Google Vertex AI

Install

pip install synapsekit[vertex]

Via the RAG facade

from synapsekit import RAG

rag = RAG(model="gemini-1.5-pro", api_key="your-gcp-project-id")
rag.add("Your document text here")

answer = rag.ask_sync("Summarize the document.")

info

Vertex AI uses Application Default Credentials (ADC). The api_key field is used as the GCP project ID. Make sure you have authenticated with gcloud auth application-default login.

Direct usage

from synapsekit.llm.vertex_ai import VertexAILLM
from synapsekit.llm.base import LLMConfig

llm = VertexAILLM(LLMConfig(
    model="gemini-1.5-pro",
    api_key="your-gcp-project-id",
    provider="vertex",
    temperature=0.3,
    max_tokens=1024,
))

async for token in llm.stream("Explain vector embeddings."):
    print(token, end="", flush=True)

Function calling

VertexAILLM supports native function calling via call_with_tools().

from synapsekit import FunctionCallingAgent, CalculatorTool
from synapsekit.llm.vertex_ai import VertexAILLM
from synapsekit.llm.base import LLMConfig

llm = VertexAILLM(LLMConfig(
    model="gemini-1.5-pro",
    api_key="your-gcp-project-id",
    provider="vertex",
))

agent = FunctionCallingAgent(
    llm=llm,
    tools=[CalculatorTool()],
)

answer = await agent.run("What is 144 divided by 12?")

How it works

SynapseKit converts OpenAI-format tool schemas to Vertex AI function declarations via Tool.from_dict(). Response function_call parts are parsed back into the standard {"id", "name", "arguments"} format. Since Vertex AI doesn't provide tool call IDs, SynapseKit generates them via uuid4.

Supported models

• gemini-1.5-pro — most capable
• gemini-1.5-flash — faster, lower cost
• gemini-1.0-pro
• Any model available in your Vertex AI project

See Vertex AI docs for the full list.

Vertex AI vs Gemini

	VertexAILLM	GeminiLLM
Auth	ADC (service accounts, gcloud)	API key
Package	google-cloud-aiplatform	google-generativeai
Best for	Production / enterprise	Development / prototyping