaluated by llm-as-judge")
     parser.add_argument(
         "--database_endpoint", type=str, default="http://localhost:6007/v1/dataprep", help="Service URL address."
     )
     parser.add_argument(
         "--embedding_endpoint", type=str, default="http://localhost:6000/v1/embeddings", help="Service URL address."
     )
+    parser.add_argument(
+        "--tei_embedding_endpoint",
+        type=str,
+        default="http://localhost:8090",
+        help="Service URL address of tei embedding.",
+    )
     parser.add_argument(
         "--retrieval_endpoint", type=str, default="http://localhost:7000/v1/retrieval", help="Service URL address."
     )