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Adding the example I build for the Cohere hackathon. It can: use a vector database to reccommend books <img width="840" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/96543a18-217b-4445-ab4b-950c7cced915"> Use a prompt template to provide information about the library <img width="834" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/996c8e0f-cab0-4213-bcc9-9baf84f1494b"> Use Cohere RAG to provide grounded results <img width="822" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/7bb4a883-5316-41a9-9d2e-19fd49a43dcb"> --------- Co-authored-by: Erick Friis <erick@langchain.dev>
72 lines
2.3 KiB
Markdown
72 lines
2.3 KiB
Markdown
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# cohere-librarian
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This template turns Cohere into a librarian.
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It demonstrates the use of a router to switch between chains that can handle different things: a vector database with Cohere embeddings; a chat bot that has a prompt with some information about the library; and finally a RAG chatbot that has access to the internet.
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For a fuller demo of the book recomendation, 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/ .
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## Environment Setup
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Set the `COHERE_API_KEY` environment variable to access the Cohere 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 cohere-librarian
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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 cohere-librarian
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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 cohere_librarian.chain import chain as cohere_librarian_chain
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add_routes(app, cohere_librarian_chain, path="/cohere-librarian")
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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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LangSmith is currently in private beta, you can sign up [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 is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://localhost:8000/docs](http://localhost:8000/docs)
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We can access the playground at [http://localhost:8000/cohere-librarian/playground](http://localhost:8000/cohere-librarian/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/cohere-librarian")
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```
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