LangChain

Using Requesty router with LangChain

Building an application with LangChain?

You can use the Requesty router to access any LLM, and get cost management, monitoring and fallbacks out-of-the-box.

Here’s an example script:

from langchain_openai import ChatOpenAI
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda
from os import getenv
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Define the prompt template
template = """You are an expert on Requesty router. The user has a question about this router:
Question: {question}
Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

# Initialize the OpenAI LLM
llm = ChatOpenAI(
    openai_api_key=getenv("REQUESTY_API_KEY"),
    openai_api_base=getenv("REQUESTY_BASE_URL"),
    model_name="openai/gpt-4o",
)

# Create a Runnable Chain
llm_chain = prompt | llm

# Define the question
question = "What application should I build now that Requesty router provides access to 600+ LLMs?"

# Run the model and get the response
response = llm_chain.invoke({"question": question})
print(response)