neuralex 0.1.0

Official Python client library for NeuraLex API

NeuraLex Python Client

Official Python client library for the NeuraLex API.

Installation

pip install neuralex

Quick Start

from neuralex import NeuraLexClient

# Initialize client with your API key
client = NeuraLexClient(api_key="nlx_your_api_key")

# Generate embeddings
response = client.embed("Hello, world!")

# Access the embedding vector
embedding = response.payload[0].embedding
print(f"Dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")

Features

• ✅ Simple and intuitive API
• ✅ Synchronous and asynchronous support
• ✅ Type hints and Pydantic models
• ✅ Automatic error handling
• ✅ Configurable semantic/term-based balance
• ✅ Batch embedding support (up to 100 inputs)

Usage

Basic Usage

from neuralex import NeuraLexClient

client = NeuraLexClient(api_key="nlx_your_api_key")

# Single text
response = client.embed("Machine learning is fascinating")
embedding = response.payload[0].embedding

Batch Embeddings

# Multiple texts
texts = [
    "First document",
    "Second document",
    "Third document"
]
response = client.embed(texts)

for item in response.payload:
    print(f"Text: {item.text}")
    print(f"Embedding dimensions: {len(item.embedding)}")
    print(f"Tokens used: {item.usage.total_tokens}")

Adjusting Semantic Weight

The semantic_weight parameter controls the balance between term-based and semantic embeddings:

• 0.0 = Pure term-based (exact keyword matching)
• 1.0 = Pure semantic (meaning-based)
• 0.5 = Balanced (default)

# More term-focused (better for keyword search)
response = client.embed(
    "Python programming",
    semantic_weight=0.3
)

# More semantic-focused (better for meaning-based search)
response = client.embed(
    "Python programming",
    semantic_weight=0.8
)

Using Context Manager

# Automatically handles connection cleanup
with NeuraLexClient(api_key="nlx_your_api_key") as client:
    response = client.embed("Hello, world!")
    print(response.payload[0].embedding[:5])

Async Usage

import asyncio
from neuralex import AsyncNeuraLexClient

async def main():
    async with AsyncNeuraLexClient(api_key="nlx_your_api_key") as client:
        response = await client.embed(["Text 1", "Text 2", "Text 3"])
        for item in response.payload:
            print(f"{item.text}: {len(item.embedding)} dimensions")

asyncio.run(main())

Error Handling

from neuralex import NeuraLexClient, AuthenticationError, RateLimitError, APIError

client = NeuraLexClient(api_key="nlx_your_api_key")

try:
    response = client.embed("Hello, world!")
except AuthenticationError:
    print("Invalid API key")
except RateLimitError:
    print("Rate limit exceeded, please wait")
except APIError as e:
    print(f"API error: {e.message} (status: {e.status_code})")
except Exception as e:
    print(f"Unexpected error: {e}")

API Reference

NeuraLexClient

Main client class for synchronous API calls.

Methods:

• embed(inputs, model="public", language="english", semantic_weight=0.5) - Generate embeddings

Parameters:

• inputs (str | List[str]): Text or list of texts to embed (max 100)
• model (str, optional): Model name (default: "public")
• language (str, optional): Language for lexeme extraction (default: "english")
• semantic_weight (float, optional): Balance between term (0.0) and semantic (1.0) (default: 0.5)

Returns: EmbeddingResponse object

AsyncNeuraLexClient

Async client class for asynchronous API calls. Same interface as NeuraLexClient but all methods are async.

Models

EmbeddingResponse

• payload (List[EmbeddingData]): List of embedding results
• model (str): Model name used
• total_usage (Usage): Total token usage across all inputs

EmbeddingData

• text (str): Original input text
• embedding (List[float]): Vector embedding
• usage (Usage): Token usage for this input

Usage

• total_tokens (int): Total tokens processed

Getting an API Key

1. Sign up at app.neuralex.ca
2. Generate a new API key
3. Keep your API key secure and never commit it to version control

License

MIT License - see LICENSE file for details