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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.
Supabase is an open-source backend platform built around managed PostgreSQL. You get a database, auto-generated REST APIs (via PostgREST), Auth, file Storage, Realtime subscriptions, and Edge Functions - with a dashboard and SQL editor on top.
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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 -
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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.
In this article, we'll build a small, memory-backed assistant with LangChain4j and Oracle AI Database. The assistant can search prior incidents, runbooks, decisions, and shift handoffs to answer questions. It can write new memories back to the database so they become searchable in any session. Additionally, all user, agent, and tool messages are logged to database table for observability and auditing.
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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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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 -
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.
Project mention: Think with your second brain: a proper Claude Code harness for Obsidian | dev.to | 2026-05-20Smart Connections (repo) is the dominant Obsidian-AI plugin and does RAG over the vault. The harness uses Smart Connections' embedding cache as a secondary signal in Hybrid Mode, not as the primary retrieval mechanism.
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git clone https://github.com/plastic-labs/honcho cd honcho docker compose up -d # Server will be on http://localhost:8000
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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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AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
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OpenMemory
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
One beautiful Ruby framework for all major AI providers. Easily build chatbots, AI agents, RAG applications, content generators, and every AI workflow in between.
Project mention: 🇺🇸 Glancer — Ask your Rails database questions in plain language | dev.to | 2026-05-27OpenAI and OpenRouter are also supported. It runs on top of RubyLLM, so any model it supports works here too. You can assign different models per role: a smarter model for query generation, a cheaper one for writing the response.
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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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prompttools
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
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Project mention: Fastembed – Lightweight Python Embedding Library | news.ycombinator.com | 2026-04-28
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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I Built a Money-Making Machine with Free AI Tools — Here is the Full Blueprint
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Show HN: I embedded 685M public texts in 32 minutes (on 8x A100, Rust, TensorRT)
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OpenAI models on Bedrock make AI deployment less messy
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SIE: Unified Inference Engine for Embeddings, Reranking, and Extraction
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I Tested 33 AI Memory Engines — Here's What Actually Works
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Show HN: Local AI server with persistent memory, RAG and plugins
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Honcho Review: Plastic Labs' Agent Memory Layer (2026)
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A note from our sponsor - SaaSHub
www.saashub.com | 7 Jun 2026
Index
What are some of the best open-source Embedding projects? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | supabase | 103,630 |
| 2 | mem0 | 57,631 |
| 3 | WeKnora | 15,967 |
| 4 | txtai | 12,627 |
| 5 | langchain4j | 12,221 |
| 6 | FlagEmbedding | 11,781 |
| 7 | seatunnel | 9,371 |
| 8 | postgresml | 6,794 |
| 9 | lance | 6,591 |
| 10 | pytorch-metric-learning | 6,325 |
| 11 | Daft | 5,551 |
| 12 | obsidian-smart-connections | 5,095 |
| 13 | honcho | 4,843 |
| 14 | text-embeddings-inference | 4,835 |
| 15 | AutoRAG | 4,810 |
| 16 | OpenMemory | 4,190 |
| 17 | RubyLLM | 3,981 |
| 18 | lightly | 3,759 |
| 19 | hub | 3,523 |
| 20 | awesome-generative-ai | 3,463 |
| 21 | towhee | 3,446 |
| 22 | prompttools | 3,037 |
| 23 | fastembed | 3,011 |
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