Indexify - Extraction and Retreival from Videos, PDF and Audio for Interactive AI Applications

LLM applications backed by Indexify will never answer outdated information.

Indexify is an open-source engine for buidling fast data pipelines for unstructured data(video, audio, images and documents) using re-usable extractors for embedding, transformation and feature extraction. LLM Applications can query transformed content friendly to LLMs by semantic search and SQL queries.

Indexify keeps vectordbs, structured databases(postgres) updated by automatically invoking the pipelines as new data is ingested into the system from external data sources.

Why use Indexify

• 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 describe data transformation and extraction of 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.
• 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.

Detailed Getting Started

To get started follow our documentation.

Quick Start

Download Indexify

curl https://tensorlake.ai | sh

Start the server

./indexify server -d

Install the Indexify Extractor and Client SDKs

pip install indexify indexify-extractors

Start an embedding extractor

indexify-extractor download hub://embedding/minilm-l6
indexify-extractor join-server minilm-l6.minilm_l6:MiniLML6Extractor

Upload some texts

from indexify import IndexifyClient
client = IndexifyClient()
client.add_extraction_policy(extractor="tensorlake/minilm-l6", name="minilml6")
client.indexes()
client.add_documents(["Adam Silver is the NBA Commissioner", "Roger Goodell is the NFL commisioner"])

Search the Index

client.search_index(name="minilm6.embedding", query="NBA commissioner", top_k=1)

Use Extracted Data in Applications

You can now use the extracted data in your application. As data is ingested by Indexify, your indexes are going to be automatically updated by Indexify. We have an example of a Langchain application here

Try out Video, Audio or PDF Extractors

We have extractors for Video, Audio and PDF as well, you can list all the available extractors

indexify-extractor list

Structured Data

Extractors which produce structured data from content, such as bounding boxes and object type, or line items of invoices are stored in structured store. You can query extracted structured data using Indexify's SQL interface.

We have an example here

Contributions

Please open an issue to discuss new features, or join our Discord group. Contributions are welcome, there are a bunch of open tasks we could use help with!

If you want to contribute on the Rust codebase, please read the developer readme.