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Top 14 Jupyter Notebook Embedding Projects
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awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
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InfluxDB
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cleora
Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
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examples
Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc. (by towhee-io)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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amazon-bedrock-samples
This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models
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entity-embed
PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.
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embedding-encoder
Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
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vector-search-azure-cosmos-db-postgresql
This sample shows how to build vector similarity search on Azure Cosmos DB for PostgreSQL using the pgvector extension and the multi-modal embeddings APIs of Azure AI Vision.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: Summarize a web page using langchain.js and Gemini in a NestJS application | dev.to | 2024-05-18Summarization Large Documents Code Lab - https://github.com/GoogleCloudPlatform/generative-ai/blob/main/language/use-cases/document-summarization/summarization_large_documents_langchain.ipynb
Project mention: Generative AI – A curated list of Generative AI tools, works, models | news.ycombinator.com | 2023-07-14
That is essentially correct. You take an object and "embed" it in a high-dimensional vector space to represent it.
For a deep dive, I highly recommend Vicki Boykis's free materials:
https://vickiboykis.com/what_are_embeddings/
This is a great guide.
Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.
With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.
When you should use language model embeddings:
- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.
For LM embedding models, many are multilingual aligned right away.
- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.
This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.
1. sbert.net
2. https://collaborate.princeton.edu/en/publications/a-simple-b...
3. https://github.com/oborchers/Fast_Sentence_Embeddings
4. https://github.com/facebookresearch/MUSE
Project mention: BMF: Frame extraction acceleration- video similarity search with Pinecone | dev.to | 2024-05-10! curl -L https://github.com/towhee-io/examples/releases/download/data/reverse_video_search.zip -O ! unzip -q -o reverse_video_search.zip
Project mention: Construyendo un asistente genAI de WhatsApp con Amazon Bedrock y Claude 3 | dev.to | 2024-05-04
Project mention: Use HNSW index on Azure Cosmos DB for PostgreSQL for similarity search | dev.to | 2024-03-14In the Jupyter Notebook provided on my GitHub repository, you'll explore text-to-image and image-to-image search scenarios. You will use the same text prompts and reference images as in the Exact Nearest Neighbors search example, allowing for a comparison of the accuracy of the results.
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).
Jupyter Notebook Embeddings related posts
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BMF: Frame extraction acceleration- video similarity search with Pinecone
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The Illustrated Word2Vec
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FastLLM by Qdrant – lightweight LLM tailored For RAG
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Use HNSW index on Azure Cosmos DB for PostgreSQL for similarity search
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What are Vector Embeddings?
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Still look familiar?
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Still look familiar?
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A note from our sponsor - InfluxDB
www.influxdata.com | 20 May 2024
Index
What are some of the best open-source Embedding projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | generative-ai | 5,640 |
| 2 | awesome-generative-ai | 2,063 |
| 3 | featureform | 1,711 |
| 4 | what_are_embeddings | 864 |
| 5 | Fast_Sentence_Embeddings | 603 |
| 6 | cleora | 477 |
| 7 | examples | 384 |
| 8 | kgtk | 342 |
| 9 | amazon-bedrock-samples | 278 |
| 10 | Research2Vec | 194 |
| 11 | entity-embed | 139 |
| 12 | embedding-encoder | 40 |
| 13 | vector-search-azure-cosmos-db-postgresql | 8 |
| 14 | emotion-classifier | 6 |
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