Blog
Engineering deep dives, release notes and talks from Starlight Search, the team behind Pulsar and EmbedAnything.
Sonam Pankaj4 min read
0.7 is all about making deployment easy and integrations to data sources. Including Prebuilt Docker Image,SearchR1 example to improve context for Agents and AWS S3 bucket Integration.
[Read post]Sonam Pankaj6 min read
Reflecting on EmbedAnything: A Year Later A year ago, I shared the story behind EmbedAnything—how we built and scaled an embedding infrastructure that has since been loved by developers at Microsoft, Meta, Tencent, AWS…
[Read post]Sonam Pankaj3 min read
Vector Streaming is popular for its low latency and high modularity features. But the best of it all is the memory leak that we are trying to bring it but haven’t been able to click with the audience yet. Why we named…
[Read post]Sonam Pankaj3 min read
After presenting at Google, PyCon DE, Berlin Buzzwords, and GDG Berlin, I was surprised by how many people approached me with questions about writing configurations, chunk sizes, and batch sizes for EmbedAnything. Since…
[Read post]Sonam Pankaj2 min read
Super Excited to share the latest development in our library, which essentially giving you more embedding choices -- Cohere and siglip, new chunking method-- late chunking and more crates that facilitates amazing…
[Read post]Sonam Pankaj3 min read
Building a Server with Lumo: A Step-by-Step Guide to MCP Integration Lumo, a powerful Rust-based agent, offers seamless integration with MCPs (Modular Control Protocols) and remarkable flexibility in implementation…
[Read post]Sonam Pankaj2 min read
In the rapidly evolving landscape of AI agents, particularly those employing Large Language Models (LLMs), observability and tracing have emerged as fundamental requirements rather than optional features. As agents…
[Read post]Sonam Pankaj5 min read
The 2025 PyCon DE event highlighted a growing but cautious interest in AI agents among the Python community. While agent technology received significant attention, many speakers and attendees expressed skepticism about…
[Read post]Sonam Pankaj & Akshay Ballal4 min read
We have received multiple requests to add different vector database for our vector streaming. So We have decided to put a detailed guide for different vector databases out there. We are happy to accept pull-request.
[Read post]Sonam Pankaj & Akshay Ballal5 min read
When working with domain-specific queries, we often struggle with the challenge of balancing in-domain and out-of-domain requests. But not anymore! With embedanything, you can leverage fine-tuned, domain-focused models…
[Read post]Sonam Pankaj1 min read
We are thrilled to share that EmbedAnything version 0.5 is out now and comprise of insane development like support for ModernBert and ReRanker models. Along with Ingestion pipeline support for DocX, and HTML let’s get…
[Read post]Sonam Pankaj & Akshay Ballal7 min read
ColPali, a late-interaction vision model, leverages this power to enable text searches within images. This means you can pinpoint the exact pages in a PDF containing relevant text, even if the text exists only as part…
[Read post]Sonam Pankaj4 min read
In March, we set out to build a local file search app. We aimed to create a tool that would make file searching faster, more innovative, and more efficient. However, we quickly hit a roadblock: no high-performance…
[Read post]Akshay Ballal & Sonam Pankaj7 min read
EmbedAnything is an open-source Rust/Python framework that lets you generate vector embeddings for any data (text, images, audio) with minimal code. It's blazing fast, memory-efficient, and can handle massive datasets…
[Read post]Sonam Pankaj5 min read
In today's data-saturated world, effective research isn't just about accessing information—it's about finding the right information efficiently. Let's explore how Gemini can function as a sophisticated research agent…
[Read post]Akshay Ballal & Sonam Pankaj4 min read
Introducing vector streaming in EmbedAnything, a feature designed to optimize large-scale document embedding. By enabling asynchronous chunking and embedding using Rust’s concurrency, it reduces memory usage and speeds…
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