add deep research agent benchmark script.

DeepResearchAgent/README.md

@@ -11,6 +11,8 @@ In this application, we leverage the deep research agent implementation of [lang
 ## Setup Deployment Environment
 
 ```
+# Configure deep_researcher.yaml with your llm model served by the vllm
+
 # get your TAVILY_API_KEY from https://app.tavily.com/
 export TAVILY_API_KEY=""
 # get your HuggingFace Access Token from https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token
@@ -21,7 +23,7 @@ export http_proxy=""
 export https_proxy=""
 export no_proxy=""
 
-
+# the shell script will initialize the OPENAI_BASE_URL and OPENAI_API_KEY for the openai compatible vllm service
 source ./set_env.sh
 ```
 

DeepResearchAgent/benchmark/accuracy/README.md

@@ -0,0 +1,29 @@
+# Deep Research Agent Benchmarks
+
+## Deploy the Deep Research Agent
+
+Follow the doc [here](https://github.com/opea-project/GenAIExamples/tree/main/DeepResearchAgent) to setup deep research agent service.
+
+## Evaluation
+
+```
+python eval.py --datasets together-search-bench --limit 1
+```
+
+The default values for arguments are:
+
+| Argument       | Default value                                | Description                                                                                         |
+| -------------- | -------------------------------------------- | --------------------------------------------------------------------------------------------------- |
+| --datasets     | together-search-bench                        | benchmark datasets, supports "smolagents:simpleqa", "hotpotqa", "simpleqa", "together-search-bench" |
+| --service-url  | http://localhost:8022/v1/deep_research_agent | the deep research agent endpoint                                                                    |
+| --llm-endpoint | http://localhost:8000/v1/                    | the llm endpoint, like vllm, for llm as judge                                                       |
+| --model        | openai/meta-llama/Llama-3.3-70B-Instruct     | the model id served by vllm, the prefix openai is the format of litellm                             |
+
+## Accuracy
+
+We randomly select 30 samples from the dataset [togethercomputer/together-search-bench](https://huggingface.co/datasets/togethercomputer/together-search-bench) randomly and compare the results of base model and deep research agent. The results show deep research agent can improve the generation quality and accuracy.
+
+| model                                                                                                                  | accuracy |
+| ---------------------------------------------------------------------------------------------------------------------- | -------- |
+| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)                          | 0.433333 |
+| deep research agent with [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) | 0.8      |

DeepResearchAgent/benchmark/accuracy/eval.py

@@ -0,0 +1,346 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import argparse
+import json
+import os
+import time
+from dataclasses import dataclass
+from typing import Any, Callable, Tuple
+
+import requests
+from datasets import load_dataset
+from litellm import completion
+from requests.exceptions import RequestException
+
+
+@dataclass
+class Result:
+    question: str
+    agent_answer: str
+    correct_answer: str
+
+
+ScoringFunction = Callable[[Result], bool]
+
+
+def llm_as_a_judge_scoring(result: Result, model_id=None, llm_service=None) -> bool:
+    prompt = f"""
+    Given the following question and answer, evaluate the answer against the correct answer:
+
+    <question>
+    {result.question}
+    </question>
+
+    <agent_answer>
+    {result.agent_answer}
+    </agent_answer>
+
+    <correct_answer>
+    {result.correct_answer}
+    </correct_answer>
+
+    Note that the agent answer might be a long text containing a lot of information or it might be a short answer.
+
+    You should read the entire text and think if the agent answers the question somewhere
+    in the text. You should try to be flexible with the answer but careful.
+
+    For example, answering with names instead of name and surname is fine.
+
+    The important thing is that the answer of the agent either contains the correct answer or is equal to the correct answer.
+
+    <reasoning>
+    The agent answer is correct because I can read that ....
+    </reasoning>
+
+    <answer>
+    1
+    </answer>
+
+    Otherwise, return
+
+    <reasoning>
+    The agent answer is incorrect because there is ...
+    </reasoning>
+
+    <answer>
+    0
+    </answer>
+
+    """
+
+    messages = [
+        {"role": "system", "content": "You are an helpful assistant that returns a number between 0 and 1."},
+        {"role": "user", "content": prompt},
+    ]
+    answer = (
+        completion(
+            model=model_id,
+            api_base=llm_service,
+            api_key="empty",
+            timeout=1200,
+            messages=messages,
+            max_tokens=1000,
+            temperature=0.0,
+        )
+        .choices[0]  # type: ignore
+        .message["content"]  # type: ignore
+    )
+
+    return bool(int(answer.split("<answer>")[1].split("</answer>")[0].strip()))
+
+
+def load_questions(dataset_names: list[str] | None = None) -> list[dict[str, str]]:
+    """Load questions from the specified Hugging Face dataset configurations.
+
+    Args:
+        dataset_names: List of dataset configurations to load
+                      Options:
+                          "smolagents:simpleqa",
+                          "hotpotqa",
+                          "simpleqa",
+                          "together-search-bench"
+                      If None, all available configurations except hotpotqa will be loaded
+
+    Returns:
+        List of question-answer pairs
+    """
+    if dataset_names is None:
+        dataset_names = ["smolagents:simpleqa"]
+
+    all_questions = []
+
+    for dataset_name in dataset_names:
+        print(f"Loading dataset: {dataset_name}")
+
+        try:
+            if dataset_name == "together-search-bench":
+                # Load Together-Search-Bench dataset
+                dataset_path = "togethercomputer/together-search-bench"
+                ds = load_dataset(dataset_path)
+                if "test" in ds:
+                    split_data = ds["test"]
+                else:
+                    print(f"No 'test' split found in dataset at {dataset_path}")
+                    continue
+
+                for i in range(len(split_data)):
+                    item = split_data[i]
+                    question_data = {
+                        "question": item["question"],
+                        "answer": item["answer"],
+                        "dataset": item.get("dataset", "together-search-bench"),
+                    }
+                    all_questions.append(question_data)
+
+                print(f"Loaded {len(split_data)} questions from together-search-bench dataset")
+                continue
+
+            elif dataset_name == "hotpotqa":
+                # Load HotpotQA dataset (using distractor version for validation)
+                ds = load_dataset("hotpotqa/hotpot_qa", "distractor", trust_remote_code=True)
+                split_name = "validation"
+            elif dataset_name == "simpleqa":
+                ds = load_dataset("basicv8vc/SimpleQA")
+                split_name = "test"
+            else:
+                # Strip "smolagents:" prefix when loading the dataset
+                actual_dataset = dataset_name.split(":")[-1]
+                ds = load_dataset("smolagents/benchmark-v1", actual_dataset)
+                split_name = "test"
+
+        except Exception as e:
+            print(f"Failed to load dataset {dataset_name}: {str(e)}")
+            continue  # Skip this dataset if it fails to load
+
+        print(f"Dataset structure for {dataset_name}: {ds}")
+        print(f"Available splits: {list(ds)}")
+
+        split_data = ds[split_name]  # type: ignore
+
+        for i in range(len(split_data)):
+            item = split_data[i]
+
+            if dataset_name == "hotpotqa":
+                # we remove questions that are easy or medium (if any) just to reduce the number of questions
+                if item["level"] != "hard":
+                    continue
+
+                question_data = {
+                    "question": item["question"],
+                    "answer": item["answer"],
+                    "dataset": dataset_name,
+                }
+            elif dataset_name == "simpleqa":
+                # Handle SimpleQA dataset format
+                question_data = {
+                    "question": item["problem"],
+                    "answer": item["answer"],
+                    "dataset": dataset_name,
+                }
+            else:
+                question_data = {
+                    "question": item["question"],
+                    "answer": item["true_answer"],
+                    "dataset": dataset_name,
+                }
+
+            all_questions.append(question_data)
+
+    print(f"Loaded {len(all_questions)} questions in total")
+    return all_questions
+
+
+def process_single_question(
+    question_data: dict[str, str],
+    agent_service: str,
+    model_id: str,
+    llm_service: str,
+) -> dict[str, Any]:
+    """Process a single benchmark question with the agent.
+
+    Args:
+        question_data: Dictionary containing question and answer
+
+    Returns:
+        Dictionary with question, answers and evaluation results
+    """
+    question = question_data["question"]
+    correct_answer = question_data["answer"]
+
+    data = {"question": question}
+
+    try:
+        res = requests.post(
+            f"{agent_service}",
+            headers={"Content-Type": "application/json"},
+            data=json.dumps(data),
+        )
+        res.raise_for_status()
+        res = res.json()
+    except RequestException as e:
+        raise Exception(f"An unexpected error occurred: {str(e)}")
+
+    agent_answer = res["answer"]
+
+    result = Result(question=question, agent_answer=agent_answer, correct_answer=correct_answer)
+
+    evaluation = llm_as_a_judge_scoring(result, model_id, llm_service)
+
+    single_benchmark_result = {
+        "question": question,
+        "correct_answer": correct_answer,
+        "agent_answer": agent_answer,
+        "evaluation": evaluation,
+        "metadata": {k: v for k, v in question_data.items() if k not in ["question", "answer"]},
+    }
+    print(single_benchmark_result)
+
+    return single_benchmark_result
+
+
+def run_benchmark(
+    questions: list[dict[str, str]],
+    agent_service: str = "http://localhost:8022/v1/deep_research_agent",
+    model_id: str = "",
+    llm_service: str = "http://localhost:8000/v1/",
+) -> Tuple[float, list[dict[str, Any]]]:
+    """Run the benchmark on a list of questions concurrently.
+
+    Args:
+        questions: List of question-answer pairs
+
+    Returns:
+        Tuple of (accuracy score, detailed results)
+    """
+
+    results = []
+    total_questions = len(questions)
+    details = []
+
+    for idx, question_data in enumerate(questions):
+        try:
+            result = process_single_question(question_data, agent_service, model_id, llm_service)
+            results.append(result["evaluation"])
+        except Exception as exc:
+            import traceback
+
+            traceback.print_exc()
+            print(f"Question {idx+1} generated an exception: {exc}")
+            results.append(0)
+            details.append({"question": questions[idx]["question"], "agent_answer": str(exc), "evaluation": 0})
+
+    return sum(results) / len(results), details
+
+
+def main():
+    """Main function to run the benchmark."""
+
+    # Set up argument parser
+    parser = argparse.ArgumentParser(description="Run scoring with benchmarking options")
+    parser.add_argument(
+        "--datasets",
+        nargs="+",
+        choices=["smolagents:simpleqa", "hotpotqa", "simpleqa", "together-search-bench"],
+        help="Specific datasets to load (default: all)",
+        default=["together-search-bench"],
+    )
+    parser.add_argument("--limit", type=int, default=None, help="Limit number of questions to process (default: all)")
+    parser.add_argument(
+        "--service-url",
+        default="http://localhost:8022/v1/deep_research_agent",
+        help="the endpoint of deep research agent.",
+    )
+    parser.add_argument(
+        "--llm-endpoint",
+        default="http://localhost:8000/v1/",
+        help="llm service for llm-as-judge.",
+    )
+    parser.add_argument(
+        "--model",
+        default="openai/meta-llama/Llama-3.3-70B-Instruct",
+        help="model id of llm service.",
+    )
+
+    args = parser.parse_args()
+
+    questions = load_questions(args.datasets)
+
+    if args.limit is not None:
+        questions = questions[: args.limit]
+        print(f"Limited to {len(questions)} questions")
+
+    results, details = run_benchmark(
+        questions, agent_service=args.service_url, model_id=args.model, llm_service=args.llm_endpoint
+    )
+
+    print(f"Completed benchmark with {results} accuracy")
+
+    benchmark_results_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "benchmark", "benchmark_results")
+    os.makedirs(benchmark_results_dir, exist_ok=True)
+
+    output_file = os.path.join(
+        benchmark_results_dir,
+        f"benchmark_{'_'.join(args.datasets)}_{time.strftime('%Y-%m-%d_%H-%M-%S', time.localtime())}.json",
+    )
+
+    output_data = {
+        "metadata": {
+            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            "datasets": args.datasets,
+            "agent_config": args.agent_config,
+            "scoring_method": "llm_as_a_judge_scoring",
+            "sample_count": len(questions),
+        },
+        "overall_accuracy": results,
+        "question_details": details,
+    }
+
+    with open(output_file, "w") as f:
+        json.dump(output_data, f, indent=2)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

DeepResearchAgent/requirements.txt

@@ -1 +1 @@
-open_deep_research # https://github.com/langchain-ai/open_deep_research
+open-deep-research==0.0.16