Example AI Task Report

Example AI Task Report

This is a sample report that demonstrates what AI Task reports look like when generated from pipeline executions. In a real report, this would contain detailed information about the execution of an AI Task pipeline.

Pipeline Configuration

The pipeline is typically configured in a YAML file with a .ai extension. Here’s an example configuration:

name: "Example Pipeline"
description: "An example pipeline that processes text"

pipe:
  - type: function
    function: aisource
    params:
      file: "input.txt"
  
  - type: llm
    tmpl: "process"
    model: "claude-3-7-sonnet-latest"
  
  - type: function
    function: airesult
    params:
      file: "output.txt"
      format: "text"

Data Flow Visualization

The following diagram illustrates the flow of data through the pipeline:

flowchart TD
    A[Empty Dictionary] --> B((aisource))
    B --> C[Dictionary with Text Content]
    C --> D((LLM Task))
    D --> E[Dictionary with Processed Content]
    E --> F((airesult))
    F --> G[Dictionary with Result File Path]

Execution Flow

The execution was monitored with detailed logging enabled. Below is the analysis of the content flow through each step of the pipeline.

Step 1: aisource Function

Function: aisource
Parameters: {'file': 'input.txt'}

Input

An empty dictionary with no keys.

Process

The function read the file input.txt and loaded its content.

Output

A dictionary with the following keys: - source-file: Path to the source file - source-content: Content of the source file - pipeout_text: Content of the source file (to be passed to the next step)

Content Transformation

Input Content: None (Empty input)

Output Content (pipeout_text):

Input pipe

This is example content from the input file. In a real report, this would contain
the actual content of the input file. This is just a placeholder to demonstrate
the format of AI Task reports.

Step 2: LLM Item

Type: llm
Template: process
Model: claude-3-7-sonnet-latest

Input

The dictionary from the previous step, with the text content in pipeout_text.

Process

The LLM item used the template process to generate a processed version of the text. The template is:

Instruction

process the following text and extract the key points:

{{ pipein_text }}

Output

The same dictionary with pipeout_text updated to contain the generated output.

Content Transformation

Input Content: Same as pipeout_text from Step 1

Output Content (pipeout_text):

Output pipe

Key points from the text:
1. This is an example AI Task report
2. It demonstrates the format of reports generated by AI Task
3. In a real report, this would contain the actual output from the LLM

Step 3: airesult Function

Function: airesult
Parameters: {'file': 'output.txt', 'format': 'text'}

Input

The dictionary from the previous step, with the processed content in pipeout_text.

Process

The function saved the processed content to the file output.txt.

Output

The same dictionary with: - pipeout_text updated to contain a message about the saved file - result-file added with the path to the saved file

Content Transformation

Input Content: Same as pipeout_text from Step 2

Output Content (pipeout_text):

Output pipe

Saved result to output.txt

Content Analysis

The AI task successfully processed the input text, extracting the key points as requested. The processed text is more concise and focused on the main points from the original content.

Performance Metrics

• Execution Time: 2.45 seconds
• Token Usage: 245 input tokens, 78 output tokens
• Model: claude-3-7-sonnet-latest