langchain/templates/cohere-librarian/cohere_librarian/chat.py

from langchain_community.llms import Cohere

chat = Cohere()
docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412) …tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' \| xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' \| xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' \| xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ``` 2024-01-02 20:32:16 +00:00			`from langchain_community.llms import Cohere`
templates[patch]: Add cohere librarian template (#14601) 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> 2023-12-13 22:34:44 +00:00
			`chat = Cohere()`