Practical overview
When it fits
Use SimplyRetrieve when you want to build and inspect a document-grounded chat workflow, particularly when local deployment and control over the knowledge source are important.
Who it is for
- Machine-learning practitioners exploring retrieval-centric or retrieval-augmented generation.
- Developers assembling chat workflows grounded in their own documents.
- Users who want to configure prompts, retrieval settings and knowledge sources in a local application.
Documented uses
- Create chat grounded in documents or other selected knowledge.
- Add or append knowledge through the GUI Knowledge tab.
- Tune prompts and retrieval settings and examine retrieval behavior.
- Experiment with different open-source language models and retrievers.
Requirements & limits
The project is available as a public GitHub repository under the MIT license. The documented launch setup uses a GPU-based Linux machine and a browser for the GUI.
- The README describes a GPU-based Linux setup and gives T4, V100 and A100 Nvidia GPUs as examples.
- Knowledge can be added through the GUI or prepared from files before launch.
- The documented file formats include PDF, TXT, DOC, DOCX, PPT, PPTX, HTML, MD and CSV; the configuration can be edited to expand formats.
- The README identifies Wizard-Vicuna-13B-Uncensored as the default language model and multilingual-e5-base as the default embedding model, while describing support for other open-source models and retrievers.
- The project’s stated stack includes Hugging Face, Gradio, PyTorch and Faiss.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Prepare documents for retrieval
Convert a collection of supported documents into a knowledge source before starting the chat tool.
Before you start
- A cloned SimplyRetrieve repository
- A GPU-based Linux machine with a Python virtual environment and required packages installed
- Documents in PDF, TXT, DOC, DOCX, PPT, PPTX, HTML, MD or CSV format
Steps
Place the documents in the chat/data/ directory.
From the chat/ directory, run the documented prepare.py command with the default_release.json configuration to write prepared data to knowledge/.
Expected outcome
A prepared knowledge directory for retrieval-based chat.
Guide limitations
- The README notes that the GUI Knowledge tab can add knowledge directly, so this preparation script is not required for that route.
- The bundled sample knowledge is described as demonstration-only, not for performance evaluation.
Start the local chat GUI
Launch SimplyRetrieve’s browser-based chat interface using the documented configuration.
Before you start
- A cloned repository on a GPU-based Linux machine
- Python virtual environment with packages installed from requirements.txt
- Knowledge prepared in advance or available through the GUI Knowledge tab
Steps
Change to the repository’s chat/ directory.
Run chat.py with the documented default_release.json configuration and CUDA device setting.
Open the displayed local-server address on port 7860 in a browser, substituting the GPU server’s IP address.
Expected outcome
The SimplyRetrieve web GUI is available for chat and retrieval configuration.
Guide limitations
- The README describes a GPU-based Linux setup and names example Nvidia GPUs; it does not establish compatibility for other configurations.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 8 October 2026
- Original website title
- RCGAI/SimplyRetrieve · Official homepage title · retained in its source language