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Generate From Contexts

If you already have prepared contexts, you can skip document processing. Simply provide these contexts to the Synthesizer, and it will generate the Goldens directly without processing documents.

LangChain
tip

This is especially helpful if you already have an embedded knowledge base. For example, if you have documents parsed and stored in a vector database, you may handle retrieving text chunks yourself.

Generate Your Goldens​

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

from deepeval.synthesizer import Synthesizer

synthesizer = Synthesizer()
synthesizer.generate_goldens_from_contexts(
    # 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 three optional parameters when using the generate_goldens_from_contexts 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 True.
  • [Optional] max_goldens_per_context: the maximum number of goldens to be generated per context. Defaulted to 2.
  • [Optional] source_files: a list of strings specifying the source of the contexts. Length of source_files MUST be the same as the length of contexts.
DID YOU KNOW?

The generate_goldens_from_docs() method calls the generate_goldens_from_contexts() method under the hood, and the only difference between the two is the generate_goldens_from_contexts() method does not contain a context construction step, but instead uses the provided contexts directly for generation.