### LintAI Web Dashboard Setup

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Start the LintAI web dashboard development server. Navigate to the frontend directory, install dependencies, and run the development script.

```bash
cd frontend
npm install
npm run dev
```

--------------------------------

### Install LintAI from PyPI

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Use this command to install the LintAI package from the Python Package Index. This is the recommended installation method.

```bash
pip install llm-validator
```

--------------------------------

### Install LintAI from Source

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Clone the repository and install LintAI locally. This is useful for development or when you need the latest unreleased features.

```bash
git clone https://github.com/SoulSniper-V2/lintai.git
cd lintai
pip install -e .
```

--------------------------------

### Example Assertions Configuration JSON

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Define validation rules for LLM outputs using a JSON file. Specify assertion types, parameters, and weights for scoring.

```json
{
  "assertions": [
    {
      "name": "max_length",
      "type": "MAX_LENGTH",
      "params": { "max_chars": 1000 },
      "weight": 0.3
    },
    {
      "name": "contains_steps",
      "type": "CONTAINS_TEXT",
      "params": { "text": "step 1" },
      "weight": 0.5
    },
    {
      "name": "no_profanity",
      "type": "NO_PATTERN",
      "params": { "pattern": "badword|offensive" },
      "weight": 0.2
    }
  ]
}
```

--------------------------------

### Example Customer Email Validator Configuration

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Defines a validator named 'customer_email' using Claude 3 Opus with assertions for professional tone, greeting presence, signature presence, and no PII.

```yaml
name: customer_email
model: claude-3-opus
assertions:
  - name: professional_tone
    type: SENTIMENT
    params: { min_positive: 0.3, max_negative: 0.2 }
    weight: 0.3
  
  - name: has_greeting
    type: CONTAINS_TEXT
    params: { text: "Dear|Hello|Hi" }
    weight: 0.1
  
  - name: has_signature
    type: CONTAINS_TEXT
    params: { text: "Sincerely|Best|Thanks" }
    weight: 0.1
  
  - name: no_pii
    type: NO_PATTERN
    params: { pattern: "\\d{3}-\\d{2}-\\d{4}" }  # SSN pattern
    weight: 0.5
```

--------------------------------

### GitHub Actions Workflow for Validation

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

A GitHub Actions workflow that checks out code, sets up Python, installs the validator, runs validation against a config file, and checks the resulting score.

```yaml
name: Validate AI Outputs
on: [push]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with: { python-version: '3.11' }
      - name: Install
        run: pip install llm-validator
      - name: Run Validation
        run: |
          llm-validate \
            --config validators/code_review.yaml \
            --output validation_results.json
      - name: Check Score
        run: |
          if [ $(jq '.score' validation_results.json) -lt 80 ]; then
            echo "Score below threshold!"
            exit 1
          fi
```

--------------------------------

### Example Code Review Validator Configuration

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Defines a validator named 'code_review' using GPT-4 with assertions for test presence, no hardcoded secrets, reasonable length, and error handling.

```yaml
name: code_review
model: gpt-4
assertions:
  - name: has_tests
    type: CONTAINS_TEXT
    params: { text: "test" }
    weight: 0.3
  
  - name: no_hardcoded_secrets
    type: NO_PATTERN
    params: { pattern: "api_key|password|secret" }
    weight: 0.4
  
  - name: reasonable_length
    type: MAX_LENGTH
    params: { max_tokens: 2000 }
    weight: 0.2
  
  - name: has_error_handling
    type: REGEX_MATCH
    params: { pattern: "except|try|catch" }
    weight: 0.1
```

--------------------------------

### LintAI CLI: Initialize Configuration

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Use the 'lintai init-config' command to generate a default configuration file for validation.

```bash
# Initialize a validation config
lintai init-config
```

--------------------------------

### Provider Selection in Python

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Demonstrates how to instantiate the LLMValidator with different LLM providers like OpenAI, Anthropic, or a local model.

```python
from llm_validator.providers import OpenAIProvider, AnthropicProvider, LocalProvider

# OpenAI
validator = LLMValidator(provider=OpenAIProvider(model="gpt-4"))

# Anthropic
validator = LLMValidator(provider=AnthropicProvider(model="claude-3-opus"))

# Local/Ollama
validator = LLMValidator(provider=LocalProvider(model="llama2"))
```

--------------------------------

### Releasing a New Version

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Steps to release a new version of the project, including committing changes, creating a version tag, and pushing the tag.

```bash
# Make changes, commit
git add -A
git commit -m "Description of changes"

# Create a version tag (follows semver)
git tag v0.1.1

```

--------------------------------

### LintAI CLI: Quick Test

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Perform a quick validation using command-line arguments for prompt, output, and rules. Useful for rapid testing.

```bash
# Quick test
llm-validate --prompt "Summarize this" --output "The text says..." --rules "max_tokens:100"
```

--------------------------------

### LintAI CLI: Validate with Config File

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Run validations using a specified configuration file with the 'lintai validate' command.

```bash
# Validate with a config file
lintai validate --config validators/my_config.yaml
```

--------------------------------

### LintAI CLI: Run Validation from Config

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Execute validation using the 'llm-validate' command with a specified configuration file.

```bash
# Run validation from config
llm-validate --config validators/sales_plan.yaml
```

--------------------------------

### Environment Variables for API Keys

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Sets environment variables for API keys required by different LLM providers.

```bash
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=...
GOOGLE_API_KEY=...
```

--------------------------------

### Running Tests with Pytest

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Commands to execute tests using pytest, including running all tests, running with code coverage, and running a specific test file.

```bash
# Run all tests
pytest tests/

# Run with coverage
pytest --cov=llm_validator tests/

# Run specific test
pytest tests/test_core.py -v
```

--------------------------------

### Trigger PyPI Release with Git Tags

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Pushing a tag to the main branch triggers an automated CI workflow for building and publishing the package to PyPI. Ensure the PYPI_API_TOKEN secret is configured in GitHub repository settings.

```bash
git push origin main
git push origin v0.1.1
```

--------------------------------

### Python API: Initialize LLMValidator

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Instantiate the LLMValidator with your chosen model and API key. This object is used to perform validation tasks.

```python
from llm_validator import LLMValidator, Assertion, AssertionType

# Initialize validator
validator = LLMValidator(
    model="gpt-4",
    api_key="your-key"
)
```

--------------------------------

### LintAI CLI: Batch Validation

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Perform batch validation on multiple test cases from a JSONL file and save the results to an output file using 'lintai batch'.

```bash
# Batch validation from JSONL
lintai batch --input test_cases.jsonl --output results.jsonl
```

--------------------------------

### LintAI CLI: Batch Validation (llm-validate)

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Conduct batch validation from a JSONL input file and output the results to a JSONL file using the 'llm-validate' command.

```bash
# Batch validation
llm-validate --input test_cases.jsonl --output results.jsonl
```

--------------------------------

### LintAI GitHub Action for CI/CD

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Integrate LintAI into your GitHub Actions workflow to automatically validate LLM outputs. Configure prompts, expected outputs, and assertion rules.

```yaml
name: Validate AI Output
on: [push]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      
      - name: Validate LLM Output
        uses: SoulSniper-V2/lintai@v0.1
        with:
          prompt: "Summarize this document"
          output: "${{ steps.generate.outputs.result }}"
          assertions-config: "./assertions.json"
          pass-threshold: 80
```

--------------------------------

### Python API: Define Assertions

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Create a list of Assertion objects to define the validation rules. Each assertion includes a name, type, parameters, and weight.

```python
# Define assertions
assertions = [
    Assertion(
        name="max_length",
        type=AssertionType.MAX_LENGTH,
        params={"max_tokens": 500},
        weight=0.3
    ),
    Assertion(
        name="no_profanity",
        type=AssertionType.NO_PATTERN,
        params={"pattern": r"(?i)badword|offensive"},
        weight=0.5
    ),
    Assertion(
        name="contains_action_plan",
        type=AssertionType.CONTAINS_TEXT,
        params={"text": "step 1", "count": 1},
        weight=0.2
    )
]
```

--------------------------------

### Python API: Validate Output

Source: https://github.com/soulsniper-v2/lintai/blob/master/README.md

Use the validator.validate() method to check an LLM's output against the defined assertions. It returns a result object with score and pass/fail status.

```python
# Validate output
result = validator.validate(
    prompt="Create a plan to increase sales",
    output="Here is a step by step plan...",
    assertions=assertions
)

print(f"Confidence Score: {result.score}/100")
print(f"Passed: {result.passed}")
print(f"Failed: {result.failed_assertions}")
```

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