Agent skill
openai
OpenAI compatibility layer for Ollama. Use the official OpenAI Python library to interact with Ollama, enabling easy migration from OpenAI and compatibility with LangChain, LlamaIndex, and other OpenAI-based tools.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/openai
SKILL.md
Ollama OpenAI Compatibility
Overview
Ollama provides an OpenAI-compatible API at /v1/* endpoints. This allows using the official openai Python library with Ollama, enabling:
- Migration - Drop-in replacement for OpenAI API
- Tool ecosystem - Works with LangChain, LlamaIndex, etc.
- Familiar interface - Standard OpenAI patterns
Quick Reference
| Endpoint | Method | Purpose |
|---|---|---|
/v1/models |
GET | List models |
/v1/completions |
POST | Text generation |
/v1/chat/completions |
POST | Chat completion |
/v1/embeddings |
POST | Generate embeddings |
Limitations
The OpenAI compatibility layer does not support:
- Show model details (
/api/show) - List running models (
/api/ps) - Copy model (
/api/copy) - Delete model (
/api/delete)
Use bazzite-ai-jupyter:chat or bazzite-ai-jupyter:ollama for these operations.
Setup
import os
from openai import OpenAI
OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://localhost:11434")
client = OpenAI(
base_url=f"{OLLAMA_HOST}/v1",
api_key="ollama" # Required by library but ignored by Ollama
)
List Models
models = client.models.list()
for model in models.data:
print(f" - {model.id}")
Text Completions
response = client.completions.create(
model="llama3.2:latest",
prompt="Why is the sky blue? Answer in one sentence.",
max_tokens=100
)
print(response.choices[0].text)
print(f"Tokens used: {response.usage.completion_tokens}")
Chat Completion
Single Turn
response = client.chat.completions.create(
model="llama3.2:latest",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain machine learning in one sentence."}
],
temperature=0.7,
max_tokens=100
)
print(response.choices[0].message.content)
print(f"Tokens used: {response.usage.total_tokens}")
Multi-Turn Conversation
messages = [
{"role": "system", "content": "You are a helpful math tutor."}
]
# Turn 1
messages.append({"role": "user", "content": "What is 2 + 2?"})
response = client.chat.completions.create(
model="llama3.2:latest",
messages=messages,
max_tokens=50
)
assistant_msg = response.choices[0].message.content
messages.append({"role": "assistant", "content": assistant_msg})
print(f"User: What is 2 + 2?")
print(f"Assistant: {assistant_msg}")
# Turn 2
messages.append({"role": "user", "content": "And what is that multiplied by 3?"})
response = client.chat.completions.create(
model="llama3.2:latest",
messages=messages,
max_tokens=50
)
print(f"User: And what is that multiplied by 3?")
print(f"Assistant: {response.choices[0].message.content}")
Streaming
stream = client.chat.completions.create(
model="llama3.2:latest",
messages=[{"role": "user", "content": "Count from 1 to 5."}],
stream=True
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
Generate Embeddings
response = client.embeddings.create(
model="llama3.2:latest",
input="Ollama makes running LLMs locally easy."
)
embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")
Error Handling
try:
response = client.chat.completions.create(
model="invalid-model",
messages=[{"role": "user", "content": "Hello"}]
)
except Exception as e:
print(f"Error: {type(e).__name__}")
Migration from OpenAI
Before (OpenAI)
from openai import OpenAI
client = OpenAI() # Uses OPENAI_API_KEY
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)
After (Ollama)
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama"
)
response = client.chat.completions.create(
model="llama3.2:latest", # Change model name
messages=[{"role": "user", "content": "Hello!"}]
)
LangChain Integration
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
base_url="http://localhost:11434/v1",
api_key="ollama",
model="llama3.2:latest"
)
response = llm.invoke("What is Python?")
print(response.content)
LlamaIndex Integration
from llama_index.llms.openai import OpenAI
llm = OpenAI(
api_base="http://localhost:11434/v1",
api_key="ollama",
model="llama3.2:latest"
)
response = llm.complete("What is Python?")
print(response.text)
Connection Health Check
import requests
def check_ollama_health(model="llama3.2:latest"):
"""Check if Ollama server is running and model is available."""
OLLAMA_HOST = os.getenv("OLLAMA_HOST", "http://localhost:11434")
try:
response = requests.get(f"{OLLAMA_HOST}/api/tags", timeout=5)
if response.status_code == 200:
models = response.json()
model_names = [m.get("name", "") for m in models.get("models", [])]
return True, model in model_names
return False, False
except requests.exceptions.RequestException:
return False, False
server_ok, model_ok = check_ollama_health()
When to Use This Skill
Use when:
- Migrating from OpenAI to local LLMs
- Using LangChain, LlamaIndex, or other OpenAI-based tools
- You prefer the OpenAI client interface
- Building applications that may switch between OpenAI and Ollama
Cross-References
bazzite-ai-jupyter:ollama- Native Ollama library (more features)bazzite-ai-jupyter:chat- Direct REST API access
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