HTTP proxy for accessing Vertex AI with the REST API interface of ollama. Optionally forwarding requests for other models to ollama. Written in Go.
Get embeddings for a text:
❯ curl localhost:22434/api/embed -d '{
"model": "text-embedding-005",
"input": "Half-orc is the best race for a barbarian."
}'
{ "embeddings": [[0.05424513295292854, -0.023687424138188362, ...]] }
- Download an archive with the executable for your hardware and operating system from GitHub Releases.
- Download a JSON file with your Google account key from Google Project Console and save it to the current directory under the name
google-account.json. - Optionally create a file
model-defaults.jsonin the current directory to change the default model parameters. - Run the server:
❯ ovai
Listening on http://localhost:22434 ...
The following properties from google-account.json are used:
{
"project_id": "...",
"private_key_id": "...",
"private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
"client_email": "...",
"scope": "https://www.googleapis.com/auth/cloud-platform", // optional, can be missing
"auth_uri": "https://www.googleapis.com/oauth2/v4/token" // optional, can be missing
}Set the environment variable PORT to override the default port 22434.
Set the environment variable DEBUG to one or more strings separated by commas to customise logging on stderr. The default value is ovai when run on the command line and ovai:srv inside the Docker container.
DEBUG value |
What will be logged |
|---|---|
ovai |
important information about the bodies of requests and responses |
ovai:srv |
methods and URLs of requests and status codes of responses |
ovai:net |
requests forwarded to Vertex AI and received responses |
ovai,ovai:* |
all information above |
Set the environment variable OLLAMA_ORIGIN to the origin of the ollama service to enable forwarding to ollama. If the requested model doesn't start with gemini, multimodalembedding, textembedding or text-embedding, the request will be forwarded to the ollama service. This can be used for using ovai as the single service with the ollama interface, which recognises both Vertex AI and ollama models.
Set the environment variable NETWORK to enforce IPV4 or IPV6. The default behaviour is to depend on the Happy Eyeballs implementation in Go and in the underlying OS. valid values:
NETWORK value |
What will be used |
|---|---|
IPV4 |
enforce the network connection via IPV4 only |
IPV6 |
enforce the network connection via IPV6 only |
For example, run a container for testing purposes with verbose logging, deleted on exit, exposing the port 22434:
docker run --rm -it -p 22434:22434 -e DEBUG=ovai,ovai:* \
-v ${PWD}/google-account.json:/usr/src/app/google-account.json \
ghcr.io/prantlf/ovai
For example, run a container named ovai in the background with custom defaults, forwarding to ollama, exposing the port 22434:
docker run --rm -dt -p 22434:22434 --name ovai \
--add-host host.docker.internal:host-gateway \
-e OLLAMA_ORIGIN=http://host.docker.internal:11434 \
-v ${PWD}/google-account.json:/usr/src/app/google-account.json \
-v ${PWD}/model-defaults.json:/usr/src/app/model-defaults.json \
prantlf/ovai
And the same task as above, only using Docker Compose (place docker-compose.yml or docker-compose-ollama.yml, if you want to use ollama too, to the current directory) to make it easier:
docker-compose up -d --wait
docker-compose -f docker-compose-ollama.yml up -d --wait
The image is available as both ghcr.io/prantlf/ovai (GitHub) or prantlf/ovai (DockerHub).
Make sure that you have installed Go 1.22.3 or newer.
git clone https://github.com/prantlf/ovai.git
cd ovai
make
Executing ./ovai, make docker-start or make docker-up will require the google-account.json file in the current directory, if you don't just proxy the calls to ollama (which needs the OLLAMA_ORIGIN environment variable).
See the original REST API documentation for details about the interface. See also the lifecycle of the Vertex AI models.
Creates a vectors from the specified input. See the available embedding models.
❯ curl localhost:22434/api/embed -d '{
"model": "textembedding-gecko@003",
"input": ["Half-orc is the best race for a barbarian."]
}'
{ "embeddings": [[0.05424513295292854, -0.023687424138188362, ...]] }
The returned vector of floats has 768 dimensions.
Previous request remains supported for compatibility:
❯ curl localhost:22434/api/embeddings -d '{
"model": "textembedding-gecko@003",
"prompt": "Half-orc is the best race for a barbarian."
}'
{ "embedding": [0.05424513295292854, -0.023687424138188362, ...] }
Generates a text using the specified prompt. See the available gemini text and chat models.
❯ curl localhost:22434/api/generate -d '{
"model": "gemini-1.5-flash-002",
"prompt": "Describe guilds from Dungeons and Dragons.",
"images": [],
"stream": false
}'
{
"model": "gemini-1.5-flash-002",
"created_at": "2024-05-10T14:10:54.885Z",
"response": "Guilds serve as organizations that bring together individuals with ...",
"done": true,
"total_duration": 13884049373,
"load_duration": 0,
"prompt_eval_count": 7,
"prompt_eval_duration: 3471012343,
"eval_count: 557,
"eval_duration: 10413037030
}
The property stream defaults to be true. The property options is optional with the following defaults:
"options": {
"num_predict": 8192,
"temperature": 1,
"top_p": 0.95,
"top_k": 40
}
Replies to a chat with the specified message history. See the available gemini text and chat models.
❯ curl localhost:22434/api/chat -d '{
"model": "gemini-1.5-pro",
"messages": [
{
"role": "system",
"content": "You are an expert on Dungeons and Dragons."
},
{
"role": "user",
"content": "What race is the best for a barbarian?",
"images": []
}
],
"stream": false
}'
{
"model": "gemini-1.5-pro",
"created_at": "2024-05-06T23:32:05.219Z",
"message": {
"role": "assistant",
"content": "Half-Orcs are a strong and resilient race, making them ideal for barbarians. ..."
},
"done": true,
"total_duration": 2325524053,
"load_duration": 0,
"prompt_eval_count": 9,
"prompt_eval_duration: 581381013,
"eval_count: 292,
"eval_duration: 1744143040
}
The property stream defaults to true. The property options is optional with the following defaults:
"options": {
"num_predict": 8192,
"temperature": 1,
"top_p": 0.95,
"top_k": 40
}
Lists available models.
❯ curl localhost:22434/api/tags
{
"models": [
{
"name": "moondream:latest",
"model": "moondream:latest",
"modified_at": "2024-06-02T16:39:32.532400236+02:00",
"size": 1738451197,
"digest": "55fc3abd386771e5b5d1bbcc732f3c3f4df6e9f9f08f1131f9cc27ba2d1eec5b",
"details": {
"parent_model": "",
"format": "gguf",
"family": "phi2",
"families": [
"phi2",
"clip"
],
"parameter_size": "1B",
"quantization_level": "Q4_0"
},
"expires_at": "0001-01-01T00:00:00Z"
}
]
}
Show information about a model.
❯ curl localhost:22434/api/show -d '{"name":"moondream"}'
{
"license": "....",
"modelfile": "...",
"parameters": "temperature 0\nstop \"\u003c|endoftext|\u003e\"\nstop \"Question:\"",
"template": "{{ if .Prompt }} Question: {{ .Prompt }}\n\n{{ end }} Answer: {{ .Response }}\n\n",
"details": {
"parent_model": "",
"format": "gguf",
"family": "phi2",
"families": [
"phi2",
"clip"
],
"parameter_size": "1B",
"quantization_level": "Q4_0"
}
}
Checks that the server is running.
❯ curl -f localhost:22434/api/ping -X HEAD
Gracefully shuts down the HTTP server and exits the process.
❯ curl localhost:22434/api/shutdown -X POST
Recognised models for embeddings: textembedding-gecko@001, textembedding-gecko@002, textembedding-gecko@003, textembedding-gecko-multilingual@001, text-multilingual-embedding-002, text-embedding-004, text-embedding-005, multimodalembedding@001.
Recognised models for content generation and chat: gemini-2.0-flash-exp, gemini-1.5-flash-001, gemini-1.5-flash-002, gemini-1.5-flash-8b-001, gemini-1.5-pro-001, gemini-1.5-pro-002, gemini-1.0-pro-vision-001, gemini-1.0-pro-001, gemini-1.0-pro-002.
Small models usable on machines with less memory and no AI accelerator:
| Name | Size |
|---|---|
| deepseek-r1:1.5b | 1.1 GB |
| gemma2:2b | 1.6 GB |
| granite3.1-dense:2b | 1.5 GB |
| granite3.1-moe:1b | 2.0 GB |
| granite3.1-moe:3b | 1.4 GB |
| granite-embedding:30m | 63 MB |
| granite-embedding:278m | 563 MB |
| internlm2:1.8b | 1.1 GB |
| llama3.2:1b | 1.3 GB |
| llama3.2:3b | 2.0 GB |
| llava-phi3 | 2.9 GB |
| moondream | 1.7 GB |
| nomic-embed-text | 274 MB |
| orca-mini | 2.0 GB |
| phi | 1.6 GB |
| phi3 | 2.2 GB |
| qwen2.5:0.5b | 397 MB |
| qwen2.5:1.5b | 986 MB |
| smollm | 990 MB |
| smollm:135m | 91 MB |
| smollm:360m | 229 MB |
| snowflake-arctic-embed2 | 1.2 GB |
| stablelm-zephyr | 1.6 GB |
| stablelm2 | 982 MB |
| tinyllama | 637 MB |
DeepSeek's first-generation of reasoning models with comparable performance to OpenAI-o1, including six dense models distilled from DeepSeek-R1 based on Llama and Qwen.
Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B.
The IBM Granite 2B and 8B models are text-only dense LLMs trained on over 12 trillion tokens of data, demonstrated significant improvements over their predecessors in performance and speed in IBM’s initial testing.
The IBM Granite 1B and 3B models are long-context mixture of experts (MoE) Granite models from IBM designed for low latency usage.
The IBM Granite Embedding 30M and 278M models models are text-only dense biencoder embedding models, with 30M available in English only and 278M serving multilingual use cases.
InternLM2.5 is a 7B parameter model tailored for practical scenarios with outstanding reasoning capability.
Meta's Llama 3.2 goes small with 1B and 3B models.
A new small LLaVA model fine-tuned from Phi 3 Mini.
moondream2 is a small vision language model designed to run efficiently on edge devices.
A commercial-friendly small language model by NVIDIA optimized for roleplay, RAG QA, and function calling.
A high-performing open embedding model with a large token context window.
A 3.8B model fine-tuned on a private high-quality synthetic dataset for information extraction, based on Phi-3.
A general-purpose model ranging from 3 billion parameters to 70 billion, suitable for entry-level hardware.
Phi-2: a 2.7B language model by Microsoft Research that demonstrates outstanding reasoning and language understanding capabilities.
Phi-3 is a family of lightweight 3B (Mini) and 14B (Medium) state-of-the-art open models by Microsoft.
Qwen 1.5 is a series of large language models by Alibaba Cloud spanning from 0.5B to 110B parameters
Qwen2 is a new series of large language models from Alibaba group
Qwen2.5 models are pretrained on Alibaba's latest large-scale dataset, encompassing up to 18 trillion tokens. The model supports up to 128K tokens and has multilingual support.
🪐 A family of small models with 135M, 360M, and 1.7B parameters, trained on a new high-quality dataset.
Snowflake's frontier embedding model. Arctic Embed 2.0 adds multilingual support without sacrificing English performance or scalability.
A lightweight chat model allowing accurate, and responsive output without requiring high-end hardware.
Stable LM 2 is a state-of-the-art 1.6B and 12B parameter language model trained on multilingual data in English, Spanish, German, Italian, French, Portuguese, and Dutch.
An experimental 1.1B parameter model trained on the new Dolphin 2.8 dataset by Eric Hartford and based on TinyLlama.
The TinyLlama project is an open endeavor to train a compact 1.1B Llama model on 3 trillion tokens.
In lieu of a formal styleguide, take care to maintain the existing coding style. Lint and test your code.
Copyright (C) 2024-2025 Ferdinand Prantl
Licensed under the MIT License.