Metadata-Version: 2.4
Name: datalegion
Version: 0.1.1
Summary: Python client for the Data Legion API
Project-URL: Homepage, https://www.datalegion.ai
Project-URL: Documentation, https://www.datalegion.ai/docs
Project-URL: Repository, https://github.com/datalegion-ai/datalegion-python
Project-URL: Issues, https://github.com/datalegion-ai/datalegion-python/issues
Author-email: Data Legion <engineering@datalegion.ai>
License: MIT
License-File: LICENSE
Keywords: api,b2b,company-enrichment,contact-data,data-enrichment,datalegion,person-enrichment,sdk
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Typing :: Typed
Requires-Python: >=3.10
Requires-Dist: httpx>=0.24.0
Requires-Dist: pydantic>=2.0
Description-Content-Type: text/markdown

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<h1 align="center">Data Legion Python SDK</h1>

<p align="center">
  <a href="https://github.com/datalegion-ai/datalegion-python/actions/workflows/test.yml"><img src="https://github.com/datalegion-ai/datalegion-python/actions/workflows/test.yml/badge.svg" alt="Tests"></a>&nbsp;&nbsp;
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</p>

The official Python client for the [Data Legion API](https://www.datalegion.ai/docs).

## Installation

```bash
pip install datalegion
```

## Quick Start

```python
from datalegion import DataLegion

client = DataLegion(api_key="legion_...")
```

Or set the `DATALEGION_API_KEY` environment variable and omit the `api_key` argument:

```python
client = DataLegion()
```

## Person Enrichment

Look up a person by email, phone, social URL, name, or other identifiers. Returns a `PersonResponse` with full type hints and dot access.

```python
person = client.person.enrich(email="john@example.com")
print(person.full_name)
print(person.job_title)
print(person.company_name)

# Multiple results
results = client.person.enrich(
    email="john@example.com",
    multiple_results=True,
    limit=5,
)
for match in results.matches:
    print(match.person.full_name, match.match_metadata.match_confidence)

# Enrich by name + company
person = client.person.enrich(
    first_name="John",
    last_name="Doe",
    company="Google",
)

# Enrich by LinkedIn URL
person = client.person.enrich(social_url="https://linkedin.com/in/johndoe")
```

## Person Search

Search for people using SQL queries.

```python
results = client.person.search(
    query="SELECT * FROM people WHERE company_name ILIKE '%google%' AND city = 'San Francisco'",
    limit=10,
)
for match in results.matches:
    print(match.person.full_name)
```

## Person Discover

Search for people using natural language.

```python
results = client.person.discover(
    query="engineers in San Francisco who worked at Google",
    limit=10,
)
for match in results.matches:
    print(match.person.full_name)
```

## Company Enrichment

Look up a company by domain, name, LinkedIn ID, or ticker symbol. Returns a `CompanyResponse` with dot access.

```python
# By domain
company = client.company.enrich(domain="google.com")
print(company.name.cleaned)
print(company.industry)
print(company.legion_employee_count)

# By name
company = client.company.enrich(name="Google")

# Multiple results
results = client.company.enrich(name="Apple", multiple_results=True, limit=3)
for match in results.matches:
    print(match.company.name.cleaned)
```

## Company Search

Search for companies using SQL queries.

```python
results = client.company.search(
    query="SELECT * FROM companies WHERE industry = 'software development'",
    limit=10,
)
for match in results.matches:
    print(match.company.name.cleaned)
```

## Company Discover

Search for companies using natural language.

```python
results = client.company.discover(
    query="AI companies with more than 100 employees",
    limit=10,
)
for match in results.matches:
    print(match.company.name.cleaned)
```

## Utilities

### Clean & Normalize Fields

```python
cleaned = client.utility.clean(
    fields={
        "email": "John.Doe+tag@gmail.com",
        "phone": "(555) 123-4567",
        "domain": "https://www.Google.com/about",
    }
)
for field, result in cleaned.results.items():
    print(f"{field}: {result.original} -> {result.cleaned}")
```

### Hash Email

```python
hashed = client.utility.hash_email(email="john@example.com")
print(hashed.hashes["sha256"])
print(hashed.hashes["md5"])
```

### Validate Data

```python
validated = client.utility.validate(
    email="john@example.com",
    phone="+15551234567",
    first_name="John",
)
print(validated.valid)
for error in validated.errors:
    print(f"{error.field}: {error.error}")
```

## Health Check

```python
health = client.health()
print(health.status)
```

## Async Usage

```python
import asyncio
from datalegion import AsyncDataLegion

async def main():
    client = AsyncDataLegion(api_key="legion_...")

    person = await client.person.enrich(email="john@example.com")
    print(person.full_name)

    company = await client.company.enrich(domain="google.com")
    print(company.name.cleaned)

    await client.close()

asyncio.run(main())
```

Or use as a context manager:

```python
async with AsyncDataLegion(api_key="legion_...") as client:
    person = await client.person.enrich(email="john@example.com")
```

## Response Types

All responses are [Pydantic](https://docs.pydantic.dev/) models with full type hints and autocomplete support. Import them directly:

```python
from datalegion import PersonResponse, CompanyResponse, PersonMatchesResponse
```

## Response Headers

After each request, these attributes are updated on the client:

```python
person = client.person.enrich(email="john@example.com")
print(client.request_id)             # unique request ID
print(client.credits_used)           # credits consumed
print(client.credits_remaining)      # credits remaining
print(client.contract_id)            # contract ID
print(client.rate_limit_limit)       # requests quota in current window
print(client.rate_limit_remaining)   # remaining requests in current window
print(client.rate_limit_reset)       # unix timestamp when window resets
print(client.rate_limit_policy)      # rate limit policy (e.g. "100/min")
print(client.retry_after)            # seconds to wait (on 429 responses)
print(client.generated_query)        # SQL from discover endpoints
print(client.correlation_id)         # echoed Correlation-ID
```

## Error Handling

```python
from datalegion import (
    DataLegion,
    AuthenticationError,
    InsufficientCreditsError,
    RateLimitError,
    ValidationError,
    APIError,
)

client = DataLegion(api_key="legion_...")

try:
    person = client.person.enrich(email="john@example.com")
except AuthenticationError as e:
    print(f"Invalid API key: {e.message}")
except InsufficientCreditsError as e:
    print(f"Out of credits: {e.message}")
except RateLimitError as e:
    print(f"Rate limited: {e.message}")
except ValidationError as e:
    print(f"Invalid request: {e.message}, details: {e.details}")
except APIError as e:
    print(f"Server error ({e.status_code}): {e.message}")
```

All exceptions inherit from `DataLegionError` and include these attributes:

- `message` - Human-readable error message
- `status_code` - HTTP status code
- `error` - Error type/code from the API
- `details` - Additional error details (if any)

## Configuration

| Parameter      | Default                      | Description                             |
| -------------- | ---------------------------- | --------------------------------------- |
| `api_key`      | `DATALEGION_API_KEY` env var | Your API key                            |
| `base_url`     | `https://api.datalegion.ai`  | API base URL                            |
| `timeout`      | `60.0`                       | Request timeout in seconds              |
| `httpx_client` | None                         | Custom httpx.Client / httpx.AsyncClient |

## Documentation

For full API documentation, visit [https://www.datalegion.ai/docs](https://www.datalegion.ai/docs).
