Topics / #embedding

Embedding projects on GitHub

Vector embeddings that represent text or data for semantic search and retrieval.

As of October 18, 2026, the most-starred Embedding projects on GitHub are openai-cookbook (76.7K stars), Langchain-Chatchat (38.7K stars) and TencentDB-Agent-Memory (29.5K stars). GitHub.Locker tracks 10 Embedding repositories with 254.8K stars combined. The fastest-growing on October 17 was TencentDB-Agent-Memory, with +103 stars in a day.

10Projects

254.8KStars in total

+171Stars in a day

Browse in the catalog →github.com/topics/embedding ↗

Hot right now

Most stars gained in a day · Oct 17

+103

TencentDB-Agent-Memory

TencentCloud/TencentDB-Agent-Memory · ★ 29.5K

A team-level memory hub for AI agents. It turns conversations, docs, and code into four reusable memory assets — Chat Memory, Skills, Wiki, and CodeGraph — that agents across a team can share, govern, and equip.

+35

all-in-rag

datawhalechina/all-in-rag · ★ 12.2K

This is a full-stack tutorial on Retrieval-Augmented Generation (RAG) for developers building LLM applications, covering theory and hands-on practice from basic concepts to production-ready RAG systems.

+16

openai-cookbook

openai/openai-cookbook · ★ 76.7K

A collection of example code and guides for accomplishing common tasks with the OpenAI API, covering things like prompting, embeddings, function calling, and retrieval.

turbovec

RyanCodrai/turbovec · ★ 17.4K

A vector index for nearest-neighbor search over embeddings, written in Rust with Python bindings. It is built on Google Research's TurboQuant quantization and aimed at RAG where memory, latency and privacy matter.

The all-time top

10 projects · by stars

#1 · openai/openai-cookbook
openai-cookbook
A collection of example code and guides for accomplishing common tasks with the OpenAI API, covering things like prompting, embeddings, function calling, and retrieval.
76.7K
#2 · chatchat-space/Langchain-Chatchat
Langchain-Chatchat
Langchain-Chatchat is an open-source RAG and Agent application that lets you build a local-knowledge-base Q&A chatbot using LLMs like ChatGLM, Qwen or Llama through the LangChain framework.
38.7K
#3 · TencentCloud/TencentDB-Agent-Memory
TencentDB-Agent-Memory
A team-level memory hub for AI agents. It turns conversations, docs, and code into four reusable memory assets — Chat Memory, Skills, Wiki, and CodeGraph — that agents across a team can share, govern, and equip.
29.5K

4
fastText
26.5Kfacebookresearch/fastText · HTML
fastText is a library from Facebook Research for efficiently learning word vector representations and training text classifiers, such as for sentiment analysis.
5
turbovec
17.4KRyanCodrai/turbovec · Rust
A vector index for nearest-neighbor search over embeddings, written in Rust with Python bindings. It is built on Google Research's TurboQuant quantization and aimed at RAG where memory, latency and privacy matter.
6
ms-swift
15.9Kmodelscope/ms-swift · Python
ms-swift is the ModelScope community's fine-tuning and deployment framework for LLMs and multimodal models, covering training, inference, evaluation, quantization and deployment in one pipeline.
- peft
- sft
- grpo
- qwen3
7
PaddleNLP
13KPaddlePaddle/PaddleNLP · Python
PaddleNLP is a large language model (LLM) and small language model (SLM) development toolkit built on the PaddlePaddle deep learning framework, offering efficient training, lossless compression, and high-performance inference for large models.
8
claude-context
12.6Kzilliztech/claude-context · TypeScript
Claude Context is an MCP server that gives AI coding agents semantic search over your entire codebase, so they can pull relevant code straight into context instead of loading whole directories.
9
Chinese-Word-Vectors
12.2KEmbedding/Chinese-Word-Vectors · Python
A collection of 100+ pre-trained Chinese word vectors (embeddings) trained with different representations, context features and corpora, plus the CA8 analogy dataset and an evaluation toolkit.
10
all-in-rag
12.2Kdatawhalechina/all-in-rag · Python
This is a full-stack tutorial on Retrieval-Augmented Generation (RAG) for developers building LLM applications, covering theory and hands-on practice from basic concepts to production-ready RAG systems.