Update crag eval with benchmark results

evals/evaluation/agent_eval/crag_eval/README.md

@@ -46,29 +46,29 @@ cd $WORKDIR/GenAIEval/evals/evaluation/agent_eval/crag_eval/preprocess_data
 bash run_data_preprocess.sh
 ```
 **Note**: This is an example of data processing. You can develop and optimize your own data processing for this benchmark.
-3. Sample queries for benchmark
+3. (Optional) Sample queries for benchmark
 The CRAG dataset has more than 4000 queries, and running all of them can be very expensive and time-consuming. You can sample a subset for benchmark. Here we provide a script to sample up to 5 queries per question_type per dynamism in each domain. For example, we were able to get 92 queries from the music domain using the script.
 ```
 bash run_sample_data.sh
 ```
 
 ## Launch agent QnA system
-Here we showcase a RAG agent in GenAIExample repo. Please refer to the README in the [AgentQnA example](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA/README.md) for more details.
+Here we showcase an agent system in OPEA GenAIExamples repo. Please refer to the README in the [AgentQnA example](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA/README.md) for more details.
 
 > **Please note**: This is an example. You can build your own agent systems using OPEA components, then expose your own systems as an endpoint for this benchmark.
 
-To launch the agent in our AgentQnA example, open another terminal and build images and launch agent system there.
+To launch the agent in our AgentQnA example on Intel Gaudi accelerators, open another terminal and follow the instructions below.
 1. Build images
 ```
 export $WORKDIR=<your-work-directory>
 cd $WORKDIR
 git clone https://github.com/opea-project/GenAIExamples.git
 cd GenAIExamples/AgentQnA/tests/
-bash 1_build_images.sh
+bash step1_build_images.sh
 ```
 2. Start retrieval tool
 ```
-bash 2_start_retrieval_tool.sh
+bash step2_start_retrieval_tool.sh
 ```
 3. Ingest data into vector database and validate retrieval tool
 ```
@@ -86,19 +86,21 @@ python3 index_data.py --host_ip $host_ip --filedir ${WORKDIR}/datasets/crag_docs
 ```
 # Go to the terminal where you launched the AgentQnA example
 cd $WORKDIR/GenAIExamples/AgentQnA/tests/
-bash 4_launch_and_validate_agent.sh
+bash step4_launch_and_validate_agent_gaudi.sh
 ```
+Note: There are two agents in the agent system: a RAG agent (as the worker agent) and a ReAct agent (as the supervisor agent). We can evaluate both agents - just need to specify the agent endpoint url in the scripts - see instructions below.
 
 ## Run CRAG benchmark
-Once you have your agent system up and running, the next step is to generate answers with agent. Change the variables in the script below and run the script. By default, it will run a sampled set of queries in music domain.
+Once you have your agent system up and running, the next step is to generate answers with agent. Change the variables in the script below and run the script. By default, it will run the entire set of queries in the music domain (in total 373 queries). You can choose to run other domains or just run a sampled subset of music domain.
 ```
 # Come back to the interactive crag-eval docker container
 cd $WORKDIR/GenAIEval/evals/evaluation/agent_eval/crag_eval/run_benchmark
+# Remember to specify the agent endpoint url in the script.
 bash run_generate_answer.sh
 ```
 
 ## Use LLM-as-judge to grade the answers
-1. Launch llm endpoint with HF TGI: in another terminal, run the command below. By default, `meta-llama/Meta-Llama-3-70B-Instruct` is used as the LLM judge.
+1. Launch llm endpoint with HF TGI: in another terminal, run the command below. By default, `meta-llama/Meta-Llama-3.1-70B-Instruct` is used as the LLM judge.
 ```
 cd llm_judge
 bash launch_llm_judge_endpoint.sh
@@ -123,3 +125,57 @@ python3 test_llm_endpoint.py
 cd $WORKDIR/GenAIEval/evals/evaluation/agent_eval/crag_eval/run_benchmark/
 bash run_grading.sh
 ```
+
+### Validation of LLM-as-judge
+We validated RAGAS answer correctness as the metric to evaluate agents. We sampled 92 queries from the full music domain dataset (up to 5 questions per sub-category for all 32 sub-categories), and conducted human evaluations on the conventional RAG answers, the single RAG agent answers and the hierarchical ReAct agent answers of the 92 queries. 
+
+We followed the criteria in the [CRAG paper](https://arxiv.org/pdf/2406.04744) to get human scores: 
+1. score 1 if the answer matches the golden answer or semantically similar.
+2. score 0 if the answer misses information, or is "I don't know", “I’m sorry I can’t find ...”, a system error such as recursion limit is hit, or a request from the system to clarify the original question.
+3. score -1 if the answer contains incorrect information.
+
+On the other hand, RAGAS `answer_correctness` score is on a scale of 0-1 and is a weighted average of 1) an F1 score and 2) similarity between answer and golden answer. The F1 score is based on the number of statements in the answer supported or not supported by the golden answer, and the number of statements in the golden answer appeared or did not appear in the answer. Please refer to [RAGAS source code](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_answer_correctness.py) for the implementation of its `answer_correctness` score. We ran RAGAS on Intel Gaudi2 accelerators. We used `meta-llama/Meta-Llama-3.1-70B-Instruct` as the LLM judge.
+
+|Setup           |Mean Human score|Mean RAGAS `answer_correctness` score|
+|----------------|-----------|------------------------------|
+|Conventional RAG|0.05       |0.37|
+|Single RAG agent|0.18       |0.43|
+|Hierarchical ReAct agent|0.22|0.54|
+
+We can see that the human scores and the RAGAS `answer_correctness` scores follow the same trend, although the two scoring methods used different grading criteria and methods. Since LLM-as-judge is more scalable for larger datasets, we decided to use RAGAS `answer_correctness` scores (produced by `meta-llama/Meta-Llama-3-70B-Instruct` as the LLM judge) for the evaluation of OPEA agents on the full CRAG music domain dataset.
+
+We have made available our scripts to calculate the mean RAGAS scores. Refer to the `run_compare_scores.sh` script in the `run_benchmark` folder.
+
+
+## Benchmark results for OPEA RAG Agents
+We have evaluated the agents (`rag_agent_llama` and `react_llama` strategies) in the OPEA AgentQnA example on CRAG music domain dataset (373 questions in total). We used `meta-llama/Meta-Llama-3.1-70B-Instruct` and we served the LLM with tgi-gaudi on 4 Intel Gaudi2 accelerator cards. Refer to the docker compose yaml files in the AgentQnA example for more details on the configurations.
+
+For the tests of conventional RAG, we used the script in the `run_benchmark` folder: `run_conv_rag.sh`. And we used the same LLM, serving configs and generation parameters as the RAG agent.
+
+The Conventional RAG and Single RAG agent use the same retriever. The Hierarchical ReAct agent uses the Single RAG agent as its retrieval tool and also has access to CRAG APIs provided by Meta as part of the CRAG benchmark.
+
+
+|Setup           |Mean RAGAS `answer_correctness` score|
+|----------------|------------------------------|
+|Conventional RAG|0.42|
+|Single RAG agent|0.43|
+|Hierarchical ReAct agent|0.53|
+
+From the results, we can see that the single RAG agent performs better than conventional RAG, while the hierarchical ReAct agent has the highest `answer_correctness` score. The reasons for such performance improvements:
+1. RAG agent rewrites query and checks the quality of retrieved documents before feeding the docs to generation. It can get docs that are more relevant to generate answers. It can also decompose complex questions into modular tasks and get related docs for each task and then aggregate info to come up with answers.
+2. Hierarchical ReAct agent was supplied with APIs to get information from knowledge graphs, and thus can supplement info to the knowledge in the retrieval vector database. So it can answer questions where conventional RAG or Single RAG agent cannot due to the lack of relevant info in vector database.
+
+Note: The performance result for the hierarchical ReAct agent is with tool selection, i.e., only give a subset of tools to agent based on query, which we found can boost agent performance when the number of tools is large. However, currently OPEA agents do not support tool selection yet. We are in the process of enabling tool selection. 
+
+### Comparison with GPT-4o-mini
+Open-source LLM serving libraries (tgi and vllm) have limited capabilities in producing tool-call objects. Although vllm improved its tool-calling capabilities recently, parallel tool calling is still not well supported. Therefore, we had to write our own prompts and output parsers for the `rag_agent_llama` and `react_llama` strategies for using open-source LLMs served with open-source serving frameworks for OPEA agent microservices.
+
+Below we show the comparisons of `meta-llama/Meta-Llama-3.1-70B-Instruct` versus OpenAI's `gpt-4o-mini-2024-07-18` on 20 sampled queries from the CRAG music domain dataset. We used human evaluation criteria outlined above. The numbers are the average scores graged by human. The parenthesis denotes the OPEA agent strategy used.
+
+|Setup|Llama3.1-70B-Instruct|gpt-4o-mini|
+|-----|---------------------|-----------|
+|Conventional RAG|0.15|0.05|
+|Single RAG agent|0.45 (`rag_agent_llama`)|0.65 (`rag_agent`)|
+|Hierarchical ReAct agent|0.55 (`react_llama`)|0.75 (`react_langgraph`)|
+
+From the comparisons on this small subset, we can see that OPEA agents using `meta-llama/Meta-Llama-3.1-70B-Instruct` with calibrated prompt templates and output parsers are only slightly behind `gpt-4o-mini-2024-07-18` with proprietary tool-calling capabilities.

evals/evaluation/agent_eval/crag_eval/docker/Dockerfile

@@ -10,7 +10,8 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y \
     git \
     poppler-utils \
     libmkl-dev \
-    curl
+    curl \
+    nano
 
 COPY requirements.txt /home/user/requirements.txt
 

evals/evaluation/agent_eval/crag_eval/docker/build_image.sh

@@ -4,8 +4,9 @@
 dockerfile=Dockerfile
 
 docker build \
+    --no-cache \
     -f ${dockerfile} . \
-    -t crag-eval:latest \
+    -t crag-eval:v1.1 \
     --network=host \
     --build-arg http_proxy=${http_proxy} \
     --build-arg https_proxy=${https_proxy} \

evals/evaluation/agent_eval/crag_eval/docker/launch_eval_container.sh

@@ -4,4 +4,4 @@
 volume=$WORKDIR
 host_ip=$(hostname -I | awk '{print $1}')
 
-docker run -it -v $volume:/home/user/ -e WORKDIR=/home/user -e HF_HOME=/home/user/hf_cache -e host_ip=$host_ip -e http_proxy=$http_proxy -e https_proxy=$https_proxy crag-eval:latest
+docker run -it --name crag_eval -v $volume:/home/user/ -e WORKDIR=/home/user -e HF_HOME=/home/user/hf_cache -e host_ip=$host_ip -e http_proxy=$http_proxy -e https_proxy=$https_proxy crag-eval:v1.1

evals/evaluation/agent_eval/crag_eval/docker/requirements.txt

@@ -3,6 +3,8 @@ evaluate
 jieba
 langchain-community
 langchain-huggingface
+langchain-openai
+nltk
 pandas
 ragas
 sentence_transformers

evals/evaluation/agent_eval/crag_eval/run_benchmark/compare_scores.py

@@ -0,0 +1,90 @@
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+
+import pandas as pd
+from scipy.stats import pearsonr, spearmanr
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--filedir", type=str, help="file directory")
+    parser.add_argument("--conv_rag", type=str, help="file with RAGAS scores for conventional RAG")
+    parser.add_argument("--ragagent", type=str, help="file with RAGAS scores for RAG agent")
+    parser.add_argument("--reactagent", type=str, help="file with RAGAS scores for React agent")
+    parser.add_argument("--human_scores_file", type=str, help="file with human scores for 3 setups")
+    return parser.parse_args()
+
+
+def merge_and_get_stats(filedir, conv_rag, ragagent, reactagent, prefix=""):
+    conv_rag_df = pd.read_csv(filedir + conv_rag)
+    ragagent_df = pd.read_csv(filedir + ragagent)
+    reactagent_df = pd.read_csv(filedir + reactagent)
+
+    conv_rag_df = conv_rag_df.rename(columns={"answer_correctness": "conv_rag_score"})
+    ragagent_df = ragagent_df.rename(columns={"answer_correctness": "ragagent_score"})
+    reactagent_df = reactagent_df.rename(columns={"answer_correctness": "reactagent_score"})
+    merged_df = pd.merge(conv_rag_df, ragagent_df, on="query")
+    merged_df = pd.merge(merged_df, reactagent_df, on="query")
+    print(merged_df.shape)
+    print(merged_df.describe())
+    merged_df.to_csv(filedir + prefix + "merged_scores.csv", index=False)
+
+    # drop rows with nan
+    merged_df_dropped = merged_df.dropna()
+    # merged_df = merged_df.reset_index(drop=True)
+    print(merged_df_dropped.shape)
+
+    # compare scores
+    print(merged_df_dropped.describe())
+    merged_df_dropped.to_csv(filedir + prefix + "merged_scores_nadropped.csv", index=False)
+    return merged_df, merged_df_dropped
+
+
+if __name__ == "__main__":
+    args = get_args()
+    filedir = args.filedir
+    conv_rag = args.conv_rag
+    ragagent = args.ragagent
+    reactagent = args.reactagent
+    human_scores_file = args.human_scores_file
+
+    # RAGAS scores
+    print("===============RAGAS scores==================")
+    merged_df, merged_df_dropped = merge_and_get_stats(filedir, conv_rag, ragagent, reactagent)
+
+    # human scores
+    print("===============Human scores==================")
+    human_scores_df = pd.read_csv(filedir + human_scores_file)
+    print(human_scores_df.describe())
+
+    human_scores_df_dropped = human_scores_df.loc[human_scores_df["query"].isin(merged_df_dropped["query"])]
+    print(human_scores_df_dropped.describe())
+    human_scores_df_dropped.to_csv(filedir + "human_scores_dropped.csv", index=False)
+
+    # concat conv_rag, ragagent, reactagent scores in merged_df_dropped
+    ragas_scores = pd.concat(
+        [
+            merged_df_dropped["conv_rag_score"],
+            merged_df_dropped["ragagent_score"],
+            merged_df_dropped["reactagent_score"],
+        ],
+        axis=0,
+    )
+    human_scores = pd.concat(
+        [
+            human_scores_df_dropped["conv_rag"],
+            human_scores_df_dropped["ragagent"],
+            human_scores_df_dropped["reactagent"],
+        ],
+        axis=0,
+    )
+
+    # calculate spearman correlation
+    print("===============Spearman correlation==================")
+    print(spearmanr(ragas_scores, human_scores))
+
+    # pearson correlation
+    print("===============Pearson correlation==================")
+    print(pearsonr(ragas_scores, human_scores))