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README.md |
stepback-qa-prompting
This template replicates the "Step-Back" prompting technique that improves performance on complex questions by first asking a "step back" question.
This technique can be combined with regular question-answering applications by doing retrieval on both the original and step-back question.
Read more about this in the paper here and an excellent blog post by Cobus Greyling here
We will modify the prompts slightly to work better with chat models in this template.
Environment Setup
Set the OPENAI_API_KEY
environment variable to access the OpenAI models.
Usage
To use this package, you should first have the LangChain CLI installed:
pip install -U langchain-cli
To create a new LangChain project and install this as the only package, you can do:
langchain app new my-app --package stepback-qa-prompting
If you want to add this to an existing project, you can just run:
langchain app add stepback-qa-prompting
And add the following code to your server.py
file:
from stepback_qa_prompting.chain import chain as stepback_qa_prompting_chain
add_routes(app, stepback_qa_prompting_chain, path="/stepback-qa-prompting")
(Optional) Let's now configure LangSmith. LangSmith will help us trace, monitor and debug LangChain applications. LangSmith is currently in private beta, you can sign up here. If you don't have access, you can skip this section
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=<your-api-key>
export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
If you are inside this directory, then you can spin up a LangServe instance directly by:
langchain serve
This will start the FastAPI app with a server running locally at http://localhost:8000
We can see all templates at http://127.0.0.1:8000/docs We can access the playground at http://127.0.0.1:8000/stepback-qa-prompting/playground
We can access the template from code with:
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/stepback-qa-prompting")