AWS Bedrock Custom Integration
Start using your own API keys with the new AWS Bedrock integration.
New SDK's with breaking changes!
We recently transitioned our SDK's from
@orquesta
to@orq-ai
across all platforms to align with our branding and focus on AI. This includes some breaking changes!
Playground V2
Today, we launched our new Playground V2. This updated layout is designed for prompt engineering. By keeping the prompt template separate from the chat messages, it's easier to fine-tune your prompt. When you clear the chat messages, the prompt template will still be there.
Claude 3 - Haiku, Sonnet and Opus are now available
With the launch of Claude 3, Anthropic is challenging the status quo. Its most advanced model 'Opus' is supposedly better than GPT-4. However, it's also around 2.5 times more expensive than GPT-4. Haiku, the cheapest and least capable model, is around the same price as GPT-3.5 and is really fast.
Hyperlinking
With the new Hyperlinking feature, you are able to take your use case from one module to the other. Switching between the Playground, Experiments, and Deployments allows you to make quick iterations throughout the whole platform. Whether you want to take your Playground setup to Experiments or your Deployment to Playground, it's all possible.
Use Groq as your LLM provider
You can now use Groq as your LLM provider. It currently hosts the following models: llama2-70b-chat, mixtral-8x7b-32768, and Gemma-7b-it.
Make your own custom LLM Evaluator
You can now create your own custom LLM evalutator. This allows you to go beyond the standard evaluators like BLEU and Valid JSON.
Use LLM as a Reference in Experiments
With this new feature, you're able to use the output of a large language model like GPT-4 as the reference for another model like Gemma-7b and Mistal-large (see image).
New Providers & Models: Mistral large, Perplexity, Gemma 7b and other models are added
Check out the newly added models on orq.ai. You can find them in the model garden.
Import from Resources in Experiments
Instead of manually adding your data sets or uploading them through a CSV file in experiments, you can now import them from the files you stored in Resources. This allows you to store and access your files in a quick and efficient manner.