Practical overview
When it fits
Use it when you need a configurable, repeatable way to compare RAG retrieval and answer-quality settings before selecting an approach for a larger dataset.
Who it is for
- Researchers comparing RAG search approaches and parameters.
- Data scientists evaluating retrieval and generated-answer quality.
- Developers tuning Azure AI Search-backed RAG workflows.
Documented uses
- Configure experiments across search parameters, query sets, search types and evaluation metrics.
- Compare text, vector and hybrid retrieval strategies.
- Prepare documents with supported loaders and explore chunking settings.
- Assess retrieval results and generated answers with quantitative and LLM-judged metrics.
- Review experiment results in generated reports and visualizations.
Requirements & limits
The repository is public and documents local execution through either a development container or local installation. Its development-container setup requires WSL; the local Conda instructions specify Python 3.11.
- Azure AI Search is the central search integration; the README also lists Azure Machine Learning, MLflow and Azure OpenAI.
- The tool supports several ingestion paths, including Azure Document Intelligence and basic LangChain loaders.
- Sampling can speed early experiments, but the project recommends a full-dataset run for more accurate results.
- The README says its solution was tested with GPT-3.5 Turbo and calls for further testing with other models.
- Cloud credentials and configuration are needed for the relevant Azure or OpenAI services.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up a local Conda environment
Prepare the repository and Python environment for the documented local installation.
Before you start
- Git
- Anaconda or Miniconda
- Python 3.11
- Windows or Mac host
Steps
Clone the repository from GitHub.
Create and activate a Conda environment named rag-experiment with Python 3.11.
Install the project dependencies with pip install .
Expected outcome
The project dependencies are installed in the Conda environment for the next setup and configuration stages.
Guide limitations
- This covers local environment setup only, not configuring cloud services or running an experiment.
- The README notes that additional dependencies may be prompted for during setup.
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 Experiment Accelerator | Configure experiments across search parameters, query sets, search types and evaluation metrics. | Cloud | The repository is public and documents local execution through either a development container or local installation. Its development-container setup requires WSL; the local Conda instructions specify Python 3.11. |
| 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
- 16 October 2026
- Original website title
- microsoft/rag-experiment-accelerator · Official homepage title · retained in its source language