Practical overview
When it fits
Use Rag Fit when you want a configurable, script-based workflow to prepare RAG datasets, fine-tune models, run inference and assess outputs. It is suited to experimentation where you need to choose processing stages and evaluation metrics.
Who it is for
- Researchers prototyping and comparing retrieval-augmented generation settings.
- Developers preparing RAG examples for model fine-tuning and inference.
- Teams evaluating generated answers with built-in or custom metrics.
Documented uses
- Load, normalize, filter and prepare data for RAG training or inference.
- Create augmented examples using retrieval, API integration and template-based prompts.
- Fine-tune models on augmented datasets with PEFT and TRL.
- Generate predictions with trained or untrained models.
- Evaluate outputs using example-level or dataset-level metrics and relevant metadata.
Requirements & limits
The code is available under the Apache 2.0 license. Installation instructions use pip from a local repository clone.
- Rag Fit is a framework and command-line toolkit, so users configure and run its modules rather than use a hosted interface.
- Training uses PEFT and TRL; models can be pushed to Hugging Face Hub.
- Evaluation supports listed built-in metrics and custom metrics, with local per-example and global dataset-level options.
- A PubmedQA tutorial and ASQA experiment configurations are provided as starting points.
- The repository says it is not an official Intel product.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Rag Fit and its optional integrations
Install the framework from a local repository clone and, if needed, add its listed Haystack or Deepeval package extras.
Before you start
- A local clone of the Rag Fit repository.
- Python package installation using pip.
Steps
From the cloned repository, run the command below.
pip install -e .For the Haystack extra, run the command below.
pip install -e .[haystack]For the Deepeval extra, run the command below.
pip install -e .[deepeval]
Expected outcome
The framework is installed in editable mode, with the selected optional package extra if requested.
Guide limitations
- These steps cover installation only, not running a data, training, inference or evaluation workflow.
- The exact behavior of the optional Haystack integration is not detailed in the installation instructions.
Compare options for Retrieval-augmented applications
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Rag Fit | Load, normalize, filter and prepare data for RAG training or inference. | CLI | The code is available under the Apache 2.0 license. Installation instructions use pip from a local repository clone. |
| Langsearch | Crawl website or local data for a knowledge base. | CLI, Self-hosted, Server | |
| Hybrid Search Rag | Combine document retrieval and language-model generation in a configurable workflow. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 9 October 2026
- Original website title
- IntelLabs/RAG-FiT · Official homepage title · retained in its source language