langchain-llm-config 0.1.6

A comprehensive LLM configuration package supporting multiple providers (OpenAI, VLLM, Gemini, Infinity) for chat assistants and embeddings

Langchain LLM Config

Yet another redundant Langchain abstraction: comprehensive Python package for managing and using multiple LLM providers (OpenAI, VLLM, Gemini, Infinity) with a unified interface for both chat assistants and embeddings.

Features

• 🤖 Multiple Chat Providers: Support for OpenAI, VLLM, and Gemini
• 🔗 Multiple Embedding Providers: Support for OpenAI, VLLM, and Infinity
• ⚙️ Unified Configuration: Single YAML configuration file for all providers
• 🚀 Easy Setup: CLI tool for quick configuration initialization
• 🔄 Easy Context Concatenation: Simplified process for combining contexts into chat
• 🔒 Environment Variables: Secure API key management
• 📦 Self-Contained: No need to import specific paths
• ⚡ Async Support: Full async/await support for all operations
• 🌊 Streaming Chat: Real-time streaming responses for interactive experiences
• 🛠️ Enhanced CLI: Environment setup and validation commands

Installation

Using pip

pip install langchain-llm-config

Using uv (recommended)

uv add langchain-llm-config

Development installation

git clone https://github.com/liux2/Langchain-LLM-Config.git
cd langchain-llm-config
uv sync --dev
uv run pip install -e .

Quick Start

1. Initialize Configuration

# Initialize config in current directory
llm-config init

# Or specify a custom location
llm-config init ~/.config/api.yaml

This creates an api.yaml file with all supported providers configured.

2. Set Up Environment Variables

# Set up environment variables and create .env file
llm-config setup-env

# Or with custom config path
llm-config setup-env --config-path ~/.config/.env

This creates a .env file with placeholders for your API keys.

3. Configure Your Providers

Edit the generated api.yaml file with your API keys and settings:

llm:
  openai:
    chat:
      api_base: "https://api.openai.com/v1"
      api_key: "${OPENAI_API_KEY}"
      model_name: "gpt-3.5-turbo"
      temperature: 0.7
      max_tokens: 8192
    embeddings:
      api_base: "https://api.openai.com/v1"
      api_key: "${OPENAI_API_KEY}"
      model_name: "text-embedding-ada-002"
  
  vllm:
    chat:
      api_base: "http://localhost:8000/v1"
      api_key: "${OPENAI_API_KEY}"
      model_name: "meta-llama/Llama-2-7b-chat-hf"
      temperature: 0.6
  
  default:
    chat_provider: "openai"
    embedding_provider: "openai"

4. Set Environment Variables

Edit the .env file with your actual API keys:

OPENAI_API_KEY=your-openai-api-key
GEMINI_API_KEY=your-gemini-api-key

5. Use in Your Code

Basic Usage (Synchronous)

from langchain_llm_config import create_assistant, create_embedding_provider
from pydantic import BaseModel, Field
from typing import List


# Define your response model
class ArticleAnalysis(BaseModel):
    summary: str = Field(..., description="Article summary")
    keywords: List[str] = Field(..., description="Key topics")
    sentiment: str = Field(..., description="Overall sentiment")


# Create an assistant without response model (raw text mode)
assistant = create_assistant(
    response_model=None,  # Explicitly set to None for raw text
    system_prompt="You are a helpful article analyzer.",
    provider="openai",  # or "vllm", "gemini"
    auto_apply_parser=False,
)

# Use the assistant for raw text output
print("=== Raw Text Mode ===")
result = assistant.ask("Analyze this article: ...")
print(result)

# Apply parser to the same assistant (modifies in place)
print("\n=== Applying Parser ===")
assistant.apply_parser(response_model=ArticleAnalysis)

# Now use the same assistant for structured output
print("\n=== Structured Mode ===")
result = assistant.ask("Analyze this article: ...")
print(result)

# Create an embedding provider
embedding_provider = create_embedding_provider(provider="openai")

# Get embeddings (synchronous)
texts = ["Hello world", "How are you?"]
embeddings = embedding_provider.embed_texts(texts)

Advanced Usage (Asynchronous)

import asyncio

# Use the assistant (asynchronous)
result = await assistant.ask_async("Analyze this article: ...")
print(result["summary"])

# Get embeddings (asynchronous)
embeddings = await embedding_provider.embed_texts_async(texts)

Streaming Chat

import asyncio
from langchain_llm_config import create_chat_streaming


async def main():
    """Main async function to run the streaming chat example"""
    # Create streaming chat assistant
    # Try with OpenAI first to test streaming
    streaming_chat = create_chat_streaming(
        provider="vllm", system_prompt="You are a helpful assistant."
    )

    print("🤖 Starting streaming chat...")
    print("Response: ", end="", flush=True)

    try:
        # Stream responses in real-time
        async for chunk in streaming_chat.chat_stream("Tell me a story"):
            if chunk["type"] == "stream":
                print(chunk["content"], end="", flush=True)
            elif chunk["type"] == "final":
                print(f"\n\nProcessing time: {chunk['processing_time']:.2f}s")
                print(f"Model used: {chunk['model_used']}")
    except Exception as e:
        print(f"\n❌ Error occurred: {e}")


if __name__ == "__main__":
    # Run the async function
    asyncio.run(main())

Supported Providers

Chat Providers

Provider	Models	Features
OpenAI	GPT-3.5, GPT-4, etc.	Streaming, function calling, structured output
VLLM	Any HuggingFace model	Local deployment, high performance
Gemini	Gemini Pro, etc.	Google's latest models

Embedding Providers

Provider	Models	Features
OpenAI	text-embedding-ada-002, etc.	High quality, reliable
VLLM	BGE, sentence-transformers	Local deployment
Infinity	Various embedding models	Fast inference

CLI Commands

# Initialize a new configuration file
llm-config init [path]

# Set up environment variables and create .env file
llm-config setup-env [path] [--force]

# Validate existing configuration
llm-config validate [path]

# Show package information
llm-config info

Advanced Usage

Custom Configuration Path

from langchain_llm_config import create_assistant

assistant = create_assistant(
    response_model=MyModel,
    config_path="/path/to/custom/api.yaml"
)

Context-Aware Conversations

# Add context to your queries
result = await assistant.ask_async(
    query="What are the main points?",
    context="This is a research paper about machine learning...",
    extra_system_prompt="Focus on technical details."
)

Direct Provider Usage

from langchain_llm_config import VLLMAssistant, OpenAIEmbeddingProvider

# Use providers directly
vllm_assistant = VLLMAssistant(
    config={"api_base": "http://localhost:8000/v1", "model_name": "llama-2"},
    response_model=MyModel
)

openai_embeddings = OpenAIEmbeddingProvider(
    config={"api_key": "your-key", "model_name": "text-embedding-ada-002"}
)

Complete Example with Error Handling

import asyncio
from langchain_llm_config import create_assistant, create_embedding_provider
from pydantic import BaseModel, Field
from typing import List

class ChatResponse(BaseModel):
    message: str = Field(..., description="The assistant's response message")
    confidence: float = Field(..., description="Confidence score", ge=0.0, le=1.0)
    suggestions: List[str] = Field(default_factory=list, description="Follow-up questions")

async def main():
    try:
        # Create assistant
        assistant = create_assistant(
            response_model=ChatResponse,
            provider="openai",
            system_prompt="You are a helpful AI assistant."
        )
        
        # Chat conversation
        response = await assistant.ask_async("What is the capital of France?")
        print(f"Assistant: {response['message']}")
        print(f"Confidence: {response['confidence']:.2f}")
        
        # Create embedding provider
        embedding_provider = create_embedding_provider(provider="openai")
        
        # Get embeddings
        texts = ["Hello world", "How are you?"]
        embeddings = await embedding_provider.embed_texts_async(texts)
        print(f"Generated {len(embeddings)} embeddings")
        
    except Exception as e:
        print(f"Error: {e}")

# Run the example
asyncio.run(main())

Configuration Reference

Environment Variables

The package supports environment variable substitution in configuration:

api_key: "${OPENAI_API_KEY}"  # Will be replaced with actual value

Configuration Structure

llm:
  provider_name:
    chat:
      api_base: "https://api.example.com/v1"
      api_key: "${API_KEY}"
      model_name: "model-name"
      temperature: 0.7
      max_tokens: 8192
      top_p: 1.0
      connect_timeout: 60
      read_timeout: 60
      model_kwargs: {}
      # ... other parameters
    embeddings:
      api_base: "https://api.example.com/v1"
      api_key: "${API_KEY}"
      model_name: "embedding-model"
      # ... other parameters
  default:
    chat_provider: "provider_name"
    embedding_provider: "provider_name"

Development

Running Tests

uv run pytest

Code Formatting

uv run black .
uv run isort .

Type Checking

uv run mypy .

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions