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RAG & Knowledge Systems

Raglab

RAGLAB is a Python framework for experimenting with retrieval-augmented generation algorithms. It includes reproductions of six algorithms, an interactive mode for understanding their behavior, and an evaluation system using ten benchmark datasets for research comparisons.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Raglab — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Interact Mode to explore algorithm behavior with built-in queries; use Evaluation Mode when preparing the data, models and retrieval setup needed to reproduce research results.

Who it is for

  • Researchers comparing retrieval-augmented generation algorithms.
  • Developers exploring RAG behavior or extending algorithms, datasets and evaluation metrics.

Documented uses

  • Explore how implemented RAG algorithms behave through interactive queries.
  • Compare RAG algorithms using shared benchmark datasets.
  • Set up evaluation workflows to reproduce research results.
  • Develop additional RAG algorithms, datasets or evaluation metrics.
Before you choose

Requirements & limits

The project is an open-source public GitHub repository under the MIT license. It is a Python framework with documented local environment and model/data setup requirements.

  • Interact Mode is documented as usable without downloading additional data, while paper-result reproduction requires training, knowledge and evaluation data.
  • The documented development environment specifies PyTorch 2.0.1, Python 3.10 and CUDA 11.8, plus Miniconda and manually installed dependencies.
  • ColBERT setup requires absolute paths in configuration and embedding processing.
  • The README lists support for 70B-plus models, vLLM and quantization; required models and compute need to be arranged for the selected experiment.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up RAGLAB and run its 10-sample Interact Mode demonstration

Install the environment, configure and start the ColBERT server, then run the documented Self-RAG Interact Mode example.

Before you start

  • Miniconda installed.
  • The RAGLAB repository cloned locally.
  • The paths to the 10-sample ColBERT index and text database, for configuring the server.

Steps

  1. Create the Conda environment from environment.yml, then install flash-attn==2.2, download spaCy's en_core_web_sm model, and download NLTK's punkt data as described in the README.

  2. Edit config/colbert_server/colbert_server-10samples.yaml so index_dbPath and text_dbPath use absolute paths to the 10-sample databases.

  3. From the repository directory, run the command below. If prompted to reprocess the ColBERT embedding due to a path error, enter yes.

    Documented command
    sh run/colbert_server/colbert_server-10samples.sh

  4. In another terminal, run the command below from the repository directory; then run the command below.

    Documented command
    sh run/colbert_server/ask_api.sh
    Documented command
    sh run/rag_inference/3-selfrag_reproduction-interact-short_form-adaptive_retrieval.sh

Expected outcome

The README describes a ColBERT server request that returns a result as an indication the server started, followed by a Self-RAG Interact Mode example using 10-sample embeddings.

Guide limitations

  • The configuration requires absolute paths to the ColBERT index and text databases.
  • This is an Interact Mode demonstration; reproducing paper results is a separate workflow that requires additional data and models.
Documentation 1 ↗
COMPARE OPTIONS

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.

ProjectDocumented purposePlatformAccess and requirements
RaglabExplore how implemented RAG algorithms behave through interactive queries.The project is an open-source public GitHub repository under the MIT license. It is a Python framework with documented local environment and model/data setup requirements.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
fate-ubw/RAGLAB · Official homepage title · retained in its source language
How we review sourcesSuggest a correction