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Setup the toolkit

Quick start

Try the default Toolkit application yourself by deploying it in a container locally. You will need to have Docker and Docker-compose >= 2.22 installed.

docker run -e COHERE_API_KEY='>>YOUR_API_KEY<<' -p 8000:8000 -p 4000:4000 ghcr.io/cohere-ai/cohere-toolkit:latest

If you need to use community features, you can run the container with the following command:

docker run -e INSTALL_COMMUNITY_DEPS='true' -e COHERE_API='>>YOUR_API_KEY<<' -p 8000:8000 -p 4000:4000 ghcr.io/cohere-ai/cohere-toolkit:latest

Go to localhost:4000 in your browser and start chatting with the model. This will use the model hosted on Cohere's platform. If you want to add your own tools or use another model, follow the instructions below to fork the repository.

Building and running locally

Clone the repo and run

make first-run

Follow the instructions to configure the model - either AWS Sagemaker, Bedrock, Azure, or Cohere's platform. This can also be done by running make setup (See Option 2 below), which will help generate a file for you, or by manually creating a .env file and copying the contents of the provided .env-template. Then replacing the values with the correct ones. For Windows systems see the detailed setup below.

Detailed environment setup

Windows
  1. Install docker
  2. Install [git]https://git-scm.com/download/win
  3. In PowerShell (Terminal), install scoop. After installing, run the following commands:
scoop bucket add extras
  1. Install miniconda using
scoop install miniconda3
conda init cmd.exe
  1. Restart PowerShell
  2. Install the following:
scoop install postgresql
scoop install make
  1. Create a new virtual environment with Python 3.11 using CMD terminal
conda create -n toolkit python=3.11
conda activate toolkit
  1. Install poetry == 1.7.1 using
pip install poetry==1.7.1
  1. Clone the repo
  2. Alternatively to make win-first-run or make win-setup, run
poetry install --with setup,community --verbose
poetry run python src/backend/cli/main.py
make migrate
make dev
  1. Navigate to https://localhost:4000 in your browser

Possible issues

  • If you encounter on error on running poetry install related to llama-cpp-python, please run the following command:
poetry source add llama-cpp-python https://abetlen.github.io/llama-cpp-python/whl/cpu
poetry source add pypi
poetry lock

and then run the commands in step 10 again. For more information and additional installation instructions, see llama-cpp-python documentation

MacOS
  1. Install Xcode. This can be done from the App Store or terminal
xcode-select --install
  1. Install docker desktop
  2. Install homebrew
  3. Install pipx. This is useful for installing poetry later.
brew install pipx
pipx ensurepath
  1. Install [postgres](brew install postgresql)
  2. Install conda using miniconda
  3. Use your environment manager to create a new virtual environment with Python 3.11
conda create -n toolkit python=3.11
  1. Install poetry >= 1.7.1
pipx install poetry

To test if poetry has been installed correctly,

conda activate toolkit
poetry --version

You should see the version of poetry (e.g. 1.8.2). If poetry is not found, try

export PATH="$HOME/.local/bin:$PATH"

And then retry poetry --version 9. Clone the repo and run make first-run 10. Navigate to https://localhost:4000 in your browser

Environment variables

Cohere Platform

  • COHERE_API_KEY: If your application will interface with Cohere's API, you will need to supply an API key. Not required if using AWS Sagemaker or Azure. Sign up at https://dashboard.cohere.com/ to create an API key.
  • NEXT_PUBLIC_API_HOSTNAME: The backend URL which the frontend will communicate with. Defaults to http://backend:8000 for use with docker compose
  • FRONTEND_HOSTNAME: The URL for the frontend client. Defaults to http://localhost:4000
  • DATABASE_URL: Your PostgreSQL database connection string for SQLAlchemy, should follow the format postgresql+psycopg2://USER:PASSWORD@HOST:PORT.
  • REDIS_URL: Your Redis connection string, should follow the format redis://USER:PASSWORD@HOST:PORT.

AWS Sagemaker

To use the toolkit with AWS Sagemaker you will first need the cohere model (a command version) which powers chat deployed in Sagemaker. Follow Cohere's guide and notebooks to deploy a command model and create an endpoint which can then be used with the toolkit.

Then you will need to set up authorization, see more details here. The default toolkit set up uses the configuration file (after aws configure sso) with the following environment variables:

  • SAGE_MAKER_REGION_NAME: The region you configured for the model.
  • SAGE_MAKER_ENDPOINT_NAME: The name of the endpoint which you created in the notebook.
  • SAGE_MAKER_PROFILE_NAME: Your AWS profile name

Bedrock

  • BEDROCK_ACCESS_KEY: Your Bedrock access key.
  • BEDROCK_SECRET_KEY: Your Bedrock secret key.
  • BEDROCK_SESSION_TOKEN: Your Bedrock session token.
  • BEDROCK_REGION_NAME: The region you configured for the model.

Hosted tools

  • PYTHON_INTERPRETER_URL: URL to the python interpreter container. Defaults to http://localhost:8080.
  • TAVILY_API_KEY: If you want to enable internet search, you will need to supply a Tavily API Key. Not required.

Deploy locally

Once your environment variables are set, you're ready to deploy the Toolkit locally! Pull the Docker images from Github Artifact registry or build files from source. See the Makefile for all available commands.

Requirements:

Option 1 - Install locally with Docker:

Ensure your shell is authenticated with GHCR.

Pull the Single Container Image from Github's Artifact Registry

docker pull ghcr.io/cohere-ai/cohere-toolkit:latest

Run the images locally:

docker run --name=cohere-toolkit -itd -e COHERE_API_KEY='Your Cohere API key here' -p 8000:8000 -p 4000:4000 ghcr.io/cohere-ai/cohere-toolkit

Option 2 - Build locally from scratch:

Option 2.1 - Run everything at once

Run make first-run to start the CLI, that will generate a .env file for you. This will also run all the DB migrations and run the containers

make first-run
Option 2.1 - Run each command separately

Run make setup to start the CLI, that will generate a .env file for you:

make setup

Then run:

make migrate
make dev

If you did not change the default port, visit http://localhost:4000/ in your browser to chat with the model.

Setup for Development

Setting up Poetry

Use for configuring and adding new retrieval chains.

Install your development dependencies:

poetry install --with dev

If you also need to install the community features, run:

poetry install --with community

To run linters, you can use make lint or separate commands:

poetry run black .
poetry run isort .

Run type checker:

  • See docs for pyright
  • Install with conda install pyright
  • Run with pyright
  • Configure in pyproject.toml under [tool.pyright]

Setting up the Environment Variables

Please confirm that you have at least one configuration of the Cohere Platform, SageMaker, Bedrock or Azure.

You have two methods to set up the environment variables:

  1. Run make setup and follow the instructions to configure it.
  2. Run cp .env-template .env and adjust the values in the .env file according to your situation.

Setting up Your Local Database

The docker-compose file should spin up a local db container with a PostgreSQL server. The first time you setup this project, and whenever new migrations are added, you will need to run:

make migrate

This will apply all existing database migrations and ensure your DB schema is up to date.

If ever you run into issues with Alembic, such as being out of sync and your DB does not contain any data you'd like to preserve, you can run:

make reset-db
make migrate
make dev

This will delete the existing db container volumes, restart the containers and reapply all migrations.

Testing the Toolkit

Run:

make dev

To spin the test_db service for you. After, you can run:

make run-tests

Making Database Model Changes

When making changes to any of the database models, such as adding new tables, modifying or removing columns, you will need to create a new Alembic migration. You can use the following Make command:

make migration

Important: If adding a new table, make sure to add the import to the model/__init__.py file! This will allow Alembic to import the models and generate migrations accordingly.

This should generate a migration on the Docker container and be copied to your local /alembic folder. Make sure the new migration gets created.

Then you can migrate the changes to the PostgreSQL Docker instance using:

make migrate