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Configuration

The YAML configuration format runs each prompt through a series of example inputs (aka "test case") and checks if they meet requirements (aka "assert").

Asserts are optional. Many people get value out of reviewing outputs manually, and the web UI helps facilitate this.

Examples

Let's imagine we're building an app that does language translation. This config runs each prompt through GPT-3.5 and Vicuna, substituting three variables:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests:
- vars:
language: French
input: Hello world
- vars:
language: German
input: How's it going?
tip

For more information on setting up a prompt file, see input and output files.

Running promptfoo eval over this config will result in a matrix view that you can use to evaluate GPT vs Vicuna.

Use assertions to validate output

Next, let's add an assertion. This automatically rejects any outputs that don't contain JSON:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests:
- vars:
language: French
input: Hello world
assert:
- type: contains-json
- vars:
language: German
input: How's it going?

We can create additional tests. Let's add a couple other types of assertions. Use an array of assertions for a single test case to ensure all conditions are met.

In this example, the javascript assertion runs Javascript against the LLM output. The similar assertion checks for semantic similarity using embeddings:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests:
- vars:
language: French
input: Hello world
assert:
- type: contains-json
- type: javascript
value: output.toLowerCase().includes('bonjour')
- vars:
language: German
input: How's it going?
assert:
- type: similar
value: was geht
threshold: 0.6 # cosine similarity
tip

To learn more about assertions, see docs on configuring expected outputs.

Avoiding repetition

Default test cases

Use defaultTest to set properties for all tests.

In this example, we use a llm-rubric assertion to ensure that the LLM does not refer to itself as an AI. This check applies to all test cases:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
defaultTest:
assert:
- type: llm-rubric
value: does not describe self as an AI, model, or chatbot
tests:
- vars:
language: French
input: Hello world
assert:
- type: contains-json
- type: javascript
value: output.toLowerCase().includes('bonjour')
- vars:
language: German
input: How's it going?
assert:
- type: similar
value: was geht
threshold: 0.6

You can also use defaultTest to override the model used for each test. This can be useful for model-graded evals:

defaultTest:
options:
provider: openai:gpt-3.5-turbo-0613

YAML references

promptfoo configurations support JSON schema references, which define reusable blocks.

Use the $ref key to re-use assertions without having to fully define them more than once. Here's an example:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests:
- vars:
language: French
input: Hello world
assert:
- $ref: '#assertionTemplates/startsUpperCase'
- vars:
language: German
input: How's it going?
assert:
- $ref: '#assertionTemplates/noAIreference'
- $ref: '#assertionTemplates/startsUpperCase'

assertionTemplates:
noAIreference:
- type: llm-rubric
value: does not describe self as an AI, model, or chatbot
startsUpperCase:
- type: javascript
value: output[0] === output[0].toUpperCase()
info

tools and functions values in providers config are not dereferenced. This is because they are standalone JSON schemas that may contain their own internal references.

Import tests from separate files

The tests config attribute takes a list of paths to files or directories. For example:

prompts: prompts.txt
providers: openai:gpt-3.5-turbo

# Load & runs all test cases matching these filepaths
tests:
# You can supply an exact filepath
- tests/tests2.yaml

# Or a glob (wildcard)
- tests/*

# Mix and match with actual test cases
- vars:
var1: foo
var2: bar

A single string is also valid:

tests: tests/*

Or a list of paths:

tests: ['tests/accuracy', 'tests/creativity', 'tests/hallucination']

Import vars from separate files

The vars attribute can point to a file or directory. For example:

tests:
- vars: path/to/vars*.yaml

You can also load individual variables from file by using the file:// prefix. For example:

tests:
- vars:
var1: some value...
var2: another value...
var3: file://path/to/var3.txt

Javascript and Python variable files are supported. For example:

tests:
- vars:
context: file://fetch_from_vector_database.py

This is useful when testing vector databases like Pinecone, Chroma, Milvus, etc.

Multiple variables in a single test case

The vars map in the test also supports array values. If values are an array, the test case will run each combination of values.

For example:

prompts: prompts.txt
providers: [openai:gpt-3.5-turbo, openai:gpt-4]
tests:
- vars:
language: [French, German, Spanish]
input: ['Hello world', 'Good morning', 'How are you?']
assert:
- type: similar
value: 'Hello world'
threshold: 0.8

Evaluates each language x input combination:

Multiple combinations of var inputs

Using nunjucks templates

In the above examples, vars values are strings. But vars can be any JSON or YAML entity, including nested objects. You can manipulate these objects in the prompt, which are nunjucks templates.

For example, consider this test case, which lists a handful of user and assistant messages in an OpenAI-compatible format:

tests:
- vars:
previous_messages:
- role: user
content: hello world
- role: assistant
content: how are you?
- role: user
content: great, thanks

The corresponding prompt.txt file simply passes through the previous_messages object using the dump and safe filters to convert the object to a JSON string:

{{ previous_messages | dump | safe }}

Running promptfoo eval -p prompt.txt -c path_to.yaml will call the Chat Completion API with the following prompt:

[
{
"role": "user",
"content": "hello world"
},
{
"role": "assistant",
"content": "how are you?"
},
{
"role": "user",
"content": "great, thanks"
}
]

Use Nunjucks templates to exert additional control over your prompt templates, including loops, conditionals, and more.

Tools and functions

promptfoo supports OpenAI tools, functions, and other provider-specific configurations like temperature, number of tokens, and so on.

To use, override the config key of the provider. See example here.

Transforming outputs

The TestCase.options.transform field is a Javascript snippet that modifies the LLM output before it is run through the test assertions.

It is a function that takes a string output and a context object:

transformFn: (output: string, context: {
vars: Record<string, any>
})

This is useful if you need to somehow transform or clean LLM output before running an eval.

For example:

# ...
tests:
- vars:
language: French
body: Hello world
options:
transform: output.toUpperCase()
# ...

Or multiline:

# ...
tests:
- vars:
language: French
body: Hello world
options:
transform: |
output = output.replace(context.vars.language, 'foo');
const words = output.split(' ').filter(x => !!x);
return JSON.stringify(words);
# ...

It also works for assertions, which is useful for picking values out of JSON:

tests:
- vars:
# ...
assert:
- type: equals
value: 'foo'
transform: output.category # Select the 'category' key from output json
tip

Use defaultTest apply a transform option to every test case in your test suite.

Config structure and organization

For detailed information on the config structure, see Configuration Reference.

If you have multiple sets of tests, it helps to split them into multiple config files. Use the --config or -c parameter to run each individual config:

promptfoo eval -c usecase1.yaml

and

promptfoo eval -c usecase2.yaml

You can run multiple configs at the same time, which will combine them into a single eval. For example:

promptfoo eval -c my_configs/*

or

promptfoo eval -c config1.yaml -c config2.yaml -c config3.yaml

Loading tests from CSV

YAML is nice, but some organizations maintain their LLM tests in spreadsheets for ease of collaboration. promptfoo supports a special CSV file format.

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests: tests.csv

promptfoo also has built-in ability to pull test cases from a Google Sheet. The sheet must be visible to "anyone with the link". For example:

prompts: [prompt1.txt, prompt2.txt]
providers: [openai:gpt-3.5-turbo, vertex:gemini-pro]
tests: https://docs.google.com/spreadsheets/d/1eqFnv1vzkPvS7zG-mYsqNDwOzvSaiIAsKB3zKg9H18c/edit?usp=sharing

Here's a full example.