langchain/templates/cohere-librarian/README.md
Leonid Ganeline 163ef35dd1
docs: templates updated titles (#25646)
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.
2024-08-23 01:19:38 -07:00

75 lines
2.2 KiB
Markdown

# Cohere - Librarian
This template turns `Cohere` into a librarian.
It demonstrates the use of:
- a router to switch between chains that handle different things
- a vector database with Cohere embeddings
- a chat bot that has a prompt with some information about the library
- a RAG chatbot that has access to the internet.
For a fuller demo of the book recommendation, consider replacing `books_with_blurbs.csv` with a larger sample from the following dataset: https://www.kaggle.com/datasets/jdobrow/57000-books-with-metadata-and-blurbs/ .
## Environment Setup
Set the `COHERE_API_KEY` environment variable to access the Cohere 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 cohere-librarian
```
If you want to add this to an existing project, you can just run:
```shell
langchain app add cohere-librarian
```
And add the following code to your `server.py` file:
```python
from cohere_librarian.chain import chain as cohere_librarian_chain
add_routes(app, cohere_librarian_chain, path="/cohere-librarian")
```
(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 is running locally at
[http://localhost:8000](http://localhost:8000)
We can see all templates at [http://localhost:8000/docs](http://localhost:8000/docs)
We can access the playground at [http://localhost:8000/cohere-librarian/playground](http://localhost:8000/cohere-librarian/playground)
We can access the template from code with:
```python
from langserve.client import RemoteRunnable
runnable = RemoteRunnable("http://localhost:8000/cohere-librarian")
```