How do agents use a durable multimodal backend?

The backend holds the corpus and the derived pieces as rows. The agent calls a retrieval function, gets passages, frames, or transcripts, and can retrieve again on the next turn. Pixeltable is that store: tables, indexes, and an HTTP route in one file.

Is agentic RAG a new model?

No. It is a retrieval loop the agent controls. The model still answers from retrieved evidence. What changes is ownership of the corpus: a durable table instead of a prompt that forgets.

Do I still need a separate vector database?

The agent needs an embedding index so similar pieces can be found. That index can live on the table that holds the chunks or frames. A separate vector database is optional; it is not the agent’s memory by itself.

Retrieval

What is agentic RAG?

Agentic RAG is retrieval an agent can call as a tool, then decide whether to retrieve again, inspect a source, or answer. The corpus, the index, and the evidence live in a durable store, so later turns reuse the same rows instead of restuffing a growing prompt.

Updated 2026-10-12 · Part of What is RAG?

How it works

The agent has a retrieval tool, not a single retrieve-then-generate chain.
Evidence is stored as rows — passages, frames, or transcripts — that later turns can query again.
The next action can retrieve, inspect a source file, or generate, using persisted artifacts rather than only the last prompt.

What it is not

It is not a chat window that re-embeds a folder on every question, and it is not an agent framework with no store underneath.

agentic RAG: this, and the thing it is confused with

agentic RAG: this, and the thing it is confused with
	This	Not this
Who retrieves	The agent, as a tool call	A single pipeline step
Memory	Rows that survive the turn	The current prompt
Evidence	Passages, frames, transcripts	A vector with no source file

Where Pixeltable fits

Pixeltable is the durable multimodal backend those tools query. @pxt.query wraps similarity search; FastAPIRouter can expose it. Database, orchestration, and serving stay in one Python file. The same file deploys to Pixeltable Cloud (Limited Beta).

import pixeltable as pxt
from pixeltable.functions.document import document_splitter
from pixeltable.functions.huggingface import sentence_transformer
from pixeltable.serving import FastAPIRouter

TableModel = pxt.model_base()
text_embed = sentence_transformer.using(model_id='all-MiniLM-L6-v2')

class Docs(TableModel, name='docs'):
    document: pxt.Document

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(document=Docs.document, separators='sentence'),
):
    __indexes__ = [
        pxt.EmbeddingIndex(text, string_embed=text_embed),
    ]

@pxt.query
def retrieve(question: str, limit: int = 5):
    sim = Chunks.text.similarity(string=question)
    return Chunks.order_by(sim, asc=False).limit(limit).select(Chunks.text, sim)

api = FastAPIRouter(name='agent-rag')
api.add_query_route(path='/retrieve', query=retrieve)

Questions

How do agents use a durable multimodal backend?: The backend holds the corpus and the derived pieces as rows. The agent calls a retrieval function, gets passages, frames, or transcripts, and can retrieve again on the next turn. Pixeltable is that store: tables, indexes, and an HTTP route in one file.
Is agentic RAG a new model?: No. It is a retrieval loop the agent controls. The model still answers from retrieved evidence. What changes is ownership of the corpus: a durable table instead of a prompt that forgets.
Do I still need a separate vector database?: The agent needs an embedding index so similar pieces can be found. That index can live on the table that holds the chunks or frames. A separate vector database is optional; it is not the agent’s memory by itself.