APIGenGenerator

Generate queries and answers for the given functions in JSON format.

The APIGenGenerator is inspired by the APIGen pipeline, which was designed to generate verifiable and diverse function-calling datasets. The task generates a set of diverse queries and corresponding answers for the given functions in JSON format.

Attributes

• system_prompt: The system prompt to guide the user in the generation of queries and answers.

• use_tools: Whether to use the tools available in the prompt to generate the queries and answers. In case the tools are given in the input, they will be added to the prompt.

• number: The number of queries to generate. It can be a list, where each number will be chosen randomly, or a dictionary with the number of queries and the probability of each. I.e: number=1, number=[1, 2, 3], number={1: 0.5, 2: 0.3, 3: 0.2} are all valid inputs. It corresponds to the number of parallel queries to generate.

• use_default_structured_output: Whether to use the default structured output or not.

Input & Output Columns

graph TD
    subgraph Dataset
        subgraph Columns
            ICOL0[examples]
            ICOL1[func_name]
            ICOL2[func_desc]
            ICOL3[tools]
        end
        subgraph New columns
            OCOL0[query]
            OCOL1[answers]
        end
    end

    subgraph APIGenGenerator
        StepInput[Input Columns: examples, func_name, func_desc, tools]
        StepOutput[Output Columns: query, answers]
    end

    ICOL0 --> StepInput
    ICOL1 --> StepInput
    ICOL2 --> StepInput
    ICOL3 --> StepInput
    StepOutput --> OCOL0
    StepOutput --> OCOL1
    StepInput --> StepOutput

Inputs

• examples (str): Examples used as few shots to guide the model.

• func_name (str): Name for the function to generate.

• func_desc (str): Description of what the function should do.

• tools (str): JSON formatted string containing the tool representation of the function.

Outputs

• query (str): The list of queries.

• answers (str): JSON formatted string with the list of answers, containing the info as a dictionary to be passed to the functions.

Examples

Generate without structured output (original implementation)

from distilabel.steps.tasks import ApiGenGenerator
from distilabel.llms import InferenceEndpointsLLM

llm=InferenceEndpointsLLM(
    model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
    generation_kwargs={
        "temperature": 0.7,
        "max_new_tokens": 1024,
    },
)
apigen = ApiGenGenerator(
    use_default_structured_output=False,
    llm=llm
)
apigen.load()

res = next(
    apigen.process(
        [
            {
                "examples": 'QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]',
                "func_name": "getrandommovie",
                "func_desc": "Returns a list of random movies from a database by calling an external API."
            }
        ]
    )
)
res
# [{'examples': 'QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]',
# 'number': 1,
# 'func_name': 'getrandommovie',
# 'func_desc': 'Returns a list of random movies from a database by calling an external API.',
# 'queries': ['I want to watch a movie tonight, can you recommend a random one from your database?',
# 'Give me 5 random movie suggestions from your database to plan my weekend.'],
# 'answers': [[{'name': 'getrandommovie', 'arguments': {}}],
# [{'name': 'getrandommovie', 'arguments': {}},
#     {'name': 'getrandommovie', 'arguments': {}},
#     {'name': 'getrandommovie', 'arguments': {}},
#     {'name': 'getrandommovie', 'arguments': {}},
#     {'name': 'getrandommovie', 'arguments': {}}]],
# 'raw_input_api_gen_generator_0': [{'role': 'system',
#     'content': "You are a data labeler. Your responsibility is to generate a set of diverse queries and corresponding answers for the given functions in JSON format.

Construct queries and answers that exemplify how to use these functions in a practical scenario. Include in each query specific, plausible values for each parameter. For instance, if the function requires a date, use a typical and reasonable date.

Ensure the query:
- Is clear and concise
- Demonstrates typical use cases
- Includes all necessary parameters in a meaningful way. For numerical parameters, it could be either numbers or words
- Across a variety level of difficulties, ranging from beginner and advanced use cases
- The corresponding result's parameter types and ranges match with the function's descriptions

Ensure the answer:
- Is a list of function calls in JSON format
- The length of the answer list should be equal to the number of requests in the query
- Can solve all the requests in the query effectively"},
#     {'role': 'user',
#     'content': 'Here are examples of queries and the corresponding answers for similar functions:
QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]

Note that the query could be interpreted as a combination of several independent requests.
Based on these examples, generate 2 diverse query and answer pairs for the function `getrandommovie`
The detailed function description is the following:
Returns a list of random movies from a database by calling an external API.

The output MUST strictly adhere to the following JSON format, and NO other text MUST be included:

Generate with structured output

from distilabel.steps.tasks import ApiGenGenerator
from distilabel.llms import InferenceEndpointsLLM

llm=InferenceEndpointsLLM(
    model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
    tokenizer="meta-llama/Meta-Llama-3.1-70B-Instruct",
    generation_kwargs={
        "temperature": 0.7,
        "max_new_tokens": 1024,
    },
)
apigen = ApiGenGenerator(
    use_default_structured_output=True,
    llm=llm
)
apigen.load()

res_struct = next(
    apigen.process(
        [
            {
                "examples": 'QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]',
                "func_name": "getrandommovie",
                "func_desc": "Returns a list of random movies from a database by calling an external API."
            }
        ]
    )
)
res_struct
# [{'examples': 'QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]',
# 'number': 1,
# 'func_name': 'getrandommovie',
# 'func_desc': 'Returns a list of random movies from a database by calling an external API.',
# 'queries': ["I'm bored and want to watch a movie. Can you suggest some movies?",
# "My family and I are planning a movie night. We can't decide on what to watch. Can you suggest some random movie titles?"],
# 'answers': [[{'arguments': {}, 'name': 'getrandommovie'}],
# [{'arguments': {}, 'name': 'getrandommovie'}]],
# 'raw_input_api_gen_generator_0': [{'role': 'system',
#     'content': "You are a data labeler. Your responsibility is to generate a set of diverse queries and corresponding answers for the given functions in JSON format.

Construct queries and answers that exemplify how to use these functions in a practical scenario. Include in each query specific, plausible values for each parameter. For instance, if the function requires a date, use a typical and reasonable date.

Ensure the query:
- Is clear and concise
- Demonstrates typical use cases
- Includes all necessary parameters in a meaningful way. For numerical parameters, it could be either numbers or words
- Across a variety level of difficulties, ranging from beginner and advanced use cases
- The corresponding result's parameter types and ranges match with the function's descriptions

Ensure the answer:
- Is a list of function calls in JSON format
- The length of the answer list should be equal to the number of requests in the query
- Can solve all the requests in the query effectively"},
#     {'role': 'user',
#     'content': 'Here are examples of queries and the corresponding answers for similar functions:
QUERY:
What is the binary sum of 10010 and 11101?
ANSWER:
[{"name": "binary_addition", "arguments": {"a": "10010", "b": "11101"}}]

Note that the query could be interpreted as a combination of several independent requests.
Based on these examples, generate 2 diverse query and answer pairs for the function `getrandommovie`
The detailed function description is the following:
Returns a list of random movies from a database by calling an external API.

Now please generate 2 diverse query and answer pairs following the above format.'}]},
# 'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct'}]

References

• APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

• Salesforce/xlam-function-calling-60k