Overview

The Target Analyzer Service is a specialized service designed to analyze and manage AI targets using LLM technology. It processes text content to determine the completion status of defined targets, taking into account dependencies between targets and additional context, making it highly effective for tracking and managing complex task completion.

• Target Completion Analysis: Intelligent analysis of target completion with detailed reasoning
• Dependency Tracking: Support for target dependencies and validation
• Context-Aware Processing: Analysis considers additional context and chat history
• Configurable Output: Control over analysis parameters and model behavior
• Detailed Reasoning: Comprehensive explanation for completion status
• Smart Dependency Management: Automatic validation of dependency chains

Traditional target tracking often involves manual status updates, binary completion flags, and simple dependency checks. The Target Analyzer Service improves this by using LLMs for intelligent completion analysis, supporting complex dependency chains, providing detailed reasoning for status, and maintaining context awareness.

Endpoints

Process Endpoint

The process endpoint performs target analysis and completion status evaluation.

API Endpoint

Endpoint Parameters

Request

content (string, required)

The text to analyze (e.g., chat history, status updates).

context (object, required)

Target definitions and additional context:

• targets (object): Contains todo and done targets
  • todo (array): List of incomplete target objects
  • done (array): List of completed target objects
• additionalContext (string, optional): Extra context for analysis

config (object, optional)

Model and analysis configuration:

• modelId (string): Model to use for analysis (default: "mistral-large-2411")
• temperature (number): Controls analysis precision (default: 0.1, range: 0.0-1.0)
• maxTokens (integer): Maximum tokens for model generation (default: 2000)

Response

status (string)

Status of the processing ("success" or "error").

result (object)

For successful responses:

• todo (array): List of incomplete targets with analysis
• done (array): List of completed targets with analysis
• Each target includes:
  • id (integer): Target identifier
  • target (string): Target description
  • done_when (string): Completion criteria
  • depends_on (array): Dependencies
  • analysis (object): Analysis result with reasoning and completion status

metadata (object)

Processing metadata:

• model (string): Model used
• created_at (string): Creation timestamp
• processed_at (string): Processing timestamp
• instance_id (string): Instance identifier
• tracking_id (string): Unique tracking ID
• mode (string): Analysis mode used

Examples

Request

Basic Request (Without Config)

Request With Custom Configuration

Response

Successful Analysis Response

Error Response

Target Configuration

The service uses a specific structure for defining targets:

Target Structure

Dependencies

• Specified through depends_on array
• Must be completed before target can be marked as done
• Supports complex dependency chains

Best Practices

Target Definition

• Write clear, specific targets
• Define unambiguous completion criteria
• Specify dependencies accurately
• Include relevant context

Content Structure

• Provide detailed status updates
• Include specific progress information
• Reference targets explicitly
• Maintain clear chat history

Configuration Tuning

• Use low temperature for precise analysis
• Adjust token limit based on content size
• Include relevant additional context

Limitations

• Targets must be predefined
• Dependencies must be explicit
• Requires clear completion criteria
• Text-based analysis only