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Client Libraries

Python
C#
Java
C++
Ruby

Automated Scenarios

Transaction Fraud Anomaly
Network Threat Detection

Dashboard Generation

Healthcare Semantic Search
Network Log Visualization

Industry Scenarios

Institutional Compliance Risk
Public Utility Energy Optimization
Algorithmic Crypto Trading
Install Docker

To get started with our container, first install Docker.

Visit Docker
Vector Clients

Browse all our implementation libraries from Java to Ruby.

Visit Client Libraries

Client Libraries

Python
C#
Java
C++
Ruby

Automated Scenarios

Transaction Fraud Anomaly
Network Threat Detection

Dashboard Generation

Healthcare Semantic Search
Network Log Visualization

Industry Scenarios

Institutional Compliance Risk
Public Utility Energy Optimization
Algorithmic Crypto Trading
Install Docker

To get started with our container, first install Docker.

Visit Docker
Vector Clients

Browse all our implementation libraries from Java to Ruby.

Visit Client Libraries

Terminal Instructions

Be sure to have Docker running 🐳

To open the terminal, follow these steps

1. On Windows, press Win + R, type "cmd", and press Enter.
2. On macOS, press Command + Space, type "Terminal", and press Enter.
3. On Linux, use your desktop environment’s application launcher and search for "Terminal".

Once the terminal is open, run the following:

docker-compose pull && docker-compose up

Developer Documentation

Welcome to Redwing Vector. This guide provides an overview of our container implementation and code integration process with Sandbox. Our documentation portal is designed to help you understand the functionality of our Vector client library and how to implement vector data types to solve unique business challenges. To provision a license key and customer id please visit https://redwing.ai/plans

Info
Ensure that these system requirements are met:
  • You are running on an arm64 or amd64 compatible machine architecture.
  • All M1, M2, and M3 class Apple devices run with arm64

Install Sandbox

This clones and establishes the Sandbox directory from which you will pull and compose the Docker container

git clone https://github.com/redwing-os/sandbox.git
cd sandbox


Set Environment Variables

Create a .env file for Cassandra in current directory which contains your container license key to run the Vector Docker container and enables allotted core utilization within VM

LICENSE_KEY=[your-license-key]
CUSTOMER_ID=[your-customer-id]
DB_HOST=db
DB_PORT=9042
DB_IMAGE=cassandra:latest
GRPC_SERVER=rust_server:50051
RUST_BACKTRACE=full
DB_STARTUP_CMD=""


To enable ScyllaDB your .env file must include a DB startup command which gets passed into the
docker-compose.yml during startup

LICENSE_KEY=[your-license-key]
CUSTOMER_ID=[your-customer-id]
DB_HOST=db
DB_PORT=9042
DB_IMAGE=scylladb/scylla:latest
GRPC_SERVER=rust_server:50051
RUST_BACKTRACE=full
DB_STARTUP_CMD="--smp 4 --memory 2G --overprovisioned 1 --listen-address=0.0.0.0 --rpc-address=0.0.0.0 --broadcast-rpc-address=127.0.0.1" # For ScyllaDB Only


Docker Pull

Be sure to have both Docker and docker-compose installed. This step pulls and runs the Docker container from Docker Hub using Sandbox's built-in   docker-compose.yml

docker-compose pull && docker-compose up


Cassandra Connect

Establishes Cassandra connection in container to run initialization commands, it will look something similar to sandbox_cassandra_1

docker ps
docker exec -it <your-docker-cassandra-container> cqlsh


Database Configuration

Establishes Cassandra tables and keyspaces for the Vector gRPC service. You can adjust these according to your requirements but keep in mind that the sample scripts and scenarios in Sandbox run according to these keyspace and table name values

CREATE KEYSPACE redwing_keyspace WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 1};
CREATE TABLE redwing_keyspace.vectors (
key text PRIMARY KEY,
vector list<float>,
created_at timestamp,
updated_at timestamp);


Install Python3 and Pip

Install all required dependencies into your system, this will change based on your runtime environment (Mac/Linux/etc), for Mac be sure to use Homebrew and use brew install python3 make sure you are in the /sandboxdirectory

sudo apt install python3-pip


Install Python Dependencies

Install all required dependencies into your system, make sure you are in the /sandboxdirectory

pip3 install grpcio grpcio-tools sentence-transformers numpy scikit-learn


Run E2E Tests

To run end-to-end tests for all core gRPC functions, make sure you are in the /sandboxdirectory and run the following

python3 test/_test_e2e.py


Explore Catalyst UI

Sandbox comes equipped with our Catalyst UI, which allows you to visualize running containers and infrastructure. To visit Catalyst copy and paste the following url into your browser.

localhost:8080


Run Generated Scenarios

To run all sample data generated scenarios, make sure you are in the /sandboxdirectory and run the following

find ./sample -maxdepth 1 -name "*.py" ! -name "vectordb_pb2*.py" -exec python3 {} \;


Wind Down

To clean up containers and wind down the sandbox environment run the following from the /sandbox directory

docker-compose down


Sandbox Environment In-Depth Documentation

Vector Terraform

Vector Terraform is an auto-provisioning system to fully deploy Redwing Vector to a cloud provider of your choice. To run the app with prompt-based auto-configuration, simply clone this repo and enter the `terraform` directory where the app is installed and run the following command:

git clone https://github.com/redwing-os/terraform.git


cd terraform
sh deploy.sh


To destroy your Terraform-managed infrastructure run the following and enter nothing for all the input prompts except region (i.e. us-east-1)

sudo sh destroy.sh


AWS Credential Configuration

Be sure to run the app from the same terminal window, or ensure the variables are saved to your .bash_profile or .bashrc

Documentation for getting AWS Access Keys is available here, IAM roles must be configured for correct usage and access to Cloudwatch.

AWS IAM Documentation

Important Notes:

  • Never commit your ~/.aws/credentials file to source control.
  • Regularly rotate your AWS access keys and always follow best security practices.
  • Ensure you have the necessary permissions in AWS to perform the actions required by the application.

Python Client Integration Guide

Welcome to the VectorDB client integration guide. This document outlines how to interact with the VectorDB service using the provided client stubs.

To get started, make sure Python3 is installed on your system: 
https://www.python.org/downloads/

Initial Setup

Before you begin, ensure you have the VectorDB client libraries installed and the gRPC service running on localhost:50051.

Create a Client

To start using the VectorDB service, initialize the client stub as follows:

import vectordb_pb2_grpc

channel = grpc.insecure_channel('localhost:50051')
stub = vectordb_pb2_grpc.VectorDBStub(channel)

Writing Data

To write data to the VectorDB service, create a VectorWriteRequest with the key and vector data:

import vectordb_pb2

write_data = vectordb_pb2.VectorWriteRequest(key="vector_key_123", vector=[0.5, 1.2, 3.4])
write_response = stub.Write(write_data)
print("Write response:", write_response)

Reading Data

To read data from the VectorDB service, use the VectorReadRequest with the desired key:

read_data = vectordb_pb2.VectorReadRequest(key="vector_key_123")
read_response = stub.Read(read_data)
print("Read response:", read_response)

Updating Data

If you need to update existing data, create a VectorUpdateRequest with the updated vector:

update_data = vectordb_pb2.VectorUpdateRequest(key="vector_key_123", vector=[2.3, 4.5, 6.7])
update_response = stub.Update(update_data)
print("Update response:", update_response)

Deleting Data

To delete data, use the VectorDeleteRequest with the key of the vector you want to remove:

delete_data = vectordb_pb2.VectorDeleteRequest(key="vector_key_123")
delete_response = stub.Delete(delete_data)
print("Delete response:", delete_response)

Batch Writing Data

For batch operations, you can send multiple write requests together using VectorBatchWriteRequest:

batch_write_data = vectordb_pb2.VectorBatchWriteRequest(vectors=[
    vectordb_pb2.VectorWriteRequest(key="vector_key_456", vector=[4.5, 5.6, 6.7]),
    vectordb_pb2.VectorWriteRequest(key="vector_key_789", vector=[7.8, 8.9, 9.0])
])
batch_write_response = stub.BatchWrite(batch_write_data)
print("Batch Write response:", batch_write_response)