Working with Ollama in Pixeltable
Ollama is a popular platform for local serving of LLMs. In this tutorial, we'll show how to integrate Ollama models into a Pixeltable workflow.
Pixeltable's Nebius Token Factory integration enables you to access Nebius language and embedding models via an OpenAI-compatible API.
Providers · Notebook · Updated
Colab runs it in your browser. If it calls a model provider, it asks for that provider's API key in a cell.
pip install pixeltable openai
NEBIUS_API_KEYnebius.chat_completions()nebius.embeddings()pxt.create_table()add_computed_column()insert()add_embedding_index()similarity()from pixeltable.functions import nebiuschat_t = pxt.create_table('nebius_demo/chat', {'input': pxt.String})messages = [{'role': 'user', 'content': chat_t.input}]chat_t.add_computed_column(output=nebius.chat_completions(messages=messages,model='Qwen/Qwen3-30B-A3B-Instruct-2507',model_kwargs={# Optional dict with parameters for the Nebius API'max_tokens': 300,'temperature': 0.7,},))chat_t.add_computed_column(response=chat_t.output.choices[0].message.content)
One cell of the notebook, copied as it is. Open it in Colab for the rest.
Ollama is a popular platform for local serving of LLMs. In this tutorial, we'll show how to integrate Ollama models into a Pixeltable workflow.
Pixeltable's OpenAI integration enables you to access OpenAI models via the OpenAI API.
Pixeltable's OpenRouter integration enables you to access multiple LLM providers through a unified API via OpenRouter.
Pixeltable's Pydantic integration enables type-safe data insertion using Pydantic models. Instead of inserting raw dictionaries, you can define structured models with validation and insert them directly into Pixeltable tables.