llama-index-vector-stores-pixeltable 0.1.1

LlamaIndex VectorStore integration for Pixeltable multimodal data infrastructure.

llama-index-vector-stores-pixeltable

LlamaIndex VectorStore integration backed by Pixeltable -- multimodal data infrastructure with built-in embedding indexes, incremental computation, and 25+ AI provider integrations.

Installation

pip install llama-index-vector-stores-pixeltable

Quick Start

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.vector_stores.pixeltable import PixeltableVectorStore

# Create the vector store
vector_store = PixeltableVectorStore(
    table_name="mydir.docs",
    embed_dim=1536,
)

# Load documents and build index
storage_context = StorageContext.from_defaults(vector_store=vector_store)
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is Pixeltable?")
print(response)

Connect to an Existing Pixeltable Table

vector_store = PixeltableVectorStore(table_name="mydir.existing_docs")
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine()

Why Pixeltable as a Vector Backend?

• Persistent and versioned: Data survives restarts; every change is tracked
• Incremental: Only new/changed rows get re-embedded
• Multimodal native: Images, video, audio, and documents alongside text
• 25+ AI providers: Built-in functions for OpenAI, Anthropic, Gemini, and more
• No external services: Embedded PostgreSQL, no Docker required

Links

• Pixeltable Docs
• GitHub
• Discord