lindr 0.2.1

Python SDK for the Lindr LLM personality evaluation API

Lindr Python SDK

Python SDK for the Lindr LLM personality evaluation API.

Lindr helps you validate fine-tuned models by measuring behavioral personality across 10 dimensions. Compare base vs. fine-tuned models and catch personality drift before deploying to production.

Installation

pip install lindr

Quick Start

import lindr

# Initialize client
client = lindr.Client(api_key="lnd_...")

# Create a persona (your target personality profile)
persona = client.personas.create(
    name="Support Agent",
    dimensions=lindr.PersonalityDimensions(
        openness=70,
        conscientiousness=85,
        extraversion=60,
        agreeableness=90,
        neuroticism=20,
        assertiveness=65,
        ambition=70,
        resilience=80,
        integrity=95,
        curiosity=75,
    ),
)

# Run baseline evaluation
samples = [
    {"id": "1", "content": "Response from base model..."},
    {"id": "2", "content": "Another response..."},
]

baseline, summary = client.evals.batch(
    name="llama-3.2-8b Baseline",
    persona_id=persona.id,
    samples=samples,
)
print(f"Avg drift: {summary.avg_drift:.1f}%")

# After fine-tuning, evaluate again
candidate, _ = client.evals.batch(
    name="llama-3.2-8b-dpo-v1",
    persona_id=persona.id,
    samples=finetuned_samples,
)

# Compare results
comparison = client.comparisons.create(
    baseline_eval_id=baseline.id,
    candidate_eval_id=candidate.id,
)
print(f"Recommendation: {comparison.recommendation}")  # "ship", "review", or "reject"

Features

• Batch Evaluations: Analyze multiple LLM responses in parallel
• 10 Personality Dimensions: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism, Assertiveness, Ambition, Resilience, Integrity, Curiosity
• A/B Comparisons: Compare baseline vs. fine-tuned models
• Ship/Review/Reject Recommendations: Get actionable guidance on model quality
• Automatic Retries: Built-in retry logic with exponential backoff
• Pagination: Efficiently iterate through large result sets
• Async Support: Full async/await support for high-throughput applications

Pagination

All list methods return paginated responses:

# Get first page
result = client.personas.list(page=1, per_page=10)

print(f"Total personas: {result.total}")
print(f"Page {result.page} of {result.total_pages}")
print(f"Has more: {result.has_more}")

# Iterate through all personas
for persona in result.items:
    print(f"- {persona.name}")

# Get next page
if result.has_more:
    next_page = client.personas.list(page=2, per_page=10)

Model Checkpoints

Track fine-tuning iterations with checkpoints:

# Register a checkpoint
checkpoint = client.checkpoints.create(
    name="llama-3.2-8b-dpo-v1",
    model_base="llama-3.2-8b",
    training_method="dpo",
    notes="First DPO fine-tune with safety data",
)

# Run eval with checkpoint tracking
eval_run, summary = client.evals.batch(
    name="DPO v1 Eval",
    persona_id=persona.id,
    checkpoint_id=checkpoint.id,
    samples=samples,
)

Datasets

Create reusable prompt datasets:

# Create a dataset
dataset = client.datasets.create(
    name="Support Scenarios",
    prompts=[
        {
            "id": "greeting",
            "messages": [{"role": "user", "content": "Hello, I need help"}],
            "category": "greeting",
        },
        {
            "id": "complaint",
            "messages": [{"role": "user", "content": "This is unacceptable!"}],
            "category": "complaint",
        },
    ],
)

# Run eval with dataset (requires monitor for generation)
eval_run, summary = client.evals.batch(
    name="Dataset Eval",
    persona_id=persona.id,
    dataset_id=dataset.id,
    monitor_id="mon_xxx",  # Monitor to generate responses
)

Error Handling

The SDK provides typed exceptions for common error cases:

import lindr

try:
    persona = client.personas.get("invalid_id")
except lindr.NotFoundError:
    print("Persona not found")
except lindr.RateLimitError as e:
    print(f"Rate limited. Retry after {e.retry_after} seconds")
except lindr.ValidationError as e:
    print(f"Validation failed: {e.details}")
except lindr.AuthenticationError:
    print("Invalid API key")
except lindr.APIError as e:
    print(f"API error ({e.status_code}): {e}")

Automatic Retries

The SDK automatically retries failed requests with exponential backoff:

# Default: 3 retries with exponential backoff
client = lindr.Client(api_key="lnd_...")

# Customize retry behavior
client = lindr.Client(
    api_key="lnd_...",
    max_retries=5,  # More retries for unreliable networks
)

# Disable retries
client = lindr.Client(
    api_key="lnd_...",
    max_retries=0,
)

Retried status codes: 429 (rate limit), 500, 502, 503, 504

Async Usage

import asyncio
import lindr

async def main():
    async with lindr.AsyncClient(api_key="lnd_...") as client:
        # List with pagination
        result = await client.personas.list()

        # Run multiple evals in parallel
        tasks = [
            client.evals.batch(
                name=f"Eval {i}",
                persona_id=persona_id,
                samples=sample_batches[i],
            )
            for i in range(5)
        ]
        results = await asyncio.gather(*tasks)

asyncio.run(main())

Configuration

Environment Variables

• LINDR_API_KEY: Your API key (alternative to passing api_key to client)
• LINDR_BASE_URL: Override API base URL (default: https://api.lindr.io/api/v1)

Client Options

client = lindr.Client(
    api_key="lnd_...",           # Required (or use LINDR_API_KEY env var)
    base_url="https://...",       # Optional: custom API endpoint
    timeout=60.0,                 # Optional: request timeout in seconds (default: 30)
    max_retries=3,                # Optional: retry attempts (default: 3)
)

API Reference

Personas

# Create
persona = client.personas.create(name="...", dimensions=dims)

# List (paginated)
result = client.personas.list(page=1, per_page=20)

# Get
persona = client.personas.get("persona_id")

# Update
persona = client.personas.update("persona_id", name="New Name")

# Delete
client.personas.delete("persona_id")

Evals

# Batch evaluation
eval_run, summary = client.evals.batch(
    name="...",
    persona_id="...",
    samples=[{"id": "1", "content": "..."}],
)

# List (paginated)
result = client.evals.list(status="completed")

# Get with results
eval_run, results = client.evals.get("eval_id", include_results=True)

# Delete
client.evals.delete("eval_id")

Comparisons

# Create comparison
comparison = client.comparisons.create(
    baseline_eval_id="...",
    candidate_eval_id="...",
)

# List (paginated)
result = client.comparisons.list()

# Get
comparison = client.comparisons.get("comparison_id")

Datasets

# Create
dataset = client.datasets.create(name="...", prompts=[...])

# List (paginated)
result = client.datasets.list()

# Get
dataset = client.datasets.get("dataset_id")

# Delete
client.datasets.delete("dataset_id")

Checkpoints

# Create
checkpoint = client.checkpoints.create(
    name="model-v1",
    model_base="llama-3.2-8b",
    training_method="lora",
)

# List (paginated)
result = client.checkpoints.list(model_base="llama-3.2-8b")

# Get
checkpoint = client.checkpoints.get("checkpoint_id")

# Delete
client.checkpoints.delete("checkpoint_id")

Documentation

Full documentation available at lindr.io/docs

License

MIT