Binary reference

ai-learner

AI learning engine for continuous improvement and performance analysis

Version9.8.4Size23.2MBAuthorWarith Al Maawali <warith@digi77.com>LicenseLicenseRef-Kodachi-SAN-1.0Websitehttps://kodachi.cloud

File Information

Property	Value
Binary Name	ai-learner
Version	9.8.4
Build Date	Not disclosed
Rust Version	1.82.0
File Size	23.2MB
Author	Warith Al Maawali <warith@digi77.com>
License	LicenseRef-Kodachi-SAN-1.0
Category	Kodachi Binary
Description	AI learning engine for continuous improvement and performance analysis
Git Commit	unknown
Metadata Generated	2026-07-11T18:16:31Z
Binary Timestamp	Unknown
JSON Data	View Raw JSON

SHA256 Checksum

315517c92aea86825636d9b12273687ca428e7dd73f7787607cf6e5fd5fc9557

Features

#	Feature
1	Feedback aggregation and analysis
2	Incremental learning with convergence detection
3	Performance tracking and trend analysis
4	Multi-format report generation (JSON, Markdown, HTML)

Security Features

Feature	Description
Input Validation	Argument parsing via clap; per-command validation is the consumer's responsibility
Rate Limiting	Not provided by cli-core
Authentication	Not provided by cli-core (see online-auth)
Encryption	Not provided by cli-core

System Requirements

Requirement	Value
OS	Linux (Debian-based)
Privileges	root/sudo for system operations
Dependencies	OpenSSL, libcurl

Global Options

Flag	Description
`-h, --help`	Print help information
`-v, --version`	Print version information
`-n, --info`	Display detailed information
`-e, --examples`	Show usage examples
`--json`	Output in JSON format
`-o, --output-format <FORMAT>`	Force output format (text\|json)
`--json-pretty`	Pretty-print JSON output with indentation
`--json-human`	Enhanced JSON output with improved formatting (like jq)
`--fields <FIELD_LIST>`	Select specific fields to include in output (comma-separated)
`--limit <NUMBER>`	Limit number of results returned
`--offset <NUMBER>`	Skip first N results (for pagination)
`-d, --work-dir <PATH>`	Working directory (defaults to auto-detected base directory)
`--port <PORT>`	Set custom port number (1024-65535)
`--log-level <LEVEL>`	Set log level (error\|warn\|info\|debug)
`--verbose`	Enable verbose output
`--quiet`	Suppress non-essential output
`--no-color`	Disable colored output
`--config <FILE>`	Use custom configuration file
`--timeout <SECS>`	Set operation timeout in seconds (optional; no default applied)
`--retry <COUNT>`	Retry attempts (optional; no default applied)

Commands

Analysis Operations

`analyze`

Analyze model performance and trends

Usage:

ai-learner analyze [OPTIONS]

Learning Operations

`learn`

Run learning cycle to improve model based on feedback

Usage:

ai-learner learn [OPTIONS]

Reporting Operations

`report`

Generate comprehensive performance reports

Usage:

ai-learner report [OPTIONS]

Status Operations

`status`

Show ai-learner status, database health, and activity metrics

Usage:

ai-learner status [OPTIONS]

Operational Scenarios

Scenario-oriented workflows generated from the binary's built-in -e --json examples.

Scenario 1: Basic Learning Operations

Run learning cycles and update models based on feedback

Step 1: Run a full learning cycle on all feedback

sudo ai-learner learn

Expected Output: Learning statistics showing improvements

Note

Processes all feedback since last run

Step 2: Run incremental learning on new feedback only

sudo ai-learner learn --incremental

Expected Output: Quick learning update with delta statistics

Step 3: Get learning results in JSON format

sudo ai-learner learn --json

Expected Output: JSON response with detailed learning metrics

Note

Useful for automated processing

Step 4: Learn with custom learning rate

sudo ai-learner learn --learning-rate 0.05

Expected Output: Learning results with adjusted convergence speed

Note

Lower rates for more stable convergence

Step 5: Learn with minimum feedback threshold

sudo ai-learner learn --min-feedback 100

Expected Output: Learning skipped if insufficient feedback available

Note

Ensures statistical significance

Step 6: Incremental learning with JSON output

sudo ai-learner learn --incremental --json

Expected Output: JSON with incremental learning delta statistics

Note

Combines fast incremental mode with structured output

Step 7: Full parameter learning with JSON output

sudo ai-learner learn --learning-rate 0.05 --min-feedback 100 --json

Expected Output: JSON with custom rate and threshold learning metrics

Note

All learning parameters combined for fine-tuned runs

Step 8: Generate signed AI policy file after learning

sudo ai-learner learn --output-policy

Expected Output: Learning cycle + ai-policy.json generated in results/

Note

Policy contains intent thresholds, tool allowlist, risk mode

Step 9: Generate policy with JSON output

sudo ai-learner learn --output-policy --json

Expected Output: JSON learning results + signed policy file written

Note

Policy is signed with SHA-256 HMAC to prevent tampering

Scenario 2: Performance Analysis

Analyze model accuracy and performance trends

Step 1: Analyze performance over the last week

sudo ai-learner analyze --period last-7-days

Expected Output: Accuracy metrics and trend analysis

Step 2: Get accuracy analysis in JSON format

sudo ai-learner analyze --metric accuracy --json

Expected Output: JSON with per-intent accuracy breakdown

Note

Supports: accuracy, confidence, f1-score

Step 3: Generate learning curve visualization data

sudo ai-learner analyze --learning-curve

Expected Output: Time-series data showing accuracy improvement

Note

Useful for identifying plateaus

Step 4: Analyze confidence metrics

sudo ai-learner analyze --metric confidence

Expected Output: Confidence score distribution and statistics

Note

Shows prediction certainty levels

Step 5: Analyze last 30 days as JSON

sudo ai-learner analyze --period last-30-days --json

Expected Output: JSON with monthly performance trends

Note

Useful for monthly reporting

Step 6: Analyze all-time data

sudo ai-learner analyze --period all-time

Expected Output: Complete historical performance analysis

Note

Shows long-term improvement trends

Step 7: Analyze F1-score metric with JSON output

sudo ai-learner analyze --metric f1-score --json

Expected Output: JSON with F1-score breakdown per intent

Note

F1-score balances precision and recall

Step 8: Learning curve data as JSON

sudo ai-learner analyze --learning-curve --json

Expected Output: JSON time-series of accuracy improvement

Note

Structured data for visualization tools

Scenario 3: AI Tier Performance

Analyze learning metrics per AI engine tier (TF-IDF, ONNX, Mistral.rs, GenAI/Ollama, Legacy LLM, Claude)

Step 1: Show accuracy breakdown across all AI tiers

sudo ai-learner analyze --metric accuracy --json

Expected Output: JSON with per-tier accuracy metrics

Note

Compares tier performance for optimization decisions

Step 2: Weekly tier performance trends

sudo ai-learner analyze --period last-7-days --json

Expected Output: JSON with weekly metrics including new tier data

Note

Tracks mistral.rs and GenAI tier improvement over time

Step 3: ONNX vs LLM routing breakdown

sudo ai-learner analyze --metric accuracy --json

Expected Output: JSON with fast-path vs slow-path query statistics

Note

Shows what percentage of queries use ONNX fast path vs LLM

Scenario 4: Report Generation

Generate comprehensive reports on learning performance

Step 1: Generate a summary report in JSON format

sudo ai-learner report

Expected Output: JSON report with all performance metrics

Step 2: Generate a Markdown report

sudo ai-learner report --format markdown

Expected Output: Formatted Markdown document with tables and graphs

Note

Great for documentation

Step 3: Generate an HTML report with visualizations

sudo ai-learner report --format html --output results/learning-report.html

Expected Output: Interactive HTML report saved to file

Note

Includes charts and graphs

Step 4: Generate weekly report

sudo ai-learner report --period last-7-days

Expected Output: JSON report covering last 7 days of activity

Note

Useful for weekly reviews

Step 5: Full report in markdown JSON format

sudo ai-learner report --period all-time --format markdown --json

Expected Output: Complete historical report in markdown format as JSON

Note

Combines all-time data with markdown formatting

Step 6: Generate HTML report to file

sudo ai-learner report --format html --output results/report.html

Expected Output: HTML report with charts saved to results/report.html

Note

File output within execution folder

Scenario 5: Integration Workflow

ai-learner commands commonly used alongside training/query cycles

Step 1: Check learner health before running learning jobs

ai-learner status --json

Expected Output: JSON status with metrics and last run timestamps

Note

Recommended first step in automation pipelines

Step 2: Inspect recent feedback trends before retraining

ai-learner analyze --period last-day --json

Expected Output: JSON analysis of recent learner performance

Note

Useful for deciding whether to run learn now

Step 3: Generate weekly markdown report for training review

ai-learner report --period last-7-days --format markdown --json

Expected Output: JSON envelope containing markdown report

Note

Good handoff artifact before ai-trainer operations

Step 4: Run incremental learning with existing feedback

sudo ai-learner learn --incremental

Expected Output: Incremental learning summary and convergence data

Note

Requires authentication

Step 5: Learn and regenerate policy artifacts in one step

sudo ai-learner learn --output-policy --json

Expected Output: JSON with learning results and policy output status

Note

Requires authentication

Step 6: Generate long-range HTML report for model tuning

ai-learner report --period all-time --format html --output results/learning-report.html

Expected Output: HTML report written to results/learning-report.html

Note

Use before planning major trainer updates

Environment Variables

Variable	Description	Default	Values
`RUST_LOG`	Set logging level	info	error\|warn\|info\|debug\|trace
`NO_COLOR`	Disable all colored output when set	unset	1\|true\|yes (any value disables color)

Exit Codes

Code	Description
4	Network error
3	Permission denied
1	General error
0	Success
2	Invalid arguments
5	File not found