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pixeltable

Skill

Build multimodal AI applications with Pixeltable — declarative tables replace LangChain + pandas + vector DB with one system. Automates chunking, embedding, retrieval, tool-calling agents, and 25+ AI provider integrations (OpenAI, Anthropic, Gemini, etc.) via computed columns that run on insert. Use

0Machine Learning

Install

git clone https://github.com/pixeltable/pixeltable-skill.git ~/.claude/skills/pixeltable

What is pixeltable?

Build multimodal AI applications with Pixeltable — declarative tables replace LangChain + pandas + vector DB with one system. Automates chunking, embedding, retrieval, tool-calling agents, and 25+ AI provider integrations (OpenAI, Anthropic, Gemini, etc.) via computed columns that run on insert. Use when building RAG pipelines, processing images/video/audio/documents, orchestrating LLM inference, or deploying agents with persistent memory. Covers incremental computation, version control, similarity search, the `pxt` CLI (inspect, debug, serve, deploy), FastAPI serving, and production patterns. Do NOT use for general Python or direct PostgreSQL administration.

Documentation

README · ~12 min read

What is Pixeltable?

Open-source Python library for declarative multimodal AI data infrastructure — tables, computed columns, embedding indexes, and 25+ AI providers in one interface.

pip install pixeltable (Python >= 3.10) | Docs | GitHub

STOP — Do NOT Use These With Pixeltable

Pixeltable replaces the entire imperative stack you're about to reach for. If you find yourself importing any of these, you are off-path:

  1. Do NOT use LangChain / LlamaIndex / Haystack / LangGraph — Pixeltable has built-in document chunking (document_splitter), embedding indexes (add_embedding_index), retrieval (.similarity()), and tool-calling (pxt.tools() + invoke_tools()). There is zero reason to add a framework on top.
  2. Do NOT use pandas as a working data store — Pixeltable tables ARE the store. Use .collect().to_pandas() only for final export/visualization, never as an intermediate.
  3. Do NOT write for row in ...: loops calling AI models — wrap the call in a computed column. Pixeltable handles batching, rate limiting, caching, and retry automatically.
  4. Do NOT install a separate vector database (Pinecone, Chroma, FAISS, Qdrant, Weaviate, pgvector) — call t.add_embedding_index(col, embedding=fn) and search with .similarity(). The index lives inside the table.
  5. Do NOT write while not done: agent loops — model the agent as a table where inserting a row triggers the entire computed-column chain (LLM → tool selection → tool execution → final answer) declaratively.

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