Agents-internal

About this Agent

The Automated Unit Test Generator Agent is designed to streamline the software development process by automatically generating unit test cases for newly written code. This AI-powered tool ensures that individual components of an application function as intended by creating comprehensive test cases based on predefined testing criteria. By integrating seamlessly into the development workflow, it helps developers maintain high code quality and reduces the time spent on manual test creation, allowing them to focus more on core development tasks.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Automated Unit Test Generator Agent:

def add_numbers(a, b):
    return a + b

a	b	expected_output	description
1	2.0	3	Normal addition
-1	-1.0	-2	Addition of two negative numbers
0	0.0	0	Adding zero values
5	-3.0	2	Positive and negative number
1.5	2.5	4.0	Floating-point addition
1000000000.0	1000000000.0	2000000000.0	Large number addition
1e-09	1e-09	2e-09	Very small number addition
abc	1.0	Error	Invalid input: non-numeric value
	1.0	Error	Invalid input: None type

Deliverable Example

Sample output delivered by the Automated Unit Test Generator Agent:

a	b	expected_output	actual_output	test_result	description
1	2.0	3	3	Passed	Normal addition
-1	-1.0	-2	-2	Passed	Addition of two negative numbers
0	0.0	0	0	Passed	Adding zero values
5	-3.0	2	2	Passed	Positive and negative number
1.5	2.5	4.0	4.0	Passed	Floating-point addition
1000000000.0	1000000000.0	2000000000.0	2000000000.0	Passed	Large number addition
1e-09	1e-09	2e-09	2e-09	Passed	Very small number addition
abc	1.0	Error	Error	Passed	Invalid input: non-numeric value
	1.0	Error	Error	Passed	Invalid input: None type

ZBrain credits and their usage

A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:

Document Embedding:

When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.

Input Processing:

When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.

Generating Output

The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.

The cost in credits for various models and processes is detailed below:

Models and Credit Consumption:

Model	Input Cost	Output Cost
GPT-4o	2,500 credits / 1M tokens	7,500 credits / 1M tokens
GPT-4	15,000 credits / 1M tokens	30,000 credits / 1M tokens
GPT-4-32k	30,000 credits / 1M tokens	60,000 credits / 1M tokens
GPT-3.5 Turbo	250 credits / 1M tokens	750 credits / 1M tokens

Embedding Models:

Model	Input Cost
text-embedding-3-small	20 credits / 1M tokens
text-embedding-3-large	130 credits / 1M tokens
ada v2	100 credits / 1M tokens

When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.

Automated Unit Test Generator Agent

About this Agent

Input Data Set

Deliverable Example

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