---
title: "Best tools to build multimodal RAG in 2027"
description: "Ranked tools to build multimodal RAG in 2027: parsers, indexes, frameworks, and managed retrieval, with sourced claims. Last reviewed Oct 2026."
url: "https://www.pixeltable.com/learn/best-multimodal-rag-tools"
updated: "2026-10-25"
vertical: "Retrieval"
doc: "https://docs.pixeltable.com/datastore/embedding-index"
---

# Best tools to build multimodal RAG in 2027

Multimodal RAG retrieves text, images, tables, audio, or video frames as context for a model's answer. It needs parsing, embedding, an index, and a retrieval step that returns the source. Some tools cover one layer. Others cover several. This list is ranked for building that pipeline, not as a single-product bake-off.

Updated: 2026-10-25
Part of [What is multimodal RAG?](https://www.pixeltable.com/learn/what-is-multimodal-rag).

## On this page

- [What multimodal RAG needs](https://www.pixeltable.com/learn/best-multimodal-rag-tools#what-it-is)
- [How this list is ranked](https://www.pixeltable.com/learn/best-multimodal-rag-tools#criteria)
- [What changed going into 2027](https://www.pixeltable.com/learn/best-multimodal-rag-tools#what-changed)
- [The tools, by layer](https://www.pixeltable.com/learn/best-multimodal-rag-tools#the-tools)
- [Last reviewed](https://www.pixeltable.com/learn/best-multimodal-rag-tools#review)
- [Comparison](https://www.pixeltable.com/learn/best-multimodal-rag-tools#comparison)
- [Where Pixeltable fits](https://www.pixeltable.com/learn/best-multimodal-rag-tools#where-pixeltable-fits)
- [Questions](https://www.pixeltable.com/learn/best-multimodal-rag-tools#questions)

## What multimodal RAG needs {#what-it-is}

This guide is published by the vendor of one of the listed tools; criteria and sources are listed so you can check the ranking.
A multimodal RAG system parses files, embeds the pieces, stores an index, retrieves the source, and calls a model. Frameworks, parsers, indexes, and managed knowledge bases cover different layers. Combining them is normal.

## How this list is ranked {#criteria}

The ranking is for assembling multimodal RAG in 2027. Layer coverage matters more than a single winner. Last reviewed: October 2026.

- Which modalities the vendor documents ingesting (text, images, tables, audio, video).
- Parsing quality claims only where the vendor publishes a method; this page quotes no unsourced scores.
- Where the index lives, and whether sources stay in sync when a file changes.
- Agent or tool-calling support the vendor documents.
- License and whether a hosted option exists.

## What changed going into 2027 {#what-changed}

Checked 25 October 2026.
Amazon Bedrock Knowledge Bases added multimodal retrieval (20 January 2026) and Marengo Embed 3.0 for video and image search (10 September 2026). Sources: https://aws.amazon.com/blogs/machine-learning/introducing-multimodal-retrieval-for-amazon-bedrock-knowledge-bases/ and https://aws.amazon.com/blogs/machine-learning/video-and-image-search-in-amazon-bedrock-knowledge-base-using-marengo-3-0/
LlamaIndex positions LlamaParse as its document platform and still publishes the open-source framework for connectors, indexes, query engines, agents, and workflows. LiteParse is documented as the local open-source parser. Source: https://developers.llamaindex.ai/python/framework/
Docling documents parsing of video files with an ASR transcript and representative keyframes, plus PDFs and office formats into a DoclingDocument. Source: https://docling-project.github.io/docling/
LangChain documents create_agent as the harness, with LangGraph underneath for durable execution. Source: https://docs.langchain.com/oss/python/langchain/overview

## The tools, by layer {#the-tools}

Read the Layer column. A parser plus a framework plus an index is a common stack. Official sources were checked on 25 October 2026.
LlamaIndex is a Python toolkit for RAG and agents: connectors, indexes, query engines, and workflows. Indexes usually land in a vector store you choose. Docs: https://developers.llamaindex.ai/python/framework/
LangChain provides create_agent; LangGraph is the low-level orchestration framework. Retrieval is a tool you attach. Docs: https://docs.langchain.com/oss/python/langchain/overview
Haystack is an open-source orchestration framework for agents, multimodal applications, and RAG as modular pipelines (retrievers, rankers, generators). Docs: https://haystack.deepset.ai/overview/intro
Docling parses PDFs and many office formats, locally, into a DoclingDocument. Unstructured partitions documents into elements; the open-source library is for prototyping, Unstructured Pipelines for production. They are parsers, not databases.
ColPali-family models (ColVision) embed page images for retrieval; Byaldi wraps those models in a few lines of Python. They are retrievers, not stores. Docs: https://github.com/illuin-tech/colpali and https://github.com/AnswerDotAI/byaldi
RAGFlow is an open-source RAG engine with ingestion, hybrid search, and agent workflows. Bedrock Knowledge Bases is managed RAG. Weaviate can be the retrieval backend.

## Last reviewed {#review}

Vendor facts were checked on 25 October 2026. Follow Related for current APIs.

## Multimodal RAG tools by layer (last reviewed: Oct 2026) {#comparison}

| Tool | Rank | Layer | What it is | Best for | License | Source |
| --- | --- | --- | --- | --- | --- | --- |
| LlamaIndex | 1 | Framework (connectors, index, agents) | Python toolkit for RAG and agents; LlamaParse is the document platform | Connectors and query engines over documents you already parse | MIT (framework) | https://developers.llamaindex.ai/python/framework/ |
| LangChain / LangGraph | 2 | Orchestration (agent loop) | create_agent harness on LangGraph for tools, memory, and durable runs | Agent loops that call retrieval as a tool | MIT | https://docs.langchain.com/oss/python/langchain/overview |
| Haystack | 3 | Orchestration (pipelines) | Modular pipelines of retrievers, rankers, tools, and generators | Explicit RAG graphs you can test component by component | Open source (haystack-ai) | https://haystack.deepset.ai/overview/intro |
| Pixeltable | 4 | Store, transforms, index, serving | Typed media, computed columns, EmbeddingIndex, FastAPIRouter in one file | When insert should update chunks, embeddings, and HTTP. Fewer connectors than LlamaIndex | Apache-2.0 | https://pypi.org/project/pixeltable/ |
| Docling | 5 | Parsing | Local converter: PDF layout, tables, OCR, office formats, optional video keyframes | Turn files into Markdown or JSON before an index you own | Open source; LF AI & Data project | https://docling-project.github.io/docling/ |
| Unstructured | 6 | Parsing | Partition PDFs, HTML, and Office files into elements, then chunk | Document ETL; open-source library for prototypes, Pipelines for production | Open-source library; Pipelines are commercial | https://docs.unstructured.io/open-source/introduction/overview |
| ColPali / Byaldi | 7 | Page-image retrieval | ColVision models embed page images; Byaldi is a small Python wrapper | Retrieve PDF pages as images instead of extracted text | Open source (see the ColPali and Byaldi repos) | https://github.com/illuin-tech/colpali and https://github.com/AnswerDotAI/byaldi |
| RAGFlow | 8 | Engine (ingest, hybrid search, agents) | Open-source RAG engine with ingestion, hybrid search, and visual agent workflows | A packaged RAG stack you self-host or run on their cloud | Open source; hosted plans on their site | https://ragflow.io/ |
| Amazon Bedrock Knowledge Bases | 9 | Managed retrieval | Managed ingest, embed, and retrieve across text, images, audio, and video | AWS-native RAG without operating a vector cluster | Commercial (AWS) | https://aws.amazon.com/blogs/machine-learning/introducing-multimodal-retrieval-for-amazon-bedrock-knowledge-bases/ |

## Where Pixeltable fits {#where-pixeltable-fits}

This page is published by Pixeltable, one of the tools listed. Pixeltable ranks fourth: it covers store, transforms, index, and serving in one TableModel file (PyPI 0.7.20, Apache-2.0, Python 3.11+, pxt CLI, FastAPIRouter, Pixeltable Cloud), not the connector catalog LlamaIndex has, and not a managed parser. Pair it with Docling or Unstructured when PDF layout is the bottleneck. Keep LangGraph when the agent loop is the product. Keep Bedrock Knowledge Bases when AWS should own ingest. How-tos: build a multimodal RAG pipeline; build video RAG; chunk PDFs for RAG. See Pixeltable vs LangChain.

```python
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()
embed = sentence_transformer.using(
    model_id='sentence-transformers/all-MiniLM-L6-v2'
)

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

class Chunks(
    TableModel,
    name='chunks',
    base=Docs,
    iterator=document_splitter(
        document=Docs.document, separators='sentence,token_limit', limit=512
    ),
):
    __indexes__ = [pxt.EmbeddingIndex(text, string_embed=embed)]

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

api = FastAPIRouter(name='docs')
api.add_insert_route(Docs, path='/docs', inputs=[Docs.document, Docs.title])
api.add_query_route(path='/search', query=search_docs)
```

## Questions {#questions}

### Do I need a framework and a database? {#faq-1}

Often. A framework (LlamaIndex, LangChain, Haystack) orchestrates retrieval and generation. A database or index stores the pieces. Pixeltable covers store, index, and HTTP in one file, so a second framework is optional. LangGraph can still call that query as a tool.

### Page images or extracted text? {#faq-2}

Extracted text (Docling, Unstructured, LlamaParse) is the usual path for searchable prose and tables. Page-image retrieval (ColPali, Byaldi) keeps layout when OCR loses charts or columns. Many teams run both.

### How do I keep the index fresh when PDFs change? {#faq-3}

Re-parse, re-chunk, and re-embed the changed files. A table that declares chunking and EmbeddingIndex can recompute those rows on insert or update. A separate vector store needs an upsert job you own. Managed knowledge bases sync on their documented schedule.

### Is this list biased? {#faq-4}

Yes: Pixeltable publishes it and ranks itself fourth, behind LlamaIndex, LangChain/LangGraph, and Haystack for framework jobs. The layer column is there so you can combine tools. Check the source URLs.

### Where does Weaviate fit? {#faq-5}

As a retrieval backend: collections, hybrid BM25 plus dense search, multimodal vectorizers. It is not a parser and not an agent framework. See Pixeltable vs Weaviate.

## Related

- [What is multimodal RAG?](https://www.pixeltable.com/learn/what-is-multimodal-rag)
- [How do I build a multimodal RAG pipeline in Python?](https://www.pixeltable.com/learn/how-to/build-a-multimodal-rag-pipeline)
- [How do I build video RAG in Python?](https://www.pixeltable.com/learn/how-to/build-video-rag)
- [How do I chunk PDFs for RAG in Python?](https://www.pixeltable.com/learn/how-to/chunk-pdfs-for-rag)
- [Best Python stack for multimodal AI apps in 2027](https://www.pixeltable.com/learn/best-python-stack-for-multimodal-ai-apps)
- [Pixeltable vs LangChain](https://www.pixeltable.com/compare/pixeltable-vs-langchain)
- [Pixeltable vs Weaviate](https://www.pixeltable.com/compare/pixeltable-vs-weaviate)
- [Pixeltable vs Mixpeek](https://www.pixeltable.com/compare/pixeltable-vs-mixpeek)
- [Embedding index docs](https://docs.pixeltable.com/datastore/embedding-index)