what_are_embeddings
A deep dive into embeddings starting from fundamentals (by veekaybee)
AI_ChatBot_Python
AI ChatBot using Python Tensorflow and Natural Language Processing (NLP) along side TFLearn (by FreeBirdsCrew)
| what_are_embeddings | AI_ChatBot_Python | |
|---|---|---|
| 3 | 1 | |
| 875 | 229 | |
| - | - | |
| 8.1 | 0.0 | |
| 2 months ago | about 1 month ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
what_are_embeddings
Posts with mentions or reviews of what_are_embeddings.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-06-15.
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SpringAI, llama3 and pgvector: bRAGging rights!
Then, I will take these essays and create embeddings from them, which I will store in Postgres, using the pgvector extension in columns of vector data type. All with the help of SpringAI abstractions and least amount of custom code. I will skip the part called "chunking". When you are dealing with very large documents, or want to isolate sections in your data (like in e-mails where you have subject, sender, etc..) you might look into doing that.
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The Illustrated Word2Vec
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/
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GPT Weekly - 3rd July Edition - Adobe’s Safety Net, Open-Source AI: Expanded Context Lengths and more.
LLMs require embeddings to work. What are embeddings? But, did you know embeddings also power the recommendation engines? Another guide on embeddings.
AI_ChatBot_Python
Posts with mentions or reviews of AI_ChatBot_Python.
We have used some of these posts to build our list of alternatives
and similar projects.
What are some alternatives?
When comparing what_are_embeddings and AI_ChatBot_Python you can also consider the following projects:
gpt-migrate - Easily migrate your codebase from one framework or language to another.