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163ef35dd1
Updated titles into a consistent format. Fixed links to the diagrams. Fixed typos. Note: The Templates menu in the navbar is now sorted by the file names. I'll try sorting the navbar menus by the page titles, not the page file names.
71 lines
2.5 KiB
Markdown
71 lines
2.5 KiB
Markdown
# Step-Back Question-Answering
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This template replicates the "Step-Back" prompting technique that improves performance on complex questions by first asking a "step back" question.
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This technique can be combined with regular question-answering applications by doing retrieval on both the original and step-back question.
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Read more about this in the [Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models](https://arxiv.org/abs/2310.06117) paper 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)
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We will modify the prompts slightly to work better with chat models in this template.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package stepback-qa-prompting
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add stepback-qa-prompting
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```
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And add the following code to your `server.py` file:
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```python
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from stepback_qa_prompting.chain import chain as stepback_qa_prompting_chain
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add_routes(app, stepback_qa_prompting_chain, path="/stepback-qa-prompting")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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You can sign up for LangSmith [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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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)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/stepback-qa-prompting")
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``` |