From 65ab75720773affc4c6a9b033cf95589b7fc9e5f Mon Sep 17 00:00:00 2001 From: Jordan Stephens Date: Wed, 24 Sep 2025 10:01:28 -0700 Subject: [PATCH 1/5] google adk sample --- samples/agentic-strands/app/Dockerfile | 9 - .../agentic-strands/app/{agent.py => main.py} | 0 samples/agentic-strands/compose.yaml | 1 - samples/google-adk/.dockerignore | 27 + samples/google-adk/.env.example | 6 + samples/google-adk/Dockerfile | 17 + samples/google-adk/README.md | 424 ++ samples/google-adk/academic-research.svg | 33 + .../google-adk/academic_research/__init__.py | 17 + samples/google-adk/academic_research/agent.py | 45 + .../google-adk/academic_research/prompt.py | 70 + .../academic_newresearch/__init__.py | 17 + .../sub_agents/academic_newresearch/agent.py | 27 + .../sub_agents/academic_newresearch/prompt.py | 70 + .../sub_agents/academic_websearch/__init__.py | 17 + .../sub_agents/academic_websearch/agent.py | 31 + .../sub_agents/academic_websearch/prompt.py | 65 + samples/google-adk/compose.yaml | 5 + samples/google-adk/deployment/deploy.py | 131 + .../google-adk/deployment/test_deployment.py | 105 + .../data/academic_research_evalset.test.json | 59 + samples/google-adk/eval/test_eval.py | 38 + samples/google-adk/main.py | 31 + samples/google-adk/poetry.lock | 5056 +++++++++++++++++ samples/google-adk/pyproject.toml | 47 + samples/google-adk/sessions.db | Bin 0 -> 36864 bytes samples/google-adk/tests/test_agents.py | 63 + 27 files changed, 6401 insertions(+), 10 deletions(-) delete mode 100644 samples/agentic-strands/app/Dockerfile rename samples/agentic-strands/app/{agent.py => main.py} (100%) create mode 100644 samples/google-adk/.dockerignore create mode 100644 samples/google-adk/.env.example create mode 100644 samples/google-adk/Dockerfile create mode 100644 samples/google-adk/README.md create mode 100644 samples/google-adk/academic-research.svg create mode 100644 samples/google-adk/academic_research/__init__.py create mode 100644 samples/google-adk/academic_research/agent.py create mode 100644 samples/google-adk/academic_research/prompt.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_newresearch/__init__.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_newresearch/agent.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_newresearch/prompt.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_websearch/__init__.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_websearch/agent.py create mode 100644 samples/google-adk/academic_research/sub_agents/academic_websearch/prompt.py create mode 100644 samples/google-adk/compose.yaml create mode 100644 samples/google-adk/deployment/deploy.py create mode 100644 samples/google-adk/deployment/test_deployment.py create mode 100644 samples/google-adk/eval/data/academic_research_evalset.test.json create mode 100644 samples/google-adk/eval/test_eval.py create mode 100644 samples/google-adk/main.py create mode 100644 samples/google-adk/poetry.lock create mode 100644 samples/google-adk/pyproject.toml create mode 100644 samples/google-adk/sessions.db create mode 100644 samples/google-adk/tests/test_agents.py diff --git a/samples/agentic-strands/app/Dockerfile b/samples/agentic-strands/app/Dockerfile deleted file mode 100644 index ea1afc16b..000000000 --- a/samples/agentic-strands/app/Dockerfile +++ /dev/null @@ -1,9 +0,0 @@ -FROM python:3.11-slim - -WORKDIR /app - -COPY . /app - -RUN pip install --no-cache-dir -r requirements.txt - -CMD ["python", "agent.py"] diff --git a/samples/agentic-strands/app/agent.py b/samples/agentic-strands/app/main.py similarity index 100% rename from samples/agentic-strands/app/agent.py rename to samples/agentic-strands/app/main.py diff --git a/samples/agentic-strands/compose.yaml b/samples/agentic-strands/compose.yaml index 5f44004f4..ace73856c 100644 --- a/samples/agentic-strands/compose.yaml +++ b/samples/agentic-strands/compose.yaml @@ -4,7 +4,6 @@ services: # domainname: example.com build: context: ./app - dockerfile: Dockerfile ports: - target: 5001 published: 5001 diff --git a/samples/google-adk/.dockerignore b/samples/google-adk/.dockerignore new file mode 100644 index 000000000..74a7bf9e0 --- /dev/null +++ b/samples/google-adk/.dockerignore @@ -0,0 +1,27 @@ +# Default .dockerignore file for Defang +**/__pycache__ +**/.direnv +**/.DS_Store +**/.envrc +**/.git +**/.github +**/.idea +**/.next +**/.vscode +**/compose.*.yaml +**/compose.*.yml +**/compose.yaml +**/compose.yml +**/docker-compose.*.yaml +**/docker-compose.*.yml +**/docker-compose.yaml +**/docker-compose.yml +**/node_modules +**/Thumbs.db +Dockerfile +*.Dockerfile +# Ignore our own binary, but only in the root to avoid ignoring subfolders +defang +defang.exe +# Ignore our project-level state +.defang \ No newline at end of file diff --git a/samples/google-adk/.env.example b/samples/google-adk/.env.example new file mode 100644 index 000000000..a0cfb8cec --- /dev/null +++ b/samples/google-adk/.env.example @@ -0,0 +1,6 @@ +# Fill in your GCP project info and rename this file to ".env". + +GOOGLE_GENAI_USE_VERTEXAI=1 +GOOGLE_CLOUD_PROJECT= +GOOGLE_CLOUD_LOCATION= +GOOGLE_CLOUD_STORAGE_BUCKET= # Only required for deployment on Agent Engine diff --git a/samples/google-adk/Dockerfile b/samples/google-adk/Dockerfile new file mode 100644 index 000000000..4118f72a7 --- /dev/null +++ b/samples/google-adk/Dockerfile @@ -0,0 +1,17 @@ +FROM python:3.11-slim + +WORKDIR /app + +# Install Poetry +RUN pip install --no-cache-dir poetry + +# Copy only poetry files first for caching +COPY pyproject.toml poetry.lock* /app/ + +# Install dependencies +RUN poetry install --no-root --no-interaction --no-ansi + +# Copy the rest of the application +COPY . /app + +CMD ["poetry", "run", "python", "main.py"] diff --git a/samples/google-adk/README.md b/samples/google-adk/README.md new file mode 100644 index 000000000..c84a6c133 --- /dev/null +++ b/samples/google-adk/README.md @@ -0,0 +1,424 @@ +# Academic Research + + +## Overview + +AI-driven agent designed to facilitate the exploration of the academic landscape surrounding seminal research works. Recognizing the challenge researchers face in navigating the expanding body of literature influenced by foundational studies, this agent offers a streamlined approach. By accepting a seminal paper identified provided as file input, the agent first analyzes the core contributions of the specified work. + +1. It then leverages specialized Agent tools to identify and retrieve via Google Search recent academic publications that cite this seminal paper, mapping its contemporary influence. +2. Finally, synthesizing the analysis of the original paper with the findings from recent citing literature, and again utilizing specialized analytical tools, the agent proposes potential future research directions. + +This capability aims to provide researchers with valuable insights into the ongoing impact of seminal research and to highlight promising avenues for novel investigation, thereby accelerating the research discovery process. + +## Agent Details + +The key features of the Academic Research include: + +| Feature | Description | +| --- | --- | +| **Interaction Type** | Conversational | +| **Complexity** | Easy | +| **Agent Type** | Multi Agent | +| **Components** | Tools: built-in Google Search | +| **Vertical** | Education | + + + +### Agent architecture: + +This diagram shows the detailed architecture of the agents and tools used +to implement this workflow. +academic researcher + +## Setup and Installation + +1. **Prerequisites** + + * Python 3.11+ + * Poetry + * For dependency management and packaging. Please follow the + instructions on the official + [Poetry website](https://python-poetry.org/docs/) for installation. + + ```bash + pip install poetry + ``` + + * A project on Google Cloud Platform + * Google Cloud CLI + * For installation, please follow the instruction on the official + [Google Cloud website](https://cloud.google.com/sdk/docs/install). + +2. **Installation** + + ```bash + # Clone this repository. + git clone https://github.com/google/adk-samples.git + cd adk-samples/python/agents/academic-research + # Install the package and dependencies. + # Note for Linux users: If you get an error related to `keyring` during the installation, you can disable it by running the following command: + # poetry config keyring.enabled false + # This is a one-time setup. + poetry install + ``` + +3. **Configuration** + + * Set up Google Cloud credentials. + + * You may set the following environment variables in your shell, or in + a `.env` file instead. + + ```bash + export GOOGLE_GENAI_USE_VERTEXAI=true + export GOOGLE_CLOUD_PROJECT= + export GOOGLE_CLOUD_LOCATION= + export GOOGLE_CLOUD_STORAGE_BUCKET= # Only required for deployment on Agent Engine + ``` + + * Authenticate your GCloud account. + + ```bash + gcloud auth application-default login + gcloud auth application-default set-quota-project $GOOGLE_CLOUD_PROJECT + ``` + +## Running the Agent + +**Using `adk`** + +ADK provides convenient ways to bring up agents locally and interact with them. +You may talk to the agent using the CLI: + +```bash +adk run academic_research +``` + +Or on a web interface: + +```bash + adk web +``` + +The command `adk web` will start a web server on your machine and print the URL. +You may open the URL, select "academic_research" in the top-left drop-down menu, and +a chatbot interface will appear on the right. The conversation is initially +blank. Here are some example requests you may ask the Academic Research to verify: + +``` +who are you +``` + +Sampled responses of these requrests are shown below in the [Example +Interaction](#example-interaction) section. + +``` +Hello! I am an AI Research Assistant. + +My purpose is to help you explore the academic landscape related to a seminal paper you're interested in. I can: + +Analyze a seminal paper you provide (Title/Authors, DOI, or URL). +Find recent academic papers that cite the seminal work using specialized tools. +Suggest potential future research directions based on the analysis and recent findings, again using specialized tools. +How can I help you today? Would you like to provide a seminal paper for analysis? +``` + +### Example Interaction + +Below is an example interaction with the Academic Research. Note that the exact output +of the agent may be different every time. Not that the user provides a pdf with the seminal paper to analyze + +``` + * user: who are you + +Hello! I am an AI Research Assistant. + +My purpose is to help you explore the academic landscape related to a seminal paper you're interested in. I can: + +Analyze a seminal paper you provide (Title/Authors, DOI, or URL). +Find recent academic papers that cite the seminal work using specialized tools. +Suggest potential future research directions based on the analysis and recent findings, again using specialized tools. +How can I help you today? Would you like to provide a seminal paper for analysis? + + * user: 1706.03762v7.pdf + * user: enclosed + +Okay, I have received the paper "Attention Is All You Need". Thank you for providing it. + +First, I will analyze the seminal paper to establish the context. + +Seminal Paper Analysis + +Seminal Paper: Attention Is All You Need, Vaswani et al., 2017 (Published at NIPS 2017) + +Authors: + +Ashish Vaswani (Google Brain) +Noam Shazeer (Google Brain) +Niki Parmar (Google Research) +Jakob Uszkoreit (Google Research) +Llion Jones (Google Research) +Aidan N. Gomez (University of Toronto / Google Brain) +Łukasz Kaiser (Google Brain) +Illia Polosukhin (Google Research) +Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. + +Summary: This paper introduces the Transformer, a novel neural network architecture designed for sequence transduction tasks, particularly machine translation. It marks a significant departure from previous state-of-the-art models which relied heavily on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) within their encoder-decoder structures. The Transformer's core innovation is its complete reliance on attention mechanisms, specifically self-attention (or intra-attention), eliminating the need for sequential processing inherent in RNNs. This allows for significantly greater parallelization during training. The architecture features an encoder and decoder, each composed of stacks of layers containing multi-head self-attention mechanisms and position-wise feed-forward networks. The paper demonstrates that the Transformer achieves superior results on English-to-German and English-to-French translation benchmarks while requiring substantially less training time than previous models. It also shows the model's applicability to other tasks like English constituency parsing. + +Key Topics/Keywords: + +Transformer model +Attention Mechanism +Self-Attention / Intra-Attention +Multi-Head Attention +Sequence Transduction +Machine Translation +Encoder-Decoder Architecture +Parallelization +Positional Encoding +Natural Language Processing (NLP) +Key Innovations: + +Proposed the Transformer architecture based entirely on attention mechanisms, removing recurrence and convolutions for sequence-to-sequence tasks. +Introduced Multi-Head Attention to allow the model to jointly attend to information from different representation subspaces at different positions. +Utilized Scaled Dot-Product Attention as an efficient attention mechanism. +Incorporated positional encodings to provide sequence order information without relying on recurrence. +Achieved state-of-the-art performance on machine translation with significantly improved training efficiency and parallelizability. +References Cited Within Seminal Paper: + +Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. +Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473, 2014. +Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V. Le. Massive exploration of neural machine translation architectures. CoRR, abs/1703.03906, 2017. +Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733, 2016. +Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. CoRR, abs/1406.1078, 2014. +Francois Chollet. Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357, 2016. +Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555, 2014. +Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. Recurrent neural network grammars. In Proc. of NAACL, 2016. +Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122v2, 2017. +Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013. +Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016. +Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001. +Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. +Zhongqiang Huang and Mary Harper. Self-training PCFG grammars with latent annotations across languages. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 832–841. ACL, August 2009. +Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410, 2016. +Łukasz Kaiser and Samy Bengio. Can active memory replace attention? In Advances in Neural Information Processing Systems, (NIPS), 2016. +Łukasz Kaiser and Ilya Sutskever. Neural GPUs learn algorithms. In International Conference on Learning Representations (ICLR), 2016. +Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099v2, 2017. +Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. Structured attention networks. In International Conference on Learning Representations, 2017. +Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. +Oleksii Kuchaiev and Boris Ginsburg. Factorization tricks for LSTM networks. arXiv preprint arXiv:1703.10722, 2017. +Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017. +Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114, 2015. +Minh-Thang Luong, Hieu Pham, and Christopher D Manning. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025, 2015. +Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. Building a large annotated corpus of english: The penn treebank. Computational linguistics, 19(2):313–330, 1993. +David McClosky, Eugene Charniak, and Mark Johnson. Effective self-training for parsing. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 152–159. ACL, June 2006. +Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A decomposable attention model. In Empirical Methods in Natural Language Processing, 2016. +Romain Paulus, Caiming Xiong, and Richard Socher. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. +Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning accurate, compact, and interpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 433–440. ACL, July 2006. +Ofir Press and Lior Wolf. Using the output embedding to improve language models. arXiv preprint arXiv:1608.05859, 2016. +Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909, 2015. +Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. +Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014. +Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end memory networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2440–2448. Curran Associates, Inc., 2015. +Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pages 3104–3112, 2014. +Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567, 2015. +Vinyals & Kaiser, Koo, Petrov, Sutskever, and Hinton. Grammar as a foreign language. In Advances in Neural Information Processing Systems, 2015. +Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016. +Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with fast-forward connections for neural machine translation. CoRR, abs/1606.04199, 2016. +Muhua Zhu, Yue Zhang, Wenliang Chen, Min Zhang, and Jingbo Zhu. Fast and accurate shift-reduce constituent parsing. In Proceedings of the 51st Annual Meeting of the ACL (Volume 1: Long Papers), pages 434–443. ACL, August 2013. +Now that I have analyzed the seminal paper, I will search for recent papers citing this work. I will look for papers published since January 2023 (covering roughly the last year and a bit). + +* user: ok + +Okay, here are the recent papers found citing "Attention Is All You Need" (Vaswani et al., 2017), published since January 2023, based on the tool's search: + +Recent Papers Citing "Attention Is All You Need" + +(Note: The list includes preprints and some non-peer-reviewed articles/blog posts identified by the search tool.) + +2025 Publications/Preprints + +Title: Tensor Product Attention Is All You Need +Author(s): Yifan Zhang, Mengxuan Fu, Ximing Xiang, Zhiyuan Zhang, Yuexiang Xie, Dongsheng Li, Dawei Yin +Publication Year: 2025 (v3 submitted April 9, 2025) +Source: arXiv +Link: https://arxiv.org/abs/2501.06425 +Title: TransMLA: Multi-head Latent Attention Is All You Need +Author(s): Mengxuan Fu, Yifan Zhang, Zhiyuan Zhang, Xiaogang Chen, Dongsheng Li, Dawei Yin +Publication Year: 2025 (v1 submitted Feb 11, 2025) +Source: arXiv +Link: https://arxiv.org/abs/2502.06816 (Link inferred) +Title: Attention is All You Need Until You Need Retention +Author(s): M. Murat Yaslioglu +Publication Year: 2025 (v1 submitted Jan 15, 2025) +Source: arXiv +Link: https://arxiv.org/abs/2501.09166 +Title: Transferable-guided Attention Is All You Need for Video Domain Adaptation +Author(s): André Sacilotti, Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida +Publication Year: 2025 (Accepted to WACV'25) +Source: IEEE/CVF WACV'25 / arXiv +Link: https://arxiv.org/abs/2407.01375 +Title: Moral Attention Is All You Need +Author(s): Mark Graves +Publication Year: 2025 (Published online Mar 17, 2025) +Source: Theology and Science +Link: https://doi.org/10.1080/14746700.2025.2472118 +Title: "Attention", "Transformers", in Neural Network "Large Language Models" (Essay referencing recent work) +Author(s): Cosma Shalizi (referencing Farrell et al., 2025) +Publication Year: 2025 (Essay updated March/April 2025) +Source: Author's Website/Blog +Link: https://www.stat.cmu.edu/~cshalizi/notebooks/attention-is-all-you-need.html +2024 Publications/Preprints/Articles + +Title: ATFF: Advanced Transformer with Multiscale Contextual Fusion for Medical Image Segmentation +Author(s): Xinping Guo, Lei Wang, Zizhen Huang, Yukun Zhang, Yaolong Han +Publication Year: 2024 (Published March 29, 2024) +Source: Journal of Computer and Communications, Vol.12 No.3 +Link: https://www.scirp.org/reference/referencespapers?referenceid=3338278 (Citation page) +Title: Multimodal Attention is all you need +Author(s): Marco Saioni, Cristina Giannone +Publication Year: 2024 (CEUR Workshop Proceedings, paper dated Dec 4, 2024) +Source: CEUR Workshop Proceedings (Evalita 2023) +Link: https://ceur-ws.org/Vol-3616/paper34.pdf (Link inferred) +Title: Attention is all you need: utilizing attention in AI-enabled drug discovery +Author(s): Yang Zhang, Caiqi Liu, Mujiexin Liu, Tianyuan Liu, Hao Lin, Cheng-Bing Huang, Lin Ning +Publication Year: 2024 (Published Jan 6, 2024) +Source: Briefings in Bioinformatics, Volume 25, Issue 1 +Link: https://doi.org/10.1093/bib/bbad467 +Title: HIERARCHICAL ENCODING AND CONDITIONAL ATTENTION IN NEURAL MACHINE TRANSLATION +Publication Year: 2024 (Published September 2024) +Source: The American Journal of Engineering and Technology, 6(09) +Link: https://doi.org/10.37547/tajet/Volume06Issue09-07 +Title: Measurements With A Quantum Vision Transformer: A Naive Approach +Publication Year: 2024 (Proceedings published 2024) +Source: EPJ Web of Conferences, Volume 295 (ICHEP 2022) +Link: https://www.epj-conferences.org/articles/epjconf/ref/2024/01/epjconf_ichep2022_12003/epjconf_ichep2022_12003.html +Title: Attention is all you need: discovering the Transformer paper (Article/Blog Post) +Author(s): Luis Serrano +Publication Year: 2024 +Source: Towards Data Science +Link: https://towardsdatascience.com/attention-is-all-you-need-discovering-the-transformer-paper-731ad0737c6d +Title: Paper Walkthrough: Attention Is All You Need (Article/Blog Post) +Author(s): Muhammad Ardi +Publication Year: 2024 (Published Nov 3, 2024) +Source: Towards Data Science +Link: https://towardsdatascience.com/paper-walkthrough-attention-is-all-you-need-69388903b711 +Title: Attention is all you need – Transformers (Article/Blog Post) +Author(s): Strahinja Zivkovic +Publication Year: 2024 (Published Nov 28, 2024) +Source: Master Data Science (Blog) +Link: https://masterdatascience.com/attention-is-all-you-need-transformers/ +Title: The Groundbreaking Transformer Paper: “Attention is All You Need” (Article/Blog Post) +Author(s): Brian Carter +Publication Year: 2024 (Published Oct 5, 2024) +Source: The Brian Carter Group (Blog) +Link: https://briancartergroup.com/the-groundbreaking-transformer-paper-attention-is-all-you-need/ +Now, based on the seminal paper "Attention Is All You Need" and these recent citing works, I will use a specialized tool to suggest potential future research directions. + +* user: ok + +Okay, here are the potential future research directions suggested by the tool, based on the seminal "Attention Is All You Need" paper and the recent citing works: + +Potential Future Research Directions + +Beyond Dot-Product Attention: Exploring Non-Linear and Learnable Similarity Metrics: Instead of just scaled dot-product, research different, perhaps non-linear or fully learned, ways to measure similarity within the attention mechanism. This could capture more complex relationships and lead to more robust or efficient models. +Intrinsically Graph-Aware Attention Mechanisms: Develop attention mechanisms that naturally understand and operate on graph structures (like molecules or social networks) without needing to force them into sequences first. +Physics-Informed and Constraint-Driven Attention: Design attention mechanisms that incorporate known physical laws, rules, or constraints directly into their structure. This could lead to more accurate and efficient models for scientific discovery and simulation. +Hybrid Neuro-Symbolic Attention Architectures: Create attention mechanisms that can interact with symbolic knowledge bases or reasoning engines, combining the strengths of deep learning and traditional AI for better reasoning and fact-checking. +Dynamic Sparsity and Adaptive Computation in Attention: Explore attention mechanisms that dynamically adjust which elements attend to each other based on context (sparse attention) or adapt the amount of computation used per element or layer, making Transformers more efficient for very long sequences or limited resources. +Biologically Plausible Attention and Memory Integration: Investigate attention architectures inspired by how biological brains handle attention and memory, potentially leading to novel models with different capabilities. +Meta-Attention for Interpretability and Controllability: Develop attention mechanisms that can observe their own attention patterns ("meta-attention"). This could improve our understanding of why the model makes certain decisions and potentially allow for more control over its focus. +Attention Mechanisms for Fundamentally Non-Sequential Data Structures: Design attention mechanisms specifically for data that doesn't have a natural order, such as sets of items or point clouds, without imposing artificial sequences. +Temporally Continuous Attention for Streaming Data: Create attention mechanisms suited for continuous, real-time data streams where the idea of a fixed sequence length doesn't apply, crucial for real-time monitoring or control. +Cross-Modal Associative Attention Kernels: Develop new attention "kernels" that can directly compute similarity between elements from different modalities (e.g., image regions and text concepts) without needing to project them into a common space first, enabling tighter multimodal integration. +This analysis provides a starting point for exploring the evolution of Transformer architectures and attention mechanisms. + +Would you like to dive deeper into any of these specific research directions, or perhaps explore another seminal paper? + +``` + +## Running Tests + +For running tests and evaluation, install the extra dependencies: + +```bash +poetry install --with dev +``` + +Then the tests and evaluation can be run from the `academic-research` directory using +the `pytest` module: + +```bash +python3 -m pytest tests +python3 -m pytest eval +``` + +`tests` runs the agent on a sample request, and makes sure that every component +is functional. `eval` is a demonstration of how to evaluate the agent, using the +`AgentEvaluator` in ADK. It sends a couple requests to the agent and expects +that the agent's responses match a pre-defined response reasonablly well. + + +## Deployment + +The Academic Co-Research can be deployed to Vertex AI Agent Engine using the following +commands: + +```bash +poetry install --with deployment +python3 deployment/deploy.py --create +``` + +When the deployment finishes, it will print a line like this: + +``` +Created remote agent: projects//locations//reasoningEngines/ +``` + +If you forgot the AGENT_ENGINE_ID, you can list existing agents using: + +```bash +python3 deployment/deploy.py --list +``` + +The output will be like: + +``` +All remote agents: + +123456789 ("academic_research") +- Create time: 2025-05-10 09:33:46.188760+00:00 +- Update time: 2025-05-10 09:34:32.763434+00:00 + +``` + +You may interact with the deployed agent using the `test_deployment.py` script +```bash +$ export USER_ID= +$ python3 deployment/test_deployment.py --resource_id=${AGENT_ENGINE_ID} --user_id=${USER_ID} +Found agent with resource ID: ... +Created session for user ID: ... +Type 'quit' to exit. +Input: Hello. What can you do for me? +Response: Hello! I'm an AI Research Assistant. I can help you analyze a seminal academic paper. + +To get started, please provide the seminal paper you wish to analyze as a PDF. +``` + +To delete the deployed agent, you may run the following command: + +```bash +python3 deployment/deploy.py --delete --resource_id=${AGENT_ENGINE_ID} +``` + +## Customization + +The Academic Research can be customized to better suit your requirements. For example: + + 1. Integrate Specialized Search Tools: Augment the agent's discovery capabilities by incorporating additional academic search functionalities, such as an ArXiv-specific search tool, to complement or replace the default search mechanism, allowing for more targeted retrieval from specialized databases and preprint archives. + 2. Implement Output Visualization: Enhance the presentation of research findings by adding modules to visualize the network of cited papers or to graphically represent suggested future research topics, thereby offering a more intuitive and interconnected understanding of the academic landscape. +3. Customize Agent Instructions: Modify the prompts guiding the academic_websearch and academic_newresearch sub-agents. This allows you to direct their analytical focus (e.g., by specifying the depth of citation analysis, emphasizing interdisciplinary connections for future research, or ensuring adherence to particular academic writing conventions) or to refine the structure and detail of their outputs. +4. Download the seminal paper via DOI or URL: Augment the agent's capabilities by enabling direct download of seminal papers. This feature would allow the agent to: +* Resolve DOI to PDF: Given a Digital Object Identifier (DOI), the agent could leverage APIs or web scraping techniques to locate and download the corresponding PDF of the paper. This streamlines the process of accessing primary sources, especially when the DOI is already known. +* Download from URL: If a direct URL to the paper's PDF or an academic repository page is provided, the agent could be configured to navigate to that URL and initiate the download. This would be particularly useful for papers available on institutional repositories, personal academic websites, or preprint servers that might not have a readily resolvable DOI. +* Integration with Reference Managers: The downloaded papers could then be automatically imported into a specified reference manager (e.g., Zotero, Mendeley, EndNote), along with their metadata, facilitating seamless organization and citation management for the user's research library. This integration would significantly enhance the efficiency of literature review and research compilation. diff --git a/samples/google-adk/academic-research.svg b/samples/google-adk/academic-research.svg new file mode 100644 index 000000000..d12552b4d --- /dev/null +++ b/samples/google-adk/academic-research.svg @@ -0,0 +1,33 @@ + + + + + +academic_coordinator + +🤖 academic_coordinator + + + +academic_websearch_agent + +🤖 academic_websearch_agent + + + +academic_coordinator->academic_websearch_agent + + + + +academic_newresearch_agent + +🤖 academic_newresearch_agent + + + +academic_coordinator->academic_newresearch_agent + + + + diff --git a/samples/google-adk/academic_research/__init__.py b/samples/google-adk/academic_research/__init__.py new file mode 100644 index 000000000..47efe77bc --- /dev/null +++ b/samples/google-adk/academic_research/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_Research: Research advice, related literature finding, research area proposals, web knowledge access.""" + +from . import agent diff --git a/samples/google-adk/academic_research/agent.py b/samples/google-adk/academic_research/agent.py new file mode 100644 index 000000000..093c82ebc --- /dev/null +++ b/samples/google-adk/academic_research/agent.py @@ -0,0 +1,45 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_Research: Research advice, related literature finding, research area proposals, web knowledge access.""" + +from google.adk.agents import LlmAgent +from google.adk.tools.agent_tool import AgentTool + +from . import prompt +from .sub_agents.academic_newresearch import academic_newresearch_agent +from .sub_agents.academic_websearch import academic_websearch_agent + +MODEL = "gemini-2.5-pro" + + +academic_coordinator = LlmAgent( + name="academic_coordinator", + model=MODEL, + description=( + "analyzing seminal papers provided by the users, " + "providing research advice, locating current papers " + "relevant to the seminal paper, generating suggestions " + "for new research directions, and accessing web resources " + "to acquire knowledge" + ), + instruction=prompt.ACADEMIC_COORDINATOR_PROMPT, + output_key="seminal_paper", + tools=[ + AgentTool(agent=academic_websearch_agent), + AgentTool(agent=academic_newresearch_agent), + ], +) + +root_agent = academic_coordinator diff --git a/samples/google-adk/academic_research/prompt.py b/samples/google-adk/academic_research/prompt.py new file mode 100644 index 000000000..929f9f22d --- /dev/null +++ b/samples/google-adk/academic_research/prompt.py @@ -0,0 +1,70 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Prompt for the academic_coordinator_agent.""" + + +ACADEMIC_COORDINATOR_PROMPT = """ +System Role: You are an AI Research Assistant. Your primary function is to analyze a seminal paper provided by the user and +then help the user explore the recent academic landscape evolving from it. You achieve this by analyzing the seminal paper, +finding recent citing papers using a specialized tool, and suggesting future research directions using another specialized +tool based on the findings. + +Workflow: + +Initiation: + +Greet the user. +Ask the user to provide the seminal paper they wish to analyze as PDF. +Seminal Paper Analysis (Context Building): + +Once the user provides the paper information, state that you will analyze the seminal paper for context. +Process the identified seminal paper. +Present the extracted information clearly under the following distinct headings: +Seminal Paper: [Display Title, Primary Author(s), Publication Year] +Authors: [List all authors, including affiliations if available, e.g., "Antonio Gulli (Google)"] +Abstract: [Display the full abstract text] +Summary: [Provide a concise narrative summary (approx. 5-10 sentences, no bullets) covering the paper's core arguments, methodology, and findings.] +Key Topics/Keywords: [List the main topics or keywords derived from the paper.] +Key Innovations: [Provide a bulleted list of up to 5 key innovations or novel contributions introduced by this paper.] +References Cited Within Seminal Paper: [Extract the bibliography/references section from the seminal paper. +List each reference on a new line using a standard citation format (e.g., Author(s). Title. Venue. Details. Date.).] +Find Recent Citing Papers (Using academic_websearch): + +Inform the user you will now search for recent papers citing the seminal work. +Action: Invoke the academic_websearch agent/tool. +Input to Tool: Provide necessary identifiers for the seminal paper. +Parameter: Specify the desired recency. Ask the user or use a default timeframe, e.g., "papers published during last year" +(e.g., since January 2025, based on the current date April 21, 2025). +Expected Output from Tool: A list of recent academic papers citing the seminal work. +Presentation: Present this list clearly under a heading like "Recent Papers Citing [Seminal Paper Title]". +Include details for each paper found (e.g., Title, Authors, Year, Source, Link/DOI). +If no papers are found in the specified timeframe, state that clearly. +The agent will provide the answer and i want you to print it to the user + +Suggest Future Research Directions (Using academic_newresearch): +Inform the user that based on the seminal paper from the seminal paper and the recent citing papers provided by the academic_websearch agent/tool, +you will now suggest potential future research directions. +Action: Invoke the academic_newresearch agent/tool. +Inputs to Tool: +Information about the seminal paper (e.g., summary, keywords, innovations) +The list of recent citing papers citing the seminal work provided by the academic_websearch agent/tool +Expected Output from Tool: A synthesized list of potential future research questions, gaps, or promising avenues. +Presentation: Present these suggestions clearly under a heading like "Potential Future Research Directions". +Structure them logically (e.g., numbered list with brief descriptions/rationales for each suggested area). + +Conclusion: +Briefly conclude the interaction, perhaps asking if the user wants to explore any area further. + +""" diff --git a/samples/google-adk/academic_research/sub_agents/academic_newresearch/__init__.py b/samples/google-adk/academic_research/sub_agents/academic_newresearch/__init__.py new file mode 100644 index 000000000..e85eb2da6 --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_newresearch/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_websearch_agent for finding research papers using search tools.""" + +from .agent import academic_newresearch_agent diff --git a/samples/google-adk/academic_research/sub_agents/academic_newresearch/agent.py b/samples/google-adk/academic_research/sub_agents/academic_newresearch/agent.py new file mode 100644 index 000000000..a9ad4a914 --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_newresearch/agent.py @@ -0,0 +1,27 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_newresearch_agent for finding new research lines""" + +from google.adk import Agent + +from . import prompt + +MODEL = "gemini-2.5-pro" + +academic_newresearch_agent = Agent( + model=MODEL, + name="academic_newresearch_agent", + instruction=prompt.ACADEMIC_NEWRESEARCH_PROMPT, +) diff --git a/samples/google-adk/academic_research/sub_agents/academic_newresearch/prompt.py b/samples/google-adk/academic_research/sub_agents/academic_newresearch/prompt.py new file mode 100644 index 000000000..7dfda6b74 --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_newresearch/prompt.py @@ -0,0 +1,70 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Prompt for the academic_newresearch_agent agent.""" + + +ACADEMIC_NEWRESEARCH_PROMPT = """ +Role: You are an AI Research Foresight Agent. + +Inputs: + +Seminal Paper: Information identifying a key foundational paper (e.g., Title, Authors, Abstract, DOI, Key Contributions Summary). +{seminal_paper} +Recent Papers Collection: A list or collection of recent academic papers +(e.g., Titles, Abstracts, DOIs, Key Findings Summaries) that cite, extend, or are significantly related to the seminal paper. +{recent_citing_papers} + +Core Task: + +Analyze & Synthesize: Carefully analyze the core concepts and impact of the seminal paper. +Then, synthesize the trends, advancements, identified gaps, limitations, and unanswered questions presented in the collection of recent papers. +Identify Future Directions: Based on this synthesis, extrapolate and identify underexplored or novel avenues for future research that logically + extend from or react to the trajectory observed in the provided papers. + +Output Requirements: + +Generate a list of at least 10 distinct future research areas. +Focus Criteria: Each proposed area must meet the following criteria: +Novelty: Represents a significant departure from current work, tackles questions not yet adequately addressed, +or applies existing concepts in a genuinely new context evident from the provided inputs. It should be not yet fully explored. +Future Potential: Shows strong potential to be impactful, influential, interesting, or disruptive within the field in the coming years. +Diversity Mandate: Ensure the portfolio of at least 10 suggestions reflects a good balance across different types of potential future directions. +Specifically, aim to include a mix of areas characterized by: +High Potential Utility: Addresses practical problems, has clear application potential, or could lead to significant real-world benefits. +Unexpectedness / Paradigm Shift: Challenges current assumptions, proposes unconventional approaches, connects previously disparate fields/concepts, or explores surprising implications. +Emerging Popularity / Interest: Aligns with growing trends, tackles timely societal or scientific questions, or opens up areas likely to attract significant research community interest. + +Format: Present the 10 research areas as a numbered list. For each area: +Provide a clear, concise Title or Theme. +Write a Brief Rationale (2-4 sentences) explaining: +What the research area generally involves. +Why it is novel or underexplored (linking back to the synthesis of the input papers). +Why it holds significant future potential (implicitly or explicitly touching upon its utility, unexpectedness, or likely popularity). + +(Optional) Identify Relevant Authors: After presenting at least 10 research areas, optionally provide a separate section titled +"Potentially Relevant Authors". In this section: +List authors, primarily drawn from the seminal or recent papers provided as input, whose expertise seems highly relevant to one or more +of the proposed future research areas. +If possible, briefly note which research area(s) each listed author's expertise aligns with most closely (e.g., "Author Name (Areas 3, 7)"). +Base this relevance on the demonstrated focus and contributions in the provided input papers. + +Example Rationale Structure (Illustrative): + +3. Title: Cross-Modal Synthesis via Disentangled Representations +Rationale: While recent papers [mention specific trend/gap, e.g., focus heavily on unimodal analysis], exploring how to generate data +in one modality (e.g., images) based purely on learned disentangled factors from another (e.g., text) remains underexplored. +This approach could lead to highly controllable generative models (utility) and potentially uncover surprising shared semantic structures +across modalities (unexpectedness), likely becoming a popular area as cross-modal learning grows. +""" diff --git a/samples/google-adk/academic_research/sub_agents/academic_websearch/__init__.py b/samples/google-adk/academic_research/sub_agents/academic_websearch/__init__.py new file mode 100644 index 000000000..1709e56f0 --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_websearch/__init__.py @@ -0,0 +1,17 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_websearch_agent for finding research papers using search tools.""" + +from .agent import academic_websearch_agent diff --git a/samples/google-adk/academic_research/sub_agents/academic_websearch/agent.py b/samples/google-adk/academic_research/sub_agents/academic_websearch/agent.py new file mode 100644 index 000000000..e63feb916 --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_websearch/agent.py @@ -0,0 +1,31 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Academic_websearch_agent for finding research papers using search tools.""" + +from google.adk import Agent +from google.adk.tools import google_search + +from . import prompt + +MODEL = "gemini-2.5-pro" + + +academic_websearch_agent = Agent( + model=MODEL, + name="academic_websearch_agent", + instruction=prompt.ACADEMIC_WEBSEARCH_PROMPT, + output_key="recent_citing_papers", + tools=[google_search], +) diff --git a/samples/google-adk/academic_research/sub_agents/academic_websearch/prompt.py b/samples/google-adk/academic_research/sub_agents/academic_websearch/prompt.py new file mode 100644 index 000000000..f193b692f --- /dev/null +++ b/samples/google-adk/academic_research/sub_agents/academic_websearch/prompt.py @@ -0,0 +1,65 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Prompt for the academic_websearch agent.""" + +ACADEMIC_WEBSEARCH_PROMPT = """ +Role: You are a highly accurate AI assistant specialized in factual retrieval using available tools. +Your primary task is thorough academic citation discovery within a specific recent timeframe. + +Tool: You MUST utilize the Google Search tool to gather the most current information. +Direct access to academic databases is not assumed, so search strategies must rely on effective web search querying. + +Objective: Identify and list academic papers that cite the seminal paper '{seminal_paper}' AND +were published (or accepted/published online) in the current year or the previous year. +The primary goal is to find at least 10 distinct citing papers for each of these years (20 total minimum, if available). + +Instructions: + +Identify Target Paper: The seminal paper being cited is {seminal_paper}. (Use its title, DOI, or other unique identifiers for searching). +Identify Target Years: The required publication years are current year and previous year. +(so if the current year is 2025, then the previous year is 2024) +Formulate & Execute Iterative Search Strategy: +Initial Queries: Construct specific queries targeting each year separately. Examples: +"cited by" "{seminal_paper}" published current year +"papers citing {seminal_paper}" publication year current year +site:scholar.google.com "{seminal_paper}" YR=current year +"cited by" "{seminal_paper}" published previous year +"papers citing {seminal_paper}" publication year previous year +site:scholar.google.com "{seminal_paper}" YR=previous year +Execute Search: Use the Google Search tool with these initial queries. +Analyze & Count: Review initial results, filter for relevance (confirming citation and year), and count distinct papers found for each year. +Persistence Towards Target (>=10 per year): If fewer than 10 relevant papers are found for either current year or previous year, +you MUST perform additional, varied searches. Refine and broaden your queries systematically: +Try different phrasing for "citing" (e.g., "references", "based on the work of"). +Use different identifiers for {seminal_paper} (e.g., full title, partial title + lead author, DOI). +Search known relevant repositories or publisher sites if applicable +(site:arxiv.org, site:ieeexplore.ieee.org, site:dl.acm.org, etc., adding the paper identifier and year constraints). +Combine year constraints with author names from the seminal paper. +Continue executing varied search queries until either the target of 10 papers per year is met, +or you have exhausted multiple distinct search strategies and angles. Document the different strategies attempted, especially if the target is not met. +Filter and Verify: Critically evaluate search results. Ensure papers genuinely cite {seminal_paper} and have +a publication/acceptance date in current year or previous year. Discard duplicates and low-confidence results. + +Output Requirements: + +Present the findings clearly, grouping results by year (current year first, then previous year). +Target Adherence: Explicitly state how many distinct papers were found for current year and how many for previous year. +List Format: For each identified citing paper, provide: +Title +Author(s) +Publication Year (Must be current year or previous year) +Source (Journal Name, Conference Name, Repository like arXiv) +Link (Direct DOI or URL if found in search results) +""" diff --git a/samples/google-adk/compose.yaml b/samples/google-adk/compose.yaml new file mode 100644 index 000000000..9ca5298f7 --- /dev/null +++ b/samples/google-adk/compose.yaml @@ -0,0 +1,5 @@ +services: + agent: + build: . + ports: + - "8080:8080" diff --git a/samples/google-adk/deployment/deploy.py b/samples/google-adk/deployment/deploy.py new file mode 100644 index 000000000..d35f06573 --- /dev/null +++ b/samples/google-adk/deployment/deploy.py @@ -0,0 +1,131 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Deployment script for Academic Research""" + + +import os + +import vertexai +from absl import app, flags +from academic_research.agent import root_agent +from dotenv import load_dotenv +from vertexai import agent_engines +from vertexai.preview.reasoning_engines import AdkApp + +FLAGS = flags.FLAGS +flags.DEFINE_string("project_id", None, "GCP project ID.") +flags.DEFINE_string("location", None, "GCP location.") +flags.DEFINE_string("bucket", None, "GCP bucket.") +flags.DEFINE_string("resource_id", None, "ReasoningEngine resource ID.") + +flags.DEFINE_bool("list", False, "List all agents.") +flags.DEFINE_bool("create", False, "Creates a new agent.") +flags.DEFINE_bool("delete", False, "Deletes an existing agent.") +flags.mark_bool_flags_as_mutual_exclusive(["create", "delete"]) + + +def create() -> None: + """Creates an agent engine for Academic Research.""" + adk_app = AdkApp(agent=root_agent, enable_tracing=True) + + remote_agent = agent_engines.create( + adk_app, + display_name=root_agent.name, + requirements=[ + "google-adk (>=0.0.2)", + "google-cloud-aiplatform[agent_engines] (>=1.91.0,!=1.92.0)", + "google-genai (>=1.5.0,<2.0.0)", + "pydantic (>=2.10.6,<3.0.0)", + "absl-py (>=2.2.1,<3.0.0)", + ], + # extra_packages=[""], + ) + print(f"Created remote agent: {remote_agent.resource_name}") + + +def delete(resource_id: str) -> None: + remote_agent = agent_engines.get(resource_id) + remote_agent.delete(force=True) + print(f"Deleted remote agent: {resource_id}") + + +def list_agents() -> None: + remote_agents = agent_engines.list() + template = """ +{agent.name} ("{agent.display_name}") +- Create time: {agent.create_time} +- Update time: {agent.update_time} +""" + remote_agents_string = "\n".join( + template.format(agent=agent) for agent in remote_agents + ) + print(f"All remote agents:\n{remote_agents_string}") + + +def main(argv: list[str]) -> None: + del argv # unused + load_dotenv() + + project_id = ( + FLAGS.project_id + if FLAGS.project_id + else os.getenv("GOOGLE_CLOUD_PROJECT") + ) + location = ( + FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION") + ) + bucket = ( + FLAGS.bucket + if FLAGS.bucket + else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET") + ) + + print(f"PROJECT: {project_id}") + print(f"LOCATION: {location}") + print(f"BUCKET: {bucket}") + + if not project_id: + print("Missing required environment variable: GOOGLE_CLOUD_PROJECT") + return + elif not location: + print("Missing required environment variable: GOOGLE_CLOUD_LOCATION") + return + elif not bucket: + print( + "Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET" + ) + return + + vertexai.init( + project=project_id, + location=location, + staging_bucket=f"gs://{bucket}", + ) + + if FLAGS.list: + list_agents() + elif FLAGS.create: + create() + elif FLAGS.delete: + if not FLAGS.resource_id: + print("resource_id is required for delete") + return + delete(FLAGS.resource_id) + else: + print("Unknown command") + + +if __name__ == "__main__": + app.run(main) diff --git a/samples/google-adk/deployment/test_deployment.py b/samples/google-adk/deployment/test_deployment.py new file mode 100644 index 000000000..1d876afb9 --- /dev/null +++ b/samples/google-adk/deployment/test_deployment.py @@ -0,0 +1,105 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Test deployment of Academic Research Agent to Agent Engine.""" + +import os + +import vertexai +from absl import app, flags +from dotenv import load_dotenv +from vertexai import agent_engines + +FLAGS = flags.FLAGS + +flags.DEFINE_string("project_id", None, "GCP project ID.") +flags.DEFINE_string("location", None, "GCP location.") +flags.DEFINE_string("bucket", None, "GCP bucket.") +flags.DEFINE_string( + "resource_id", + None, + "ReasoningEngine resource ID (returned after deploying the agent)", +) +flags.DEFINE_string("user_id", None, "User ID (can be any string).") +flags.mark_flag_as_required("resource_id") +flags.mark_flag_as_required("user_id") + + +def main(argv: list[str]) -> None: # pylint: disable=unused-argument + + load_dotenv() + + project_id = ( + FLAGS.project_id + if FLAGS.project_id + else os.getenv("GOOGLE_CLOUD_PROJECT") + ) + location = ( + FLAGS.location if FLAGS.location else os.getenv("GOOGLE_CLOUD_LOCATION") + ) + bucket = ( + FLAGS.bucket + if FLAGS.bucket + else os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET") + ) + + project_id = os.getenv("GOOGLE_CLOUD_PROJECT") + location = os.getenv("GOOGLE_CLOUD_LOCATION") + bucket = os.getenv("GOOGLE_CLOUD_STORAGE_BUCKET") + + if not project_id: + print("Missing required environment variable: GOOGLE_CLOUD_PROJECT") + return + elif not location: + print("Missing required environment variable: GOOGLE_CLOUD_LOCATION") + return + elif not bucket: + print( + "Missing required environment variable: GOOGLE_CLOUD_STORAGE_BUCKET" + ) + return + + vertexai.init( + project=project_id, + location=location, + staging_bucket=f"gs://{bucket}", + ) + + agent = agent_engines.get(FLAGS.resource_id) + print(f"Found agent with resource ID: {FLAGS.resource_id}") + session = agent.create_session(user_id=FLAGS.user_id) + print(f"Created session for user ID: {FLAGS.user_id}") + print("Type 'quit' to exit.") + while True: + user_input = input("Input: ") + if user_input == "quit": + break + + for event in agent.stream_query( + user_id=FLAGS.user_id, session_id=session["id"], message=user_input + ): + if "content" in event: + if "parts" in event["content"]: + parts = event["content"]["parts"] + for part in parts: + if "text" in part: + text_part = part["text"] + print(f"Response: {text_part}") + + agent.delete_session(user_id=FLAGS.user_id, session_id=session["id"]) + print(f"Deleted session for user ID: {FLAGS.user_id}") + + +if __name__ == "__main__": + app.run(main) diff --git a/samples/google-adk/eval/data/academic_research_evalset.test.json b/samples/google-adk/eval/data/academic_research_evalset.test.json new file mode 100644 index 000000000..c4fe956e6 --- /dev/null +++ b/samples/google-adk/eval/data/academic_research_evalset.test.json @@ -0,0 +1,59 @@ +{ + "eval_set_id": "academic_research_evalset", + "name": "academic_research_evalset", + "description": null, + "eval_cases": [ + { + "eval_id": "hello", + "conversation": [ + { + "invocation_id": "e-ea7feda9-2e21-43c8-9149-97d4c48db2a3", + "user_content": { + "parts": [ + { + "video_metadata": null, + "thought": null, + "inline_data": null, + "code_execution_result": null, + "executable_code": null, + "file_data": null, + "function_call": null, + "function_response": null, + "text": "hello" + } + ], + "role": "user" + }, + "final_response": { + "parts": [ + { + "video_metadata": null, + "thought": null, + "inline_data": null, + "code_execution_result": null, + "executable_code": null, + "file_data": null, + "function_call": null, + "function_response": null, + "text": "Hello! I am an AI Research Assistant. I can help you analyze a seminal paper, find recent citing papers, and suggest future research directions.\n\nTo begin, please provide the seminal paper you wish to analyze as a PDF." + } + ], + "role": null + }, + "intermediate_data": { + "tool_uses": [], + "intermediate_responses": [] + }, + "creation_timestamp": 1748011266.735581 + } + ], + "session_input": { + "app_name": "academic_research", + "user_id": "user", + "state": {} + }, + "creation_timestamp": 1748011280.510737 + } + ], + "creation_timestamp": 1748011130.5057902 +} diff --git a/samples/google-adk/eval/test_eval.py b/samples/google-adk/eval/test_eval.py new file mode 100644 index 000000000..9c0a6cfa9 --- /dev/null +++ b/samples/google-adk/eval/test_eval.py @@ -0,0 +1,38 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Basic evalualtion for Academic Research""" + +import pathlib + +import dotenv +import pytest +from google.adk.evaluation.agent_evaluator import AgentEvaluator + +pytest_plugins = ("pytest_asyncio",) + + +@pytest.fixture(scope="session", autouse=True) +def load_env(): + dotenv.load_dotenv() + + +@pytest.mark.asyncio +async def test_all(): + """Test the agent's basic ability on a few examples.""" + await AgentEvaluator.evaluate( + "academic_research", + str(pathlib.Path(__file__).parent / "data"), + num_runs=5, + ) diff --git a/samples/google-adk/main.py b/samples/google-adk/main.py new file mode 100644 index 000000000..6c8863f94 --- /dev/null +++ b/samples/google-adk/main.py @@ -0,0 +1,31 @@ +import os + +import uvicorn +from fastapi import FastAPI +from google.adk.cli.fast_api import get_fast_api_app + +# Example session service URI (e.g., SQLite) +SESSION_SERVICE_URI = "sqlite:///./sessions.db" +# Example allowed origins for CORS +ALLOWED_ORIGINS = ["http://localhost", "http://localhost:8080", "*"] +# Set web=True if you intend to serve a web interface, False otherwise +SERVE_WEB_INTERFACE = True + +# Call the function to get the FastAPI app instance +# Ensure the agent directory name ('u') matches your agent folder +app: FastAPI = get_fast_api_app( + agents_dir=".", + session_service_uri=SESSION_SERVICE_URI, + allow_origins=ALLOWED_ORIGINS, + web=SERVE_WEB_INTERFACE, +) + +# You can add more FastAPI routes or configurations below if needed +# Example: +# @app.get("/hello") +# async def read_root(): +# return {"Hello": "World"} + +if __name__ == "__main__": + # Use the PORT environment 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"AI-driven agent designed to facilitate the exploration of the academic landscape" +authors = [{ name = "Antonio Gulli", email = "gulli@google.com" }] +license = "Apache License 2.0" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.9" +google-adk = "^1.0.0" +google-genai = "^1.9.0" +pydantic = "^2.10.6" +python-dotenv = "^1.0.1" +google-cloud-aiplatform = { version = "^1.93", extras = [ + "adk", + "agent-engines", +] } + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +google-adk = { extras = ["eval"], version = "^1.0.0" } +google-cloud-aiplatform = { version = "^1.93", extras = [ + "adk", + "agent-engines", + "evaluation", +] } +pytest = "^8.3.5" +black = "^25.1.0" +pytest-asyncio = "^0.26.0" +pandas = "^2.2.3" +tabulate = "^0.9.0" + +[tool.poetry.group.deployment] +optional = true + +[tool.poetry.group.deployment.dependencies] +absl-py = "^2.2.1" + +[tool.pytest.ini_options] +asyncio_mode = "auto" + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/samples/google-adk/sessions.db b/samples/google-adk/sessions.db new file mode 100644 index 0000000000000000000000000000000000000000..76cb7fbe48a0af952e79e29ab13c86472258813f GIT binary patch literal 36864 zcmeI%J#X7a7{GBdb|g!%lOP=g4+hTg5DOL3$gYcSjV#ly5Ls$vHE585U?kRNA(1M1 z3{s#O;-Et_=Zj?NmN`SdP{!O*re0!84bV)&p8&;|Baip|o`;r&JNdcpNmu+9gndVf zUE`Kvn#O%048vGfYfi1{qL1~OE@rwjXS;0NeRX^7<_BXX_tv=a=H|P#_c#7p{detk z?(OP7bMB)F0R#|0009ILK;SBYziZiSVSU|v)tAnbp6fn!eHjfuSE?Gj=6I{kq-P2C?7OPwBhT={nLG zHFe@h2K~$D2TmwGrziHC&AL@-BoB>4-|htcLC;nHh)*=QVHkv}r8`cDM$q>Ro77+< z=XBg!zPIyj((I{mhG9IAZg;Z7pIWu!O6!q$U_BDsUbiIDgqOrcVoPG2z@(?cX3MG_ zHS{gp^RE^y>(FXhjjDB$b`puJ?(w=s(QJqVt8S_KtX58{l>;kZ{CXu*_+i~NJiqHc zi~i`TUnRB^%OE+nhXrhxhuhqX$Jeuktu6EXc~bbv=vCsR8%^wHre1-G2K^qS!HjC) z(%W!BGW&w$fl0=vsw5t$N^osmOIMfCR2tH8j8ag{7xyxmLTzgn#Y7->dEy}Z;_gzm zu(@fT-$_t(&qI^(-trX5`26RIe9?eaoV&}J!qMieILD*2%j5m)=U(D{$JGe5*GppAb Date: Mon, 29 Sep 2025 09:48:28 -0700 Subject: [PATCH 2/5] remove dockerfile --- samples/google-adk/Dockerfile | 17 ----------------- 1 file changed, 17 deletions(-) delete mode 100644 samples/google-adk/Dockerfile diff --git a/samples/google-adk/Dockerfile b/samples/google-adk/Dockerfile deleted file mode 100644 index 4118f72a7..000000000 --- a/samples/google-adk/Dockerfile +++ /dev/null @@ -1,17 +0,0 @@ -FROM python:3.11-slim - -WORKDIR /app - -# Install Poetry -RUN pip install --no-cache-dir poetry - -# Copy only poetry files first for caching -COPY pyproject.toml poetry.lock* /app/ - -# Install dependencies -RUN poetry install --no-root --no-interaction --no-ansi - -# Copy the rest of the application -COPY . /app - -CMD ["poetry", "run", "python", "main.py"] From ad8f216e53278ab1fbde9f1c55eea771abbcf3a5 Mon Sep 17 00:00:00 2001 From: Jordan Stephens Date: Mon, 29 Sep 2025 09:57:50 -0700 Subject: [PATCH 3/5] readme --- samples/google-adk/README.md | 425 +++-------------------------------- 1 file changed, 26 insertions(+), 399 deletions(-) diff --git a/samples/google-adk/README.md b/samples/google-adk/README.md index c84a6c133..3d25d435d 100644 --- a/samples/google-adk/README.md +++ b/samples/google-adk/README.md @@ -1,424 +1,51 @@ -# Academic Research +# Google ADK Sample +[![1-click-deploy](https://raw.githubusercontent.com/DefangLabs/defang-assets/main/Logos/Buttons/SVG/deploy-with-defang.svg)](https://portal.defang.dev/redirect?url=https%3A%2F%2Fgithub.com%2Fnew%3Ftemplate_name%3Dsample-google-adk-template%26template_owner%3DDefangSamples) -## Overview +This is a simple sample demonstrating how to deploy agents build with Google ADK to Defang. -AI-driven agent designed to facilitate the exploration of the academic landscape surrounding seminal research works. Recognizing the challenge researchers face in navigating the expanding body of literature influenced by foundational studies, this agent offers a streamlined approach. By accepting a seminal paper identified provided as file input, the agent first analyzes the core contributions of the specified work. +## Prerequisites -1. It then leverages specialized Agent tools to identify and retrieve via Google Search recent academic publications that cite this seminal paper, mapping its contemporary influence. -2. Finally, synthesizing the analysis of the original paper with the findings from recent citing literature, and again utilizing specialized analytical tools, the agent proposes potential future research directions. +1. Download [Defang CLI](https://github.com/DefangLabs/defang) +2. (Optional) If you are using [Defang BYOC](https://docs.defang.io/docs/concepts/defang-byoc) authenticate with your cloud provider account +3. (Optional for local development) [Docker CLI](https://docs.docker.com/engine/install/) -This capability aims to provide researchers with valuable insights into the ongoing impact of seminal research and to highlight promising avenues for novel investigation, thereby accelerating the research discovery process. -## Agent Details +## Development -The key features of the Academic Research include: - -| Feature | Description | -| --- | --- | -| **Interaction Type** | Conversational | -| **Complexity** | Easy | -| **Agent Type** | Multi Agent | -| **Components** | Tools: built-in Google Search | -| **Vertical** | Education | - - - -### Agent architecture: - -This diagram shows the detailed architecture of the agents and tools used -to implement this workflow. -academic researcher - -## Setup and Installation - -1. **Prerequisites** - - * Python 3.11+ - * Poetry - * For dependency management and packaging. Please follow the - instructions on the official - [Poetry website](https://python-poetry.org/docs/) for installation. - - ```bash - pip install poetry - ``` - - * A project on Google Cloud Platform - * Google Cloud CLI - * For installation, please follow the instruction on the official - [Google Cloud website](https://cloud.google.com/sdk/docs/install). - -2. **Installation** - - ```bash - # Clone this repository. - git clone https://github.com/google/adk-samples.git - cd adk-samples/python/agents/academic-research - # Install the package and dependencies. - # Note for Linux users: If you get an error related to `keyring` during the installation, you can disable it by running the following command: - # poetry config keyring.enabled false - # This is a one-time setup. - poetry install - ``` - -3. **Configuration** - - * Set up Google Cloud credentials. - - * You may set the following environment variables in your shell, or in - a `.env` file instead. - - ```bash - export GOOGLE_GENAI_USE_VERTEXAI=true - export GOOGLE_CLOUD_PROJECT= - export GOOGLE_CLOUD_LOCATION= - export GOOGLE_CLOUD_STORAGE_BUCKET= # Only required for deployment on Agent Engine - ``` - - * Authenticate your GCloud account. - - ```bash - gcloud auth application-default login - gcloud auth application-default set-quota-project $GOOGLE_CLOUD_PROJECT - ``` - -## Running the Agent - -**Using `adk`** - -ADK provides convenient ways to bring up agents locally and interact with them. -You may talk to the agent using the CLI: +To run the application locally, you can use the following command: ```bash -adk run academic_research +docker compose up --build ``` -Or on a web interface: - -```bash - adk web -``` - -The command `adk web` will start a web server on your machine and print the URL. -You may open the URL, select "academic_research" in the top-left drop-down menu, and -a chatbot interface will appear on the right. The conversation is initially -blank. Here are some example requests you may ask the Academic Research to verify: - -``` -who are you -``` - -Sampled responses of these requrests are shown below in the [Example -Interaction](#example-interaction) section. - -``` -Hello! I am an AI Research Assistant. - -My purpose is to help you explore the academic landscape related to a seminal paper you're interested in. I can: - -Analyze a seminal paper you provide (Title/Authors, DOI, or URL). -Find recent academic papers that cite the seminal work using specialized tools. -Suggest potential future research directions based on the analysis and recent findings, again using specialized tools. -How can I help you today? Would you like to provide a seminal paper for analysis? -``` - -### Example Interaction - -Below is an example interaction with the Academic Research. Note that the exact output -of the agent may be different every time. Not that the user provides a pdf with the seminal paper to analyze - -``` - * user: who are you - -Hello! I am an AI Research Assistant. - -My purpose is to help you explore the academic landscape related to a seminal paper you're interested in. I can: - -Analyze a seminal paper you provide (Title/Authors, DOI, or URL). -Find recent academic papers that cite the seminal work using specialized tools. -Suggest potential future research directions based on the analysis and recent findings, again using specialized tools. -How can I help you today? Would you like to provide a seminal paper for analysis? - - * user: 1706.03762v7.pdf - * user: enclosed - -Okay, I have received the paper "Attention Is All You Need". Thank you for providing it. - -First, I will analyze the seminal paper to establish the context. - -Seminal Paper Analysis - -Seminal Paper: Attention Is All You Need, Vaswani et al., 2017 (Published at NIPS 2017) - -Authors: - -Ashish Vaswani (Google Brain) -Noam Shazeer (Google Brain) -Niki Parmar (Google Research) -Jakob Uszkoreit (Google Research) -Llion Jones (Google Research) -Aidan N. Gomez (University of Toronto / Google Brain) -Łukasz Kaiser (Google Brain) -Illia Polosukhin (Google Research) -Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. - -Summary: This paper introduces the Transformer, a novel neural network architecture designed for sequence transduction tasks, particularly machine translation. It marks a significant departure from previous state-of-the-art models which relied heavily on recurrent neural networks (RNNs) or convolutional neural networks (CNNs) within their encoder-decoder structures. The Transformer's core innovation is its complete reliance on attention mechanisms, specifically self-attention (or intra-attention), eliminating the need for sequential processing inherent in RNNs. This allows for significantly greater parallelization during training. The architecture features an encoder and decoder, each composed of stacks of layers containing multi-head self-attention mechanisms and position-wise feed-forward networks. The paper demonstrates that the Transformer achieves superior results on English-to-German and English-to-French translation benchmarks while requiring substantially less training time than previous models. It also shows the model's applicability to other tasks like English constituency parsing. - -Key Topics/Keywords: - -Transformer model -Attention Mechanism -Self-Attention / Intra-Attention -Multi-Head Attention -Sequence Transduction -Machine Translation -Encoder-Decoder Architecture -Parallelization -Positional Encoding -Natural Language Processing (NLP) -Key Innovations: - -Proposed the Transformer architecture based entirely on attention mechanisms, removing recurrence and convolutions for sequence-to-sequence tasks. -Introduced Multi-Head Attention to allow the model to jointly attend to information from different representation subspaces at different positions. -Utilized Scaled Dot-Product Attention as an efficient attention mechanism. -Incorporated positional encodings to provide sequence order information without relying on recurrence. -Achieved state-of-the-art performance on machine translation with significantly improved training efficiency and parallelizability. -References Cited Within Seminal Paper: - -Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. -Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473, 2014. -Denny Britz, Anna Goldie, Minh-Thang Luong, and Quoc V. Le. Massive exploration of neural machine translation architectures. CoRR, abs/1703.03906, 2017. -Jianpeng Cheng, Li Dong, and Mirella Lapata. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733, 2016. -Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statistical machine translation. CoRR, abs/1406.1078, 2014. -Francois Chollet. Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357, 2016. -Junyoung Chung, Çaglar Gülçehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555, 2014. -Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith. Recurrent neural network grammars. In Proc. of NAACL, 2016. -Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122v2, 2017. -Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013. -Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016. -Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and Jürgen Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001. -Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. -Zhongqiang Huang and Mary Harper. Self-training PCFG grammars with latent annotations across languages. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 832–841. ACL, August 2009. -Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410, 2016. -Łukasz Kaiser and Samy Bengio. Can active memory replace attention? In Advances in Neural Information Processing Systems, (NIPS), 2016. -Łukasz Kaiser and Ilya Sutskever. Neural GPUs learn algorithms. In International Conference on Learning Representations (ICLR), 2016. -Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. Neural machine translation in linear time. arXiv preprint arXiv:1610.10099v2, 2017. -Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush. Structured attention networks. In International Conference on Learning Representations, 2017. -Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. -Oleksii Kuchaiev and Boris Ginsburg. Factorization tricks for LSTM networks. arXiv preprint arXiv:1703.10722, 2017. -Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017. -Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. Multi-task sequence to sequence learning. arXiv preprint arXiv:1511.06114, 2015. -Minh-Thang Luong, Hieu Pham, and Christopher D Manning. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025, 2015. -Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. Building a large annotated corpus of english: The penn treebank. Computational linguistics, 19(2):313–330, 1993. -David McClosky, Eugene Charniak, and Mark Johnson. Effective self-training for parsing. In Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, pages 152–159. ACL, June 2006. -Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit. A decomposable attention model. In Empirical Methods in Natural Language Processing, 2016. -Romain Paulus, Caiming Xiong, and Richard Socher. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017. -Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning accurate, compact, and interpretable tree annotation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 433–440. ACL, July 2006. -Ofir Press and Lior Wolf. Using the output embedding to improve language models. arXiv preprint arXiv:1608.05859, 2016. -Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909, 2015. -Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017. -Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929–1958, 2014. -Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end memory networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2440–2448. Curran Associates, Inc., 2015. -Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pages 3104–3112, 2014. -Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. CoRR, abs/1512.00567, 2015. -Vinyals & Kaiser, Koo, Petrov, Sutskever, and Hinton. Grammar as a foreign language. In Advances in Neural Information Processing Systems, 2015. -Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016. -Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. Deep recurrent models with fast-forward connections for neural machine translation. CoRR, abs/1606.04199, 2016. -Muhua Zhu, Yue Zhang, Wenliang Chen, Min Zhang, and Jingbo Zhu. Fast and accurate shift-reduce constituent parsing. In Proceedings of the 51st Annual Meeting of the ACL (Volume 1: Long Papers), pages 434–443. ACL, August 2013. -Now that I have analyzed the seminal paper, I will search for recent papers citing this work. I will look for papers published since January 2023 (covering roughly the last year and a bit). - -* user: ok - -Okay, here are the recent papers found citing "Attention Is All You Need" (Vaswani et al., 2017), published since January 2023, based on the tool's search: - -Recent Papers Citing "Attention Is All You Need" - -(Note: The list includes preprints and some non-peer-reviewed articles/blog posts identified by the search tool.) - -2025 Publications/Preprints - -Title: Tensor Product Attention Is All You Need -Author(s): Yifan Zhang, Mengxuan Fu, Ximing Xiang, Zhiyuan Zhang, Yuexiang Xie, Dongsheng Li, Dawei Yin -Publication Year: 2025 (v3 submitted April 9, 2025) -Source: arXiv -Link: https://arxiv.org/abs/2501.06425 -Title: TransMLA: Multi-head Latent Attention Is All You Need -Author(s): Mengxuan Fu, Yifan Zhang, Zhiyuan Zhang, Xiaogang Chen, Dongsheng Li, Dawei Yin -Publication Year: 2025 (v1 submitted Feb 11, 2025) -Source: arXiv -Link: https://arxiv.org/abs/2502.06816 (Link inferred) -Title: Attention is All You Need Until You Need Retention -Author(s): M. Murat Yaslioglu -Publication Year: 2025 (v1 submitted Jan 15, 2025) -Source: arXiv -Link: https://arxiv.org/abs/2501.09166 -Title: Transferable-guided Attention Is All You Need for Video Domain Adaptation -Author(s): André Sacilotti, Samuel Felipe dos Santos, Nicu Sebe, Jurandy Almeida -Publication Year: 2025 (Accepted to WACV'25) -Source: IEEE/CVF WACV'25 / arXiv -Link: https://arxiv.org/abs/2407.01375 -Title: Moral Attention Is All You Need -Author(s): Mark Graves -Publication Year: 2025 (Published online Mar 17, 2025) -Source: Theology and Science -Link: https://doi.org/10.1080/14746700.2025.2472118 -Title: "Attention", "Transformers", in Neural Network "Large Language Models" (Essay referencing recent work) -Author(s): Cosma Shalizi (referencing Farrell et al., 2025) -Publication Year: 2025 (Essay updated March/April 2025) -Source: Author's Website/Blog -Link: https://www.stat.cmu.edu/~cshalizi/notebooks/attention-is-all-you-need.html -2024 Publications/Preprints/Articles - -Title: ATFF: Advanced Transformer with Multiscale Contextual Fusion for Medical Image Segmentation -Author(s): Xinping Guo, Lei Wang, Zizhen Huang, Yukun Zhang, Yaolong Han -Publication Year: 2024 (Published March 29, 2024) -Source: Journal of Computer and Communications, Vol.12 No.3 -Link: https://www.scirp.org/reference/referencespapers?referenceid=3338278 (Citation page) -Title: Multimodal Attention is all you need -Author(s): Marco Saioni, Cristina Giannone -Publication Year: 2024 (CEUR Workshop Proceedings, paper dated Dec 4, 2024) -Source: CEUR Workshop Proceedings (Evalita 2023) -Link: https://ceur-ws.org/Vol-3616/paper34.pdf (Link inferred) -Title: Attention is all you need: utilizing attention in AI-enabled drug discovery -Author(s): Yang Zhang, Caiqi Liu, Mujiexin Liu, Tianyuan Liu, Hao Lin, Cheng-Bing Huang, Lin Ning -Publication Year: 2024 (Published Jan 6, 2024) -Source: Briefings in Bioinformatics, Volume 25, Issue 1 -Link: https://doi.org/10.1093/bib/bbad467 -Title: HIERARCHICAL ENCODING AND CONDITIONAL ATTENTION IN NEURAL MACHINE TRANSLATION -Publication Year: 2024 (Published September 2024) -Source: The American Journal of Engineering and Technology, 6(09) -Link: https://doi.org/10.37547/tajet/Volume06Issue09-07 -Title: Measurements With A Quantum Vision Transformer: A Naive Approach -Publication Year: 2024 (Proceedings published 2024) -Source: EPJ Web of Conferences, Volume 295 (ICHEP 2022) -Link: https://www.epj-conferences.org/articles/epjconf/ref/2024/01/epjconf_ichep2022_12003/epjconf_ichep2022_12003.html -Title: Attention is all you need: discovering the Transformer paper (Article/Blog Post) -Author(s): Luis Serrano -Publication Year: 2024 -Source: Towards Data Science -Link: https://towardsdatascience.com/attention-is-all-you-need-discovering-the-transformer-paper-731ad0737c6d -Title: Paper Walkthrough: Attention Is All You Need (Article/Blog Post) -Author(s): Muhammad Ardi -Publication Year: 2024 (Published Nov 3, 2024) -Source: Towards Data Science -Link: https://towardsdatascience.com/paper-walkthrough-attention-is-all-you-need-69388903b711 -Title: Attention is all you need – Transformers (Article/Blog Post) -Author(s): Strahinja Zivkovic -Publication Year: 2024 (Published Nov 28, 2024) -Source: Master Data Science (Blog) -Link: https://masterdatascience.com/attention-is-all-you-need-transformers/ -Title: The Groundbreaking Transformer Paper: “Attention is All You Need” (Article/Blog Post) -Author(s): Brian Carter -Publication Year: 2024 (Published Oct 5, 2024) -Source: The Brian Carter Group (Blog) -Link: https://briancartergroup.com/the-groundbreaking-transformer-paper-attention-is-all-you-need/ -Now, based on the seminal paper "Attention Is All You Need" and these recent citing works, I will use a specialized tool to suggest potential future research directions. - -* user: ok - -Okay, here are the potential future research directions suggested by the tool, based on the seminal "Attention Is All You Need" paper and the recent citing works: - -Potential Future Research Directions - -Beyond Dot-Product Attention: Exploring Non-Linear and Learnable Similarity Metrics: Instead of just scaled dot-product, research different, perhaps non-linear or fully learned, ways to measure similarity within the attention mechanism. This could capture more complex relationships and lead to more robust or efficient models. -Intrinsically Graph-Aware Attention Mechanisms: Develop attention mechanisms that naturally understand and operate on graph structures (like molecules or social networks) without needing to force them into sequences first. -Physics-Informed and Constraint-Driven Attention: Design attention mechanisms that incorporate known physical laws, rules, or constraints directly into their structure. This could lead to more accurate and efficient models for scientific discovery and simulation. -Hybrid Neuro-Symbolic Attention Architectures: Create attention mechanisms that can interact with symbolic knowledge bases or reasoning engines, combining the strengths of deep learning and traditional AI for better reasoning and fact-checking. -Dynamic Sparsity and Adaptive Computation in Attention: Explore attention mechanisms that dynamically adjust which elements attend to each other based on context (sparse attention) or adapt the amount of computation used per element or layer, making Transformers more efficient for very long sequences or limited resources. -Biologically Plausible Attention and Memory Integration: Investigate attention architectures inspired by how biological brains handle attention and memory, potentially leading to novel models with different capabilities. -Meta-Attention for Interpretability and Controllability: Develop attention mechanisms that can observe their own attention patterns ("meta-attention"). This could improve our understanding of why the model makes certain decisions and potentially allow for more control over its focus. -Attention Mechanisms for Fundamentally Non-Sequential Data Structures: Design attention mechanisms specifically for data that doesn't have a natural order, such as sets of items or point clouds, without imposing artificial sequences. -Temporally Continuous Attention for Streaming Data: Create attention mechanisms suited for continuous, real-time data streams where the idea of a fixed sequence length doesn't apply, crucial for real-time monitoring or control. -Cross-Modal Associative Attention Kernels: Develop new attention "kernels" that can directly compute similarity between elements from different modalities (e.g., image regions and text concepts) without needing to project them into a common space first, enabling tighter multimodal integration. -This analysis provides a starting point for exploring the evolution of Transformer architectures and attention mechanisms. - -Would you like to dive deeper into any of these specific research directions, or perhaps explore another seminal paper? - -``` - -## Running Tests - -For running tests and evaluation, install the extra dependencies: - -```bash -poetry install --with dev -``` - -Then the tests and evaluation can be run from the `academic-research` directory using -the `pytest` module: - -```bash -python3 -m pytest tests -python3 -m pytest eval -``` - -`tests` runs the agent on a sample request, and makes sure that every component -is functional. `eval` is a demonstration of how to evaluate the agent, using the -`AgentEvaluator` in ADK. It sends a couple requests to the agent and expects -that the agent's responses match a pre-defined response reasonablly well. - - ## Deployment -The Academic Co-Research can be deployed to Vertex AI Agent Engine using the following -commands: - -```bash -poetry install --with deployment -python3 deployment/deploy.py --create -``` - -When the deployment finishes, it will print a line like this: - -``` -Created remote agent: projects//locations//reasoningEngines/ -``` +> [!NOTE] +> Download [Defang CLI](https://github.com/DefangLabs/defang) -If you forgot the AGENT_ENGINE_ID, you can list existing agents using: +### Defang Playground +Deploy your application to the Defang Playground by opening up your terminal and typing: ```bash -python3 deployment/deploy.py --list -``` - -The output will be like: - +defang compose up ``` -All remote agents: - -123456789 ("academic_research") -- Create time: 2025-05-10 09:33:46.188760+00:00 -- Update time: 2025-05-10 09:34:32.763434+00:00 -``` +### BYOC (AWS) -You may interact with the deployed agent using the `test_deployment.py` script -```bash -$ export USER_ID= -$ python3 deployment/test_deployment.py --resource_id=${AGENT_ENGINE_ID} --user_id=${USER_ID} -Found agent with resource ID: ... -Created session for user ID: ... -Type 'quit' to exit. -Input: Hello. What can you do for me? -Response: Hello! I'm an AI Research Assistant. I can help you analyze a seminal academic paper. +If you want to deploy to your own cloud account, you can use Defang BYOC: -To get started, please provide the seminal paper you wish to analyze as a PDF. -``` +1. [Authenticate your AWS account](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html), and check that you have properly set your environment variables like `AWS_PROFILE`, `AWS_REGION`, `AWS_ACCESS_KEY_ID`, and `AWS_SECRET_ACCESS_KEY`. +2. Run in a terminal that has access to your AWS environment variables: + ```bash + defang --provider=aws compose up -To delete the deployed agent, you may run the following command: +--- -```bash -python3 deployment/deploy.py --delete --resource_id=${AGENT_ENGINE_ID} -``` +Title: Google ADK -## Customization +Short Description: A simple sample demonstrating how to deploy agents build with Google ADK to Defang. -The Academic Research can be customized to better suit your requirements. For example: +Tags: Google ADK, Python, Agents - 1. Integrate Specialized Search Tools: Augment the agent's discovery capabilities by incorporating additional academic search functionalities, such as an ArXiv-specific search tool, to complement or replace the default search mechanism, allowing for more targeted retrieval from specialized databases and preprint archives. - 2. Implement Output Visualization: Enhance the presentation of research findings by adding modules to visualize the network of cited papers or to graphically represent suggested future research topics, thereby offering a more intuitive and interconnected understanding of the academic landscape. -3. Customize Agent Instructions: Modify the prompts guiding the academic_websearch and academic_newresearch sub-agents. This allows you to direct their analytical focus (e.g., by specifying the depth of citation analysis, emphasizing interdisciplinary connections for future research, or ensuring adherence to particular academic writing conventions) or to refine the structure and detail of their outputs. -4. Download the seminal paper via DOI or URL: Augment the agent's capabilities by enabling direct download of seminal papers. This feature would allow the agent to: -* Resolve DOI to PDF: Given a Digital Object Identifier (DOI), the agent could leverage APIs or web scraping techniques to locate and download the corresponding PDF of the paper. This streamlines the process of accessing primary sources, especially when the DOI is already known. -* Download from URL: If a direct URL to the paper's PDF or an academic repository page is provided, the agent could be configured to navigate to that URL and initiate the download. This would be particularly useful for papers available on institutional repositories, personal academic websites, or preprint servers that might not have a readily resolvable DOI. -* Integration with Reference Managers: The downloaded papers could then be automatically imported into a specified reference manager (e.g., Zotero, Mendeley, EndNote), along with their metadata, facilitating seamless organization and citation management for the user's research library. This integration would significantly enhance the efficiency of literature review and research compilation. +Languages: python From 817f583355df45dda9fff52ab45106b3393da6f0 Mon Sep 17 00:00:00 2001 From: Jordan Stephens Date: Mon, 29 Sep 2025 11:26:04 -0700 Subject: [PATCH 4/5] add gcp byoc instructions --- samples/google-adk/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/samples/google-adk/README.md b/samples/google-adk/README.md index 3d25d435d..996a0f287 100644 --- a/samples/google-adk/README.md +++ b/samples/google-adk/README.md @@ -31,6 +31,15 @@ Deploy your application to the Defang Playground by opening up your terminal and defang compose up ``` +### BYOC (GCP) + +If you want to deploy to your own cloud account, you can use Defang BYOC: + +1. [Authenticate your GCP account](https://cloud.google.com/sdk/docs/quickstart). +2. Run in a terminal that has access to your GCP project id: + ```bash + GCP_PROJECT_ID=my-project-12345 defang --provider=gcp compose up + ### BYOC (AWS) If you want to deploy to your own cloud account, you can use Defang BYOC: From a02f870f401ab7deffccb7f36c815f82cd60c7ed Mon Sep 17 00:00:00 2001 From: Jordan Stephens Date: Mon, 29 Sep 2025 11:27:30 -0700 Subject: [PATCH 5/5] add a healthcheck --- samples/google-adk/compose.yaml | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/samples/google-adk/compose.yaml b/samples/google-adk/compose.yaml index 9ca5298f7..efeec00a1 100644 --- a/samples/google-adk/compose.yaml +++ b/samples/google-adk/compose.yaml @@ -3,3 +3,14 @@ services: build: . ports: - "8080:8080" + healthcheck: + test: + [ + "CMD", + "python3", + "-c", + "import urllib.request; exit(0) if urllib.request.urlopen('http://localhost:8080/').status==200 else exit(1)", + ] + interval: 30s + timeout: 10s + retries: 5