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Exploring LLM Embeddings

Blog post

Read my blog post which describes the details of this project.

Built with

  • Python 3.11.1
  • pip 23.2.1

Setup

First install Python dependencies

cd app
pip install -r requirements.txt

Then download NLTK models

import nltk
nltk.download('punkt')

# This is for lemmatization
nltk.download('wordnet')

Then add a .env file to the root with your OPENAI_API_KEY

OPENAI_API_KEY=YOU_KEY_HERE

Mode

  • create - Creates the embeddings model for a given file
  • query - Queries the document's embeddings model with a given query to see what it is most similar to
  • extract - Helps debug the doc extraction logic by showing how the given file is split up
  • test - Explores comparing 3 pieces of text to learn how similar or different they are based on their embeddings
  • create_dict - Creates an embeddings model for a list of words
  • query_dict - Queries the dictionary's embeddings model with a given query to see what it is most similar to
  • analyze - Performance analysis, clustering, categorization on the document model (must first run create and create_dict)

Examples

Explore the parsing and splitting of an input file

./run.sh -m extract -f pair_programming.pdf

Create the embeddings model for a file

./run.sh -m create -f pair_programming.pdf

Create a model for a pre-defined word list

./run.sh -m create_dict

**Query the dictionary word model **

./run.sh -m query_dict -q "hotdogs are the best food"

Analyze the embeddings the file leveraging the embeddings model and dictionary model This leads to unexpected results which may mean simply negating the embeddings is not enough to get the opposite meaning. This may also mean embedding space is not symmetric, which also is not surprising.

./run.sh -m analyze -f pair_programming.pdf

Query the model so see what is similar to the query

./run.sh -m query -q "absolute correctness is an illusion"  -f pair_programming.pdf

Compare embeddings in general to see similarity

./run.sh -m test -t1 "people are bad and should not be trusted" -t2 "hotdogs are the best food" -t3 "I hate everyone" 


./run.sh -m test -t1 "UFOs are real and have visited earth on many occasions" -t2 "Bigfoot lives in my backyard" -t3 "the NY Jets have had a long losing streak"

Query a single word in the dict and see the most similar and different words based on its embedding

./run.sh -m query_word -q eating

Find the words representing the mid-point of the embeddings of two words

./run.sh -m word_midpoint -q dread -q2 hope

Notes

SPACY

If you want to use spacy instead of NLTK for text splitting you must download it's language model

python -m spacy download en_core_web_sm