oci-genai-service

Production-ready Python toolkit for Oracle Cloud Infrastructure Generative AI Service.

Features

• Unified client that auto-routes between OpenAI-compatible and native OCI APIs
• Chat, streaming, and vision with Llama 4, Grok, GPT-OSS, and Cohere models
• Function calling with a @tool decorator that auto-generates OpenAI-format schemas
• Text embeddings via Cohere Embed v3 models (native OCI SDK)
• Oracle AI Vector Search integration for production-grade vector storage
• RAG pipeline with Docling document loading, recursive chunking, and retrieval-augmented generation
• Tool-calling agent with memory, multi-turn sessions, and configurable reasoning loops
• Guardrails for content moderation, PII detection, and prompt injection defense
• Thinking trace extraction for reasoning models (Grok Code, GPT-OSS)
• CLI for interactive chat, model browsing, embeddings, and RAG operations
• 5 auth methods: User Principal, API Key, Instance Principal, Resource Principal, Session Token

Quick Install

pip install oci-genai-service

Quick Start

Chat Completion

from oci_genai_service import GenAIClient

client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")
response = client.chat("Explain Oracle AI Vector Search in 3 sentences.")
print(response.text)

Streaming

from oci_genai_service import GenAIClient

client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")

for chunk in client.chat("Write a haiku about databases.", stream=True):
    print(chunk, end="", flush=True)

Vision (Image + Text)

from oci_genai_service import GenAIClient

client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")

response = client.chat(
    "Describe this architecture diagram.",
    model="meta.llama-3.2-90b-vision-instruct",
    images=["diagram.png"],
)
print(response.text)

RAG with Oracle Vector Search

from oci_genai_service import GenAIClient, RAGPipeline, OracleVectorStore

client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")
store = OracleVectorStore(
    dsn="localhost:1521/FREEPDB1",
    user="genai",
    password="genai",
)

pipeline = RAGPipeline(client=client, vector_store=store)
pipeline.ingest("quarterly-report.pdf")

result = pipeline.query("What was revenue in Q4?")
print(result.text)
for source in result.sources:
    print(f"  [{source['score']:.2f}] {source['chunk'][:80]}...")

Agent with Tools

from oci_genai_service import GenAIClient, Agent, tool

@tool
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"72F and sunny in {city}"

@tool
def get_population(city: str) -> int:
    """Get population of a city."""
    return 1_000_000

client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")
agent = Agent(client=client, tools=[get_weather, get_population])

result = agent.run("What's the weather and population in Austin?")
print(result.text)
print(f"Tool calls: {len(result.tool_calls)}, Steps: {result.steps}")

Authentication

Method	auth_type	When to Use
User Principal	user_principal	Local development with ~/.oci/config (default)
API Key	api_key	Simple token-based auth, no OCI config needed
Instance Principal	instance_principal	Running on OCI compute instances
Resource Principal	resource_principal	OCI Functions, Data Science jobs
Session Token	session	Temporary session-based authentication

Environment Variables

export OCI_COMPARTMENT_ID="ocid1.compartment.oc1..xxx"
export OCI_REGION="us-chicago-1"           # default
export OCI_PROFILE="DEFAULT"               # OCI config profile
export OCI_CONFIG_FILE="~/.oci/config"     # OCI config path
export OCI_GENAI_API_KEY="your-api-key"    # triggers api_key auth

Explicit Config

from oci_genai_service import GenAIClient, AuthConfig

# User Principal (default)
client = GenAIClient(compartment_id="ocid1.compartment.oc1..xxx")

# API Key
client = GenAIClient(api_key="your-key", compartment_id="ocid1.compartment.oc1..xxx")

# Instance Principal
config = AuthConfig(auth_type="instance_principal", compartment_id="ocid1.compartment.oc1..xxx")
client = GenAIClient(config=config)

CLI

The oci-genai command is installed automatically.

Interactive Chat

oci-genai chat
# Model: meta.llama-4-maverick
# You> What is Oracle Cloud?
# Assistant> ...

One-shot Chat

oci-genai chat "Explain RAG in one paragraph" --model meta.llama-3.3-70b-instruct

Vision

oci-genai chat "Describe this image" --image photo.jpg --model meta.llama-3.2-90b-vision-instruct

Browse Models

# List all models
oci-genai models list

# Filter by vendor
oci-genai models list --vendor meta

# Filter by capability
oci-genai models list --capability vision

# Model details
oci-genai models info meta.llama-4-maverick

Embeddings

oci-genai embed "Oracle AI Vector Search" --model cohere.embed-english-v3.0

RAG

# Ingest documents
oci-genai rag ingest ./docs/ --dsn localhost:1521/FREEPDB1 --user genai --password genai

# Query
oci-genai rag query "What are the main findings?" --dsn localhost:1521/FREEPDB1

Oracle Vector Search Setup

Start Oracle Database 26ai Free with Docker Compose:

# docker-compose.yml
services:
  oracle-db:
    image: gvenzl/oracle-free:26-slim
    ports:
      - "1521:1521"
    environment:
      ORACLE_PASSWORD: genai
      APP_USER: genai
      APP_USER_PASSWORD: genai
    volumes:
      - oracle-data:/opt/oracle/oradata

volumes:
  oracle-data:

docker compose up -d

# Verify it's ready
docker compose logs -f oracle-db  # wait for "DATABASE IS READY TO USE"

The OracleVectorStore will auto-create the vector table on first use.

Supported Models

Chat Models (OpenAI-Compatible API)

Model ID	Name	Vendor	Capabilities	Context
meta.llama-4-scout	Llama 4 Scout	Meta	chat, function_calling, streaming	131K
meta.llama-4-maverick	Llama 4 Maverick	Meta	chat, function_calling, streaming	131K
meta.llama-3.3-70b-instruct	Llama 3.3 70B Instruct	Meta	chat, function_calling, streaming	131K
meta.llama-3.1-405b-instruct	Llama 3.1 405B Instruct	Meta	chat, function_calling, streaming	131K
meta.llama-3.1-70b-instruct	Llama 3.1 70B Instruct	Meta	chat, function_calling, streaming	131K
xai.grok-4.1-fast	Grok 4.1 Fast	xAI	chat, streaming	131K
xai.grok-4	Grok 4	xAI	chat, streaming	131K
xai.grok-4-fast	Grok 4 Fast	xAI	chat, streaming	131K
xai.grok-3	Grok 3	xAI	chat, streaming	131K
xai.grok-3-mini	Grok 3 Mini	xAI	chat, streaming	131K
xai.grok-3-mini-fast	Grok 3 Mini Fast	xAI	chat, streaming	131K
xai.grok-code-fast-1	Grok Code Fast 1	xAI	chat, streaming, thinking	131K
openai.gpt-oss-120b	GPT-OSS 120B	OpenAI	chat, streaming, thinking	131K
openai.gpt-oss-20b	GPT-OSS 20B	OpenAI	chat, streaming	131K

Vision Models (OpenAI-Compatible API)

Model ID	Name	Vendor	Capabilities	Context
meta.llama-3.2-90b-vision-instruct	Llama 3.2 90B Vision	Meta	chat, vision, streaming	131K
meta.llama-3.2-11b-vision-instruct	Llama 3.2 11B Vision	Meta	chat, vision, streaming	131K

Chat Models (Native OCI API)

Model ID	Name	Vendor	Capabilities	Context
cohere.command-a	Command A	Cohere	chat, streaming	262K
cohere.command-r-plus	Command R+	Cohere	chat, streaming	131K
cohere.command-r	Command R	Cohere	chat, streaming	131K

Embedding Models (Native OCI API)

Model ID	Name	Vendor	Dimensions
cohere.embed-english-v3.0	Embed English v3.0	Cohere	1024
cohere.embed-multilingual-v3.0	Embed Multilingual v3.0	Cohere	1024
cohere.embed-english-light-v3.0	Embed English Light v3.0	Cohere	384
cohere.embed-multilingual-light-v3.0	Embed Multilingual Light v3.0	Cohere	384

Examples

See the examples/ directory for complete working examples:

• chat_basic.py — simple chat completion
• chat_streaming.py — streaming output
• vision.py — image analysis with vision models
• function_calling.py — tool use with @tool decorator
• embeddings.py — text embeddings
• rag_pipeline.py — full RAG with Oracle Vector Search
• agent.py — tool-calling agent with memory
• guardrails.py — content moderation and PII detection
• thinking.py — reasoning trace extraction

License

Copyright (c) 2024, 2025 Oracle and/or its affiliates.

Licensed under the Universal Permissive License v1.0 (UPL-1.0). See LICENSE for details.