tangerine-backend

🍊

A work in progress

Local Environment Setup for Linux / Intel Macs

The local dev environment uses ollama to serve the LLM.

You may require further tweaks to properly make use of your GPU. Refer to the ollama docker image documentation.

1. Make sure git-lfs is installed:

   Fedora: `sudo dnf install git-lfs`
   MacOS: `brew install git-lfs`
   
   git lfs install
   

2. Create the directory which will house the local environment data:

   mkdir data
   

3. Create a directory to house the embedding model and download the snowflake-arctic-embed-m-long model:

   mkdir data/embeddings
   git clone https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long \
     data/embeddings/snowflake-arctic-embed-m-long
   

4. Invoke docker compose (postgres data will persist in data/postgres):

   docker compose up --build
   

5. Pull the mistral LLM (data will persist in data/ollama):

   docker exec tangerine-ollama ollama pull mistral
   

6. The API can now be accessed on http://localhost:5000

Local Environment Setup for Apple Silicon Macs

Some of the images used in the docker-compose.yml wont work on Apple Silicon Macs. In order to develop on those system you will need to start some of the processes manually.

You'll ned to have the following installed and working before proceeding on:

• Brew
• Pipenv
• Pyenv
• Docker or Podman

1. Install Ollama

brew install ollama

2. Start Ollama

ollama serve

3. Pull the language and embedding models

ollama pull mistral
ollama pull nomic-embed-text

4. Install the C API for Postgres (libpq)

brew install libpq

Tip

For Apple Silicon Macs, you'll need to export the following environment variable to avoid the issues with ligpg c library errors: export PATH="/opt/homebrew/opt/libpq/bin:$PATH" export LDFLAGS="-L/opt/homebrew/opt/libpq/lib" export CPPFLAGS="-I/opt/homebrew/opt/libpq/include"

5. Start the Vector database

docker run -e POSTGRES_PASSWORD="citrus" -e POSTGRES_USER="citrus" -e POSTGRES_DB="citrus" -e POSTGRES_HOST_AUTH_METHOD=trust -p 5432:5432 pgvector/pgvector:pg16

6. Prepare the Python virtual environment:

pipenv --python=3.11
pipenv install
pipenv shell

7. Start Tangerine Backend

Note

The default tangerine port, 5000, is already claimed by Bonjour on Macs, so we need to use a different port instead.

flask run --host=127.0.0.1 --port=8000

You can now communicate with the API on port 8000

curl -XGET 127.0.0.1:8000/api/agents
{
    "data": []
}

Run Tangerine Frontend Locally

To run the UI in a development environment, see tangerine-frontend

Populate Data from S3

You can populate the vector database and list of agents by processing documents pulled from an S3 bucket. To do so you'll need to do the following:

1. Export environment variables that contain your S3 bucket auth info:

export AWS_ACCESS_KEY_ID="MYKEYID"
export AWS_DEFAULT_REGION="us-east-1"
export AWS_ENDPOINT_URL_S3="https://s3.us-east-1.amazonaws.com"
export AWS_SECRET_ACCESS_KEY="MYACCESSKEY"
export BUCKET="mybucket"

2. Create an s3.yaml file that describes your agents and the documents they should ingest. See s3-example.yaml for an example.

3. Run the ingest script

flask s3sync

4. Start the server and you should see your data available via the API.

Available API Paths

Path	Method	Description
/api/agents	GET	Get a list of all agents
/api/agents	POST	Create a new agent
/api/agents/<id>	GET	Get an agent
/api/agents/<id>	PUT	Update an agent
/api/agents/<id>	DELETE	Delete an agent
/api/agents/<id>/chat	POST	Chat with an agent
/api/agents/<id>/documents	POST	Agent document uploads
/api/agents/<id>/documents	DELETE	Delete agent documents
/api/agentDefaults	GET	Get agent default settings
/ping	GET	Health check