lighthouse-llm-registry 0.1.1

A global reusable LLM Registry wrapper for LangChain models with unified error mapping for Lighthouse Agents Factory

LLM Registry

A global, reusable LLM Registry wrapper for LangChain chat models. It enables unified model configuration management, lazy loading, and dynamic resolution of model providers (OpenAI, Groq, Anthropic, and Google Gemini).

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Installation & Setup

[!NOTE] The distribution package name is lighthouse-llm-registry (hyphen), and the Python import package name is llm_registry (underscore).

1. Installation

Install the core package:

pip install lighthouse-llm-registry

To install specific provider SDK dependencies:

# OpenAI support
pip install "lighthouse-llm-registry[openai]"

# Groq support
pip install "lighthouse-llm-registry[groq]"

# Anthropic support
pip install "lighthouse-llm-registry[anthropic]"

# Google Gemini support
pip install "lighthouse-llm-registry[google]"

# Install all supported providers
pip install "lighthouse-llm-registry[all]"

If installing from local source distribution:

pip install ".[all]"

2. API Keys Configuration

Create a .env file in the root of your target project:

OPENAI_API_KEY=your-openai-key
GROQ_API_KEY=your-groq-key
ANTHROPIC_API_KEY=your-anthropic-key
GOOGLE_API_KEY=your-gemini-key
LOCAL_MODEL_LINK=http://192.168.101.88:11434/api/chat
LOCAL_MODEL=llama3.1:8b

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Usage Guide

1. Registration (Application Startup)

Register your model configurations once at application startup (e.g., in your app's main entry point):

# main.py
from dotenv import load_dotenv
from llm_registry import global_registry, ModelConfig

# Load environment keys from .env
load_dotenv()

# Register OpenAI config (defaults to standard temperature/max_tokens rules)
global_registry.register_model_config(
    "primary-chat",
    ModelConfig(
        provider="openai",
        model_name="gpt-4o-mini",
        temperature=0.7
    )
)

# Register Groq config
global_registry.register_model_config(
    "fast-chat",
    ModelConfig(
        provider="groq",
        model_name="llama-3.1-8b-instant",
        temperature=0.2
    )
)

# Register Anthropic config (supports models like claude-opus-4-8)
global_registry.register_model_config(
    "legacy-chat",
    ModelConfig(
        provider="anthropic",
        model_name="claude-opus-4-8"
    )
)

# Register Local Ollama config (falls back to LOCAL_MODEL_LINK and LOCAL_MODEL if parameters are omitted)
global_registry.register_model_config(
    "local-chat",
    ModelConfig(
        provider="local",
        model_name=""
    )
)

2. Resolution (Anywhere in your code)

Resolve and query the model anywhere inside your application without importing specific provider SDKs:

# agents/agent.py
from llm_registry import global_registry

class SimpleAgent:
    def __init__(self):
        # Dynamically resolve model from the global registry
        self.model = global_registry.get_model("fast-chat")
      
    def respond(self, query: str) -> str:
        # Standard LangChain invoke call
        response = self.model.invoke(query)
        return response.content

# Example for Local Ollama Model:
local_model = global_registry.get_model("local-chat")
local_response = local_model.invoke([
    {"role": "user", "content": "How are you today?"}
])
print(local_response)  # Returns direct text output

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Running Playground tests

An interactive chat test script is included under tests/chat_test.py to check your connections. Run it directly:

python tests/chat_test.py