metadata: {"signature": "0x2252ea9aa3fc72a2c6c7c8d0bf073deee1392bc47e48381d41ec3a823d96e272583d8c4a5b0c15b57883488b9cab5e7cd09a45485f92a6bf5825f1c74af59ebd1c", "application": {"round": "0x0c0412ddb846096ea1e37de717921ebf4fef9a39", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "TetBf9FcNHNHOKmifHMLCjw6n5SafqD1DAGt2KMDa2VYVl3L6vVTxq+1kZMNHkX4", "encryptedSymmetricKey": "b85de328f480e844651f763694487627b6b138bd17dad681ff47525f493132ccd0c77cf4dbf638ed7ee3a93527ee58064dd56a12545daa79f2471ee5eb027a9973cedd53a17d3a1a6f36e82b1b4c8dbb6ec3a80fd5d4d651385a8ff9308ac48d5c56daa8cbe96ce9517b0950abf44e8661a7a07c06998391c21aadbb3916905c0000000000000020281580c37f9eaa69d3c2a825f97f2368e1f9d2ff5e2cccc7d57f111d941987bace56541297cdf15cd70e5de4972d4582"}}, {"type": "short-answer", "answer": "Gitcoin, 2K donation from VitaDAO and 10K donation from HEALES", "hidden": false, "question": "Funding Sources", "questionId": 1}, {"type": "number", "answer": "6", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "short-answer", "answer": "antonkulaga", "hidden": true, "question": "Telegram username", "questionId": 3}, {"type": "paragraph", "answer": "15000", "hidden": true, "question": "Total prior funding for project in USD", "questionId": 4}, {"type": "paragraph", "answer": "- Anton Kulaga: [GitHub](https://github.com/antonkulaga), [LinkedIn](https://www.linkedin.com/in/antonkulaga/)\n- Olga Borysova: [LinkedIn](https://www.linkedin.com/in/olga-borysova-79390884/)\n- Newton Winter: [GitHub](https://github.com/winternewt)\n- marfantyk: [GitHub](https://github.com/marfantyk)\n- Alex Karmazin: [GitHub](https://github.com/Alex-Karmazin), [LinkedIn](https://www.linkedin.com/in/alex-karmazin-ua/)\n- Nikhil Yadala: [GitHub](https://github.com/nikhilYadala), [LinkedIn](https://www.linkedin.com/in/nikhilyadala/)\n", "hidden": true, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 5}, {"type": "short-answer", "answer": "16", "hidden": true, "question": "How old is the project? (Months)", "questionId": 6}, {"type": "paragraph", "answer": "## Problem Statement:\n\nLarge Language Models (LLMs) like ChatGPT, Claude, and Gemini, as well as open-source models like LLAMA 3, 3.1, and Mixtral, have limitations in biomedical research, personal health, genetics, and longevity research. This is due to:\n\n- **Lack of specialized knowledge:** LLMs aren't trained on in-depth biological data.\n- **Lack of knowledge of existing gene and gene-products databases.**\n- **Poor referencing:** Despite recent improvements, LLMs still struggle to cite reliable sources.\n- **\"Hallucinations\":** LLMs can invent false details, especially in complex areas.\n- **Unreliable tool calls and small context windows on open-source models** (i.e., LLAMA 3.1).\n\nLifespan intervention information and partial reprogramming results are precious for longevity research but are not known by existing LLMs. This requires a targeted retrieval strategy to extract and process the relevant data from currently unstructured sources. \n\nSome commercial projects try to address specific aspects of these problems. However, more open-source solutions, developed by and for researchers and citizen scientists, are needed.\n\nThe Longevity Genie project tackles the limitations of LLMs in health and longevity research by utilizing the following techniques and approaches:\n\n- **Incorporating up-to-date information:** The project utilizes Retrieval-Augmented Generation (RAG) and Hybrid Search, utilizing vector-based and keyword-based queries to expand LLMs' knowledge with high-quality, reliable sources, ensuring accurate and comprehensive information retrieval.\n- **Domain-Specific Focus:** The project indexes pre-selected scientific papers emphasizing longevity research to provide targeted and relevant insights. Queries to specialized databases are used to supplement answers with relevant facts.\n- **Agent-Based Approach:** Specialized agents are developed to refine how LLMs query, use, and structure domain-specific information, resulting in improved and more precise scientific responses.\n- **Collaborative Origins:** Initiated at the Zuzalu hackathon, the project has received support and contributions from VitaDao, HEALES, and Rostock University, fostering a collaborative effort in advancing LLM capabilities in health and longevity research.\n\n## Current Solution and Progress:\n\nThe project currently features the following (See Project Description for full details and links):\n\n- **Custom Assistants:** The Genetics Genie custom assistant supports multiple models, providing specialized knowledge in genetics and clinical trials.\n- **Custom GPTs:** Several custom GPTs have been developed, each utilizing different databases and hybrid search techniques.\n- **Aging-Related Papers Datasets:** Hosted on [Hugging Face](https://huggingface.co/longevity-genie) to support and enhance research and development in the field. We have implemented scientific indexing using OpenSearch and bge-large embedding which requires refactoring.\n- **Infrastructure libraries** to deal with open-source agents ([just-agents](https://github.com/longevity-genie/just-agents/)) and research papers (indexpaper, getpaper) in the Longevity Genie GitHub organization.\n\n## Proposed Solution Improvements:\n\nThe project currently relies on the proprietary OpenAI API and technologies, which, while producing high-quality output, are limited in availability and raise privacy concerns. However, the recent open-source advancements, particularly the release of LLAMA-3.1, have bridged the gap between commercial and open-source models. This offers an opportunity to break free from proprietary dependencies, enabling fully open and on-premise deployment while maintaining high-quality output.\n\nFurther expansion of biomedical databases and sources available to the model through RAG highlights the need for inter-domain knowledge about bio-object links and interconnections to improve model reasoning. Integrating biomedical knowledge graphs (KG) with large language models (LLMs) through open-source projects like [Biocypher/Biochatter](https://github.com/biocypher) and [KG_RAG](https://github.com/BaranziniLab/KG_RAG) holds promise for such improvements.\n\nTo expedite longevity research, systematization and organization of existing and novel information about partial reprogramming techniques, interventions, and drug-interactions would be valuable. Extracting such information requires a combined approach using agent-chains, custom-tailored prompts, and multi-modal LLM calls to interpret scientific article figures for advanced reasoning.\n\n## Roadmap:\n\n- **Phase 1, Q3 2024 - Q4 2025:**\n  - Refine prompts and code to support newly added open-source models to achieve the level of output quality on par with proprietary models.\n  - Add BioCypher format support and KG-RAG capabilities.\n  - Reimplement indexing of scientific papers with semantic segmentation (which splits articles by meaning and not by length).\n\n- **Phase 2 (Q4 2024 - Q1 2025):**\n  - Reindex scientific papers with more advanced embeddings.\n  - Introduce available BioCypher graphs (Drug-Interactions, CKG) for retrieval.\n  - Add multimodal queries to enhance RAG results with textual transcription of scientific papers figures.\n  - Introduce capabilities to organize unstructured reprogramming techniques and protocols into tables.\n\n- **Phase 3 (Q1 2025 - Q2 2025):**\n  - Conduct internal and controlled user group testing.\n  - Optimize performance and quality based on user feedback.\n", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 7}, {"type": "paragraph", "answer": "Our project directly supports the decentralized science (DeSci) movement in a few key ways:\n\n1. **Democratization of Specialized AI Tools**: We aim to make advanced language models for biomedical and longevity research accessible to a wide range of researchers, not just those with significant resources. By providing open-source, easy-to-use tools with a variety of backend options (including open-source language models), we lower the barriers to entry for smaller labs and independent researchers to leverage cutting-edge AI capabilities.\n\n2. **Enabling Transparency and Reproducibility**: The open-source nature of our tools aligns with DeSci principles of transparency and reproducibility in scientific research. Researchers can inspect the models, customize them, and validate findings - a crucial aspect of trustworthy, decentralized science.\n\n3. **Support for Longevity Use Cases**: Our specific focus on longevity research supports a key DeSci priority - accelerating progress in vital areas of human health and well-being. By empowering researchers to better leverage language models for longevity-related investigations, we hope to drive breakthroughs that improve quality of life.\n\n4. **Fostering Collaborative Innovation**: The open and accessible nature of our tools encourages collaboration among researchers, which is a core tenet of the DeSci movement. Researchers can build upon each other's work, share insights, and rapidly advance the field through collective efforts.\n\nIn summary, our project directly contributes to the DeSci mission of democratizing scientific research, promoting transparency, and accelerating progress in critical areas like longevity - all of which are essential for a more decentralized, equitable, and impactful scientific ecosystem.\"", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 8}, {"type": "paragraph", "answer": "\u2154 will be used to compensate for effort. \u2153 will be used on IT infrastructure and computation.", "hidden": false, "question": "Describe how funds from the grants program will be used for your project. ", "questionId": 9}, {"type": "paragraph", "answer": "oeth:0xe913a267DA2C57704A500D2B5CA207968dF142D5", "hidden": true, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta) using a different project payout wallet address, please share it here.", "questionId": 10}, {"type": "paragraph", "answer": "https://checker.gitcoin.co/public/project/show/longevity-genie \n\nThis was the initial round when the project just started during a hackathon. All the core features (Hybrid Search, Agents, databases) and components except the initial module  and RAG [Longevity GPT](https://asklongevitygpt.com/)", "hidden": false, "question": "If this project participated in past grant rounds, please share any new updates or milestones since the last round.", "questionId": 11}, {"type": "paragraph", "answer": "Collaboration with [HEALES](https://heales.org/) and [IBIMA Group at Rostock University, Germany](https://ibima.med.uni-rostock.de/)", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 12}, {"type": "checkbox", "answer": ["Yes"], "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 13}, {"type": "multiple-choice", "answer": "I already read the above exclusion criteria and understand those exclusion criteria are not permitted and will result in rejection or removal from the round.", "hidden": true, "question": "Please read the following exclusion criteria that are not permitted and will result in rejection or removal from the round.", "questionId": 14}], "project": {"id": "0xa8accb6ea853b273231c6410d29440c9593329e74b5ffa19b3349a7eb9549056", "title": "Longevity Genie", "logoImg": "bafkreibgf2f3d6lahxa53uzyzmtskuyl35arrzii2znrek7raev4bvqrgq", "metaPtr": {"pointer": "bafkreihenpitwmaww7jfedcc7uqt2rquwtuyq2kivfbsd4im7fbxyzvlv4", "protocol": "undefined"}, "website": "https://longevity-genie.github.io", "bannerImg": "bafkreidkibavvvbsckh73p72frac62hhilcxxx7wxyc3rs6dj6lsoli3f4", "createdAt": 1692052572842, "userGithub": " antonkulaga", "credentials": {"github": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..8UY0duFZBO6x4b9bvrlRQwI7TvB5smr7WuuewmebV4jegZoHVPHc_cyR9NYKRI64puk6AkJS9dxjstfr3hrJBQ", "type": "Ed25519Signature2018", "created": "2024-08-08T21:39:10.482Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-08T21:39:10.482Z", "expirationDate": "2024-11-06T21:39:10.482Z", "credentialSubject": {"id": "did:pkh:eip155:1:0xBc7BeB2d44Eb0Cddf6c0F2560a21475c2E2aC19C", "hash": "v0.0.0:r0kUiWSMqjJbd+4W0SZZE5bGkFm5Q3cM2AkYzs1Gi0w=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextGithubOrg#longevity-genie#842436"}}, "twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..dEwlCRPBAZoCuPstoUlShFIBgdDGOLU_sEdrh0ME1o4VUIzimx26Mlnn_dh-XWC3MMlaq0NalxK6KTSELR5NDQ", "type": "Ed25519Signature2018", "created": "2024-08-08T21:38:33.662Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2024-08-08T21:38:33.661Z", "expirationDate": "2024-11-06T21:38:33.661Z", "credentialSubject": {"id": "did:pkh:eip155:1:0xBc7BeB2d44Eb0Cddf6c0F2560a21475c2E2aC19C", "hash": "v0.0.0:zYJ0YKL8PLWq97eeaty6tyZt6OHVkkbkERgI4QB98fk=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#longevity_genie"}}}, "description": "Imagine having a personal genie who helps you navigate the complex world of health, genetics, and longevity research. The Longevity Genie project brings this vision to life using advanced language models to provide accurate and reliable information.\n\nCurrent language models have limitations when it comes to delivering precise information on health and longevity. We're addressing this challenge by integrating cutting-edge technologies like **Retrieval-Augmented Generation (RAG)**, **Hybrid Search**, and **AI Agent Workflows**. These enhancements allow our models to access high-quality research papers and databases, making them more reliable and accurate.\n\n### Core Components:\n\n- **[GitHub Organization](https://github.com/longevity-genie):** Open-source LLM agents enabling searches through multiple [aging-related databases](https://github.com/longevity-genie/longdata).\n- **[Project Website](https://longevity-genie.github.io/):** Central hub for the Longevity Genie project.\n- **[Genetics Genie](https://chat.longevity-genie.info/):** A custom assistant supporting multiple models, knowledgeable in genetics and clinical trials.\n\n### Custom GPTs:\n\n- **[Genetics Genie GPT](https://chat.openai.com/g/g-HnvTUyFeq-genetics-genie):** A custom GPT utilizing multiple databases and hybrid search for in-depth genetics research.\n- **[Clinical-Trials Genie](https://chat.openai.com/g/g-tpOIOEeWm-clinical-trials-genie):** A GPT leveraging ClinicalTrials.gov to provide up-to-date clinical trial information.\n- **[DrugAge Genie](https://chat.openai.com/g/g-tpOIOEeWm-clinical-trials-genie):** An agent that uses the DrugAge database for drug research and information.\n- **[AnAge Genie](https://chat.openai.com/g/g-I8OGEVL4X-animal-ageing-genie):** A custom GPT retrieving species age information from the AnAge database.\n- **[Longevity GPT](https://asklongevitygpt.com/):** The initial module implementing RAG and advanced referencing techniques.\n\n### Supporting Tools:\n\n- **[Just-Agents](https://github.com/longevity-genie/just-agents/):** A framework to organize agent workflows without unnecessary complexity.\n- **[getpaper](https://github.com/longevity-genie/getpaper)** and **[indexpaper](https://github.com/longevity-genie/indexpaper):** Libraries for handling research papers.\n- **[Aging-related Papers Datasets](https://huggingface.co/longevity-genie):** Datasets hosted on Hugging Face to enhance research and development.", "lastUpdated": 0, "projectGithub": "longevity-genie", "projectTwitter": "longevity_genie"}, "recipient": "0x410e98D59B2B9596Dc6fE9b801f8d222B606d90c"}}