semvec/pypi-packages huggingface dataset:
langgate-transform 0.4.1
Parameter transformation utilities for LangGate
License: MIT
Requires Python: >=3.13

# LangGate Transform

Parameter transformation utilities for LangGate AI Gateway.

This package provides the core parameter transformation logic used by both:
- Local registry clients to transform parameters for direct model calls
- The Envoy external processor to transform parameters in the proxy

It implements a declarative approach to parameter transformation that can be:
1. Used directly in Python applications
2. Potentially reimplemented in other languages (like Go) for external processors
3. Extended with custom transformation rules

## Parameter Transformation Precedence

The transformation module follows a specific precedence order when applying parameter transformations:

### Defaults (applied only if key doesn't exist yet):
1. Model-specific defaults (highest precedence for defaults)
2. Pattern defaults (matching patterns applied in config order)
3. Service provider defaults
4. Global defaults (lowest precedence for defaults)

### Overrides/Removals/Renames (applied in order, later steps overwrite/modify earlier ones):
1. Input parameters (initial state)
2. Service-level API keys and base URLs
3. Service-level overrides, removals, renames
4. Pattern-level overrides, removals, renames (matching patterns applied in config order)
5. Model-specific overrides, removals, renames (highest precedence)
6. Model ID (always overwritten with service_model_id)
7. Environment variable substitution (applied last to all string values)
langgate-server 0.4.1
Server implementation for LangGate AI Gateway
License: MIT
Requires Python: >=3.13

# LangGate Server

Server implementation for the LangGate AI Gateway. This package provides the FastAPI server implementation that hosts the LangGate registry API.

## Features

- FastAPI-based API server
- Registry endpoints for model information
- Configurable settings through environment variables or config files
- Structured logging with request context

## Usage

```python
# Run the server with uvicorn
import uvicorn
from langgate.server import app

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=4000)
```

## Docker Deployment

The LangGate repository includes a Dockerfile specifically for running this server:

```bash
# Build the Docker image
docker build -t langgate-server .

# Run the container
docker run -p 4000:4000 langgate-server
```

You can also use the provided Docker Compose setup for a complete deployment (which runs the server behind an Envoy proxy on port 10000):

```bash
# Start the full LangGate stack
make compose-up

# For development with hot reloading
make compose-dev
```

## Configuration

The server can be configured through:
- Environment variables
- A `.env` file in your working directory
- A langgate_config.yaml file in your working directory
- Command-line arguments to the uvicorn server

See the main LangGate documentation for more details on configuration options.
langgate-sdk 0.4.1
Convenience SDK for LangGate AI Gateway
License: MIT
Requires Python: >=3.13

# LangGate SDK

Convenience SDK for LangGate AI Gateway.

This package provides a combined client that gives access to both model registry information and parameter transformation for LLMs and image generation models in a single interface. It's designed for applications that need both capabilities but want a simpler integration.
langgate-registry 0.4.1
Model registry implementation for LangGate AI Gateway
License: MIT
Requires Python: >=3.13

# LangGate Registry

Model registry implementation for LangGate AI Gateway.

This package provides the core registry implementation used by LangGate, including:

1. The registry model system - defining model metadata, capabilities, and costs for both LLMs and image generation models
2. Configuration loading and management - loading from YAML and environment variables with modality-aware structure
3. `LocalRegistryClient` - for embedding the registry directly in applications with support for both text and image models

This package is used both:
- As a standalone library for applications embedding the registry directly
- As a core component of the LangGate server when deployed as a microservice