Agentic Patterns

What it covers

• Pattern 1: Prompt Chaining
• Pattern 2: Routing
• Pattern 3: Parallelization
• Pattern 4: Tool Use
• Pattern 5: Evaluator-Optimizer
• Pattern 6: Orchestrator-Worker
• Strategy A: ReAct
• Strategy B: Planning

What it uses

Install

terminal

$pip install pixeltable openai

Environment variables it names

OPENAI_API_KEY

Pixeltable calls

openai.chat_completions()openai.invoke_tools()pxt.create_table()@pxt.udfpxt.tools()pxt.udf()add_computed_column()insert()

A look at the code

# Step 1: generate an outline
chain.add_computed_column(
    outline_response=openai.chat_completions(
        messages=[
            {
                'role': 'user',
                'content': 'Create a 3-point outline for a short article about: '
                + chain.topic,
            }
        ],
        model='gpt-4o-mini',
    )
)
chain.add_computed_column(
    outline=chain.outline_response.choices[0].message.content.astype(
        pxt.String
    )
)

One cell of the notebook, copied as it is. Open it in Colab for the rest.

• Build a RAG pipeline

  Create a retrieval-augmented generation system that answers questions using your documents as context.

  Updated Apr 10, 2026Open in Colab

• LLM as judge

  Score model output with a second model, as a chain of computed columns

  Recipe · Updated Sep 8, 2026

• Document Indexing and RAG

  In this tutorial, we'll demonstrate how RAG operations can be implemented in Pixeltable. In particular, we'll develop a RAG application that summarizes a collection of PDF documents and uses ChatGPT to answer questions about them.

  Updated Oct 30, 2026Open in Colab

• RAG Operations in Pixeltable

  In this tutorial, we'll explore Pixeltable's flexible handling of RAG operations on unstructured text.

  Updated Jan 24, 2026Open in Colab