Why Multimodal RAG
Traditional RAG only scratches the surface. Most enterprise knowledge lives in images, video, audio, and documents -- not just text files.
Text-Only RAG Misses 80% of Data
Most RAG frameworks only index text documents, ignoring the images, videos, audio, and PDFs that make up the majority of enterprise data.
Mixpeek extracts features from every modality -- video frames, audio transcripts, document layouts, images -- and indexes them into a unified vector space for retrieval.
Cross-Modal Understanding
Query with text, retrieve text. Single-modal pipelines cannot find a video clip that matches a text description or an image that answers a question.
Query with text and retrieve matching video frames. Query with an image and find similar documents. Any modality in, any modality out.
Production-Ready Infrastructure
Most RAG tools are frameworks that require you to stitch together embedding models, vector databases, orchestration, and GPU infrastructure yourself.
Mixpeek is managed infrastructure -- Ray clusters, Qdrant vector search, 50+ extractors, and composable retriever pipelines out of the box. Not a framework, a platform.
RAG Pipeline Architecture
A complete pipeline from data ingestion to generation, with every stage composable and configurable.
Data Sources
S3, GCS, Azure Blob, APIs
Feature Extraction
50+ extractors for every modality
Vector Index
Qdrant hybrid search
Retriever Pipeline
Composable stages
Generation
LLM integration
Mix and match extractors, search methods, filters, and rerankers to build the exact retrieval pipeline your application needs. No rigid workflows -- just building blocks.
Key Capabilities
Everything you need to build, deploy, and scale production RAG pipelines across every data modality.
Multimodal Embedding Generation
Generate embeddings from text, images, video, audio, and documents using state-of-the-art models.
- Unified embedding space across modalities
- Support for custom embedding models
- Batch and real-time embedding pipelines
Composable Retriever Stages
Build retrieval pipelines from modular stages -- filter, search, rerank, and transform in any order.
- Chain multiple retrieval strategies
- Metadata filtering and boolean logic
- Custom reranking with cross-encoders
Hybrid Search
Combine vector similarity, keyword matching, and metadata filtering in a single query.
- Vector + BM25 keyword search
- Weighted score fusion
- Faceted metadata filtering
Custom Feature Extractors
Bring your own models or use built-in extractors for OCR, transcription, object detection, and more.
- 50+ built-in extractors
- Plugin your own models via Docker
- GPU-accelerated processing on Ray
Real-Time & Batch Processing
Ingest data in real-time via webhooks or process large datasets in batch with automatic scaling.
- Event-driven ingestion triggers
- Auto-scaling Ray GPU clusters
- Progress tracking and batch monitoring
Self-Hosted or Managed Cloud
Run Mixpeek in your own VPC for data sovereignty or use our managed cloud for zero-ops deployment.
- BYO Cloud deployment option
- Managed multi-tenant cloud
- Air-gapped environment support
RAG Patterns
From simple text retrieval to agentic tool-use -- Mixpeek supports every RAG architecture pattern.
Simple RAG
The classic pattern: embed text documents, retrieve relevant chunks by semantic similarity, and generate answers with an LLM.
Text Documents --> Embed --> Vector Search --> LLM --> Answer
Multimodal RAG
Go beyond text. Embed images, video frames, audio segments, and documents into a shared vector space. Query with any modality and retrieve across all of them.
Images + Video + Audio + Text --> Feature Extraction --> Unified Index --> Cross-Modal Retrieval --> LLM --> Rich Answer
Agentic RAG
Let an AI agent decide which retriever to call, what filters to apply, and how to combine results. The agent uses retrieval as a tool within a larger reasoning loop.
User Query --> Agent Reasoning --> Tool Selection --> Retriever Call --> Re-rank --> Agent Synthesis --> Final Answer
Mixpeek RAG vs. Alternatives
See how Mixpeek compares to popular RAG frameworks and platforms.
| Feature | Mixpeek | LangChain | LlamaIndex | Vectara |
|---|---|---|---|---|
| Multimodal Support | Native (video, image, audio, text, PDF) | Limited (text-focused, manual integrations) | Limited (text-focused, some image) | Text + some document parsing |
| Infrastructure | Managed Ray + Qdrant clusters | Framework only (BYO infrastructure) | Framework only (BYO infrastructure) | Managed (text-only pipeline) |
| Deployment Options | Managed, Dedicated, BYO Cloud | Self-managed only | Self-managed or LlamaCloud | Managed SaaS only |
| Embedding Generation | Built-in (50+ extractors) | BYO models | BYO models | Built-in (text only) |
| Custom Extractors | Plugin system (Docker-based) | Custom code required | Custom code required | Not supported |
| Production Readiness | Enterprise SLAs, monitoring, auto-scaling | Depends on your infrastructure | Depends on your infrastructure | Enterprise SLAs (text only) |
Build Multimodal RAG in Minutes
A simple Python API to create and execute multimodal retrieval pipelines.
from mixpeek import Mixpeek
client = Mixpeek(api_key="YOUR_API_KEY")
# Create a multimodal retriever
retriever = client.retrievers.create(
name="multimodal-rag",
namespace="my-namespace",
stages=[
{
"type": "feature_search",
"method": "hybrid",
"query": {
"text": "manufacturing defect on assembly line",
"modalities": ["video", "image", "text"]
},
"limit": 20
},
{
"type": "rerank",
"model": "cross-encoder",
"limit": 5
}
]
)
# Execute the retriever
results = client.retrievers.execute(
retriever_id=retriever.id,
query="Show me quality control failures from last week",
filters={
"metadata.department": "manufacturing",
"metadata.date": {"$gte": "2026-03-01"}
}
)
# Results include matched video frames, images, and documents
for result in results:
print(f"Score: {result.score}")
print(f"Type: {result.modality}")
print(f"Content: {result.content}")Frequently Asked Questions
What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that enhances large language model (LLM) outputs by first retrieving relevant information from an external knowledge base, then using that context to generate more accurate, grounded responses. Instead of relying solely on training data, the LLM gets up-to-date, domain-specific context at query time.
What is multimodal RAG?
Multimodal RAG extends traditional text-only RAG to work across all data types -- images, video, audio, and documents. Instead of limiting retrieval to text chunks, multimodal RAG embeds and indexes content from every modality into a unified vector space, enabling cross-modal retrieval where a text query can surface relevant video frames or images.
How is Mixpeek different from LangChain or LlamaIndex?
LangChain and LlamaIndex are frameworks -- they provide abstractions for building RAG pipelines, but you still need to manage your own embedding models, vector databases, GPU infrastructure, and scaling. Mixpeek is managed infrastructure: it includes 50+ feature extractors, Ray GPU clusters, Qdrant vector search, and composable retriever pipelines out of the box. You also get native multimodal support that goes far beyond text.
What data types can I use with multimodal RAG?
Mixpeek supports text documents, PDFs, images (JPEG, PNG, TIFF, WebP), video (MP4, MOV, AVI), audio (MP3, WAV, FLAC), and more. Each data type has dedicated feature extractors that handle OCR, transcription, object detection, scene understanding, and embedding generation.
Can I use my own embedding models?
Yes. Mixpeek supports custom embedding models via its plugin system. You can package your model in a Docker container and deploy it as a custom feature extractor that runs on Mixpeek's Ray GPU clusters. You can also use any of the 50+ built-in extractors.
Does Mixpeek support self-hosted RAG deployments?
Yes. Mixpeek offers three deployment options: Managed Cloud (fully managed by Mixpeek), Dedicated Cloud (single-tenant in Mixpeek's cloud), and BYO Cloud (deployed in your own VPC on AWS, GCP, or Azure). The BYO Cloud option gives you complete data sovereignty while Mixpeek manages the software.
What is agentic RAG?
Agentic RAG is a pattern where an AI agent uses retrieval as a tool within a larger reasoning loop. Instead of a fixed retrieval pipeline, the agent dynamically decides which retrievers to call, what filters to apply, and how to combine results based on the user's query. This enables more flexible, context-aware retrieval strategies.
How does Mixpeek handle large-scale RAG pipelines?
Mixpeek uses Ray for distributed GPU processing and Qdrant for scalable vector search. Ingestion pipelines automatically scale across GPU clusters based on workload. Batch processing handles millions of documents with progress tracking, and retriever pipelines are optimized for low-latency queries at scale.