- Keeps processing in different flows separate so that data can go to different stores / collections etc.
- Potentially supports different processing flows
- Tidies the processing API with common base-classes for e.g. LLMs, and automatic configuration of 'clients' to use the right queue names in a flow
* Break out enums for different model types
* Add model detection for inference profiles in US and EU
* Encapsulate model handling, make it easier to manage
* - More AWS Boto3 settings (profile and session key)
- Align environment variable and profile setting names with AWS
conventions.
Hopefully this should be able to run from an EC2 instance just with role
setting.
* Tweak naming to all make sense, added rate limit detect
* - Refactored retry for rate limits into the base class
- ConsumerProducer is derived from Consumer to simplify code
- Added rate_limit_count metrics for rate limit events
* Add rate limit events to VertexAI and Google AI Studio
* Added Grafana rate limit dashboard
* Add rate limit handling to all LLMs
- Change templates to interpolate environment variables in docker compose
- Change templates to invoke secrets for environment variable credentials in K8s configuration
- Update LLMs to pull in credentials from environment variables if not specified
* - Locked 0.11 packages to 0.11 deps
- Added 'trustgraph' uber-package which installs the rest
- Added dependency to set package versions before building packages
* Bump version
