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What is Mixpeek?

Mixpeek is a multimodal data processing pipeline. It allows you to define custom functions that processes changes from your S3 bucket, then automatically writes the structured outputs to your database.

It captures your unstructured object changes consistent, transforms them and inserts the structured output into your downstream database, so your application has fresh, structured data, always.

Mixpeek doesn’t require any changes to your existing retrieval architecture. It acts as an ETL/indexing tool that operates entirely in the background, empowering you to treat your object storage and database as one entity.

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Demo

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Highlights

Multimodal Data Handling

Supports images, videos, audio, text, and documents.

Automated Pipeline

Processes data through extraction, generation, and embedding.

Seamless Integration

Connect unstructured object storage like Amazon S3 with downstream structured databases like MongoDB.

Real-Time Updates

Ensures always fresh and structured data for robust querying by using robust sync architecture

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Use Cases

Check the use case section for live demos, code, videos, and guides

  • Search Engines: Enhance search capabilities with rich, multimodal data.
  • Recommendation Systems: Improve recommendations using diverse data sources.
  • Content Management: Streamline and manage multimedia content efficiently.
  • Data Analytics: Perform advanced analytics with structured, multimodal data.
  • Knowledge Graphs: Build and maintain comprehensive knowledge graphs with real-time updates.
  • RAG (Retrieval-Augmented Generation): Use the structured data to power advanced AI models for more accurate and contextually relevant outputs.

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How Does It Work?

Comprehensive achitecture guide
  1. Connect Datastores: Integrate your existing datastores, such as S3 and MongoDB.
  2. Data Ingestion: Add files (images, videos, audio, text, documents) to S3.
  3. Pipeline Processing: Define and enable your Mixpeek pipeline, so each of tehse files is processed according to your logic like:
    • Extraction: Extracts relevant information from each modality.
    • Generation: Generates AI-powered outputs like summaries and tags.
    • Embedding: Creates vectors for each modality.
  4. Database Insertion: Processed data is inserted into the customer’s database as a single atomic unit.
  5. Access and Query: Access the freshly structured data to build rich query pipelines.

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