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Fast Extraction Pipelines and Retrieval for AI Applications at Any Scale

Indexify High Level

Indexify is data framework for building LLM Applications that derives knowledge from enterprise or consumer data to make decisions or answer questions. It shines in use-cases where ground truth sources of information changes over time. Most data sources in the real world are unstructured and originate from PDFs, clicks on websites, emails, text messages or videos. Indexify brings information from unstructured data sources to LLMs continuously for production applications.

It provides the following primitives -

  1. Real Time Data Pipelines - Declarative data transformation and extraction graphs that execute whenever new data is ingested.
  2. Extractor SDK - SDK to write embedding and un-structured data extraction algorithms that can be used by real-time pipelines.
  3. Storage Interface - Extracted data is automatically stored in vector stores, structured data stores and blob stores.
  4. Retrieval API - Exposes data from vector stores and structured stores using Semantic Search APIs and SQL.

Why Use Indexify

Building a data-intensive product with LLMs often involves -

  1. Ingesting new data, extracting structured information, or embedding and writing them to storage.
  2. Setting up fault tolerant pipelines to do the ingestion-extraction process continuously as new data is generated by humans or business processes.
  3. Running compute intensive models for extraction efficiently in pipelines.
  4. Serving fresh data by searching vector indexes and structured stores to LLMs for making accurate decisions.

While there are many data frameworks for unstructured data, they are primarily optimized for prototyping applications locally. Reliable data infrastructure for production use cases is designed to be distributed on many machines to allow scale-outs, fault-tolerant to hardware or software crashes, predictable latencies and throughput, and observable to help troubleshoot.

Indexify runs locally without any dependencies, making it easy to build applications and test and iterate locally. It does so without sacrificing the properties that make data systems shine in production environments. Applications built with Indexify can run on laptops and can run unchanged in production. Indexify can auto-scale, is distributed, fault-tolerant, and is fully observable with predictable latencies and throughput.

Start Using Indexify

Dive into Getting Started to learn how to use Indexify.

If you would like to learn some common use-cases -

  1. Learn how to build production grade RAG Applications
  2. Extract PDF, Videos and Audio to extract embedding and structured data.

Features

  • Makes Unstructured Data Queryable with SQL and Semantic Search
  • Real Time Extraction Engine to keep indexes automatically updated as new data is ingested.
  • Create Extraction Graph to create multi-step workflows for data transformation, embedding and structured extraction.
  • Incremental Extraction and Selective Deletion when content is deleted or updated.
  • Extractor SDK allows adding new extraction capabilities, and many readily available extractors for PDF, Image and Video indexing and extraction.
  • Multi-Tenant from the ground up, Namespaces to isolate sensitive data.
  • Works with any LLM Framework including Langchain, DSPy, etc.
  • Runs on your laptop during prototyping and also scales to 1000s of machines on the cloud.
  • Works with many Blob Stores, Vector Stores and Structured Databases
  • We have even Open Sourced Automation to deploy to Kubernetes in production.