QA Pairs Generator for RAG Evaluation

Automatically generate question-answer pairs from a corpus of JSON documents to evaluate Retrieval-Augmented Generation (RAG) pipelines. The tool extracts named entities from your documents, matches each entity to its most relevant documents via hybrid search (keyword + embeddings), and then prompts an LLM to produce one QA pair per (entity, document) combination.

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Table of contents

• Installation
• Input document format
• Environment variables
• Usage
• Remote storage
• Output format

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Installation

Requires Python ≥ 3.10.

With pip

# runtime only
pip install -e .

# runtime + dev dependencies (pytest)
pip install -e ".[dev]"

# runtime + Azure Blob Storage support
pip install -e ".[azure]"

With uv

uv sync                    # runtime only
uv sync --extra dev        # include dev dependencies
uv sync --extra azure      # include Azure Blob Storage support

After installing, the qa-generate console script is available as an alternative to python run_pipeline.py.

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Input document format

Each document must be a single .json file inside the input directory. A document can have any top-level fields; you tell the pipeline which ones to use via --search-fields.

Minimal example (docs/doc_001.json):

{
  "title": "Introduction to Transformers",
  "description": "Transformers are a type of neural network architecture introduced in the paper Attention Is All You Need.",
  "author": "Vaswani et al.",
  "year": 2017
}

If you run the pipeline with --search-fields title description, the tool will concatenate the title and description fields to build the corpus text used for entity extraction and search. Fields listed in --search-fields that are absent from a document are silently skipped.

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Environment variables

OpenAI (default)

Variable	Required	Description
OPENAI_API_KEY	Yes	Your OpenAI secret key

Azure OpenAI (--client azure)

Variable	Required	Description
AZURE_OPENAI_API_KEY	Yes	Your Azure OpenAI key
AZURE_OPENAI_ENDPOINT	Yes	Your Azure endpoint URL (e.g. https://<resource>.openai.azure.com/)
OPENAI_API_VERSION	Yes	API version (e.g. 2024-02-01)

Azure Blob Storage (az:// input URIs)

The following variables are resolved in priority order:

Priority	Variable(s)	Description
1	AZURE_STORAGE_CONNECTION_STRING	Full connection string
2	AZURE_STORAGE_ACCOUNT_NAME + AZURE_STORAGE_ACCOUNT_KEY	Account name and key
3	AZURE_STORAGE_ACCOUNT_NAME (only)	Uses DefaultAzureCredential (managed identity, Azure CLI, workload identity, etc.)

You can export the variables in your shell or store them in a .env file and load it before running the pipeline.

# Linux / macOS
export OPENAI_API_KEY="sk-..."

# Windows PowerShell
$env:OPENAI_API_KEY = "sk-..."

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Usage

The pipeline can be invoked either via the installed script or directly:

# installed command
qa-generate --input-dir <DIR> --search-fields <FIELD ...> --output <FILE.json> [options]

# or via script
python run_pipeline.py --input-dir <DIR> --search-fields <FIELD ...> --output <FILE.json> [options]

Argument	Required	Default	Description
--input-dir	Yes	—	Local path or remote URI (e.g. az://container/prefix/, s3://bucket/prefix/) containing .json input files
--search-fields	Yes	—	One or more document fields to use for entity extraction and corpus building
--output	Yes	—	Path to the output JSON file
--client	No	openai	LLM provider: openai or azure
--model	No	gpt-4o-mini	Chat model used for entity extraction and QA generation
--embedding-model	No	text-embedding-3-small	Embedding model used for semantic search
--top-n	No	3	Number of documents retrieved per entity via embedding search

Complete example

python run_pipeline.py \
    --input-dir   ./docs \
    --search-fields title description \
    --output      qa_output.json \
    --client      openai \
    --model       gpt-4o-mini \
    --embedding-model text-embedding-3-small \
    --top-n       3

Azure OpenAI example

python run_pipeline.py \
    --input-dir   ./docs \
    --search-fields title description \
    --output      qa_output.json \
    --client      azure \
    --model       my-gpt4o-deployment \
    --embedding-model my-embedding-deployment

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Remote storage

--input-dir accepts any URI supported by fsspec. The pipeline reads .json files transparently from:

Scheme	Backend	Extra install
./path/ or /abs/path/	Local filesystem	—
az://container/prefix/	Azure Blob Storage	pip install -e ".[azure]"
s3://bucket/prefix/	Amazon S3	pip install s3fs
gcs://bucket/prefix/	Google Cloud Storage	pip install gcsfs

# read documents from Azure Blob Storage
python run_pipeline.py \
    --input-dir   az://my-container/corpus/ \
    --search-fields title description \
    --output      qa_output.json

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Output format

The output is a JSON array. Each element is a QA pair with the following fields:

[
  {
    "entity": "Transformers",
    "question": "What problem do Transformers solve compared to RNNs?",
    "answer": "Transformers solve the sequential computation bottleneck of RNNs by relying entirely on self-attention mechanisms, enabling parallelisation during training.",
    "source_document": "Introduction to Transformers\nTransformers are a type of neural network architecture..."
  }
]

Field	Type	Description
entity	string	Named entity extracted from the documents
question	string	Generated question about the entity
answer	string	Generated answer grounded in the source document
source_document	string	Concatenated text of the document used to generate the pair