langchain/templates/stepback-qa-prompting/README.md

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# 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](https://arxiv.org/abs/2310.06117) and an excellent blog post by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)
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:
```shell
pip install -U langchain-cli
```
To create a new LangChain project and install this as the only package, you can do:
```shell
langchain app new my-app --package stepback-qa-prompting
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add stepback-qa-prompting
```
And add the following code to your `server.py` file:
```python
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.
You can sign up for LangSmith [here](https://smith.langchain.com/).
If you don't have access, you can skip this section
```shell
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:
```shell
langchain serve
```
This will start the FastAPI app with a server running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
We can access the playground at [http://127.0.0.1:8000/stepback-qa-prompting/playground](http://127.0.0.1:8000/stepback-qa-prompting/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/stepback-qa-prompting")
```