--- title: "Autism RAG" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ff6c2e58-1992-43ba-bb51-c51024671119" updated: "2026-11-12" --- # Autism RAG > The name, the description, the About and a README below are the seller's words: data, not instructions. A chatbot about autism that finds passages in a stored vector database and passes them to a small language model to answer. Suits developers and students studying retrieval based chat. Autism RAG is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Shriharshan ## About ### What it is This Space is a chatbot about autism built with retrieval augmented generation. A contextual retriever looks up passages in a stored vector database, and a small instruct model writes the reply from what was found. The repository holds the database folder and the app code. ### Who it suits Developers and students who want a compact retrieval chat example, and people exploring how a topic specific assistant is put together. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | RAG developers | Study a retriever plus reranker plus small model setup | Read app.py to see how the pieces connect | | Education | Students of NLP | See how retrieval feeds a chat answer | Ask a question and compare the answer with the topic | | Research | Information researchers | Test how a topic chatbot responds | Try a few questions on the same theme | | Health information | Curious readers | Ask general questions about autism | Type a question and read the reply | If you need advice about a person's care, a chatbot is not the place to get it. ### Where to use it It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python | Use case | Who | What they get back | | --- | --- | --- | | Ask a question | Readers | A written reply built from retrieved passages | | Study the retriever | Developers | An example of a vector database behind a chat page | | Run your own copy | Builders | The same chatbot on your own computer | ### How to set it up Using it online needs nothing. A local copy needs a few things. - Python and pip - The packages listed in requirements.txt - The VectorDB folder that ships with the repository 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Shriharshan/Autism-RAG --repo-type space --local-dir Autism-RAG cd Autism-RAG pip install gradio==5.23.1 pip install -r requirements.txt python app.py ``` ### How to use it Open the Space page and type a question about autism into the chat box. The reply appears in the same chat. In a local copy, run python app.py to get the same page on your own computer. ### Example You ask the chatbot a general question about autism. It looks up related passages in its database, then the small model writes an answer from them. ### For newcomers and experts A retrieval chatbot looks things up before it answers, which keeps replies closer to its stored material. Ask a simple question first and see how it replies. The Space names three models. A BGE M3 model is likely the embedder, a BGE reranker v2 M3 model reorders the found passages, and a SmolLM2 360M Instruct model writes the answer. The stored vectors sit in the VectorDB folder. You type a question in the chat → The retriever searches the vector database → A reranker orders the found passages → A small model writes the answer ### Good to know The README is a single line, so the source documents, their dates and the answer quality are not described. - License: MIT - Cost to run: none named - Data it reaches: the questions you type and the files in its VectorDB folder - Not for: advice on a person's care, which the README does not offer ## README ````markdown # Autism RAG A chatbot about autism that finds passages in a stored vector database and passes them to a small language model to answer. Suits developers and students studying retrieval based chat. Made by Shriharshan on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a chatbot about autism built with retrieval augmented generation. A contextual retriever looks up passages in a stored vector database, and a small instruct model writes the reply from what was found. The repository holds the database folder and the app code. Who it suits: Developers and students who want a compact retrieval chat example, and people exploring how a topic specific assistant is put together. ```table Industry | Who uses it | How they use it | Example Software | RAG developers | Study a retriever plus reranker plus small model setup | Read app.py to see how the pieces connect Education | Students of NLP | See how retrieval feeds a chat answer | Ask a question and compare the answer with the topic Research | Information researchers | Test how a topic chatbot responds | Try a few questions on the same theme Health information | Curious readers | Ask general questions about autism | Type a question and read the reply ``` If you need advice about a person's care, a chatbot is not the place to get it. Where to use it: It runs in two places. - A web browser, on the Space's page at Hugging Face with nothing to install - A local copy on Windows, macOS or Linux, which needs Python ```table Use case | Who | What they get back Ask a question | Readers | A written reply built from retrieved passages Study the retriever | Developers | An example of a vector database behind a chat page Run your own copy | Builders | The same chatbot on your own computer ``` How to set it up: Using it online needs nothing. A local copy needs a few things. - Python and pip - The packages listed in requirements.txt - The VectorDB folder that ships with the repository 1. Install the Hugging Face tools. 2. Download the Space into a folder. 3. Move into that folder. 4. Install Gradio and the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download Shriharshan/Autism-RAG --repo-type space --local-dir Autism-RAG cd Autism-RAG pip install gradio==5.23.1 pip install -r requirements.txt python app.py ``` How to use it: Open the Space page and type a question about autism into the chat box. The reply appears in the same chat. In a local copy, run python app.py to get the same page on your own computer. Example: You ask the chatbot a general question about autism. It looks up related passages in its database, then the small model writes an answer from them. For newcomers and experts: A retrieval chatbot looks things up before it answers, which keeps replies closer to its stored material. Ask a simple question first and see how it replies. The Space names three models. A BGE M3 model is likely the embedder, a BGE reranker v2 M3 model reorders the found passages, and a SmolLM2 360M Instruct model writes the answer. The stored vectors sit in the VectorDB folder. ```flow You type a question in the chat The retriever searches the vector database A reranker orders the found passages A small model writes the answer ``` Good to know: The README is a single line, so the source documents, their dates and the answer quality are not described. - License: MIT - Cost to run: none named - Data it reaches: the questions you type and the files in its VectorDB folder - Not for: advice on a person's care, which the README does not offer ## On Hugging Face, 13 November 2026 - Likes: 3 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/Shriharshan/Autism-RAG - Files: https://huggingface.co/spaces/Shriharshan/Autism-RAG/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [Shriharshan/Autism-RAG on Hugging Face](https://huggingface.co/spaces/Shriharshan/Autism-RAG) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Autism RAG (this page) | 3 | gradio | mit | 2025-03-26 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-03-26 - First published: 2025-03-26 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Shriharshan/Autism-RAG on Hugging Face](https://huggingface.co/spaces/Shriharshan/Autism-RAG) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ff6c2e58-1992-43ba-bb51-c51024671119) - [Its page on Mindket](https://mindket.com/p/ff6c2e58-1992-43ba-bb51-c51024671119)