langchain/templates/skeleton-of-thought/README.md
2023-11-26 19:31:41 -05:00

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# skeleton-of-thought
Implements "Skeleton of Thought" from [this](https://sites.google.com/view/sot-llm) paper.
This technique makes it possible to generate longer generates more quickly by first generating a skeleton, then generating each point of the outline.
## Environment Setup
Set the `OPENAI_API_KEY` environment variable to access the OpenAI models.
To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key.
## 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 skeleton-of-thought
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add skeleton-of-thought
```
And add the following code to your `server.py` file:
```python
from skeleton_of_thought import chain as skeleton_of_thought_chain
add_routes(app, skeleton_of_thought_chain, path="/skeleton-of-thought")
```
(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](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 is 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/skeleton-of-thought/playground](http://127.0.0.1:8000/skeleton-of-thought/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/skeleton-of-thought")
```