Synthesizer

Quick Summary

deepeval offers a data Synthesizer for anyone to easily generate evaluation datasets from scratch. The Synthesizer class is a synthetic data generator that first uses an LLM to generate a series of inputs, before evolving each input to make them more complex and realistic. These evolved inputs are then used to create a list of synthetic Goldens, which makes up your synthetic EvaluationDataset.

Did You Know?

deepeval's Synthesizer uses the data evolution method to generate large volumes of data across various complexity levels to make synthetic data more realistic. This method was originally introduced by the developers of Evol-Instruct and WizardML.

For those interested, here is a great article on how deepeval's synthesizer was built.

Creating An Synthesizer

deepeval's Synthesizer can be used as a standalone or within an EvaluationDataset. To begin, create a Synthesizer:

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()

There are two optional parameters when creating a Synthesizer:

• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to gpt-4o.
• [Optional] multithreading: a boolean which when set to True, enables concurrent generation of goldens. Defaulted to True.

Using Synthesizer As A Standalone

There are 2 approaches a deepeval's Synthesizer can generate synthetic Goldens:

1. Generating synthetic Goldens using context extracted from documents.
2. Generating synthetic Goldens from a list of provided context.

Generating From Documents

To generate synthetic Goldens from documents, simply provide a list of document paths:

info

The generate_goldens_from_docs method employs a token-based text splitter to manage document chunking, meaning the chunk_size and chunk_overlap parameters do not guarantee exact context sizes. This approach is designed to ensure meaningful and coherent context extraction, but might lead to variations in the expected size of each context.

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
synthesizer.generate_goldens_from_docs(
    document_paths=['example.txt', 'example.docx', 'example.pdf'],
    max_goldens_per_document=2
)

There are one mandatory and six optional parameters when using the generate_goldens_from_docs method:

• document_paths: a list strings, representing the path to the documents from which contexts will be extracted from. Supported documents types include: .txt, .docx, and .pdf.
• [Optional] include_expected_output: a boolean which when set to True, will additionally generate an expected_output for each synthetic Golden. Defaulted to False.
• [Optional] max_goldens_per_document: the maximum number of golden data points to be generated for each document. Defaulted to 5.
• [Optional] chunk_size: specifies the size of text chunks (in characters) to be considered for context extraction within each document. Defaulted to 1024.
• [Optional] chunk_overlap: an int that determines the overlap size between consecutive text chunks during context extraction. Defaulted to 0.
• [Optional] num_evolutions: the number of evolution steps to apply to each generated input. This parameter controls the complexity and diversity of the generated dataset by iteratively refining and evolving the initial inputs. Defaulted to 1.
• [Optional] enable_breadth_evolve: a boolean which when set to True, introduces a wider variety of context modifications, enhancing the dataset's diversity. Defaulted to False.

note

deepeval uses an LLM and an embedder model during context and query generation during data synthesization. You can choose to use Azure's OpenAI models if you wish by running the following commands in the terminal:

deepeval set-azure-openai --openai-endpoint=<endpoint> \
    --openai-api-key=<api_key> \
    --deployment-name=<deployment_name> \
    --openai-api-version=<openai_api_version> \
    --model-version=<model_version>

Then, run this to set the Azure OpenAI embedder:

deepeval set-azure-openai-embedding --embedding_deployment-name=<embedding_deployment_name>

Generating From Provided Contexts

deepeval also allows you to generate synthetic Goldens from a manually provided a list of context instead of directly generating from your documents.

tip

This is especially helpful if you already have an indexed and/or embedded knowledge base. For example, if you already have documents parsed and stored in an existing vector database, you may consider handling the logic to retrieve text chunks yourself.

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
synthesizer.generate_goldens(
    # Provide a list of context for synthetic data generation
    contexts=[
        ["The Earth revolves around the Sun.", "Planets are celestial bodies."],
        ["Water freezes at 0 degrees Celsius.", "The chemical formula for water is H2O."],
    ]
)

There are one mandatory and four optional parameters when using the generate_goldens method:

• contexts: a list of context, where each context is itself a list of strings, ideally sharing a common theme or subject area.
• [Optional] include_expected_output: a boolean which when set to True, will additionally generate an expected_output for each synthetic Golden. Defaulted to False.
• [Optional] max_goldens_per_context: the maximum number of golden data points to be generated from each context. Adjusting this parameter can influence the size of the resulting dataset. Defaulted to 2.
• [Optional] num_evolutions: the number of evolution steps to apply to each generated input. This parameter controls the complexity and diversity of the generated dataset by iteratively refining and evolving the initial inputs. Defaulted to 1.
• [Optional] enable_breadth_evolve: a boolean indicating whether to enable breadth evolution strategies during data generation. When set to True, it introduces a wider variety of context modifications, enhancing the dataset's diversity. Defaulted to False.

caution

You can also optionally generate expected_outputs alongside each golden, but you should always aim to cross-check any generated expected output.

Saving Generated Goldens

To not accidentally lose any generated synthetic Golden, you can use the save_as() method:

synthesizer.save_as(
    file_type='json', # or 'csv'
    directory="./synthetic_data"
)

Using Synthesizer Within An Evaluation Dataset

An EvaluationDataset also has the generate_goldens_from_docs and generate_goldens methods, which under the hood is powered by the Synthesizer's implementation.

info

Except for an additional option to accept a custom Synthesizer as argument, the generate_goldens_from_docs and generate_goldens methods in an EvaluationDataset accepts the exact same arguments as those on a Synthesizer.

You can optionally specify a custom Synthesizer when calling generate_goldens_from_docs and generate_goldens through the EvaluationDataset interface if for example, you wish to use a custom LLM to generate synthetic data. If no Synthesizer is provided, the default Synthesizer configuration is used.

To begin, optionally create a custom Synthesizer:

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer(model="gpt-3.5-turbo")

Then, provide it as an argument to generate_goldens_from_docs:

from deepeval.dataset import EvaluationDataset
...

dataset = EvaluationDataset()
dataset.generate_goldens_from_docs(
    synthesizer=synthesizer,
    document_paths=['example.pdf'],
)

Or, to generate_goldens:

...

dataset.generate_goldens(
    synthesizer=synthesizer,
    contexts=[
        ["The Earth revolves around the Sun.", "Planets are celestial bodies."],
        ["Water freezes at 0 degrees Celsius.", "The chemical formula for water is H2O."],
    ]
)

Lastly, don't forget to call save_as() to perserve any generated synthetic Golden:

saved_path = dataset.save_as(
    file_type='json',  # or 'csv'
    directory="./synthetic_data"
)

tip

The save_as() method returns a string to the path the dataset was saved to, just in case you need to use it in code later on.