Practical overview
When it fits
Use it when you are building or adapting the model layer of a RAG retrieval pipeline, rather than looking for a hosted RAG application. Its training code supports embedding, ColBERT-style, and reranker models; the lightweight inference package focuses on reranking.
Who it is for
- Machine-learning practitioners developing retrieval components for RAG systems.
- Python developers who need a unified interface for reranker inference.
- Teams adapting open-source embedding or reranker models to domain-specific retrieval tasks.
Documented uses
- Fine-tune embedding models for a document retrieval pipeline.
- Train or distill rerankers to reorder retrieved passages.
- Build a ColBERT-style retrieval component.
- Call supported reranker models through a shared Python interface.
Requirements & limits
The repository is public and lists an MIT license. The README documents a Python/Conda training setup and a separate pip installation path for reranker inference.
- Training requires Python 3.8 and repository dependencies; the README advises choosing a PyTorch version compatible with local CUDA before installing requirements.
- Training instructions are organized by model type in separate repository subdirectories.
- The reranker package documents two long-document handling approaches: maximum-length truncation or splitting and taking the maximum score.
- The project describes multi-GPU training strategies including DeepSpeed and FSDP.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the training environment
Prepare the documented Python environment and dependencies for training RAG retrieval models.
Before you start
- Conda
- Python 3.8
- A PyTorch build compatible with local CUDA, if using CUDA
Steps
Create and activate a Conda environment named rag-retrieval with Python 3.8.
If using CUDA, install a compatible PyTorch version before the remaining dependencies.
Install the repository requirements with pip install -r requirements.txt.
Expected outcome
The documented training environment and requirements are installed; model-specific training procedures are in the relevant subdirectory README files.
Guide limitations
- This setup guide does not cover running a training job.
- Training details differ by model type.
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 Retrieval | Fine-tune embedding models for a document retrieval pipeline. | Linux, macOS, Windows | The repository is public and lists an MIT license. The README documents a Python/Conda training setup and a separate pip installation path for reranker inference. |
| 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
- 8 October 2026
- Original website title
- NovaSearch-Team/RAG-Retrieval · Official homepage title · retained in its source language