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Top 23 Embedding Open-Source Projects
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supabase
The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.
Project mention: How to Set Up a Free Cloud Database and Add Your First Table with Supabase | dev.to | 2026-09-30Navigate to supabase.com and select Start your project.
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AppSignal
AppSignal knows why the f*#k it crashed. Stop vibe-debugging. Every exception, every backtrace, grouped so you see patterns, not noise.
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WeKnora
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Project mention: WeKnora – LLM-Powered Document Understanding and Retrieval Framework | news.ycombinator.com | 2025-12-14 -
RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
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langchain4j
LangChain4j is an idiomatic, open-source Java library for building LLM-powered applications on the JVM. It offers a unified API over popular LLM providers and vector stores, and makes implementing tool calling (including MCP support), agents and RAG easy. It integrates seamlessly with enterprise Java frameworks like Quarkus and Spring Boot.
Project mention: Building a Multi-Agent Hiring Workflow with LangChain4j and LangGraph4j on Spring Boot | dev.to | 2026-08-05I wanted to actually learn LangChain4j and LangGraph4j beyond the "hello world" chat example, so I built something with enough moving parts to force real decisions: a hiring workflow where several AI agents independently score a candidate, a graph aggregates their opinions into a routing decision, and a human gets pulled in whenever the signals aren't clean. It's langchain4j-sample on Spring Boot, with LangGraph4j's Postgres checkpointer for state.
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InsForge
The all-in-one, open-source backend platform for agentic coding. InsForge gives your coding agent database, auth, storage, compute, hosting, and AI gateway to ship full-stack apps end-to-end.
Project mention: Bringing Razorpay to InsForge: Building a Production-Grade Payment Flow | dev.to | 2026-06-08PR #1382 — Foundational Provider & Sync PR #1485 — Webhook Infrastructure PR #1490 — Runtime Payment Flow
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Project mention: txtai VS verified-memory-vault - a user suggested alternative | libhunt.com/r/txtai | 2026-08-25
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Thank you for publishing this! I absolutely love small embedding models, and have used them on a number of projects (both commercial and hobbyist). I look forward to checking this one out!
I don't know if this is too much to ask, but something that would really help me adopt your model is to include a fine-tuning setup. The BGE series of embeddings-models has been my go-to for a couple of years now -- not because it's the best-performing in the leaderboards, but because they make it so incredibly easy to fine-tune the model [0]. Give it a JSONL file of a bunch of training triplets, and you can fine-tune the base models on your own dataset. I appreciate you linking to the paper on the recipe for training this type of model -- how close to turnkey is your model to helping me do transfer learning with my own dataset? I looked around for a fine-tuning example of this model, and didn't happen to see anything, but I would be very interested in trying this one out.
Does support for fine-tuning already exist? If so, then I would be able to switch to this model away from BGE immediately.
* [0] - https://github.com/FlagOpen/FlagEmbedding/tree/master/exampl...
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Project mention: Building Metadata Capabilities in Apache SeaTunnel: A Committer’s Journey | dev.to | 2026-05-28
PR Link Description https://github.com/apache/seatunnel/pull/5663 Added save_mode functionality to SeaTunnel https://github.com/apache/seatunnel/pull/10402 Integrated Gravitino with SeaTunnel https://github.com/apache/seatunnel/pull/10586 Designed the metadata SPI interface for SeaTunnel https://github.com/apache/seatunnel/pull/10657 Enhanced the metadata SPI interface for SeaTunnel https://github.com/apache/seatunnel/pull/10838 Added dynamic metadata functionality based on the metadata SPI interface
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Pinned Honcho source tree
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lance
Open Lakehouse Format for Multimodal AI. Convert from Parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..
Project mention: Accelerating Multimodal Vector DB with Hugging Face + LanceDB | dev.to | 2026-02-17 -
One addition: Postgres for all your GPU-based machine learning training and inference needs: https://github.com/postgresml/postgresml
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pytorch-metric-learning
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
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Daft
High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale
Project mention: 650GB of Data (Delta Lake on S3). Polars vs. DuckDB vs. Daft vs. Spark | news.ycombinator.com | 2025-11-13Hey everyone, I'm a software engineer at Eventual, the team behind Daft! Huge thanks to the op for the benchmark, we're a huge fan of your blog posts and this gave us some really useful insights. For context, Daft is a high-performance data processing engine for AI workloads that works both on single-node and distributed setups.
We're actively looking into the results of the benchmark and hope to share some of our findings soon. From initial results, we found a lot of potential optimizations we could make to our deltalake reader to improve parallelism and our groupby operator to improve pipelining for count aggregations. We're hoping to roll our these improvements over the next couple of releases.
If you're interested to learn more about our findings, check out our GitHub (https://github.com/Eventual-Inc/Daft) or follow us on Twitter (https://x.com/daftengine) and LinkedIn (https://www.linkedin.com/showcase/daftengine) for updates. Also if Daft sounds interesting to you, give us a try via pip install daft!
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obsidian-smart-connections
Find related notes and excerpts while writing. Your link building copilot displays relevant content in graph + list view. A local embedding model powers semantic search. Zero setup. No API key.
SmartConnections but heads up, Connections Pro asks $300/year, or, more for the plugin than for an LLM subscription, more than Microsoft Office for that matter.
https://smartconnections.app
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AutoRAG
AutoRAG: Now your agent can find anything in your computer. It gets smarter if you are using it frequently.
Project mention: RAGFlow + MCP: Turning Your Best RAG Config Into a Production Assistant | dev.to | 2026-07-12Use measurement tools (AutoRAG, RAGBuilder) to find the best RAG settings for your data.
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Project mention: Cut token waste across your entire team with the MCP Optimizer | dev.to | 2026-03-11
The operator defaults to BAAI/bge-small-en-v1.5 as the model and runs the HuggingFace Text Embeddings Inference server. You can increase the replica count via spec.replicas to match your team's throughput needs. One shared instance serves every vMCP in the namespace. For all available configuration options, see the Optimizer docs.
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LongMemory
Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc.
Project mention: Show HN: A local-first memory store for LLM agents (SQLite) | news.ycombinator.com | 2025-12-14edit:
from gpt5.2 with prompt:
<< 'adversarial review request. please look at the github link for signs of being written by llm ( extra points if you can point to the llm that generated it ) https://github.com/CaviraOSS/OpenMemory'
>> I can’t prove it’s LLM-written from the outside, but the README (at least) has a lot of “LLM smell.” I’d put it at high likelihood of AI-assisted marketing/docs copy, with some sections bordering on “generated then lightly edited.”
but then it adds a list of style reason why it could be generated by llm
<< “Extra points”: which LLM wrote it?
Most likely: Claude 3.5 Sonnet–style output
<< if i were to point to comments in readme and code, what would you say upon re-review
>> Comments that narrate the obvious (especially line-by-line)
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RubyLLM
The Ruby-native AI framework. Chats, agents, tools, images, audio, and video through one consistent API, in plain Ruby or Rails.
Project mention: Building Autofatturala: Italian Self-Invoicing Is Crazy, Rails + RubyLLM Made It Simple | dev.to | 2026-08-11I built a product to make this bearable: Autofatturala. In this article I want to tell you the story of the journey: how absurd the problem is, and how surprisingly easy it was to build the solution with Rails and RubyLLM. I will show some real code, but not everything. Some parts of the product stay in the kitchen.
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awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
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towhee
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
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Project mention: I Tried to Learn RAG and Got Stuck on API Costs. Turns Out My CPU Could Do the Expensive Bit. | dev.to | 2026-09-13
Recently I came back to the project and discovered fastembed, and had one of those slightly embarrassing engineering moments where you realize the problem that made you abandon something months ago never actually needed the solution you'd assumed it needed.
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NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Embeddings discussion
Embeddings related posts
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Jev in Depth: Can It Reshape Agent Search?
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Fastembed – Rust library for generating vector embeddings, reranking locally
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I Tried to Learn RAG and Got Stuck on API Costs. Turns Out My CPU Could Do the Expensive Bit.
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I Built the SQLite of Vector Search in ~120KB of Pure C & SIMD: 3,000x Faster Cold Starts and Zero Dependencies for AI Agents
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Semantic Search in C++ without Python, libtorch or ONNX Runtime
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The attack is a sleeping sentence your logs cannot see
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I Gave Claude Code a Brain. Then I Measured What It Costs to Ask for One.
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A note from our sponsor - SaaSHub
www.saashub.com | 6 Oct 2026
Index
What are some of the best open-source Embedding projects? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | supabase | 111,036 |
| 2 | WeKnora | 31,580 |
| 3 | RAG_Techniques | 29,660 |
| 4 | langchain4j | 13,199 |
| 5 | InsForge | 13,061 |
| 6 | txtai | 12,990 |
| 7 | FlagEmbedding | 12,210 |
| 8 | seatunnel | 9,697 |
| 9 | honcho | 7,455 |
| 10 | lance | 7,133 |
| 11 | postgresml | 6,825 |
| 12 | pytorch-metric-learning | 6,336 |
| 13 | Daft | 5,790 |
| 14 | obsidian-smart-connections | 5,481 |
| 15 | AutoRAG | 5,109 |
| 16 | text-embeddings-inference | 5,065 |
| 17 | LongMemory | 4,513 |
| 18 | RubyLLM | 4,424 |
| 19 | lightly | 3,816 |
| 20 | awesome-generative-ai | 3,546 |
| 21 | hub | 3,522 |
| 22 | towhee | 3,452 |
| 23 | fastembed | 3,228 |
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AppSignal knows why the f*#k it crashed.
Stop vibe-debugging. Every exception, every backtrace, grouped so you see patterns, not noise.
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