Beta: Firebase Genkit is in Beta, which means that it is not subject to any SLA or deprecation policy and could change in backwards-incompatible ways. Throughout the Beta period, Firebase Genkit and its documentation will be updated and improved.

Evaluation

Evaluations are a form of testing that helps you validate your LLM's responses and ensure they meet your quality bar.

Firebase Genkit supports third-party evaluation tools through plugins, paired with powerful observability features that provide insight into the runtime state of your LLM-powered applications. Genkit tooling helps you automatically extract data including inputs, outputs, and information from intermediate steps to evaluate the end-to-end quality of LLM responses as well as understand the performance of your system's building blocks.

For example, if you have a RAG flow, Genkit will extract the set of documents that was returned by the retriever so that you can evaluate the quality of your retriever while it runs in the context of the flow as shown below with the Genkit faithfulness and answer relevancy metrics:

import { genkit } from 'genkit';
import { genkitEval, GenkitMetric } from '@genkit-ai/evaluator';
import { vertexAI, textEmbedding004, gemini15Flash } from '@genkit-ai/vertexai';

const ai = genkit({
  plugins: [
    vertexAI(),
    genkitEval({
      judge: gemini15Flash,
      metrics: [GenkitMetric.FAITHFULNESS, GenkitMetric.ANSWER_RELEVANCY],
      embedder: textEmbedding004, // GenkitMetric.ANSWER_RELEVANCY requires an embedder
    }),
  ],
  // ...
});

Note: The configuration above requires installing the genkit, @genkit-ai/google-ai, @genkit-ai/evaluator and @genkit-ai/vertexai packages.

  npm install @genkit-ai/evaluator @genkit-ai/vertexai

Start by defining a set of inputs that you want to use as an input dataset called testInputs.json. This input dataset represents the test cases you will use to generate output for evaluation.

["Cheese", "Broccoli", "Spinach and Kale"]

You can then use the eval:flow command to evaluate your flow against the test cases provided in testInputs.json.

genkit eval:flow menuSuggestionFlow --input testInputs.json

You can then see evaluation results in the Developer UI by running:

genkit start

Then navigate to localhost:4000/evaluate.

Alternatively, you can provide an output file to inspect the output in a JSON file.

genkit eval:flow menuSuggestionFlow --input testInputs.json --output eval-result.json

Note: Below you can see an example of how an LLM can help you generate the test cases.

Supported evaluators

Genkit evaluators

Genkit includes a small number of native evaluators, inspired by RAGAS, to help you get started:

Faithfulness
Answer Relevancy
Maliciousness

Evaluator plugins

Genkit supports additional evaluators through plugins like the VertexAI Rapid Evaluators via the VertexAI Plugin.

Advanced use

eval:flow is a convenient way to quickly evaluate the flow, but sometimes you might need more control over evaluation steps. This may occur if you are using a different framework and already have some output you would like to evaluate. You can perform all the steps that eval:flow performs semi-manually.

You can batch run your Genkit flow and add a unique label to the run which then will be used to extract an evaluation dataset (a set of inputs, outputs, and contexts).

Run the flow over your test inputs:

genkit flow:batchRun myRagFlow test_inputs.json --output flow_outputs.json --label customLabel

Extract the evaluation data:

genkit eval:extractData myRagFlow --label customLabel --output customLabel_dataset.json

The exported data will be output as a JSON file with each testCase in the following format:

[
  {
    "testCaseId": string,
    "input": string,
    "output": string,
    "context": array of strings,
    "traceIds": array of strings,
  }
]

The data extractor will automatically locate retrievers and add the produced docs to the context array. By default, eval:run will run against all configured evaluators, and like eval:flow, results for eval:run will appear in the evaluation page of Developer UI, located at localhost:4000/evaluate.

Custom extractors

You can also provide custom extractors to be used in eval:extractData and eval:flow commands. Custom extractors allow you to override the default extraction logic giving you more power in creating datasets and evaluating them.

To configure custom extractors, add a tools config file named genkit-tools.conf.js to your project root if you don't have one already.

cd $GENKIT_PROJECT_HOME
touch genkit-tools.conf.js

In the tools config file, add the following code:

module.exports = {
  evaluators: [
    {
      actionRef: '/flow/myFlow',
      extractors: {
        context: { outputOf: 'foo-step' },
        output: 'bar-step',
      },
    },
  ],
};

In this sample, you configure an extractor for myFlow flow. The config overrides the extractors for context and output fields and uses the default logic for the input field.

The specification of the evaluation extractors is as follows:

evaluators field accepts an array of EvaluatorConfig objects, which are scoped by flowName
extractors is an object that specifies the extractor overrides. The current supported keys in extractors are [input, output, context]. The acceptable value types are:
- string - this should be a step name, specified as a string. The output of this step is extracted for this key.
- { inputOf: string } or { outputOf: string } - These objects represent specific channels (input or output) of a step. For example, { inputOf: 'foo-step' } would extract the input of step foo-step for this key.
- (trace) => string; - For further flexibility, you can provide a function that accepts a Genkit trace and returns a string, and specify the extraction logic inside this function. Refer to genkit/genkit-tools/common/src/types/trace.ts for the exact TraceData schema.

Note: The extracted data for all these steps will be a JSON string. The tooling will parse this JSON string at the time of evaluation automatically. If providing a function extractor, make sure that the output is a valid JSON string. For example: "Hello, world!" is not valid JSON; "\"Hello, world!\"" is valid.

Running on existing datasets

To run evaluation over an already extracted dataset:

genkit eval:run customLabel_dataset.json

To output to a different location, use the --output flag.

genkit eval:flow menuSuggestionFlow --input testInputs.json --output customLabel_evalresult.json

To run on a subset of the configured evaluators, use the --evaluators flag and provide a comma-separated list of evaluators by name:

genkit eval:run customLabel_dataset.json --evaluators=genkit/faithfulness,genkit/answer_relevancy

Synthesizing test data using an LLM

Here's an example flow that uses a PDF file to generate possible questions users might be asking about it.

import { genkit, run, z } from "genkit";
import { googleAI, gemini15Flash } from "@genkit-ai/googleai";
import { chunk } from "llm-chunk";

const ai = genkit({ plugins: [googleAI()] });

export const synthesizeQuestions = ai.defineFlow(
  {
    name: "synthesizeQuestions",
    inputSchema: z.string().describe("PDF file path"),
    outputSchema: z.array(z.string()),
  },
  async (filePath) => {
    filePath = path.resolve(filePath);
    const pdfTxt = await run("extract-text", () => extractText(filePath));

    const chunks = await run("chunk-it", async () =>
      chunk(pdfTxt, chunkingConfig)
    );

    const questions: string[] = [];
    for (var i = 0; i < chunks.length; i++) {
      const qResponse = await ai.generate({
        model: gemini15Flash,
        prompt: {
          text: `Generate one question about the text below: ${chunks[i]}`,
        },
      });
      questions.push(qResponse.text);
    }
    return questions;
  }
);

You can then use this command to export the data into a file and use for evaluation.

genkit flow:run synthesizeQuestions '"my_input.pdf"' --output synthesizedQuestions.json