Refolk

Top Embeddings repositories on GitHub

Models, libraries, and infrastructure for vector representations of text and media.

Ranked by stars across 535 repositories tagged embeddings. Refreshed daily.

  1. 1
    supabase/supabase★ 114,524 · ⑂ 15,767

    The Postgres development platform. Supabase gives you a dedicated Postgres database to build your web, mobile, and AI applications.

    • firebase
    • supabase
    • realtime
    • postgrest
    • postgres
    • postgresql
  2. 2
    thedotmack/claude-mem★ 112,688 · ⑂ 9,792

    Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

    • ai
    • ai-agents
    • ai-memory
    • anthropic
    • artificial-intelligence
    • claude
  3. 3
    Tencent/WeKnora★ 36,481 · ⑂ 4,986

    Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

    • agent
    • agentic
    • ai
    • golang
    • llm
    • ollama
  4. 4
    NirDiamant/RAG_Techniques★ 30,227 · ⑂ 3,700

    This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.

    • rag
    • tutorials
    • langchain
    • llama-index
    • llms
    • python
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  6. 5
    RyanCodrai/turbovec★ 17,458 · ⑂ 1,485

    A vector index built on TurboQuant, written in Rust with Python bindings

    • ann
    • avx512
    • embeddings
    • faiss
    • nearest-neighbor
    • neon
  7. 6
    langchain4j/langchain4j★ 13,356 · ⑂ 2,658

    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.

    • huggingface
    • java
    • langchain
    • openai
    • chatgpt
    • gpt
  8. 7
    InsForge/InsForge★ 13,284 · ⑂ 1,248

    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.

    • ai
    • ai-agents
    • coding
    • oauth2
    • postgresql
    • deno
  9. 8
    neuml/txtai★ 13,064 · ⑂ 962

    💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows

    • python
    • search
    • nlp
    • semantic-search
    • vector-search
    • txtai
  10. 9
    FlagOpen/FlagEmbedding★ 12,297 · ⑂ 945

    Retrieval and Retrieval-augmented LLMs

    • embeddings
    • information-retrieval
    • llm
    • sentence-embeddings
    • text-semantic-similarity
    • retrieval-augmented-generation
  11. 10
    Embedding/Chinese-Word-Vectors★ 12,252 · ⑂ 2,321

    100+ Chinese Word Vectors 上百种预训练中文词向量

    • chinese
    • chinese-word-segmentation
    • embeddings
    • word-embeddings
    • vectors-trained
    • embedding
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  13. 11
    h2oai/h2ogpt★ 11,928 · ⑂ 1,294

    Private chat with local GPT with document, images, video, etc. 100% private, Apache 2.0. Supports oLLaMa, Mixtral, llama.cpp, and more. Demo: https://gpt.h2o.ai/ https://gpt-docs.h2o.ai/

    • chatgpt
    • llm
    • ai
    • embeddings
    • generative
    • gpt
  14. 12
    apache/seatunnel★ 9,784 · ⑂ 2,460

    SeaTunnel is a multimodal, high-performance, distributed, massive data integration tool.

    • data-integration
    • high-performance
    • offline
    • real-time
    • apache
    • batch
  15. 13
    lance-format/lance★ 7,257 · ⑂ 924

    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..

    • machine-learning
    • computer-vision
    • data-format
    • deep-learning
    • python
    • apache-arrow
  16. 14
    postgresml/postgresml★ 6,826 · ⑂ 364

    Postgres with GPUs for ML/AI apps.

    • ml
    • machine-learning
    • ai
    • ann
    • artificial-intelligence
    • classification
  17. 15
    MinishLab/semble★ 6,361 · ⑂ 286

    Fast and Accurate Code Search for Agents. Uses 99% fewer tokens than grep+read

    • agents
    • code-search
    • embeddings
    • mcp
    • mcp-server
    • model-context-protocol
  18. 16

    Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.

    • metric-learning
    • deep-learning
    • computer-vision
    • machine-learning
    • pytorch
    • deep-metric-learning
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  20. 17
    ARahim3/DigUp★ 6,234 · ⑂ 493

    Search your computer by what's in your files: photos, PDFs, documents, recordings, videos and code. On-device on Mac, Windows and Linux, built on Google DeepMind's EmbeddingGemma 2.

    • embeddinggemma
    • embeddings
    • llama-cpp
    • local-first
    • macos
    • menubar-app
  21. 18
    Eventual-Inc/Daft★ 5,827 · ⑂ 585

    High-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale

    • machine-learning
    • python
    • data-engineering
    • distributed-computing
    • rust
    • big-data
  22. 19

    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.

    • chatgpt
    • embeddings
    • claude
    • gemini
    • obsidian
    • obsidian-plugin

Find engineers shipping Embeddings

The list above ranks the most-starred public repositories tagged with the Embeddings topic, drawn from the public GitHub graph. Across 535 repositories tagged this way, the maintainers and top contributors are a tight cluster of the people actually building Embeddings.

Looking for engineers who’ve worked on Embeddings for real, not just listed it on LinkedIn? The fastest path is the contributor list of these repos. Their commits, issues, and READMEs are public proof of depth.

Refolk turns this list into a search. Ask for “maintainers of top Embeddings repos who are hiring”, “Embeddings engineers in San Francisco”, or “founders shipping Embeddings” and Refolk returns a ranked shortlist with sources.

How this list is built

Refolk searched GitHub for public repositories tagged with the Embeddings topic, ranked them by stargazer count, and kept those with at least 50 stars. The list refreshes once a day.

Last refreshed: Wed, 04 Nov 2026 19:47:15 GMT

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Common questions

How are these repositories ranked?

By stars, with forks and recent activity as tiebreakers, read from the public GitHub API. The methodology section above has the details.

How fresh is the data?

The ranking re-renders at least daily. Last refreshed: Wed, 04 Nov 2026 19:47:15 GMT.

Can I find the maintainers and contributors behind these repos?

Yes. Stars rank the projects; I can rank the engineers - maintainers, top contributors, and the people they work with. You start with 500 free credits, no card required.

Can I use this list for hiring?

That's the point. I read hiring signals across GitHub, LinkedIn, and the open web, so a repo list turns into a shortlist of engineers worth talking to.

Try it on the search you came here for

Stop building boolean strings. Just describe the person.

Type one sentence. I plan the search, read GitHub, public LinkedIn and Crunchbase records, and the open web as it is right now, and hand back a ranked list with the reason next to every name.

  1. 01Describe them

    One plain sentence. Role, city, stack, stage, whatever matters to you.

  2. 02I read the web live

    GitHub, public LinkedIn and Crunchbase records, the open web. Not a database that went stale last quarter.

  3. 03You read the shortlist

    Ranked, with the reasoning under every name. Open a profile, ask a follow-up, narrow it down.

  • No boolean, no filters, no seat to buy. One box.
  • Read at search time, so a profile updated yesterday counts today.
  • Every step visible as it runs, every name with its reason.

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