RAG Experiment Accelerator: a config-driven harness for Azure AI Search retrieval experiments

The problem: RAG tuning is usually done by hand, one index at a time

Retrieval quality depends on a stack of choices that interact: chunk size, analyzer, embedding model, search type, re-ranking, and how the query is rewritten. Changing one and re-measuring means rebuilding an index and re-running a query set. The README describes the accelerator as a tool to "expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern." The intended users are named directly: researchers, data scientists, and developers who want to test search and OpenAI hyperparameters, compare search strategies, and find the best combination. That is a narrow audience. If you already have a retrieval stack in production and only want to add regression tests, this project is aimed at a different job: exploring a parameter space before you commit to a configuration.

What actually varies between runs: the config file drives index creation

The mechanism is config-driven, and the README is explicit that the tool "creates multiple search indexes based on hyperparameter configurations available in the config file." One run can therefore produce several indexes, each representing a different combination of chunking and search settings. On top of that, the tool generates query sets (described as diverse and customizable), runs them against each index, and evaluates the answers. Search types listed include pure text, pure vector, cross-vector, multi-vector, and hybrid. Two optional stages sit between retrieval and evaluation: sub-querying, where a complex user query is broken into smaller sub-queries to gather context, and re-ranking, where Azure AI Search responses are re-scored by an LLM against the query. The evaluation layer then splits into end-to-end metrics (generated answer versus ground-truth answer, using distance-based, cosine and semantic similarity) and component metrics judged by an LLM, such as context recall and answer relevance, plus retrieval metrics like MAP@k. Reports with visualizations are generated at the end. The data flow is therefore: config, index build, query generation, retrieval, optional re-rank, answer generation, metrics, report.

Document loading and chunking are where the project has the most opinion

Most of the README's technical detail is about ingestion. Two loader families exist: Azure Document Intelligence and basic LangChain loaders. When the 'prebuilt-layout' model is selected, the tool uses a custom loader rather than the LangChain path. According to the README, that custom loader formats tables with column headers into key-value pairs, strips page numbers, footers, and other parts judged irrelevant to the LLM, and removes recurring patterns using regex. Because each table row becomes a text line, chunking is done recursively by paragraph and line so a row is not split mid-way. The README also states that the custom loader falls back to the simpler 'prebuilt-layout' model when 'prebuilt-layout' fails, which reads like a documentation error rather than a design choice, and is worth confirming in the source before you rely on fallback behaviour. Any other Document Intelligence model goes through LangChain and returns the raw API response. That means chunk quality, and therefore your metrics, can differ between loader paths for reasons that have nothing to do with the search parameters you are trying to compare. Keep the loader fixed across a comparison.

Getting it running: dev container or local install, then a config

The README documents two compute paths: a development container, or a local install on the host machine. The container route requires WSL plus Docker Desktop, Visual Studio Code, and the VS Code Remote-Containers extension, with Windows users pointed at Ubuntu 22.04.3 LTS from the Microsoft Store. The README notes that the container installs all required software for you. The project needs three Azure-side dependencies: an Azure AI Search service, Azure OpenAI or the OpenAI API, and Azure Machine Learning resources, which is where MLFlow tracking fits. One concrete constraint is stated plainly: semantic search is available in Azure AI Search at Basic tier or higher, so a Free-tier search service will not cover every search type the tool advertises. The README also carries a maintenance instruction after the March 2024 content sampling change: rebuild your environment if you have used the tool before, because of new dependencies. That is a signal about how tightly the environment is pinned to the code at any given commit.

Content sampling is a speed knob with a stated accuracy cost

The most recent change described in the README is content sampling, added on 18 March 2024. It samples the dataset by a specified percentage, but not uniformly: the data is clustered by content first, then the percentage is taken across each cluster, so the sample is spread rather than concentrated in one part of the corpus. The stated purpose is representative results in the sample. The README is honest about the limit: results should be roughly indicative of the full dataset within about a 10 percent margin, and once an approach has been identified, running on the full dataset is recommended for accurate results. Treat sampling as a screening step. If two configurations differ by less than that margin, the sample cannot separate them, and you are paying for a full run either way.

Where it stops being the right tool

This is an experiment harness, and the README never presents it as anything else. It orchestrates index builds, query generation, LLM judging and report writing. Those are batch operations with LLM calls in the loop, which makes them slow and token-expensive compared with a retrieval endpoint. If what you need is a low-latency query path serving real users, this project is the wrong layer. There is also an Azure dependency that runs through every part of it: Azure AI Search for indexes, Azure OpenAI or OpenAI for generation and judging, Azure Machine Learning for tracking, Document Intelligence for the richer loaders. A team running pgvector or Elasticsearch would have to rewrite the retrieval and evaluation layers rather than configure them. The licence is a second open question. The repository metadata reports NOASSERTION, meaning no standard licence identifier was detected, and the README does not state terms. Until you read the LICENSE file in the repository, you cannot assume the permissions you get with, say, an MIT-licensed library.

How it differs from promptflow and RAGAS

Microsoft's promptflow is a general orchestration layer for LLM flows, with a visual editor and its own evaluation tooling; you build a flow and then evaluate it. RAGAS is a Python metrics library you import into your own test code, and it does not build indexes or call a search service. The accelerator sits between them: it owns the whole loop from index creation through report generation, which is why it needs a config file instead of a function call, and why it is tied to Azure AI Search in a way neither of the others is. The practical difference shows up in week two. With RAGAS you write a pytest file and get metrics inside CI. With the accelerator you edit a config, rebuild indexes, and read a generated report. The second is better for exploring an unfamiliar corpus; the first is better for guarding a configuration you have already chosen.

Maintenance cost and what to verify before you commit

The repository's default branch is development, not main, which is worth knowing before you pin a commit. No releases were retrieved in the material available, so there is no tagged version to depend on; you track the branch or a commit hash. The README's own instruction to rebuild the environment after a dependency change suggests upgrades are not drop-in. The dependency surface is broad (LangChain, Document Intelligence, MLFlow, Azure SDKs), and each of those moves independently, so budget for periodic environment rebuilds rather than in-place upgrades. On licensing, NOASSERTION means you should open the LICENSE file yourself and, if the terms matter commercially, have someone qualified read them; nothing here should be taken as legal advice. A reasonable first hour: clone the repository, read the LICENSE file, confirm your Azure AI Search tier supports semantic search, and run the sample config against a small slice of your own documents before trusting any comparison the tool produces.

Editorial conclusion

Adopt it if your corpus already lives in Azure AI Search and you need to compare chunking, analyzer and search-type combinations under one metrics pipeline. Do not adopt it if you need a low-latency retrieval service, a non-Azure vector store, or a library you can import into an existing test suite. Before committing, verify three things: the exact licence terms, whether your Azure AI Search tier supports semantic search (the README states Basic or higher), and whether the sampling path is acceptable for your dataset, since the README puts sampled results within roughly a 10 percent margin of the full run.

Official sources

1. Issues
2. microsoft/rag-experiment-accelerator on GitHub
3. Project website
4. README