Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Top 14 Jupyter Notebook Embedding Projects
-
-
awesome-generative-ai
A curated list of Generative AI tools, works, models, and references (by filipecalegario)
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
-
-
cleora
Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
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)
-
-
entity-embed
PyTorch library for transforming entities like companies, products, etc. into vectors to support scalable Record Linkage / Entity Resolution using Approximate Nearest Neighbors.
-
embedding-encoder
Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
-
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.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
I've used the code based on similar examples from GitHub [1]. According to docs [2], imagegeneration@005 was released on the 11th, so I guessed it's Imagen 2, though there are no confirmations.
[1] https://github.com/GoogleCloudPlatform/generative-ai/blob/ma...
[2] https://console.cloud.google.com/vertex-ai/publishers/google...
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:
Project mention: FastLLM by Qdrant – lightweight LLM tailored For RAG | news.ycombinator.com | 2024-04-01
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...
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).
The latest post mention was on 2024-04-19.
Jupyter Notebook Embeddings related posts
- The Illustrated Word2Vec
- FastLLM by Qdrant – lightweight LLM tailored For RAG
- Use HNSW index on Azure Cosmos DB for PostgreSQL for similarity search
- What are Vector Embeddings?
- Still look familiar?
- Still look familiar?
- Still look familiar?
-
A note from our sponsor - InfluxDB
www.influxdata.com | 19 Apr 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 | 5,330 |
| 2 | awesome-generative-ai | 1,957 |
| 3 | featureform | 1,674 |
| 4 | what_are_embeddings | 834 |
| 5 | fastembed | 741 |
| 6 | Fast_Sentence_Embeddings | 603 |
| 7 | cleora | 472 |
| 8 | examples | 367 |
| 9 | kgtk | 336 |
| 10 | Research2Vec | 193 |
| 11 | entity-embed | 138 |
| 12 | embedding-encoder | 40 |
| 13 | vector-search-azure-cosmos-db-postgresql | 8 |
| 14 | emotion-classifier | 6 |
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com