langchainkit 0.1.9

LangChainKit makes it easier to work with Qwen3 models via vLLM, and simplifies the process of prompting LLMs to return structured outputs using LangChain and Langfuse.

LangChainKit

LangChainKit simplifies the process of prompting LLMs to return structured outputs using LangChain and LangFuse.

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🚀 Features

• 🔧 Simplified Qwen3 + vLLM integration
  Automatically configure enable_thinking and other complex settings for Qwen3 models when using vLLM.

• 🧠 Structured Output via LangChain
  Easily prompt the LLM to generate structured outputs, including batch prompting support, with minimal setup.

• 📊 LangFuse Integration
  Track and evaluate LLM performance using LangFuse, without writing boilerplate code.

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Installation

pip install langchainkit

Quick Start

Configuration

Set up your environment variables in .env file:

DEEPSEEK_API_KEY=your deepseek api key
MOONSHOT_API_KEY=...
OPENROUTER_API_KEY=...
ARK_API_KEY=...
DASHSCOPE_API_KEY=...
LOCAL_VLLM_BASE_URL=http://172.20.14.28:8000/v1
LOCAL_VLLM_API_KEY=...

LANGFUSE_SECRET_KEY=...
LANGFUSE_PUBLIC_KEY=...
LANGFUSE_HOST=...

from langchainkit import GeneralLLM,prompt_parsing
from pydantic import BaseModel
from dotenv import load_dotenv

load_dotenv() # load .env file

llm = GeneralLLM.deepseek_chat()

class Response(BaseModel):
    answer: str
    confidence: float

result = prompt_parsing(
    model=Response,
    failed_model=Response(answer="no_answer", confidence=0.0),
    query="What is the capital of France?",
    llm=llm,
    use_langfuse=False 
)
print(result.answer)  # "Paris"
print(result.confidence)  # 1.0

result = prompt_parsing(
    model=Response,
    failed_model=Response(answer="no_answer", confidence=0.0),
    query=["What is the capital of France?",
           "What is the capital of Germany?",
           "What is the capital of Italy?"],
    llm=llm,
    use_langfuse=False
)
for each in result:
    print(each.answer)
    print(each.confidence)
# Paris
# 0.95
# Berlin
# 0.95
# Rome
# 1.0

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• LangChain for the core framework
• vLLM for high-throughput LLM inference
• Langfuse for observability and monitoring