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RAG & Knowledge Systems

Mrag

MRAG is a Python framework implementing Multi-Head RAG, a retrieval-augmented generation method that uses Transformer multi-head attention activations as retrieval keys to find multiple documents for queries spanning different content aspects.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mrag — 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 MRAG to explore or evaluate retrieval for questions whose answers require several distinct sources, especially when those sources may be far apart in embedding space.

Who it is for

  • Researchers studying multi-aspect retrieval and RAG methods.
  • Developers building or evaluating systems that need complementary documents from semantically distant sources.

Documented uses

  • Retrieve multiple documents relevant to different facets of a query.
  • Generate dataset and query embeddings for retrieval experiments.
  • Import embedding data into a vector database and evaluate retrieval strategies.
  • Visualize evaluation results with generated plots.
Before you choose

Requirements & limits

The GitHub repository is public. Its supplied repository metadata does not specify a license.

  • Requires Python 3.9 or newer; the installation instructions also install Docker Compose.
  • Synthetic query generation uses OpenAI LLMs and incurs a cost; prepared Wikipedia-based data and queries are available in the repository.
  • The documented embedding command uses Salesforce/SFR-Embedding-Mistral and generates attention-head embeddings for the last layer.
  • The workflow separates data generation, embedding, vector database setup and import, evaluation, and plotting.

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

Install MRAG from source

Set up the framework for use or code modification.

Before you start

  • Python 3.9 or newer
  • A Python environment, if you use one
  • Docker Compose

Steps

  1. Clone the MRAG repository and change into its directory.

  2. For standard use, run the command below; for code modification, run the command below.

    Documented command
    pip install .
    Documented command
    pip install -e .

  3. Install Docker Compose with apt-get install docker-compose.

Expected outcome

MRAG is installed from the source repository in standard or editable mode.

Guide limitations

  • This covers installation only, not running a retrieval workflow.
  • The README's later synthetic query-generation step uses OpenAI LLMs and incurs a cost.
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
MragRetrieve multiple documents relevant to different facets of a query.The GitHub repository is public. Its supplied repository metadata does not specify a license.
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
16 October 2026
Original website title
spcl/MRAG · Official homepage title · retained in its source language
How we review sourcesSuggest a correction