Does this skill require a running Ollama server?

Yes, the skill requires an Ollama server to be running and accessible, by default at http://localhost:11434, unless configured via the OLLAMA_HOST environment variable.

How does it handle conversation history?

The skill provides a clean Pythonic pattern for multi-turn conversations by appending messages to a list, which is then passed back to the chat API.

Is embedding generation supported?

Yes, the skill provides documentation and examples for generating vector embeddings from text input using any compatible Ollama model.

Can I use this skill for streaming responses?

Yes, it includes standard implementation patterns for both non-streaming and streaming responses for both text generation and chat completions.

Does it support model management commands?

Absolutely. You can list available models, check which models are currently running in VRAM, pull new models, and delete or copy existing ones.

Ollama Python Integration

Name: Ollama Python Integration
Author: atrawog

byatrawog

数据科学与机器学习

Integrates local LLM inference into Python applications using the official Ollama library for text generation, chat, and model management.

关于

This skill enables Claude to interact seamlessly with the official Ollama Python library, providing a high-level interface for local large language model (LLM) orchestration. It covers essential operations including chat completions, text generation, and embeddings, alongside comprehensive model management tasks like pulling, listing, and deleting models. Ideal for developers building AI-powered Python applications, it simplifies the process of managing local model lifecycles and implementing multi-turn conversations with production-ready patterns like streaming and robust error handling.

主要功能

Comprehensive model lifecycle management including pulling and copying
0 GitHub stars
Vector embedding generation for local RAG and semantic search
Built-in health checks and performance metric tracking
Seamless text generation and multi-turn chat completions
Real-time streaming response handling for interactive apps

使用场景

Automating model orchestration and environment setup for local LLMs
Building local AI-powered Python applications and agents
Generating text embeddings for semantic search or local RAG pipelines

关于

主要功能

Comprehensive model lifecycle management including pulling and copying
0 GitHub stars
Vector embedding generation for local RAG and semantic search
Built-in health checks and performance metric tracking
Seamless text generation and multi-turn chat completions
Real-time streaming response handling for interactive apps

使用场景

Automating model orchestration and environment setup for local LLMs
Building local AI-powered Python applications and agents
Generating text embeddings for semantic search or local RAG pipelines