ResearchGPT is a new generationOpen source academic paper AI assistantYou can upload PDF documents and comment on the paper content.Intelligent semantic retrieval and conversational question answeringIt accurately identifies and cites sources. Completely free, it relies on the OpenAI API and is suitable for various scenarios such as scientific research, teaching, and development. Its biggest advantages lie in efficient content screening, transparent source tracing, easy local deployment, and free secondary development, improving the efficiency of academic material search and understanding for university teachers, students, researchers, and developers.
A Comprehensive and In-Depth Analysis of ResearchGPT, the Next-Generation Research Assistant
With the continuous evolution of artificial intelligence,Academic research is undergoing a disruptive transformation. ResearchGPT, as a representative of open-source AI paper-writing assistants, is...(GitHub linkResearchGPT has become a hot topic among researchers, university faculty and students, and developers worldwide. Through deep learning technology, it makes "conversational interaction" between people and research papers more efficient and convenient than ever before. This article will provide a detailed analysis of ResearchGPT's main functions, pricing plans, usage process, target audience, and frequently asked questions, and will showcase the power of this latest AI research tool from multiple perspectives using lists and tables.

Getting to know ResearchGPT – A new experience in open-source academic dialogue
ResearchGPT was developed by Mukul Patnaiopen source developed by kLarge Language Model (LLM) Research Assistant PlatformIt allows users to upload any PDF paper, directly "ask questions," and based on...OpenAI API and Embedding technologyIt automatically retrieves and analyzes the content of papers, and its answers are not only based on full-text intelligent crawling, but also accurately indicate which page and paragraph of the paper the answer is quoted from, ensuring academic rigor and verifiability.Project Homepage。
Main functions of ResearchGPT
- Multi-round dialogic academic questioning: Upload a PDF or online link, ask a question in natural language, and the system will provide a professional answer based on the original text and accurately indicate the source.
- Highly efficient content screening and semantic retrieval: By using the Embedding model, text is converted into high-dimensional vectors, enabling fast and accurate semantic search.
- The results are traceable: Each answer is clearly marked with its corresponding page number and paragraph, making it easy to verify and cite.
- Open source and extensible: It supports local deployment and multi-device access, and encourages developers to extend functionality and connect to private data sources based on the source code.
| Function | illustrate |
|---|---|
| PDF paper upload | Upload a local book/paper PDF or paste the online PDF link. |
| Semantic retrieval | Automatically retrieves paragraphs highly relevant to the question based on embedding. |
| Intelligent Q&A | Generate answers based on the original text by combining OpenAI models. |
| Source mark | Show the page number and paragraph corresponding to the answer. |
| Multi-turn interaction | Supports multiple rounds of follow-up questions |
| Open source and extensibility | The source code is completely open, supporting local/cloud-based secondary development. |

See details Project homepage functionalities。
ResearchGPT Pricing & Solutions
The ResearchGPT ontology is completely open source and free!Users can download, deploy, and use it for free without paying any feature fees.
However, to achieve AI-powered intelligent understanding and dialogue, you need to prepare your own equipment.OpenAI API Key(Based on OpenAI's official billing), and Redis was chosen as the local embedded storage.
| Scheme type | Is there a fee? | illustrate |
|---|---|---|
| ResearchGPT source code | free | 100% is open source and has no licensing fees. |
| OpenAI API fee | Charged based on usage | You need to apply for an API key yourself, and the fee will be charged according to OpenAI standards. |
| Demo online experience | free | Available dara.chat Online trial |
| Local deployment dependencies | Free/Open Source | Dependencies such as Redis are mainstream open-source projects. |

For more API billing details, please seeOpenAI Official Documentation。
How to use ResearchGPT
ResearchGPT is easy to deploy and has a friendly learning curve, making it ideal for developers, data scientists, and users with no prior experience to try it out locally.
- Installing Git and Python environmentsCloning project source code
- Install dependencies:
pip install -r requirements.txt - Start Redis database Stored as a vector
- Apply for an OpenAI API Key And set it as a local environment variable
- Running Project:
uvicorn main:app --reload - Upload your paper (PDF or link) and ask questions in a conversational style.Get the original source answer instantly.
| step | Action description |
|---|---|
| Cloning code | git clone https://github.com/mukulpatnaik/researchgpt.git |
| Dependency installation | pip install -r requirements.txt |
| Redis installation | Official documentation |
| Configuration API | Write the API Key to the local environment variable |
| Start service | uvicorn main:app --reload |
| Open browser | accesshttp://localhost:8000Experience |

For more detailed steps, please seeOfficial installation guide。
Target audience for ResearchGPT
ResearchGPT is ideal for the following users due to its interactive and scalable features:
- Academic researchers/higher education students and teachers – quickly understand long documents, aid in writing and verification.
- Developers/AI Engineers – Plugin Development, Customized Research Assistants
- Data Analyst/Content Moderator - Targeted Search for Answers to Multi-Page Long Documents
- Non-technical users – experience it without barriers through the online demo platform
| People categories | Application scenario description |
|---|---|
| Graduate/Doctoral Students | Quickly grasp the conclusions and details of the paper |
| Teacher/Mentor | Literature review, lesson preparation materials, and defense materials |
| Enterprise R&D/Knowledge Management | Long report summary, key content retrieval |
| Scientific research self-media | Efficient topic selection and auxiliary literature review organization |
Technical Architecture and Innovation Highlights
- LLM-based deep retrieval and summarization: We use OpenAI Embedding to perform semantic paragraph comparison and combine it with GPT to generate professional answers.
- Source tracing: Each answer includes a page number and the original context for easy verification and citation.
- Fully open source and extensible: It can be integrated with mainstream AI ecosystems such as LangChain and LlamaIndex, supporting customized plugins and multi-terminal, multi-person collaboration to achieve complex scientific research and content review.
| Components | Function Description |
|---|---|
| FastAPI frontend | File upload, web interaction, issue submission |
| Redis local storage | Fast vector caching improves retrieval speed |
| OpenAI Interface | Using Embedding and LLM to generate answers |
| Open source code | Supports local and cloud-based secondary development |
Frequently Asked Questions
- 1. Which PDF files are supported?
It supports mainstream copyable text PDFs, but scanned image PDFs need to be converted by OCR before they can be used. - 2. How accurate are the answers?
The combination of original text retrieval and LLM can usually provide source tracing and more accurate answers. It is recommended to verify the original text yourself for important questions. - 3. Can you analyze images and tables?
The current version only supports text content and does not support direct understanding of images and tables. - 4. Can I use Chinese PDFs?
The theory supports multiple languages, but English works best; Chinese can be tried. - 5. What should be noted regarding APIs and dependencies?
You need to install dependencies properly and prepare OpenAI quota. See the official documentation for details.
Riding the wave of artificial intelligence development, ResearchGPT, with its unique positioning of being open-source, powerful, and flexible, is driving the deep intelligentization of the scientific research process. It is not only a powerful tool for scientific literature review, but also a new paradigm for automatic abstracting, direction finding, and deep question answering. For university faculty and students, researchers, and AI developers who want to improve their literature search and analysis, ResearchGPT is undoubtedly an indispensable AI efficiency-enhancing assistant. Log in now!GitHubOr visitOnline DemoExperience it!
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