Integration: Pixeltable
Document Store and Retriever backed by Pixeltable multimodal data infrastructure.
Authors
Pixeltable
Table of Contents
Overview
Pixeltable is open-source Python data infrastructure for multimodal AI. It provides persistent, versioned tables that store text, images, video, audio, and documents alongside embeddings and metadata, with incremental computation via computed columns.
This integration provides two components:
PixeltableDocumentStore— a HaystackDocumentStorebacked by a Pixeltable table with a built-in embedding index.PixeltableRetriever— a HaystackRetrievercomponent that performs vector similarity search.
The .table property exposes the underlying Pixeltable table when you need computed columns, version history, or multimodal operations beyond the Haystack Document Store interface.
Requires Pixeltable >= 0.6.8.
Installation
pip install haystack-pixeltable
Usage
Document Store
from haystack import Document
from haystack_pixeltable import PixeltableDocumentStore
store = PixeltableDocumentStore(
table_name="myproject.docs",
embedding_dimension=1536,
)
store.write_documents([
Document(
content="Pixeltable is multimodal data infrastructure.",
embedding=[0.1] * 1536,
meta={"category": "infra"},
),
Document(
content="Haystack is a framework for building RAG pipelines.",
embedding=[0.2] * 1536,
meta={"category": "frameworks"},
),
])
print(store.count_documents()) # 2
Retriever
from haystack_pixeltable import PixeltableDocumentStore, PixeltableRetriever
store = PixeltableDocumentStore(
table_name="myproject.docs",
embedding_dimension=1536,
)
retriever = PixeltableRetriever(document_store=store, top_k=5)
result = retriever.run(query_embedding=[0.1] * 1536)
for doc in result["documents"]:
print(f"{doc.content} (score: {doc.score:.3f})")
In a Haystack Pipeline
from haystack import Pipeline
from haystack.components.embedders import (
SentenceTransformersTextEmbedder,
SentenceTransformersDocumentEmbedder,
)
from haystack.components.writers import DocumentWriter
from haystack_pixeltable import PixeltableDocumentStore, PixeltableRetriever
store = PixeltableDocumentStore(
table_name="rag.knowledge",
embedding_dimension=384,
)
# Indexing pipeline
indexing = Pipeline()
indexing.add_component("embedder", SentenceTransformersDocumentEmbedder())
indexing.add_component("writer", DocumentWriter(document_store=store))
indexing.connect("embedder", "writer")
# Query pipeline
query = Pipeline()
query.add_component("embedder", SentenceTransformersTextEmbedder())
query.add_component("retriever", PixeltableRetriever(document_store=store, top_k=5))
query.connect("embedder.embedding", "retriever.query_embedding")
result = query.run({"embedder": {"text": "What is multimodal AI?"}})
Filtering
The Document Store supports the Haystack filter specification:
# Simple comparison
store.filter_documents(
filters={"field": "meta.category", "operator": "==", "value": "science"}
)
# Compound AND
store.filter_documents(filters={
"operator": "AND",
"conditions": [
{"field": "meta.category", "operator": "==", "value": "science"},
{"field": "meta.score", "operator": ">", "value": 0.5},
],
})
# Compound OR
store.filter_documents(filters={
"operator": "OR",
"conditions": [
{"field": "meta.source", "operator": "==", "value": "arxiv"},
{"field": "meta.source", "operator": "==", "value": "pubmed"},
],
})
Pixeltable Escape Hatch
Access the underlying Pixeltable table for operations beyond the Haystack interface:
store = PixeltableDocumentStore(
table_name="myproject.docs", embedding_dimension=1536
)
t = store.table
# Add a computed column that summarizes every document on insert
import pixeltable.functions.openai as openai
t.add_computed_column(
summary=openai.chat_completions(
messages=[{"role": "user", "content": t.content}],
model="gpt-4o-mini",
).choices[0].message.content,
if_exists="ignore",
)
# Query with Pixeltable's API
results = t.select(t.content, t.summary).collect()