Overview

The project implements AI DIAL API for language models and embeddings from Vertex AI.

Supported models

The following models support POST SERVER_URL/openai/deployments/DEPLOYMENT_NAME/chat/completions endpoint along with optional support of /tokenize and /truncate_prompt endpoints:

Model	Deployment name	Modality	/tokenize	/truncate_prompt	tools/functions support
Gemini 2.0 Pro	gemini-2.0-pro-exp-02-05	(text/pdf/image/audio/video)-to-text	✅	✅	✅
Gemini 2.0 Flash Thinking	gemini-2.0-flash-thinking-exp-01-21	text-to-text	✅	✅	❌
Gemini 2.0 Flash	gemini-2.0-flash-(exp|001)	(text/pdf/image/audio/video)-to-text	✅	✅	✅
Gemini 2.0 Flash Lite	gemini-2.0-flash-lite-preview-02-05	(text/pdf/image/audio/video)-to-text	✅	✅	❌
Gemini 1.5 Pro	gemini-1.5-pro-(preview-0409|001|002)	(text/pdf/image/audio/video)-to-text	✅	✅	✅
Gemini 1.5 Flash	gemini-1.5-flash-(001|002)	(text/pdf/image/audio/video)-to-text	✅	✅	✅
Gemini 1.0 Pro Vision	gemini-pro-vision	(text/pdf/image/video)-to-text	✅	✅	❌
Gemini 1.0 Pro	gemini-pro	text-to-text	✅	✅	✅
Claude 3 Opus	claude-3-opus@20240229	(text/image)-to-text	✅	✅	✅
Claude 3.5 Sonnet v2	claude-3-5-sonnet-v2@20241022	(text/image)-to-text	✅	✅	✅
Claude 3.5 Sonnet	claude-3-5-sonnet@20240620	(text/image)-to-text	✅	✅	✅
Claude 3.5 Haiku	claude-3-5-haiku@20241022	text-to-text	✅	✅	✅
Claude 3 Haiku	claude-3-haiku@20240307	(text/image)-to-text	✅	✅	✅
Imagen 2	imagegeneration@005	text-to-image	✅	✅	❌
PaLM 2 Chat Bison	chat-bison@001	text-to-text	✅	✅	❌
PaLM 2 Chat Bison	chat-bison@002	text-to-text	✅	✅	❌
PaLM 2 Chat Bison	chat-bison-32k@002	text-to-text	✅	✅	❌
Codey for Code Chat	codechat-bison@001	text-to-text	✅	✅	❌
Codey for Code Chat	codechat-bison@002	text-to-text	✅	✅	❌
Codey for Code Chat	codechat-bison-32k@002	text-to-text	✅	✅	❌

The models that support /truncate_prompt do also support max_prompt_tokens request parameter.

The following models support SERVER_URL/openai/deployments/DEPLOYMENT_NAME/embeddings endpoint:

Model	Deployment name	Language support	Modality
Gecko Embeddings for Text V1	textembedding-gecko@001	English	text-to-embedding
Gecko Embeddings for Text V3	textembedding-gecko@003	English	text-to-embedding
Embeddings for Text	text-embedding-004	English	text-to-embedding
Gecko Embeddings for Text Multilingual	textembedding-gecko-multilingual@001	Multilingual	text-to-embedding
Embeddings for Text Multilingual	text-multilingual-embedding-002	Multilingual	text-to-embedding
Multimodal embeddings	multimodalembedding@001	English	(text/image)-to-embedding

Developer environment

This project uses Python>=3.11 and Poetry>=1.6.1 as a dependency manager.

Check out Poetry's documentation on how to install it on your system before proceeding.

To install requirements:

poetry install

This will install all requirements for running the package, linting, formatting and tests.

IDE configuration

The recommended IDE is VSCode. Open the project in VSCode and install the recommended extensions.

The VSCode is configured to use PEP-8 compatible formatter Black.

Alternatively you can use PyCharm.

Set-up the Black formatter for PyCharm manually or install PyCharm>=2023.2 with built-in Black support.

Run

Run the development server:

make serve

Open localhost:5001/docs to make sure the server is up and running.

Environment Variables

Copy .env.example to .env and customize it for your environment:

Variable	Default	Description
GOOGLE_APPLICATION_CREDENTIALS		Filepath to JSON with credentials
DEFAULT_REGION		Default region for Vertex AI (e.g. "us-central1")
GCP_PROJECT_ID		GCP project ID
LOG_LEVEL	INFO	Log level. Use DEBUG for dev purposes and INFO in prod
AIDIAL_LOG_LEVEL	WARNING	AI DIAL SDK log level
WEB_CONCURRENCY	1	Number of workers for the server
DIAL_URL		URL of the core DIAL server. Optional. Used to access images stored in the DIAL File storage
COMPATIBILITY_MAPPING	{}	A JSON dictionary that maps VertexAI deployments that aren't supported by the Adapter to the VertexAI deployments that are supported by the Adapter (see the Supported models section). Find more details in the compatibility mode section.

Running unsupported VertexAI models in the compatibility mode

The Adapter supports a predefined list of VertexAI deployments. The Supported models section lists the models. These models could be accessed via /openai/deployments/{deployment_name}/(chat_completions|embeddings) endpoints. The Adapter won't recognize any other deployment name and will result in 404 error.

Now, suppose VertexAI has just released a new version of a model, e.g. gemini-2.0-flash-006 which is a better version of an older gemini-2.0-flash-001 model.

Immediately after the release, the former model is unsupported by the Adapter, but the latter is supported. Therefore, the request to openai/deployments/gemini-2.0-flash-006/chat/completions will result in 404 error.

It will take some time for the Adapter to catch up with VertexAI - support the v6 model and publish the release with the fix.

What to do in the meantime? Presumably, the v6 model is backward compatible with v1, so we may try to run v6 in the compatibility mode - that is to convince the Adapter to process v6 request as if it's v1 request with the only difference that the final upstream request to AWS Bedrock will be to v6 and not v1.

The COMPATIBILITY_MAPPING env variable enables exactly this scenario.

When it's defined like this:

COMPATIBILITY_MAPPING={"gemini-2.0-flash-006": "gemini-2.0-flash-001"}

the Adapter will be able to handle requests to the gemini-2.0-flash-006 deployment. The requests will be processed by the same pipeline as gemini-2.0-flash-001, but the call to AWS Bedrock will be done to gemini-2.0-flash-006 deployment name.

Naturally, this will only work if the APIs of v1 and v6 deployments are compatible:

1. The requests utilizing the modalities supported by both v1 and v6 will work just fine.
2. However, the requests with modalities that are supported by v6 and aren't supported by v1, won't be processed correctly. You will have to wait until the Adapter supports the v6 deployment natively.

When a version of the Adapter supporting the v6 model is released, you may migrate to it and safely remove the entry from the COMPATIBILITY_MAPPING dictionary.

Note that a mapping such as this one would be ineffectual:

COMPATIBILITY_MAPPING={"gemini-2.0-flash-006": "imagegeneration@005"}

since the APIs and capabilities of these two models are drastically different.

Load balancing

If you use DIAL Core load balancing mechanism, you can provide extraData upstream setting the region to use for a particular upstream:

{
  "upstreams": [
    {
      "extraData": {
        "region": "us-central1"
      }
    },
    {
      "extraData": {
        "region": "us-east5"
      }
    }
  ]
}

The default region defined by the environment variable DEFAULT_REGION is used when the extraData.region isn't provided.

Note

The region configuration is only supported for Gemini 2.0 and Anthropic models.

Docker

Run the server in Docker:

make docker_serve

Lint

Run the linting before committing:

make lint

To auto-fix formatting issues run:

make format

Test

Run unit tests locally:

make test

Run unit tests in Docker:

make docker_test

Run integration tests locally:

make integration_tests

Clean

To remove the virtual environment and build artifacts:

make clean