Agents-internal

About this Agent

The Code Quality Analysis Agent is an AI-powered digital assistant designed to enhance software development processes by automatically reviewing code. It ensures adherence to coding standards and best practices, identifies potential issues such as syntax errors, security vulnerabilities, and inefficient code. This agent streamlines the code review process, providing developers with valuable insights and suggestions for improvement, ultimately leading to more robust and secure applications.

Accuracy
TBD

Speed
TBD

Input Data Set

Sample of data set required for Code Quality Analysis Agent:

Sample code with errors

def example_function():
    x = 10
    y = 20
    print(x + y
def unused_function():
    pass
def potentialsecurityissue(password):
    # Hardcoded secret
    secretkey = "mysecret_key"
    print(f"The secret key is {secret_key}")

Deliverable Example

Sample output delivered by the Code Quality Analysis Agent:

Line Number	Issue Type	Description	Severity
4	Syntax Error	Syntax error: missing closing parenthesis	High
7	Unused Code	Unused function: The function "unused_function" is defined but never used	Low
10	Security Vulnerability	Hardcoded secret key detected	Medium

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.

Code Quality Analysis Agent

About this Agent

Input Data Set

Deliverable Example

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