├── arduino-agent
├── tmp
│ ├── .gitkeep
│ └── tmp.ino
├── tools
│ ├── __init__.py
│ └── arduino.py
├── .env.example
├── img
│ └── img.png
├── requirements.txt
├── main.py
├── tasks.py
├── agents.py
└── README.md
├── locust-guide
├── src
│ ├── api
│ │ ├── __init__.py
│ │ ├── schemas.py
│ │ └── main.py
│ └── locustfile.py
├── static
│ ├── diagram.gif
│ ├── locust_home.png
│ ├── locust_graph.png
│ └── youtube_thumbnail.jpg
├── requirements.txt
├── docker-compose.yml
├── pyproject.toml
├── Dockerfile
└── Makefile
├── rick-llm
├── .python-version
├── src
│ ├── lambda
│ │ ├── __init__.py
│ │ ├── ssh-key
│ │ │ └── .gitkeep
│ │ ├── requirements_lambda.txt
│ │ └── request.json
│ ├── rick_llm
│ │ ├── __init__.py
│ │ ├── exceptions.py
│ │ ├── finetune.py
│ │ ├── model_utils.py
│ │ ├── constants.py
│ │ └── trainer.py
│ └── download_model.py
├── img
│ ├── finetune.png
│ ├── lambda.png
│ ├── ollama.png
│ ├── unsloth.png
│ ├── lambda_ip.png
│ ├── lambda_ssh.png
│ ├── lambda_home.png
│ ├── dataset_viewer.png
│ ├── project_design.png
│ ├── rick_and_morty.png
│ ├── rick_ollama_chat.png
│ └── huggingface_dataset.png
├── .env.example
├── .pre-commit-config.yaml
├── pyproject.toml
├── ollama_files
│ └── Modelfile
└── Makefile
├── vector-twin
├── src
│ ├── app
│ │ ├── __init__.py
│ │ ├── assets
│ │ │ ├── logo.png
│ │ │ └── celebrities
│ │ │ │ ├── rihanna
│ │ │ │ ├── rihanna_1.jpg
│ │ │ │ ├── rihanna_2.jpg
│ │ │ │ ├── rihanna_3.jpg
│ │ │ │ └── rihanna_4.jpg
│ │ │ │ ├── zendaya
│ │ │ │ ├── zendaya_1.jpg
│ │ │ │ ├── zendaya_2.jpg
│ │ │ │ ├── zendaya_3.jpg
│ │ │ │ └── zendaya_4.jpg
│ │ │ │ ├── elon_musk
│ │ │ │ ├── elon_musk_1.jpg
│ │ │ │ ├── elon_musk_2.jpg
│ │ │ │ ├── elon_musk_3.jpg
│ │ │ │ └── elon_musk_4.jpg
│ │ │ │ ├── gal_gadot
│ │ │ │ ├── gal_gadot_1.jpg
│ │ │ │ ├── gal_gadot_2.jpg
│ │ │ │ ├── gal_gadot_3.jpg
│ │ │ │ └── gal_gadot_4.jpg
│ │ │ │ ├── megan_fox
│ │ │ │ ├── megan_fox_1.jpg
│ │ │ │ ├── megan_fox_2.jpg
│ │ │ │ ├── megan_fox_3.jpg
│ │ │ │ └── megan_fox_4.jpg
│ │ │ │ ├── tom_ellis
│ │ │ │ ├── tom_ellis_1.jpg
│ │ │ │ ├── tom_ellis_2.jpg
│ │ │ │ ├── tom_ellis_3.jpg
│ │ │ │ └── tom_ellis_4.jpg
│ │ │ │ ├── tom_hardy
│ │ │ │ ├── tom_hardy_1.jpg
│ │ │ │ ├── tom_hardy_2.jpg
│ │ │ │ ├── tom_hardy_3.jpg
│ │ │ │ └── tom_hardy_4.jpg
│ │ │ │ ├── zac_efron
│ │ │ │ ├── zac_efron_1.jpg
│ │ │ │ ├── zac_efron_2.jpg
│ │ │ │ ├── zac_efron_3.jpg
│ │ │ │ └── zac_efron_4.jpg
│ │ │ │ ├── bill_gates
│ │ │ │ ├── bill_gates_1.jpg
│ │ │ │ ├── bill_gates_2.jpg
│ │ │ │ ├── bill_gates_3.jpg
│ │ │ │ └── bill_gates_4.jpg
│ │ │ │ ├── ellen_page
│ │ │ │ ├── ellen_page_1.jpg
│ │ │ │ ├── ellen_page_2.jpg
│ │ │ │ ├── ellen_page_3.jpg
│ │ │ │ └── ellen_page_4.jpg
│ │ │ │ ├── emma_stone
│ │ │ │ ├── emma_stone_1.jpg
│ │ │ │ ├── emma_stone_2.jpg
│ │ │ │ ├── emma_stone_3.jpg
│ │ │ │ └── emma_stone_4.jpg
│ │ │ │ ├── inbar_lavi
│ │ │ │ ├── inbar_lavi_1.jpg
│ │ │ │ ├── inbar_lavi_2.jpg
│ │ │ │ ├── inbar_lavi_3.jpg
│ │ │ │ └── inbar_lavi_4.jpg
│ │ │ │ ├── jeff_bezos
│ │ │ │ ├── jeff_bezos_1.jpg
│ │ │ │ ├── jeff_bezos_2.jpg
│ │ │ │ ├── jeff_bezos_3.jpg
│ │ │ │ └── jeff_bezos_4.jpg
│ │ │ │ ├── rami_malek
│ │ │ │ ├── rami_malek_1.jpg
│ │ │ │ ├── rami_malek_2.jpg
│ │ │ │ ├── rami_malek_3.jpg
│ │ │ │ └── rami_malek_4.jpg
│ │ │ │ ├── tom_cruise
│ │ │ │ ├── tom_cruise_1.jpg
│ │ │ │ ├── tom_cruise_2.jpg
│ │ │ │ ├── tom_cruise_3.jpg
│ │ │ │ └── tom_cruise_4.jpg
│ │ │ │ ├── adriana_lima
│ │ │ │ ├── adriana_lima_1.jpg
│ │ │ │ ├── adriana_lima_2.jpg
│ │ │ │ ├── adriana_lima_3.jpg
│ │ │ │ └── adriana_lima_4.jpg
│ │ │ │ ├── alex_lawther
│ │ │ │ ├── alex_lawther_1.jpg
│ │ │ │ ├── alex_lawther_2.jpg
│ │ │ │ ├── alex_lawther_3.jpg
│ │ │ │ └── alex_lawther_4.jpg
│ │ │ │ ├── alvaro_morte
│ │ │ │ ├── alvaro_morte_1.jpg
│ │ │ │ ├── alvaro_morte_2.jpg
│ │ │ │ ├── alvaro_morte_3.jpg
│ │ │ │ └── alvaro_morte_4.jpg
│ │ │ │ ├── amanda_crew
│ │ │ │ ├── amanda_crew_1.jpg
│ │ │ │ ├── amanda_crew_2.jpg
│ │ │ │ ├── amanda_crew_3.jpg
│ │ │ │ └── amanda_crew_4.jpg
│ │ │ │ ├── amber_heard
│ │ │ │ ├── amber_heard_1.jpg
│ │ │ │ ├── amber_heard_2.jpg
│ │ │ │ ├── amber_heard_3.jpg
│ │ │ │ └── amber_heard_4.jpg
│ │ │ │ ├── andy_samberg
│ │ │ │ ├── andy_samberg_1.jpg
│ │ │ │ ├── andy_samberg_2.jpg
│ │ │ │ ├── andy_samberg_3.jpg
│ │ │ │ └── andy_samberg_4.jpg
│ │ │ │ ├── barack_obama
│ │ │ │ ├── barack_obama_1.jpg
│ │ │ │ ├── barack_obama_2.jpg
│ │ │ │ ├── barack_obama_3.jpg
│ │ │ │ └── barack_obama_4.jpg
│ │ │ │ ├── ben_affleck
│ │ │ │ ├── ben_affleck_1.jpg
│ │ │ │ ├── ben_affleck_2.jpg
│ │ │ │ ├── ben_affleck_3.jpg
│ │ │ │ └── ben_affleck_4.jpg
│ │ │ │ ├── bobby_morley
│ │ │ │ ├── bobby_morley_1.jpg
│ │ │ │ ├── bobby_morley_2.jpg
│ │ │ │ ├── bobby_morley_3.jpg
│ │ │ │ └── bobby_morley_4.jpg
│ │ │ │ ├── brie_larson
│ │ │ │ ├── brie_larson_1.jpg
│ │ │ │ ├── brie_larson_2.jpg
│ │ │ │ ├── brie_larson_3.jpg
│ │ │ │ └── brie_larson_4.jpg
│ │ │ │ ├── chris_evans
│ │ │ │ ├── chris_evans_1.jpg
│ │ │ │ ├── chris_evans_2.jpg
│ │ │ │ ├── chris_evans_3.jpg
│ │ │ │ └── chris_evans_4.jpg
│ │ │ │ ├── chris_pratt
│ │ │ │ ├── chris_pratt_1.jpg
│ │ │ │ ├── chris_pratt_2.jpg
│ │ │ │ ├── chris_pratt_3.jpg
│ │ │ │ └── chris_pratt_4.jpg
│ │ │ │ ├── eliza_taylor
│ │ │ │ ├── eliza_taylor_1.jpg
│ │ │ │ ├── eliza_taylor_2.jpg
│ │ │ │ ├── eliza_taylor_3.jpg
│ │ │ │ └── eliza_taylor_4.jpg
│ │ │ │ ├── emma_watson
│ │ │ │ ├── emma_watson_1.jpg
│ │ │ │ ├── emma_watson_2.jpg
│ │ │ │ ├── emma_watson_3.jpg
│ │ │ │ └── emma_watson_4.jpg
│ │ │ │ ├── grant_gustin
│ │ │ │ ├── grant_gustin_1.jpg
│ │ │ │ ├── grant_gustin_2.jpg
│ │ │ │ ├── grant_gustin_3.jpg
│ │ │ │ └── grant_gustin_4.jpg
│ │ │ │ ├── henry_cavil
│ │ │ │ ├── henry_cavil_1.jpg
│ │ │ │ ├── henry_cavil_2.jpg
│ │ │ │ ├── henry_cavil_3.jpg
│ │ │ │ └── henry_cavil_4.jpg
│ │ │ │ ├── hugh_jackman
│ │ │ │ ├── hugh_jackman_1.jpg
│ │ │ │ ├── hugh_jackman_2.jpg
│ │ │ │ ├── hugh_jackman_3.jpg
│ │ │ │ └── hugh_jackman_4.jpg
│ │ │ │ ├── irina_shayk
│ │ │ │ ├── irina_shayk_1.jpg
│ │ │ │ ├── irina_shayk_2.jpg
│ │ │ │ ├── irina_shayk_3.jpg
│ │ │ │ └── irina_shayk_4.jpg
│ │ │ │ ├── jason_momoa
│ │ │ │ ├── jason_momoa_1.jpg
│ │ │ │ ├── jason_momoa_2.jpg
│ │ │ │ ├── jason_momoa_3.jpg
│ │ │ │ └── jason_momoa_4.jpg
│ │ │ │ ├── jimmy_fallon
│ │ │ │ ├── jimmy_fallon_1.jpg
│ │ │ │ ├── jimmy_fallon_2.jpg
│ │ │ │ ├── jimmy_fallon_3.jpg
│ │ │ │ └── jimmy_fallon_4.jpg
│ │ │ │ ├── johnny_depp
│ │ │ │ ├── johnny_depp_1.jpg
│ │ │ │ ├── johnny_depp_2.jpg
│ │ │ │ ├── johnny_depp_3.jpg
│ │ │ │ └── johnny_depp_4.jpg
│ │ │ │ ├── josh_radnor
│ │ │ │ ├── josh_radnor_1.jpg
│ │ │ │ ├── josh_radnor_2.jpg
│ │ │ │ ├── josh_radnor_3.jpg
│ │ │ │ └── josh_radnor_4.jpg
│ │ │ │ ├── keanu_reeves
│ │ │ │ ├── keanu_reeves_1.jpg
│ │ │ │ ├── keanu_reeves_2.jpg
│ │ │ │ ├── keanu_reeves_3.jpg
│ │ │ │ └── keanu_reeves_4.jpg
│ │ │ │ ├── lionel_messi
│ │ │ │ ├── lionel_messi_1.jpg
│ │ │ │ ├── lionel_messi_2.jpg
│ │ │ │ ├── lionel_messi_3.jpg
│ │ │ │ └── lionel_messi_4.jpg
│ │ │ │ ├── logan_lerman
│ │ │ │ ├── logan_lerman_1.jpg
│ │ │ │ ├── logan_lerman_2.jpg
│ │ │ │ ├── logan_lerman_3.jpg
│ │ │ │ └── logan_lerman_4.jpg
│ │ │ │ ├── mark_ruffalo
│ │ │ │ ├── mark_ruffalo_1.jpg
│ │ │ │ ├── mark_ruffalo_2.jpg
│ │ │ │ ├── mark_ruffalo_3.jpg
│ │ │ │ └── mark_ruffalo_4.jpg
│ │ │ │ ├── miley_cyrus
│ │ │ │ ├── miley_cyrus_1.jpg
│ │ │ │ ├── miley_cyrus_2.jpg
│ │ │ │ ├── miley_cyrus_3.jpg
│ │ │ │ └── miley_cyrus_4.jpg
│ │ │ │ ├── nadia_hilker
│ │ │ │ ├── nadia_hilker_1.jpg
│ │ │ │ ├── nadia_hilker_2.jpg
│ │ │ │ ├── nadia_hilker_3.jpg
│ │ │ │ └── nadia_hilker_4.jpg
│ │ │ │ ├── pedro_alonso
│ │ │ │ ├── pedro_alonso_1.jpg
│ │ │ │ ├── pedro_alonso_2.jpg
│ │ │ │ ├── pedro_alonso_3.jpg
│ │ │ │ └── pedro_alonso_4.jpg
│ │ │ │ ├── penn_badgley
│ │ │ │ ├── penn_badgley_1.jpg
│ │ │ │ ├── penn_badgley_2.jpg
│ │ │ │ ├── penn_badgley_3.jpg
│ │ │ │ └── penn_badgley_4.jpg
│ │ │ │ ├── selena_gomez
│ │ │ │ ├── selena_gomez_1.jpg
│ │ │ │ ├── selena_gomez_2.jpg
│ │ │ │ ├── selena_gomez_3.jpg
│ │ │ │ └── selena_gomez_4.jpg
│ │ │ │ ├── taylor_swift
│ │ │ │ ├── taylor_swift_1.jpg
│ │ │ │ ├── taylor_swift_2.jpg
│ │ │ │ ├── taylor_swift_3.jpg
│ │ │ │ └── taylor_swift_4.jpg
│ │ │ │ ├── tom_holland
│ │ │ │ ├── tom_holland_1.jpg
│ │ │ │ ├── tom_holland_2.jpg
│ │ │ │ ├── tom_holland_3.jpg
│ │ │ │ └── tom_holland_4.jpg
│ │ │ │ ├── zoe_saldana
│ │ │ │ ├── zoe_saldana_1.jpg
│ │ │ │ ├── zoe_saldana_2.jpg
│ │ │ │ ├── zoe_saldana_3.jpg
│ │ │ │ └── zoe_saldana_4.jpg
│ │ │ │ ├── anne_hathaway
│ │ │ │ ├── anne_hathaway_1.jpg
│ │ │ │ ├── anne_hathaway_2.jpg
│ │ │ │ ├── anne_hathaway_3.jpg
│ │ │ │ └── anne_hathaway_4.jpg
│ │ │ │ ├── avril_lavigne
│ │ │ │ ├── avril_lavigne_1.jpg
│ │ │ │ ├── avril_lavigne_2.jpg
│ │ │ │ ├── avril_lavigne_3.jpg
│ │ │ │ └── avril_lavigne_4.jpg
│ │ │ │ ├── camila_mendes
│ │ │ │ ├── camila_mendes_1.jpg
│ │ │ │ ├── camila_mendes_2.jpg
│ │ │ │ ├── camila_mendes_3.jpg
│ │ │ │ └── camila_mendes_4.jpg
│ │ │ │ ├── emilia_clarke
│ │ │ │ ├── emilia_clarke_1.jpg
│ │ │ │ ├── emilia_clarke_2.jpg
│ │ │ │ ├── emilia_clarke_3.jpg
│ │ │ │ └── emilia_clarke_4.jpg
│ │ │ │ ├── jake_mcdorman
│ │ │ │ ├── jake_mcdorman_1.jpg
│ │ │ │ ├── jake_mcdorman_2.jpg
│ │ │ │ ├── jake_mcdorman_3.jpg
│ │ │ │ └── jake_mcdorman_4.jpg
│ │ │ │ ├── jeremy_renner
│ │ │ │ ├── jeremy_renner_1.jpg
│ │ │ │ ├── jeremy_renner_2.jpg
│ │ │ │ ├── jeremy_renner_3.jpg
│ │ │ │ └── jeremy_renner_4.jpg
│ │ │ │ ├── lili_reinhart
│ │ │ │ ├── lili_reinhart_1.jpg
│ │ │ │ ├── lili_reinhart_2.jpg
│ │ │ │ ├── lili_reinhart_3.jpg
│ │ │ │ └── lili_reinhart_4.jpg
│ │ │ │ ├── margot_robbie
│ │ │ │ ├── margot_robbie_1.jpg
│ │ │ │ ├── margot_robbie_2.jpg
│ │ │ │ ├── margot_robbie_3.jpg
│ │ │ │ └── margot_robbie_4.jpg
│ │ │ │ ├── maria_pedraza
│ │ │ │ ├── maria_pedraza_1.jpg
│ │ │ │ ├── maria_pedraza_2.jpg
│ │ │ │ ├── maria_pedraza_3.jpg
│ │ │ │ └── maria_pedraza_4.jpg
│ │ │ │ ├── sophie_turner
│ │ │ │ ├── sophie_turner_1.jpg
│ │ │ │ ├── sophie_turner_2.jpg
│ │ │ │ ├── sophie_turner_3.jpg
│ │ │ │ └── sophie_turner_4.jpg
│ │ │ │ ├── stephen_amell
│ │ │ │ ├── stephen_amell_1.jpg
│ │ │ │ ├── stephen_amell_2.jpg
│ │ │ │ ├── stephen_amell_3.jpg
│ │ │ │ └── stephen_amell_4.jpg
│ │ │ │ ├── anthony_mackie
│ │ │ │ ├── anthony_mackie_1.jpg
│ │ │ │ ├── anthony_mackie_2.jpg
│ │ │ │ ├── anthony_mackie_3.jpg
│ │ │ │ └── anthony_mackie_4.jpg
│ │ │ │ ├── barbara_palvin
│ │ │ │ ├── barbara_palvin_1.jpg
│ │ │ │ ├── barbara_palvin_2.jpg
│ │ │ │ ├── barbara_palvin_3.jpg
│ │ │ │ └── barbara_palvin_4.jpg
│ │ │ │ ├── brian_j._smith
│ │ │ │ ├── brian_j._smith_1.jpg
│ │ │ │ ├── brian_j._smith_2.jpg
│ │ │ │ ├── brian_j._smith_3.jpg
│ │ │ │ └── brian_j._smith_4.jpg
│ │ │ │ ├── christian_bale
│ │ │ │ ├── christian_bale_1.jpg
│ │ │ │ ├── christian_bale_2.jpg
│ │ │ │ ├── christian_bale_3.jpg
│ │ │ │ └── christian_bale_4.jpg
│ │ │ │ ├── dwayne_johnson
│ │ │ │ ├── dwayne_johnson_1.jpg
│ │ │ │ ├── dwayne_johnson_2.jpg
│ │ │ │ ├── dwayne_johnson_3.jpg
│ │ │ │ └── dwayne_johnson_4.jpg
│ │ │ │ ├── elizabeth_lail
│ │ │ │ ├── elizabeth_lail_1.jpg
│ │ │ │ ├── elizabeth_lail_2.jpg
│ │ │ │ ├── elizabeth_lail_3.jpg
│ │ │ │ └── elizabeth_lail_4.jpg
│ │ │ │ ├── jessica_barden
│ │ │ │ ├── jessica_barden_1.jpg
│ │ │ │ ├── jessica_barden_2.jpg
│ │ │ │ ├── jessica_barden_3.jpg
│ │ │ │ └── jessica_barden_4.jpg
│ │ │ │ ├── kiernen_shipka
│ │ │ │ ├── kiernen_shipka_1.jpg
│ │ │ │ ├── kiernen_shipka_2.jpg
│ │ │ │ ├── kiernen_shipka_3.jpg
│ │ │ │ └── kiernen_shipka_4.jpg
│ │ │ │ ├── krysten_ritter
│ │ │ │ ├── krysten_ritter_1.jpg
│ │ │ │ ├── krysten_ritter_2.jpg
│ │ │ │ ├── krysten_ritter_3.jpg
│ │ │ │ └── krysten_ritter_4.jpg
│ │ │ │ ├── lindsey_morgan
│ │ │ │ ├── lindsey_morgan_1.jpg
│ │ │ │ ├── lindsey_morgan_2.jpg
│ │ │ │ ├── lindsey_morgan_3.jpg
│ │ │ │ └── lindsey_morgan_4.jpg
│ │ │ │ ├── melissa_fumero
│ │ │ │ ├── melissa_fumero_1.jpg
│ │ │ │ ├── melissa_fumero_2.jpg
│ │ │ │ ├── melissa_fumero_3.jpg
│ │ │ │ └── melissa_fumero_4.jpg
│ │ │ │ ├── morgan_freeman
│ │ │ │ ├── morgan_freeman_1.jpg
│ │ │ │ ├── morgan_freeman_2.jpg
│ │ │ │ ├── morgan_freeman_3.jpg
│ │ │ │ └── morgan_freeman_4.jpg
│ │ │ │ ├── natalie_dormer
│ │ │ │ ├── natalie_dormer_1.jpg
│ │ │ │ ├── natalie_dormer_2.jpg
│ │ │ │ ├── natalie_dormer_3.jpg
│ │ │ │ └── natalie_dormer_4.jpg
│ │ │ │ ├── richard_harmon
│ │ │ │ ├── richard_harmon_1.jpg
│ │ │ │ ├── richard_harmon_2.jpg
│ │ │ │ ├── richard_harmon_3.jpg
│ │ │ │ └── richard_harmon_4.jpg
│ │ │ │ ├── robert_de_niro
│ │ │ │ ├── robert_de_niro_1.jpg
│ │ │ │ ├── robert_de_niro_2.jpg
│ │ │ │ ├── robert_de_niro_3.jpg
│ │ │ │ └── robert_de_niro_4.jpg
│ │ │ │ ├── tom_hiddleston
│ │ │ │ ├── tom_hiddleston_1.jpg
│ │ │ │ ├── tom_hiddleston_2.jpg
│ │ │ │ ├── tom_hiddleston_3.jpg
│ │ │ │ └── tom_hiddleston_4.jpg
│ │ │ │ ├── ursula_corbero
│ │ │ │ ├── ursula_corbero_1.jpg
│ │ │ │ ├── ursula_corbero_2.jpg
│ │ │ │ ├── ursula_corbero_3.jpg
│ │ │ │ └── ursula_corbero_4.jpg
│ │ │ │ ├── chris_hemsworth
│ │ │ │ ├── chris_hemsworth_1.jpg
│ │ │ │ ├── chris_hemsworth_2.jpg
│ │ │ │ ├── chris_hemsworth_3.jpg
│ │ │ │ └── chris_hemsworth_4.jpg
│ │ │ │ ├── dominic_purcell
│ │ │ │ ├── dominic_purcell_1.jpg
│ │ │ │ ├── dominic_purcell_2.jpg
│ │ │ │ ├── dominic_purcell_3.jpg
│ │ │ │ └── dominic_purcell_4.jpg
│ │ │ │ ├── elizabeth_olsen
│ │ │ │ ├── elizabeth_olsen_1.jpg
│ │ │ │ ├── elizabeth_olsen_2.jpg
│ │ │ │ ├── elizabeth_olsen_3.jpg
│ │ │ │ └── elizabeth_olsen_4.jpg
│ │ │ │ ├── gwyneth_paltrow
│ │ │ │ ├── gwyneth_paltrow_1.jpg
│ │ │ │ ├── gwyneth_paltrow_2.jpg
│ │ │ │ ├── gwyneth_paltrow_3.jpg
│ │ │ │ └── gwyneth_paltrow_4.jpg
│ │ │ │ ├── maisie_williams
│ │ │ │ ├── maisie_williams_1.jpg
│ │ │ │ ├── maisie_williams_2.jpg
│ │ │ │ ├── maisie_williams_3.jpg
│ │ │ │ └── maisie_williams_4.jpg
│ │ │ │ ├── mark_zuckerberg
│ │ │ │ ├── mark_zuckerberg_1.jpg
│ │ │ │ ├── mark_zuckerberg_2.jpg
│ │ │ │ ├── mark_zuckerberg_3.jpg
│ │ │ │ └── mark_zuckerberg_4.jpg
│ │ │ │ ├── morena_baccarin
│ │ │ │ ├── morena_baccarin_1.jpg
│ │ │ │ ├── morena_baccarin_2.jpg
│ │ │ │ ├── morena_baccarin_3.jpg
│ │ │ │ └── morena_baccarin_4.jpg
│ │ │ │ ├── natalie_portman
│ │ │ │ ├── natalie_portman_1.jpg
│ │ │ │ ├── natalie_portman_2.jpg
│ │ │ │ ├── natalie_portman_3.jpg
│ │ │ │ └── natalie_portman_4.jpg
│ │ │ │ ├── brenton_thwaites
│ │ │ │ ├── brenton_thwaites_1.jpg
│ │ │ │ ├── brenton_thwaites_2.jpg
│ │ │ │ ├── brenton_thwaites_3.jpg
│ │ │ │ └── brenton_thwaites_4.jpg
│ │ │ │ ├── cristiano_ronaldo
│ │ │ │ ├── cristiano_ronaldo_1.jpg
│ │ │ │ ├── cristiano_ronaldo_2.jpg
│ │ │ │ ├── cristiano_ronaldo_3.jpg
│ │ │ │ └── cristiano_ronaldo_4.jpg
│ │ │ │ ├── jennifer_lawrence
│ │ │ │ ├── jennifer_lawrence_1.jpg
│ │ │ │ ├── jennifer_lawrence_2.jpg
│ │ │ │ ├── jennifer_lawrence_3.jpg
│ │ │ │ └── jennifer_lawrence_4.jpg
│ │ │ │ ├── katharine_mcphee
│ │ │ │ ├── katharine_mcphee_1.jpg
│ │ │ │ ├── katharine_mcphee_2.jpg
│ │ │ │ ├── katharine_mcphee_3.jpg
│ │ │ │ └── katharine_mcphee_4.jpg
│ │ │ │ ├── leonardo_dicaprio
│ │ │ │ ├── leonardo_dicaprio_1.jpg
│ │ │ │ ├── leonardo_dicaprio_2.jpg
│ │ │ │ ├── leonardo_dicaprio_3.jpg
│ │ │ │ └── leonardo_dicaprio_4.jpg
│ │ │ │ ├── madelaine_petsch
│ │ │ │ ├── madelaine_petsch_1.jpg
│ │ │ │ ├── madelaine_petsch_2.jpg
│ │ │ │ ├── madelaine_petsch_3.jpg
│ │ │ │ └── madelaine_petsch_4.jpg
│ │ │ │ ├── rebecca_ferguson
│ │ │ │ ├── rebecca_ferguson_1.jpg
│ │ │ │ ├── rebecca_ferguson_2.jpg
│ │ │ │ ├── rebecca_ferguson_3.jpg
│ │ │ │ └── rebecca_ferguson_4.jpg
│ │ │ │ ├── robert_downey_jr
│ │ │ │ ├── robert_downey_jr_1.jpg
│ │ │ │ ├── robert_downey_jr_2.jpg
│ │ │ │ ├── robert_downey_jr_3.jpg
│ │ │ │ └── robert_downey_jr_4.jpg
│ │ │ │ ├── wentworth_miller
│ │ │ │ ├── wentworth_miller_1.jpg
│ │ │ │ ├── wentworth_miller_2.jpg
│ │ │ │ ├── wentworth_miller_3.jpg
│ │ │ │ └── wentworth_miller_4.jpg
│ │ │ │ ├── alexandra_daddario
│ │ │ │ ├── alexandra_daddario_1.jpg
│ │ │ │ ├── alexandra_daddario_2.jpg
│ │ │ │ ├── alexandra_daddario_3.jpg
│ │ │ │ └── alexandra_daddario_4.jpg
│ │ │ │ ├── danielle_panabaker
│ │ │ │ ├── danielle_panabaker_1.jpg
│ │ │ │ ├── danielle_panabaker_2.jpg
│ │ │ │ ├── danielle_panabaker_3.jpg
│ │ │ │ └── danielle_panabaker_4.jpg
│ │ │ │ ├── katherine_langford
│ │ │ │ ├── katherine_langford_1.jpg
│ │ │ │ ├── katherine_langford_2.jpg
│ │ │ │ ├── katherine_langford_3.jpg
│ │ │ │ └── katherine_langford_4.jpg
│ │ │ │ ├── marie_avgeropoulos
│ │ │ │ ├── marie_avgeropoulos_1.jpg
│ │ │ │ ├── marie_avgeropoulos_2.jpg
│ │ │ │ ├── marie_avgeropoulos_3.jpg
│ │ │ │ └── marie_avgeropoulos_4.jpg
│ │ │ │ ├── millie_bobby_brown
│ │ │ │ ├── millie_bobby_brown_1.jpg
│ │ │ │ ├── millie_bobby_brown_2.jpg
│ │ │ │ ├── millie_bobby_brown_3.jpg
│ │ │ │ └── millie_bobby_brown_4.jpg
│ │ │ │ ├── scarlett_johansson
│ │ │ │ ├── scarlett_johansson_1.jpg
│ │ │ │ ├── scarlett_johansson_2.jpg
│ │ │ │ ├── scarlett_johansson_3.jpg
│ │ │ │ └── scarlett_johansson_4.jpg
│ │ │ │ ├── tuppence_middleton
│ │ │ │ ├── tuppence_middleton_1.jpg
│ │ │ │ ├── tuppence_middleton_2.jpg
│ │ │ │ ├── tuppence_middleton_3.jpg
│ │ │ │ └── tuppence_middleton_4.jpg
│ │ │ │ ├── alycia_dabnem_carey
│ │ │ │ ├── alycia_dabnem_carey_1.jpg
│ │ │ │ ├── alycia_dabnem_carey_2.jpg
│ │ │ │ ├── alycia_dabnem_carey_3.jpg
│ │ │ │ └── alycia_dabnem_carey_4.jpg
│ │ │ │ ├── neil_patrick_harris
│ │ │ │ ├── neil_patrick_harris_1.jpg
│ │ │ │ ├── neil_patrick_harris_2.jpg
│ │ │ │ ├── neil_patrick_harris_3.jpg
│ │ │ │ └── neil_patrick_harris_4.jpg
│ │ │ │ ├── sarah_wayne_callies
│ │ │ │ ├── sarah_wayne_callies_1.jpg
│ │ │ │ ├── sarah_wayne_callies_2.jpg
│ │ │ │ ├── sarah_wayne_callies_3.jpg
│ │ │ │ └── sarah_wayne_callies_4.jpg
│ │ │ │ └── shakira_isabel_mebarak
│ │ │ │ ├── shakira_isabel_mebarak_1.jpg
│ │ │ │ ├── shakira_isabel_mebarak_2.jpg
│ │ │ │ ├── shakira_isabel_mebarak_3.jpg
│ │ │ │ └── shakira_isabel_mebarak_4.jpg
│ │ ├── requirements.txt
│ │ └── Dockerfile
│ ├── vector_twin
│ │ ├── __init__.py
│ │ ├── scripts
│ │ │ ├── __init__.py
│ │ │ ├── delete_zenml_secrets.py
│ │ │ └── create_zenml_secrets.py
│ │ ├── qdrant
│ │ │ ├── __init__.py
│ │ │ └── client.py
│ │ ├── settings.py
│ │ └── models.py
│ └── embedding_pipeline
│ │ ├── __init__.py
│ │ ├── .dockerignore
│ │ ├── requirements.txt
│ │ ├── configs
│ │ ├── embedding_generation_local.yaml
│ │ └── embedding_generation_qdrant_cloud.yaml
│ │ ├── steps
│ │ ├── __init__.py
│ │ ├── embedding_generation.py
│ │ └── hf_datasets.py
│ │ ├── pipeline.py
│ │ ├── settings.py
│ │ └── run.py
├── .env.example
├── img
│ ├── cloud_run.png
│ ├── streamlit_app.png
│ ├── twin_celebrity.png
│ └── embedding_pipeline.png
├── .pre-commit-config.yaml
├── docker-compose.yml
├── cloudbuild.yaml
├── pyproject.toml
├── LICENSE
└── Makefile
├── vision-rag-complex-pdf
├── .gitignore
├── .python-version
├── .vscode
│ └── settings.json
├── data
│ ├── pdf
│ │ ├── JRCR-18-8-5353-4.pdf
│ │ ├── JRCR-18-8-5353-5.pdf
│ │ ├── JRCR-18-9-5436-6.pdf
│ │ ├── JRCR-18-9-5436-7.pdf
│ │ ├── JRCR-19-1-5506-4.pdf
│ │ ├── JRCR-19-4-5651-7.pdf
│ │ ├── JRCR-19-4-5651-8.pdf
│ │ ├── JRCR-19-4-5651-9.pdf
│ │ ├── JRCR-19-4-5693-4.pdf
│ │ ├── JRCR-19-5-5719-5.pdf
│ │ ├── JRCR-19-5-5719-6.pdf
│ │ └── JRCR-19-4-5651-10.pdf
│ └── metadata.json
├── infrastructure
│ ├── bash_scripts
│ │ ├── ollama.sh
│ │ ├── send_code.sh
│ │ ├── ssh_keys.sh
│ │ ├── install_runpodctl.sh
│ │ └── pre_setup.sh
│ └── python_scripts
│ │ └── create_runpod.py
├── app.py
├── .env.example
├── .pre-commit-config.yaml
├── document_processor
│ └── pdf_to_png.py
├── src
│ └── vision_model_loader.py
└── Makefile
├── static
├── neural_hub.png
├── diagrams
│ ├── rick_llm.png
│ ├── locust_guide.png
│ ├── arduino_agent.png
│ └── linkedin_influencer_agent.png
└── thumbnails
│ ├── rick_llm.png
│ ├── arduino_agent.png
│ ├── locust_guide.png
│ └── linkedin_influencer_agent.png
├── linkedin-influencer-agent
├── tools
│ ├── __init__.py
│ ├── utils.py
│ └── linkedin.py
├── run_tool.py
├── img
│ ├── img.png
│ └── architecture.png
├── requirements.txt
├── .env.example
├── main.py
├── tasks.py
└── README.md
└── LICENSE
/arduino-agent/tmp/.gitkeep:
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/arduino-agent/tools/__init__.py:
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/locust-guide/src/api/__init__.py:
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/rick-llm/.python-version:
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1 | 3.12
2 |
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/rick-llm/src/lambda/__init__.py:
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/rick-llm/src/rick_llm/__init__.py:
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/vector-twin/src/app/__init__.py:
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/rick-llm/src/lambda/ssh-key/.gitkeep:
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/vision-rag-complex-pdf/.gitignore:
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1 | .env
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/vector-twin/src/vector_twin/__init__.py:
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1 |
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/arduino-agent/.env.example:
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1 | OPENAI_API_KEY=""
2 |
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/vector-twin/src/embedding_pipeline/__init__.py:
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1 |
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/vector-twin/src/vector_twin/scripts/__init__.py:
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1 |
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/vision-rag-complex-pdf/.python-version:
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1 | 3.13
2 |
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/vector-twin/src/embedding_pipeline/.dockerignore:
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1 | app/
2 |
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/static/neural_hub.png:
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/arduino-agent/img/img.png:
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/rick-llm/img/ollama.png:
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/rick-llm/img/unsloth.png:
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/vision-rag-complex-pdf/.vscode/settings.json:
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1 | {
2 | "python.defaultInterpreterPath": ".venv/bin/python"
3 | }
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/rick-llm/img/lambda_home.png:
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/static/diagrams/rick_llm.png:
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/static/thumbnails/rick_llm.png:
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/vector-twin/.env.example:
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1 | QDRANT_URL=
2 | QDRANT_API_KEY=
3 |
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/vector-twin/img/cloud_run.png:
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https://raw.githubusercontent.com/neural-maze/neural-hub/HEAD/vector-twin/img/cloud_run.png
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/linkedin-influencer-agent/tools/__init__.py:
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1 | from .linkedin import scrape_linkedin_posts_tool, scrape_linkedin_posts_fn
2 |
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/locust-guide/static/diagram.gif:
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/rick-llm/img/dataset_viewer.png:
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/static/diagrams/locust_guide.png:
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/linkedin-influencer-agent/run_tool.py:
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1 | from tools import scrape_linkedin_posts_fn
2 |
3 | print(scrape_linkedin_posts_fn())
4 |
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/locust-guide/static/locust_home.png:
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/static/diagrams/arduino_agent.png:
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/static/thumbnails/arduino_agent.png:
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/static/thumbnails/locust_guide.png:
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/vector-twin/img/streamlit_app.png:
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/vector-twin/img/twin_celebrity.png:
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/vector-twin/src/app/assets/logo.png:
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/vector-twin/src/app/requirements.txt:
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1 | streamlit
2 | facenet-pytorch
3 | qdrant-client
4 | python-dotenv
5 | pydantic-settings
6 |
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/linkedin-influencer-agent/img/img.png:
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/locust-guide/requirements.txt:
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1 | fastapi[standard]
2 | transformers
3 | redis
4 | fastapi-cache2
5 | pydantic
6 | locust
7 | torch
8 |
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/locust-guide/static/locust_graph.png:
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/vector-twin/src/embedding_pipeline/requirements.txt:
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1 | zenml[server]==0.68.1
2 | datasets
3 | facenet-pytorch
4 | qdrant-client
5 | tqdm
6 |
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/locust-guide/static/youtube_thumbnail.jpg:
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/arduino-agent/requirements.txt:
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1 | crewai==0.28.8
2 | crewai-tools==0.2.3
3 | langchain-core==0.1.45
4 | langchain-openai==0.0.5
5 | python-dotenv==1.0.0
6 |
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/locust-guide/docker-compose.yml:
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1 | services:
2 | api:
3 | build: .
4 | container_name: fastapi_locust_example
5 | ports:
6 | - "8000:8000"
7 |
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1 | OPENAI_API_KEY="PUT_YOUR_OPENAI_API_KEY_HERE"
2 | HUGGINGFACE_TOKEN="PUT_YOUR_HUGGINGFACE_TOKEN_HERE"
3 | LAMBDA_API_KEY="PUT_YOUR_LAMBDA_API_KEY_HERE"
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5 | tf_keras==2.17.0
6 | Jinja2==3.1.4
7 |
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/vision-rag-complex-pdf/infrastructure/bash_scripts/ollama.sh:
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1 | #!/bin/bash
2 |
3 | curl -fsSL https://ollama.com/install.sh | sh
4 |
5 | ollama serve &
6 |
7 | sleep 10
8 |
9 | ollama pull gemma3:27b
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/locust-guide/src/api/schemas.py:
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1 | from pydantic import BaseModel
2 |
3 | class TextInput(BaseModel):
4 | text: str
5 |
6 | class SentimentOutput(BaseModel):
7 | label: str
8 | score: float
9 |
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/vision-rag-complex-pdf/app.py:
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1 | from src.vision_rag import vision_rag
2 |
3 | user_query = f"""
4 |
5 | Your query related to your .pdf content
6 |
7 | """
8 |
9 | vision_rag(user_query=user_query)
10 |
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/vision-rag-complex-pdf/.env.example:
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1 | DATA_FOLDER_PATH="/root/data"
2 | COLQWEN_MODEL="vidore/colqwen2-v1.0-hf"
3 | QDRANT_COLLECTION_NAME="vision_rag"
4 | QDRANT_URL="XXXX"
5 | QDRANT_API_KEY=XXXX
6 | RUNPOD_API_KEY=XXXX
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/vision-rag-complex-pdf/.pre-commit-config.yaml:
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1 | repos:
2 | - repo: https://github.com/astral-sh/ruff-pre-commit
3 | rev: v0.1.6
4 | hooks:
5 | - id: ruff
6 | args: [--fix, --exit-non-zero-on-fix]
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/vector-twin/src/embedding_pipeline/configs/embedding_generation_local.yaml:
--------------------------------------------------------------------------------
1 | settings:
2 | docker:
3 | requirements: requirements.txt
4 |
5 | steps:
6 | sample_dataset:
7 | parameters:
8 | sample_size: 100
9 |
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/vector-twin/src/embedding_pipeline/configs/embedding_generation_qdrant_cloud.yaml:
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1 | settings:
2 | docker:
3 | requirements: requirements.txt
4 |
5 | steps:
6 | sample_dataset:
7 | parameters:
8 | sample_size: 3000
9 |
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/arduino-agent/tmp/tmp.ino:
--------------------------------------------------------------------------------
1 | void setup() {
2 | }
3 |
4 | void loop() {
5 | digitalWrite(9, HIGH);
6 | delay(1000);
7 |
8 | digitalWrite(11, HIGH);
9 | delay(1000);
10 |
11 | digitalWrite(10, HIGH);
12 | delay(1000);
13 | }
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/vector-twin/src/embedding_pipeline/steps/__init__.py:
--------------------------------------------------------------------------------
1 | from .hf_datasets import load_hf_dataset, sample_dataset
2 | from .embedding_generation import generate_embeddings
3 |
4 | __all__ = ["load_hf_dataset", "sample_dataset", "generate_embeddings"]
5 |
--------------------------------------------------------------------------------
/linkedin-influencer-agent/requirements.txt:
--------------------------------------------------------------------------------
1 | selenium==4.19.0
2 | lxml==5.2.1
3 | beautifulsoup4==4.12.3
4 | python-dotenv==1.0.0
5 | crewai==0.28.8
6 | crewai-tools==0.1.7
7 | langchain-core==0.1.45
8 | langchain-openai==0.0.5
9 | langchain-mistralai==0.1.2
10 |
--------------------------------------------------------------------------------
/rick-llm/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
1 | repos:
2 | - repo: https://github.com/astral-sh/ruff-pre-commit
3 | rev: v0.3.4 # Use the latest version of Ruff
4 | hooks:
5 | - id: ruff
6 | args: [--fix, --exit-non-zero-on-fix]
7 | - id: ruff-format
8 |
--------------------------------------------------------------------------------
/vector-twin/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
1 | repos:
2 | - repo: https://github.com/astral-sh/ruff-pre-commit
3 | rev: v0.3.4 # Use the latest version of Ruff
4 | hooks:
5 | - id: ruff
6 | args: [--fix, --exit-non-zero-on-fix]
7 | - id: ruff-format
8 |
--------------------------------------------------------------------------------
/linkedin-influencer-agent/.env.example:
--------------------------------------------------------------------------------
1 | OPENAI_API_KEY="your_openai_api_key"
2 | MISTRAL_API_KEY="your_mistral_api_key"
3 | SERPER_API_KEY="your_serper_api_key"
4 | LINKEDIN_EMAIL="your_linkedin_email"
5 | LINKEDIN_PASSWORD="your_linkedin_password"
6 | LINKEDIN_PROFILE_NAME="moteropedrido"
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/qdrant/__init__.py:
--------------------------------------------------------------------------------
1 | from .client import get_qdrant_client
2 | from .utils import create_collection, insert_image_embedding, get_top_k_similar_images
3 |
4 | __all__ = ["get_qdrant_client", "create_collection", "insert_image_embedding", "get_top_k_similar_images"]
--------------------------------------------------------------------------------
/rick-llm/src/lambda/request.json:
--------------------------------------------------------------------------------
1 | {
2 | "name": "rick-finetune-instance",
3 | "region_name": "us-east-1",
4 | "instance_type_name": "gpu_1x_a100_sxm4",
5 | "ssh_key_names": [
6 | "rick-public-key"
7 | ],
8 | "file_system_names": [],
9 | "quantity": 1
10 | }
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/scripts/delete_zenml_secrets.py:
--------------------------------------------------------------------------------
1 | from zenml.client import Client
2 |
3 | client = Client()
4 |
5 | try:
6 | client.delete_secret(
7 | name_id_or_prefix="qdrant"
8 | )
9 | except KeyError:
10 | print("Secret not found. Skipping deletion ...")
11 |
--------------------------------------------------------------------------------
/vector-twin/src/app/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM python:3.11-slim
2 |
3 | WORKDIR /app
4 |
5 | COPY src/app/requirements.txt /app/requirements.txt
6 |
7 | RUN pip install -r requirements.txt
8 |
9 | COPY src/app /app
10 | COPY src/vector_twin /app/vector_twin
11 |
12 | EXPOSE 8501
13 |
14 | CMD ["streamlit", "run", "main.py"]
15 |
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/scripts/create_zenml_secrets.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | from zenml.client import Client
4 | from dotenv import load_dotenv
5 |
6 | load_dotenv()
7 |
8 | client = Client()
9 |
10 | client.create_secret(
11 | name="qdrant",
12 | values={"QDRANT_URL": os.getenv("QDRANT_URL"), "QDRANT_PORT": os.getenv("QDRANT_PORT")}
13 | )
14 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/infrastructure/bash_scripts/send_code.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | set -e
4 |
5 | ZIP_NAME="project_code.zip"
6 | HOST="runpod-pytorch"
7 |
8 | echo "Code zipped in: ${ZIP_NAME}..."
9 | zip -r "$ZIP_NAME" . -x ".venv/*" "uv.lock" "*.git*" "node_modules/*" "__pycache__/*"
10 |
11 | echo "Sending ${ZIP_NAME} to the remote host."
12 |
13 | OUTPUT=$(runpodctl send "$ZIP_NAME")
14 | echo "$OUTPUT"
15 |
16 |
--------------------------------------------------------------------------------
/locust-guide/pyproject.toml:
--------------------------------------------------------------------------------
1 | [project]
2 | name = "locust-example"
3 | version = "0.1.0"
4 | description = "Add your description here"
5 | readme = "README.md"
6 | requires-python = ">=3.12"
7 | dependencies = [
8 | "fastapi-cache2>=0.2.2",
9 | "fastapi[standard]>=0.115.12",
10 | "locust>=2.33.2",
11 | "pydantic>=2.10.6",
12 | "redis>=5.2.1",
13 | "torch>=2.6.0",
14 | "transformers>=4.50.1",
15 | ]
16 |
--------------------------------------------------------------------------------
/vector-twin/docker-compose.yml:
--------------------------------------------------------------------------------
1 | services:
2 | qdrant:
3 | image: qdrant/qdrant:latest
4 | ports:
5 | - 6333:6333
6 | volumes:
7 | - qdrant_data:/qdrant/storage
8 | app:
9 | build:
10 | context: .
11 | dockerfile: src/app/Dockerfile
12 | ports:
13 | - 8501:8501
14 | environment:
15 | - QDRANT_URL=qdrant
16 | - QDRANT_PORT=6333
17 | depends_on:
18 | - qdrant
19 | volumes:
20 | qdrant_data:
21 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/infrastructure/bash_scripts/ssh_keys.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | set -e
3 |
4 | SSH_DIR="$HOME/.ssh"
5 | KEY_PATH="$SSH_DIR/id_ed25519"
6 |
7 | mkdir -p "$SSH_DIR"
8 | chmod 700 "$SSH_DIR"
9 |
10 | if [ ! -f "$KEY_PATH" ]; then
11 | echo "Generating new SSH key in $KEY_PATH"
12 | ssh-keygen -t ed25519 -f "$KEY_PATH" -N ""
13 | else
14 | echo "SSH key already exists in $KEY_PATH"
15 | fi
16 |
17 | echo "Public SSH key:"
18 | cat "$KEY_PATH.pub"
19 |
--------------------------------------------------------------------------------
/rick-llm/pyproject.toml:
--------------------------------------------------------------------------------
1 | [project]
2 | name = "rick-llm"
3 | version = "0.1.0"
4 | description = "Add your description here"
5 | readme = "README.md"
6 | requires-python = ">=3.12"
7 | dependencies = [
8 | "bitsandbytes==0.42.0",
9 | "datasets>=3.2.0",
10 | "openai>=1.58.1",
11 | "pre-commit>=4.0.1",
12 | "python-dotenv>=1.0.1",
13 | "ruff>=0.8.4",
14 | "torch>=2.5.1",
15 | "tqdm>=4.67.1",
16 | "transformers>=4.47.1",
17 | "trl>=0.13.0",
18 | ]
19 |
--------------------------------------------------------------------------------
/locust-guide/Dockerfile:
--------------------------------------------------------------------------------
1 | FROM python:3.12-slim
2 |
3 | # Install uv.
4 | COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
5 |
6 | # Set the working directory.
7 | WORKDIR /app
8 |
9 | # Install the application dependencies.
10 | COPY uv.lock pyproject.toml README.md ./
11 | RUN uv sync --frozen --no-cache
12 |
13 | # Copy the application into the container.
14 | COPY src/api api/
15 |
16 | CMD ["/app/.venv/bin/fastapi", "run", "api/main.py", "--port", "8000", "--host", "0.0.0.0"]
17 |
--------------------------------------------------------------------------------
/arduino-agent/main.py:
--------------------------------------------------------------------------------
1 | from crewai import Crew
2 | from dotenv import load_dotenv
3 |
4 | from agents import sketch_programmer_agent, arduino_uploader_agent
5 | from tasks import sketch_programming_task, arduino_uploading_task
6 |
7 | load_dotenv()
8 |
9 |
10 | crew = Crew(
11 | agents=[sketch_programmer_agent, arduino_uploader_agent],
12 | tasks=[sketch_programming_task, arduino_uploading_task],
13 | )
14 |
15 | result = crew.kickoff()
16 |
17 | print(result)
18 |
--------------------------------------------------------------------------------
/rick-llm/src/rick_llm/exceptions.py:
--------------------------------------------------------------------------------
1 | class UnslothNotInstalledError(ImportError):
2 | """Exception raised when Unsloth is not installed.
3 |
4 | This exception should be raised when attempting to use Unsloth-dependent
5 | functionality without having Unsloth installed in the environment.
6 | """
7 |
8 | def __init__(
9 | self,
10 | message="Unsloth is required but not installed. Remember finetune should be run in a Lambda Labs instance",
11 | ):
12 | self.message = message
13 | super().__init__(self.message)
14 |
--------------------------------------------------------------------------------
/locust-guide/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: build up down logs clean restart
2 |
3 | # Build and start the application
4 | api-up:
5 | docker-compose up --build -d
6 |
7 | # Build the application
8 | api-build:
9 | docker-compose build
10 |
11 | # Stop and remove containers
12 | api-down:
13 | docker-compose down
14 |
15 | # View logs
16 | api-logs:
17 | docker-compose logs -f api
18 |
19 | # Remove all containers and images
20 | api-clean:
21 | docker-compose down --rmi all --volumes --remove-orphans
22 |
23 | # Restart the application
24 | api-restart:
25 | docker-compose restart api
26 |
27 | # Run locust
28 | run-locust:
29 | locust -f src/locustfile.py
30 |
--------------------------------------------------------------------------------
/locust-guide/src/locustfile.py:
--------------------------------------------------------------------------------
1 | import json
2 | import os
3 | import random
4 |
5 | from locust import HttpUser, constant_pacing, task
6 |
7 |
8 | def load_json_file(file_path: str):
9 | with open(file_path, 'r') as file:
10 | data = json.load(file)
11 | return data
12 |
13 | COMMENTS = load_json_file(
14 | os.path.join(os.path.dirname(__file__), "data/example_comments.json")
15 | )
16 |
17 | class LoadTestSentiment(HttpUser):
18 | wait_time = constant_pacing(1)
19 |
20 | @task
21 | def post_sentiment(self):
22 | comment = random.choice(COMMENTS)
23 | self.client.post("/predict-sentiment", json=comment)
24 |
--------------------------------------------------------------------------------
/linkedin-influencer-agent/main.py:
--------------------------------------------------------------------------------
1 | from crewai import Crew
2 | from dotenv import load_dotenv
3 |
4 | from agents import linkedin_scraper_agent, web_researcher_agent, doppelganger_agent
5 | from tasks import scrape_linkedin_task, web_research_task, create_linkedin_post_task
6 |
7 | load_dotenv()
8 |
9 |
10 | crew = Crew(
11 | agents=[
12 | linkedin_scraper_agent,
13 | web_researcher_agent,
14 | doppelganger_agent
15 | ],
16 | tasks=[
17 | scrape_linkedin_task,
18 | web_research_task,
19 | create_linkedin_post_task
20 | ]
21 | )
22 |
23 | result = crew.kickoff()
24 |
25 |
26 | print("Here is the result: ")
27 | print(result)
28 |
--------------------------------------------------------------------------------
/vector-twin/src/embedding_pipeline/pipeline.py:
--------------------------------------------------------------------------------
1 | from settings import settings # type: ignore
2 | from steps import (generate_embeddings, load_hf_dataset, # type: ignore
3 | sample_dataset)
4 | from zenml import pipeline
5 | from zenml.config import DockerSettings
6 | from zenml.config.docker_settings import DockerBuildConfig
7 |
8 |
9 | docker_settings = DockerSettings(build_config=DockerBuildConfig(dockerignore=".dockerignore"))
10 |
11 | @pipeline(settings={"docker": docker_settings})
12 | def embedding_pipeline(use_qdrant_cloud: bool):
13 | dataset = load_hf_dataset()
14 | sampled_dataset = sample_dataset(dataset)
15 | generate_embeddings(sampled_dataset, use_qdrant_cloud)
16 |
--------------------------------------------------------------------------------
/vector-twin/src/embedding_pipeline/settings.py:
--------------------------------------------------------------------------------
1 | from pydantic_settings import BaseSettings, SettingsConfigDict
2 |
3 |
4 | class Settings(BaseSettings):
5 | model_config = SettingsConfigDict(env_file=".env", extra="ignore")
6 |
7 | # Qdrant URL and API Key
8 | QDRANT_HOST: str | None = "localhost"
9 | QDRANT_API_KEY: str | None = None
10 | QDRANT_PORT: int | None = 6333
11 |
12 |
13 | # Qdrant Collection (for both local and cloud)
14 | QDRANT_COLLECTION_NAME: str = "celebrities"
15 | QDRANT_VECTOR_DIMENSIONS: int = 512
16 |
17 | # Hugging Face Dataset Name
18 | DATASET_NAME: str = "lansinuote/simple_facenet"
19 |
20 |
21 | settings = Settings()
22 |
23 | print(settings)
24 |
--------------------------------------------------------------------------------
/rick-llm/src/rick_llm/finetune.py:
--------------------------------------------------------------------------------
1 | import os
2 |
3 | import dotenv
4 |
5 | from model_utils import initialize_model, setup_peft_model
6 | from trainer import ModelTrainer
7 |
8 | dotenv.load_dotenv()
9 |
10 |
11 | def main():
12 | # Initialize model
13 | model, tokenizer = initialize_model()
14 | model = setup_peft_model(model)
15 |
16 | # Train model
17 | trainer = ModelTrainer(model, tokenizer)
18 | trainer_instance = trainer.setup_trainer()
19 | trainer_instance.train()
20 |
21 | # Push to hub
22 | model.push_to_hub_gguf(
23 | "theneuralmaze/RickLLama-3.1-8B",
24 | tokenizer,
25 | token=os.getenv("HUGGINGFACE_TOKEN"),
26 | )
27 |
28 |
29 | if __name__ == "__main__":
30 | main()
31 |
--------------------------------------------------------------------------------
/arduino-agent/tasks.py:
--------------------------------------------------------------------------------
1 | from textwrap import dedent
2 |
3 | from crewai import Task
4 |
5 | from agents import sketch_programmer_agent, arduino_uploader_agent
6 |
7 | sketch_programming_task = Task(
8 | description=dedent(
9 | "Write a Sketch script that can be immediately uploaded to an Arduino. Just the Arduino code, nothing else."),
10 | expected_output=dedent("A plain Sketch script that can be copy and pasted directly into the Arduino CLI"),
11 | agent=sketch_programmer_agent,
12 | output_file="./tmp/tmp.ino",
13 | )
14 |
15 | arduino_uploading_task = Task(
16 | description=dedent(
17 | "Compile and Upload the the Sketch script into the Arduino"),
18 | expected_output=dedent("Just compile the code and upload it into the Arduino"),
19 | agent=arduino_uploader_agent,
20 | )
21 |
22 |
--------------------------------------------------------------------------------
/rick-llm/src/rick_llm/model_utils.py:
--------------------------------------------------------------------------------
1 | from exceptions import UnslothNotInstalledError
2 | from constants import MAX_SEQ_LENGTH, MODEL_CONFIG, PEFT_CONFIG
3 |
4 | try:
5 | from unsloth import FastLanguageModel # type: ignore
6 | except ImportError:
7 | raise UnslothNotInstalledError
8 |
9 |
10 | def initialize_model():
11 | """Initialize and return the base model and tokenizer."""
12 | model, tokenizer = FastLanguageModel.from_pretrained(
13 | model_name=MODEL_CONFIG["model_name"],
14 | max_seq_length=MAX_SEQ_LENGTH,
15 | load_in_4bit=MODEL_CONFIG["load_in_4bit"],
16 | dtype=MODEL_CONFIG["dtype"],
17 | )
18 | return model, tokenizer
19 |
20 |
21 | def setup_peft_model(model):
22 | """Apply PEFT configuration to the model."""
23 | return FastLanguageModel.get_peft_model(model, **PEFT_CONFIG)
24 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/infrastructure/bash_scripts/install_runpodctl.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | set -e
4 |
5 | echo "Searching RUNPOD API KEY in .env"
6 |
7 | if [ ! -f .env ]; then
8 | echo ".env not found."
9 | exit 1
10 | fi
11 |
12 | API_KEY=$(grep -E '^RUNPOD_API_KEY=' .env | cut -d '=' -f2- | tr -d '"')
13 |
14 | if [ -z "$API_KEY" ]; then
15 | echo "RUNPOD API KEY not defined in .env"
16 | exit 1
17 | fi
18 |
19 | echo "RUNPOD API KEY found."
20 |
21 | echo "Downloading runpodctl."
22 | wget -q https://github.com/Run-Pod/runpodctl/releases/download/v1.14.3/runpodctl-linux-amd64 -O runpodctl
23 | chmod +x runpodctl
24 | sudo mv runpodctl /usr/local/bin/runpodctl
25 |
26 | echo "runpodctl installed: $(runpodctl --version)"
27 |
28 | echo "Enabling runpodctl with RUNPOD API KEY."
29 | runpodctl config --apiKey "$API_KEY"
30 |
31 | echo "runpodctl installed and configured."
32 |
--------------------------------------------------------------------------------
/rick-llm/ollama_files/Modelfile:
--------------------------------------------------------------------------------
1 | FROM unsloth.Q8_0.gguf
2 |
3 | TEMPLATE """{{ if .System }}<|im_start|>system
4 | {{ .System }}<|im_end|>{{ end }}{{ if .Prompt }}
5 | <|im_start|>user
6 | {{ .Prompt }}<|im_end|>{{ end }}
7 | <|im_start|>assistant
8 | {{ .Response }}<|im_end|><|end_of_text|>"""
9 |
10 | PARAMETER stop "<|finetune_right_pad_id|>"
11 | PARAMETER stop "<|eot_id|>"
12 | PARAMETER stop "<|end_header_id|>"
13 | PARAMETER stop "<|start_header_id|>"
14 | PARAMETER stop "<|python_tag|>"
15 | PARAMETER stop "<|eom_id|>"
16 | PARAMETER stop "<|end_of_text|>"
17 | PARAMETER stop "<|im_end|>"
18 | PARAMETER stop "<|reserved_special_token_"
19 | PARAMETER temperature 0.8
20 | PARAMETER min_p 0.1
21 | SYSTEM "You are an interdimensional genius scientist named Rick Sanchez.
22 | Be brutally honest, use sharp wit, and sprinkle in some scientific jargon.
23 | Don't shy away from dark humor or existential truths, but always provide a solution (even if it's unconventional)."
--------------------------------------------------------------------------------
/vector-twin/cloudbuild.yaml:
--------------------------------------------------------------------------------
1 | steps:
2 | # Build the container image
3 | - name: 'gcr.io/cloud-builders/docker'
4 | args: ['build', '-t', '$LOCATION-docker.pkg.dev/$PROJECT_ID/vector-twin-app/app:latest', '-f', 'src/app/Dockerfile', '.']
5 | # Push the container image to Artifact Registry
6 | - name: 'gcr.io/cloud-builders/docker'
7 | args: ['push', '$LOCATION-docker.pkg.dev/$PROJECT_ID/vector-twin-app/app:latest']
8 | # Deploy container image to Cloud Run
9 | - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk'
10 | entrypoint: gcloud
11 | args: ['run', 'deploy', 'vector-twin',
12 | '--image', '$LOCATION-docker.pkg.dev/$PROJECT_ID/vector-twin-app/app:latest',
13 | '--port', '8501',
14 | '--region', '$LOCATION',
15 | '--platform', 'managed',
16 | '--project', '$PROJECT_ID',
17 | '--memory', '2Gi',
18 | '--update-secrets', 'QDRANT_API_KEY=QDRANT_API_KEY:latest,QDRANT_URL=QDRANT_URL:latest'
19 | ]
20 | images:
21 | - $LOCATION-docker.pkg.dev/$PROJECT_ID/vector-twin-app/app:latest
--------------------------------------------------------------------------------
/rick-llm/src/rick_llm/constants.py:
--------------------------------------------------------------------------------
1 | MAX_SEQ_LENGTH = 2048
2 |
3 | MODEL_CONFIG = {
4 | "model_name": "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
5 | "load_in_4bit": False,
6 | "dtype": None,
7 | }
8 |
9 | PEFT_CONFIG = {
10 | "r": 32,
11 | "lora_alpha": 64,
12 | "lora_dropout": 0,
13 | "target_modules": [
14 | "q_proj",
15 | "k_proj",
16 | "v_proj",
17 | "up_proj",
18 | "down_proj",
19 | "o_proj",
20 | "gate_proj",
21 | ],
22 | "use_rslora": True,
23 | "use_gradient_checkpointing": "unsloth",
24 | }
25 |
26 | TRAINING_ARGS = {
27 | "learning_rate": 2e-4,
28 | "lr_scheduler_type": "linear",
29 | "per_device_train_batch_size": 4,
30 | "gradient_accumulation_steps": 4,
31 | "num_train_epochs": 5,
32 | "logging_steps": 1,
33 | "optim": "adamw_8bit",
34 | "weight_decay": 0.01,
35 | "warmup_steps": 5,
36 | "output_dir": "output",
37 | "seed": 0,
38 | "report_to": "none",
39 | }
40 |
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/qdrant/client.py:
--------------------------------------------------------------------------------
1 | from functools import lru_cache
2 |
3 | from qdrant_client import QdrantClient # type: ignore
4 | from vector_twin.settings import settings # type: ignore
5 |
6 |
7 | @lru_cache(maxsize=1)
8 | def get_qdrant_client(use_qdrant_cloud: bool = True) -> QdrantClient:
9 | """Gets a configured Qdrant client instance.
10 |
11 | Args:
12 | use_qdrant_cloud (bool, optional): Whether to connect to Qdrant Cloud or local instance.
13 | Defaults to True.
14 |
15 | Returns:
16 | QdrantClient: Configured Qdrant client connected to either cloud or local instance.
17 | """
18 | if use_qdrant_cloud:
19 | return QdrantClient(
20 | url=settings.QDRANT_URL,
21 | port=settings.QDRANT_PORT,
22 | api_key=settings.QDRANT_API_KEY
23 | )
24 | else:
25 | return QdrantClient(
26 | host=settings.QDRANT_HOST,
27 | port=settings.QDRANT_PORT
28 | )
29 |
--------------------------------------------------------------------------------
/vector-twin/pyproject.toml:
--------------------------------------------------------------------------------
1 | [tool.poetry]
2 | name = "vector-twin"
3 | version = "0.1.0"
4 | description = "Find your Twin Celebrity in Vector Space"
5 | authors = ["MichaelisTrofficus "]
6 | license = "MIT"
7 | readme = "README.md"
8 |
9 | [tool.poetry.dependencies]
10 | python = ">=3.11,<3.13"
11 | facenet-pytorch = "^2.6.0"
12 | python-dotenv = "^1.0.1"
13 | qdrant-client = "^1.12.1"
14 | datasets = "^3.1.0"
15 | zenml = {extras = ["server"], version = "^0.68.1"}
16 | matplotlib = "^3.9.2"
17 | tqdm = "^4.67.0"
18 | poethepoet = "^0.30.0"
19 | pre-commit = "^4.0.1"
20 | click = "8.1.3"
21 | google-cloud-storage = "^2.18.2"
22 | fastapi-cache = "^0.1.0"
23 | fastapi-cache2 = "^0.2.2"
24 | redis = "^5.2.0"
25 | pydantic-settings = "^2.6.1"
26 | opencv-python = "^4.10.0.84"
27 | streamlit = "^1.40.1"
28 |
29 |
30 | [tool.poetry.group.dev.dependencies]
31 | jupyter = "^1.1.1"
32 |
33 | [build-system]
34 | requires = ["poetry-core"]
35 | build-backend = "poetry.core.masonry.api"
36 |
--------------------------------------------------------------------------------
/locust-guide/src/api/main.py:
--------------------------------------------------------------------------------
1 | from fastapi import FastAPI, HTTPException
2 | from .schemas import TextInput, SentimentOutput
3 |
4 | from transformers import pipeline
5 |
6 | from functools import lru_cache
7 |
8 |
9 | @lru_cache(maxsize=1)
10 | def get_sentiment_analyzer():
11 | """Initialize and cache the sentiment analysis pipeline"""
12 | return pipeline("sentiment-analysis")
13 |
14 | app = FastAPI()
15 |
16 | @app.get("/")
17 | async def root():
18 | return {"message": "This is an example API"}
19 |
20 | @app.post("/predict-sentiment")
21 | async def predict_sentiment(input: TextInput) -> SentimentOutput:
22 | try:
23 | sentiment_analyzer = get_sentiment_analyzer()
24 | result = sentiment_analyzer(input.text)
25 | sentiment = result[0]
26 | return SentimentOutput(
27 | label=sentiment["label"],
28 | score=sentiment["score"]
29 | )
30 | except Exception as e:
31 | raise HTTPException(status_code=500, detail="Error processing request") from e
32 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/data/metadata.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "pdf_name": "JRCR-18-8-5353.pdf",
4 | "theme": "subcutaneous forehead",
5 | "author": "Levi Sanchez Ramirez",
6 | "year": 2024
7 | },
8 | {
9 | "pdf_name": "JRCR-18-9-5436.pdf",
10 | "theme": "pancreatitis",
11 | "author": "Thamir Hashim",
12 | "year": 2024
13 | },
14 | {
15 | "pdf_name": "JRCR-19-1-5506.pdf",
16 | "theme": "bipartite scaphoide",
17 | "author": "Cherif Zaitouni O",
18 | "year": 2025
19 | },
20 | {
21 | "pdf_name": "JRCR-19-4-5651.pdf",
22 | "theme": "breast cancer",
23 | "author": "Mark Andrew Rowley",
24 | "year": 2025
25 | },
26 | {
27 | "pdf_name": "JRCR-19-4-5693.pdf",
28 | "theme": "liver gallstones",
29 | "author": "Lianhai Zhao1",
30 | "year": 2025
31 | },
32 | {
33 | "pdf_name": "JRCR-19-5-5719.pdf",
34 | "theme": "thyroid cancer",
35 | "author": "Yan Zhihua",
36 | "year": 2025
37 | }
38 | ]
39 |
40 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/document_processor/pdf_to_png.py:
--------------------------------------------------------------------------------
1 | from pdf2image import convert_from_path
2 | import os
3 | from dotenv import load_dotenv
4 |
5 | load_dotenv(".env")
6 |
7 | DATA_FOLDER_PATH = os.getenv("DATA_FOLDER_PATH")
8 |
9 | PDF_FOLDER_PATH = os.path.join(DATA_FOLDER_PATH, "pdf")
10 | PNG_FOLDER_PATH = os.path.join(DATA_FOLDER_PATH, "png")
11 |
12 | for pdf_file in os.listdir(PDF_FOLDER_PATH):
13 |
14 | if pdf_file.lower().endswith(".pdf"):
15 |
16 | pdf_path = os.path.join(PDF_FOLDER_PATH, pdf_file)
17 | pdf_base_name = os.path.splitext(pdf_file)[0]
18 |
19 | try:
20 | pages = convert_from_path(pdf_path, dpi=200)
21 |
22 | for i, page in enumerate(pages, start=1):
23 | output_filename = f"{pdf_base_name}_{i}.png"
24 | output_path = os.path.join(PNG_FOLDER_PATH, output_filename)
25 | page.save(output_path, "PNG")
26 | print(f"Saved: {output_filename}")
27 | except Exception as e:
28 | print(f"Processing error: {pdf_file}: {e}")
29 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2024 The Neural Maze
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
--------------------------------------------------------------------------------
/vector-twin/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2024 The Neural Maze
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
--------------------------------------------------------------------------------
/rick-llm/src/download_model.py:
--------------------------------------------------------------------------------
1 | import os
2 | from huggingface_hub import hf_hub_download
3 |
4 |
5 | def download_model(repo_id, filename, dest_folder="ollama_files"):
6 | """
7 | Download a model file from Hugging Face Hub.
8 |
9 | Args:
10 | repo_id (str): The Hugging Face repository ID (e.g., "theneuralmaze/RickLLama-3.1-8B")
11 | filename (str): The name of the file to download
12 | dest_folder (str): The destination folder
13 | """
14 | os.makedirs(dest_folder, exist_ok=True)
15 |
16 | try:
17 | downloaded_path = hf_hub_download(
18 | repo_id=repo_id,
19 | filename=filename,
20 | local_dir=dest_folder,
21 | local_dir_use_symlinks=False,
22 | )
23 | print(f"Successfully downloaded {filename} to {downloaded_path}")
24 | except Exception as e:
25 | print(f"Error downloading model: {e}")
26 |
27 |
28 | if __name__ == "__main__":
29 | # Based on the repository shown in the finetune.py file
30 | repo_id = "theneuralmaze/RickLLama-3.1-8B"
31 |
32 | # Download the GGUF model file
33 | download_model(repo_id, "unsloth.Q8_0.gguf")
34 |
--------------------------------------------------------------------------------
/vector-twin/Makefile:
--------------------------------------------------------------------------------
1 | # ZenML project configuration and pipeline execution
2 | .PHONY: configure-zenml run-cloud run-local clean start-local-qdrant stop-local-qdrant
3 |
4 | # Configure ZenML project and stack
5 | configure-zenml:
6 | @echo "Configuring ZenML project..."
7 | cd src && zenml init && zenml stack set default
8 | @echo "Deleting existing ZenML secrets..."
9 | PYTHONPATH=src python src/vector_twin/scripts/delete_zenml_secrets.py
10 | @echo "Creating new ZenML secrets..."
11 | PYTHONPATH=src python src/vector_twin/scripts/create_zenml_secrets.py
12 |
13 | # Run local app
14 | start-local-app: configure-zenml
15 | @echo "Starting local Qdrant instance..."
16 | docker compose -f docker-compose.yml up -d
17 | @echo "Running embedding pipeline with local Qdrant..."
18 | PYTHONPATH=src python src/embedding_pipeline/run.py
19 |
20 | # Stop local app
21 | stop-local-app:
22 | @echo "Stopping local Qdrant instance..."
23 | docker compose -f docker-compose.yml down
24 |
25 | # Run prod app
26 | insert-embeddings-qdrant-cloud: configure-zenml
27 | @echo "Running embedding pipeline with Qdrant Cloud..."
28 | PYTHONPATH=src python src/embedding_pipeline/run.py --use-qdrant-cloud
29 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/src/vision_model_loader.py:
--------------------------------------------------------------------------------
1 | import os
2 | import torch
3 | from transformers import ColQwen2ForRetrieval, ColQwen2Processor
4 | from transformers.utils.import_utils import is_flash_attn_2_available
5 | from dotenv import load_dotenv
6 |
7 | load_dotenv("colqwen_qdrant_meetup/.env")
8 |
9 | _colqwen_model = None
10 | _colqwen_processor = None
11 |
12 | COLQWEN_MODEL = os.getenv("COLQWEN_MODEL")
13 |
14 | device = "cuda" if torch.cuda.is_available() else "cpu"
15 |
16 | def get_colqwen_model():
17 | global _colqwen_model
18 | if _colqwen_model is None:
19 | _colqwen_model = ColQwen2ForRetrieval.from_pretrained(
20 | COLQWEN_MODEL,
21 | torch_dtype=torch.bfloat16,
22 | device_map=device,
23 | attn_implementation="flash_attention_2" if is_flash_attn_2_available() else "sdpa",
24 | )
25 | return _colqwen_model
26 |
27 | def get_colqwen_processor():
28 | global _colqwen_processor
29 | if _colqwen_processor is None:
30 | _colqwen_processor = ColQwen2Processor.from_pretrained(
31 | COLQWEN_MODEL,
32 | device_map=device
33 | )
34 | return _colqwen_processor
--------------------------------------------------------------------------------
/rick-llm/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: lambda-commands
2 |
3 | create-hf-dataset:
4 | echo "Creating HF dataset"
5 | python src/dataset.py
6 |
7 | generate-ssh-key:
8 | python src/lambda/commands.py generate-ssh-key
9 |
10 | list-ssh-keys:
11 | python src/lambda/commands.py list-ssh-keys
12 |
13 | list-instances:
14 | python src/lambda/commands.py list-instances
15 |
16 | list-instance-types:
17 | python src/lambda/commands.py list-types
18 |
19 | get-lambda-ip:
20 | python src/lambda/commands.py get-ip
21 |
22 | launch-lambda-instance:
23 | python src/lambda/commands.py launch
24 |
25 | lambda-help:
26 | python src/lambda/commands.py
27 |
28 | lambda-setup:
29 | echo "Installing dependencies"
30 | sudo apt update && sudo apt upgrade -y
31 | sudo apt install curl libcurl4-openssl-dev -y
32 | pip install -r requirements_lambda.txt -q
33 | pip install torchvision@https://download.pytorch.org/whl/cu121/torchvision-0.20.1%2Bcu121-cp310-cp310-linux_x86_64.whl
34 | pip install transformers==4.47.1
35 |
36 | finetune:
37 | echo "Finetuning Rick LLM"
38 | python src/rick_llm/finetune.py
39 |
40 | terminate-instance:
41 | python src/lambda/commands.py terminate
42 |
43 | download-model:
44 | echo "Downloading model files"
45 | python src/download_model.py
46 |
--------------------------------------------------------------------------------
/vector-twin/src/embedding_pipeline/steps/embedding_generation.py:
--------------------------------------------------------------------------------
1 | from uuid import uuid4
2 |
3 | from datasets import Dataset # type: ignore
4 | from tqdm import tqdm # type: ignore
5 | from zenml import step
6 | from zenml.logger import get_logger
7 |
8 | from vector_twin.models import initialize_models, process_single_image
9 | from vector_twin.qdrant import get_qdrant_client, create_collection, insert_image_embedding
10 |
11 |
12 | logger = get_logger(__name__)
13 |
14 |
15 | @step
16 | def generate_embeddings(dataset: Dataset, use_qdrant_cloud: bool = True) -> None:
17 | """Generates embeddings for the dataset and stores them in Qdrant vector database.
18 |
19 | Args:
20 | dataset: Dataset containing celebrity images and labels
21 | use_qdrant_cloud: If True, connects to Qdrant Cloud using URL and API key.
22 | If False, connects to local Qdrant instance.
23 | """
24 | qdrant_client = get_qdrant_client(use_qdrant_cloud)
25 | create_collection(qdrant_client)
26 |
27 | device, mtcnn, resnet = initialize_models()
28 |
29 | for row in tqdm(dataset):
30 | img_embedding = process_single_image(row["image"], device, mtcnn, resnet)
31 | insert_image_embedding(qdrant_client, img_embedding, str(uuid4()), row['label'])
32 |
--------------------------------------------------------------------------------
/vector-twin/src/embedding_pipeline/run.py:
--------------------------------------------------------------------------------
1 | import click
2 | from pipeline import embedding_pipeline # type: ignore
3 |
4 |
5 | @click.command(
6 | help="""
7 | ZenML Starter project.
8 |
9 | Run the ZenML starter project with basic options.
10 |
11 | Examples:
12 |
13 | # Run the training pipeline
14 | python run.py
15 | """
16 | )
17 | @click.option(
18 | "--enable-cache",
19 | is_flag=True,
20 | default=False,
21 | help="Enabling caching for the pipeline run.",
22 | )
23 | @click.option(
24 | "--use-qdrant-cloud",
25 | is_flag=True,
26 | default=False,
27 | help="Whether to use Qdrant Cloud or local Qdrant",
28 | )
29 | def main(
30 | use_qdrant_cloud: bool,
31 | enable_cache: bool = False,
32 | ):
33 | pipeline_args: dict = {}
34 |
35 | if not enable_cache:
36 | pipeline_args["enable_cache"] = False
37 |
38 | if use_qdrant_cloud:
39 | pipeline_args["config_path"] = "src/embedding_pipeline/configs/embedding_generation_qdrant_cloud.yaml"
40 | else:
41 | pipeline_args["config_path"] = "src/embedding_pipeline/configs/embedding_generation_local.yaml"
42 |
43 |
44 | embedding_pipeline.with_options(**pipeline_args)(use_qdrant_cloud=use_qdrant_cloud)
45 |
46 |
47 | if __name__ == "__main__":
48 | main()
49 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/Makefile:
--------------------------------------------------------------------------------
1 | .PHONY: setup-vision-rag pre-setup pdf2png png2qdrant setup-pod ssh ollama lint
2 |
3 | setup-vision-rag:
4 | @bash infrastructure/bash_scripts/setup.sh
5 |
6 | pre-setup:
7 | @bash infrastructure/bash_scripts/pre_setup.sh
8 |
9 | pdf2png:
10 | @echo "Processing .pdf files to .png"
11 | @python document_processor/pdf_to_png.py
12 |
13 | png2qdrant:
14 | @echo "Transforming .png files into tensors and indexing in Qdrant collection."
15 | @python document_processor/png_to_qdrant.py
16 |
17 | setup-pod:
18 | @echo "🔑 Verificando claves SSH..."
19 | @bash infrastructure/bash_scripts/ssh_keys.sh
20 | @echo "🚀 Creando pod..."
21 | @python infrastructure/python_scripts/create_runpod.py
22 | @echo "⏳ Esperando que el pod inicie..."
23 | @sleep 30
24 | @echo "⚙️ Configurando SSH..."
25 | @python infrastructure/python_scripts/config_ssh.py
26 | @echo "📦 Instalando y configurando runpodctl..."
27 | @bash infrastructure/bash_scripts/install_runpodctl.sh
28 | @echo "🖥️ Abriendo VSCode conectado al pod remoto..."
29 | @positron --remote ssh-remote+runpod-pytorch
30 | @echo "Send code"
31 | @bash infrastructure/bash_scripts/send_code.sh
32 |
33 | ssh:
34 | @ssh runpod-pytorch
35 |
36 | ollama:
37 | @bash infrastructure/bash_scripts/ollama.sh
38 |
39 |
40 |
41 |
42 |
43 |
44 |
45 |
46 |
47 |
48 |
49 |
--------------------------------------------------------------------------------
/linkedin-influencer-agent/tasks.py:
--------------------------------------------------------------------------------
1 | from crewai import Task
2 | from textwrap import dedent
3 | from agents import linkedin_scraper_agent, web_researcher_agent, doppelganger_agent
4 |
5 |
6 | scrape_linkedin_task = Task(
7 | description=dedent(
8 | "Scrape a LinkedIn profile to get some relevant posts"),
9 | expected_output=dedent("A list of LinkedIn posts obtained from a LinkedIn profile"),
10 | agent=linkedin_scraper_agent,
11 | )
12 |
13 | web_research_task = Task(
14 | description=dedent(
15 | "Get valuable and high quality web information about the comparison between Llama 2 and Llama 3"),
16 | expected_output=dedent("Your task is to gather high quality information about the comparison"
17 | " between Llama 2 and Llama 3"),
18 | agent=web_researcher_agent,
19 | )
20 |
21 | create_linkedin_post_task = Task(
22 | description=dedent(
23 | "Create a LinkedIn post comparing Llama 2 and Llama 3 following the writing-style "
24 | "expressed in the scraped LinkedIn posts."
25 | ),
26 | expected_output=dedent("A high-quality and engaging LinkedIn post comparing Llama 2 and Llama 3."
27 | " The LinkedIn post must follow"
28 | " the same writing-style as the one expressed in the scraped LinkedIn posts"),
29 | agent=doppelganger_agent,
30 | )
31 |
32 | create_linkedin_post_task.context = [scrape_linkedin_task, web_research_task]
33 |
--------------------------------------------------------------------------------
/arduino-agent/agents.py:
--------------------------------------------------------------------------------
1 | import os
2 | from textwrap import dedent
3 |
4 | from crewai import Agent
5 | from langchain_openai import ChatOpenAI
6 |
7 | from tools.arduino import CompileAndUploadToArduinoTool
8 |
9 | tool = CompileAndUploadToArduinoTool(
10 | ino_file_dir="./tmp",
11 | board_fqbn="arduino:avr:uno",
12 | port="/dev/cu.usbmodem1201"
13 | )
14 |
15 | llama3 = ChatOpenAI(
16 | model="llama3",
17 | base_url="http://localhost:11434/v1")
18 | openai_llm = ChatOpenAI(api_key=os.environ.get("OPENAI_API_KEY"), model="gpt-3.5-turbo-0125")
19 |
20 |
21 | sketch_programmer_agent = Agent(
22 | role="Sketch Programmer",
23 | goal=dedent(
24 | """Write a Sketch script for Arduino that lights a red led in digital pin 11,
25 | a blue led in digital pin 10 and a green led in digital pin 9 with a time
26 | between the three of 1 second."""),
27 | backstory=dedent(
28 | """
29 | You are an experienced Sketch programmer who really enjoys programming Arduinos
30 | """
31 | ),
32 | verbose=True,
33 | allow_delegation=False,
34 | llm=llama3,
35 | )
36 |
37 | arduino_uploader_agent = Agent(
38 | role="Arduino Uploader Agent",
39 | goal="Your goal is to compile and upload the received arduino script using a tool",
40 | backstory=dedent(
41 | """
42 | You are a hardware geek.
43 | """
44 | ),
45 | verbose=True,
46 | allow_delegation=False,
47 | tools=[tool]
48 | )
49 |
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/settings.py:
--------------------------------------------------------------------------------
1 | from pydantic_settings import BaseSettings, SettingsConfigDict
2 |
3 | try:
4 | from zenml.client import Client
5 | from zenml.exceptions import EntityExistsError
6 |
7 | ZENML_ENVIRONMENT = True
8 |
9 | except ImportError:
10 | ZENML_ENVIRONMENT = False
11 |
12 |
13 | class Settings(BaseSettings):
14 | model_config = SettingsConfigDict(env_file=".env", extra="ignore", env_file_encoding="utf-8")
15 |
16 | # Qdrant URL, port and API key
17 | QDRANT_HOST: str = "localhost"
18 | QDRANT_URL: str = ""
19 | QDRANT_PORT: int = 6333
20 | QDRANT_API_KEY: str | None = None
21 |
22 | # Qdrant Collection (for both local and cloud)
23 | QDRANT_COLLECTION_NAME: str = "celebrities"
24 | QDRANT_VECTOR_DIMENSIONS: int = 512
25 |
26 | # Hugging Face Dataset Name
27 | DATASET_NAME: str = "lansinuote/simple_facenet"
28 |
29 | @classmethod
30 | def load_settings(cls):
31 | """Load settings from ZenML secret store if available."""
32 | try:
33 | client = Client()
34 | secrets = client.get_secret("qdrant")
35 | if "QDRANT_URL" in secrets.secret_values:
36 | cls.QDRANT_URL = secrets.secret_values["QDRANT_URL"]
37 | if "QDRANT_PORT" in secrets.secret_values:
38 | cls.QDRANT_PORT = int(secrets.secret_values["QDRANT_PORT"])
39 | except EntityExistsError:
40 | # Secret not found, use default values
41 | pass
42 | return cls()
43 |
44 |
45 | if ZENML_ENVIRONMENT:
46 | settings = Settings.load_settings()
47 | else:
48 | settings = Settings()
49 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/infrastructure/bash_scripts/pre_setup.sh:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 |
3 | set -e
4 |
5 | # Rust install for uv usage
6 | curl https://sh.rustup.rs -sSf | sh -s -- -y
7 |
8 | # Rust path for uv shortcuts
9 | export PATH="$HOME/.cargo/bin:$HOME/.local/bin:$PATH"
10 | source "$HOME/.cargo/env"
11 |
12 | # System packages
13 | sudo apt update && sudo apt install -y unzip zip \
14 | make build-essential libssl-dev zlib1g-dev \
15 | libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
16 | libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev \
17 | libffi-dev liblzma-dev
18 |
19 | # uv installation
20 | if ! command -v uv &> /dev/null; then
21 | curl -LsSf https://astral.sh/uv/install.sh | sh
22 | export PATH="$HOME/.local/bin:$PATH"
23 | command -v uv >/dev/null || { echo "uv is not in PATH."; exit 1; }
24 | else
25 | echo "uv already installed"
26 | fi
27 |
28 | # pyproject.toml
29 | if [ ! -f pyproject.toml ]; then
30 | echo "pyproject.toml not found."
31 | exit 1
32 | fi
33 |
34 | # uv dependencies in pyproject.toml
35 | uv sync
36 |
37 | # Virtual environment activation
38 | if [ -f .venv/bin/activate ]; then
39 | source .venv/bin/activate
40 | echo "Virtual environment activated."
41 | else
42 | echo "Virtual environment not found."
43 | exit 1
44 | fi
45 |
46 | # dev tools installation
47 | uv add --dev ruff pre-commit
48 |
49 | export PYTHONPATH=/root
50 | echo 'export PYTHONPATH=/root' >> ~/.bashrc
51 |
52 | # pre-commit installation
53 | if [ -f .pre-commit-config.yaml ]; then
54 | pre-commit install
55 | echo "pre-commit installed."
56 | else
57 | echo ".pre-commit-config.yaml not found."
58 | fi
--------------------------------------------------------------------------------
/linkedin-influencer-agent/tools/utils.py:
--------------------------------------------------------------------------------
1 | from bs4 import BeautifulSoup
2 |
3 |
4 | def parse_html_content(page_source: str):
5 | """
6 | Parses the HTML from the LinkedIn's profile and returns a collection of LinkedIn posts. We don't need
7 | all of them, just a few, since we can get the "writing-style" very easily.
8 |
9 | Args:
10 | page_source: The HTML content
11 |
12 | Returns:
13 | A list of div containers representing a collection of LinkedIn posts
14 | """
15 | linkedin_soup = BeautifulSoup(page_source.encode("utf-8"), "lxml")
16 |
17 | containers = linkedin_soup.find_all("div", {"class": "feed-shared-update-v2"})
18 | containers = [container for container in containers if 'activity' in container.get('data-urn', '')]
19 | return containers
20 |
21 |
22 | def get_post_content(container, selector, attributes):
23 | """
24 | Gets the content of a LinkedIn post container
25 | Args:
26 | container: The div container
27 | selector: The selector
28 | attributes: Attributes to be fetched
29 |
30 | Returns:
31 | The post content
32 | """
33 | try:
34 | element = container.find(selector, attributes)
35 | if element:
36 | return element.text.strip()
37 | except Exception as e:
38 | print(e)
39 | return ""
40 |
41 |
42 | def get_linkedin_posts(page_source: str):
43 | containers = parse_html_content(page_source)
44 | posts = []
45 |
46 | for container in containers:
47 | post_content = get_post_content(container, "div", {"class": "update-components-text"})
48 | posts.append(post_content)
49 |
50 | return posts
51 |
--------------------------------------------------------------------------------
/arduino-agent/tools/arduino.py:
--------------------------------------------------------------------------------
1 | import re
2 | import subprocess
3 |
4 | from crewai_tools import BaseTool
5 |
6 |
7 | class CompileAndUploadToArduinoTool(BaseTool):
8 | name: str = "CompileAndUploadToArduinoTool"
9 | description: str = "Compiles and Uploads an Arduino Sketch script to an Arduino"
10 | ino_file_dir: str = "The directory that contains the ino file"
11 | board_fqbn: str = "The board type, e.g. 'arduino:avr:uno'"
12 | port: str = "The port where the Arduino is connected"
13 |
14 | def __init__(self, ino_file_dir: str, board_fqbn: str, port: str, **kwargs):
15 | super().__init__(**kwargs)
16 | self.ino_file_dir = ino_file_dir
17 | self.board_fqbn = board_fqbn
18 | self.port = port
19 |
20 | def _fix_ino_file(self):
21 | """
22 | This is a helper method for fixing the output .ino file when Llama3 adds some unintended text
23 | that invalidates the compilation.
24 | """
25 | with open(f"{self.ino_file_dir}/tmp.ino", "r") as f:
26 | content = f.read()
27 |
28 | pattern = r'```.*?\n(.*?)```'
29 | match = re.search(pattern, content, re.DOTALL).group(1).strip()
30 |
31 | with open(f"{self.ino_file_dir}/tmp.ino", "w") as f:
32 | f.write(match)
33 |
34 | def _run(self):
35 | self._fix_ino_file()
36 |
37 | try:
38 | subprocess.check_call([
39 | "arduino-cli", "compile", "--fqbn", self.board_fqbn, self.ino_file_dir
40 | ])
41 | subprocess.check_call([
42 | "arduino-cli", "upload", "--port", self.port, "--fqbn", self.board_fqbn, self.ino_file_dir
43 | ])
44 | except subprocess.CalledProcessError:
45 | return "Compilation failed"
46 |
47 | return "Code successfully uploaded to the board"
48 |
--------------------------------------------------------------------------------
/rick-llm/src/rick_llm/trainer.py:
--------------------------------------------------------------------------------
1 | from datasets import load_dataset
2 | from transformers import TrainingArguments
3 | from trl import SFTTrainer
4 | from constants import MAX_SEQ_LENGTH, TRAINING_ARGS
5 | from exceptions import UnslothNotInstalledError
6 |
7 | try:
8 | from unsloth import standardize_sharegpt, apply_chat_template, is_bfloat16_supported # type: ignore
9 | except ImportError:
10 | raise UnslothNotInstalledError
11 |
12 |
13 | class ModelTrainer:
14 | def __init__(self, model, tokenizer):
15 | self.model = model
16 | self.tokenizer = tokenizer
17 |
18 | def _load_dataset(self):
19 | dataset = load_dataset(
20 | "theneuralmaze/rick-and-morty-transcripts-sharegpt", split="train"
21 | )
22 | return standardize_sharegpt(dataset)
23 |
24 | def _prepare_dataset(self, dataset):
25 | chat_template = """<|im_start|>system
26 | {SYSTEM}<|im_end|>
27 | <|im_start|>user
28 | {INPUT}<|im_end|>
29 | <|im_start|>assistant
30 | {OUTPUT}<|im_end|>"""
31 |
32 | return apply_chat_template(
33 | dataset,
34 | tokenizer=self.tokenizer,
35 | chat_template=chat_template,
36 | )
37 |
38 | def setup_trainer(self):
39 | dataset = self._load_dataset()
40 | processed_dataset = self._prepare_dataset(dataset)
41 |
42 | training_args = TrainingArguments(
43 | **TRAINING_ARGS,
44 | fp16=not is_bfloat16_supported(),
45 | bf16=is_bfloat16_supported(),
46 | )
47 |
48 | return SFTTrainer(
49 | model=self.model,
50 | tokenizer=self.tokenizer,
51 | train_dataset=processed_dataset,
52 | dataset_text_field="text",
53 | max_seq_length=MAX_SEQ_LENGTH,
54 | dataset_num_proc=2,
55 | packing=True,
56 | args=training_args,
57 | )
58 |
--------------------------------------------------------------------------------
/vector-twin/src/embedding_pipeline/steps/hf_datasets.py:
--------------------------------------------------------------------------------
1 |
2 | from datasets import (Dataset, load_dataset, # type: ignore
3 | load_dataset_builder)
4 | from zenml import step
5 | from zenml.logger import get_logger
6 |
7 | from settings import settings
8 |
9 | logger = get_logger(__name__)
10 |
11 | @step
12 | def load_hf_dataset(dataset_name: str = "lansinuote/simple_facenet") -> Dataset:
13 | """Loads and returns the HuggingFace dataset for facial recognition.
14 |
15 | This function loads the dataset specified by DATASET_NAME constant using the
16 | HuggingFace datasets library. It prints information about available splits
17 | and the size of the training dataset.
18 |
19 | Args:
20 | dataset_name: The name of the dataset to load.
21 |
22 | Returns:
23 | dict: A dictionary containing the loaded dataset with 'train' as the key
24 | and the corresponding dataset object as the value.
25 | """
26 | dataset_builder = load_dataset_builder(settings.DATASET_NAME)
27 | logger.info(f"Available splits:\n{dataset_builder.info.splits}")
28 |
29 | dataset = load_dataset(dataset_name, split="train")
30 | logger.info(f"Number of samples in the dataset: {len(dataset)}")
31 |
32 | return dataset
33 |
34 |
35 | @step
36 | def sample_dataset(dataset: Dataset, sample_size: int = 3000) -> Dataset:
37 | """Creates a random sample of the given dataset.
38 |
39 | Args:
40 | dataset (Dataset): The input dataset to sample from.
41 | sample_size (int, optional): Number of samples to select. Defaults to 3000.
42 |
43 | Returns:
44 | Dataset: A new dataset containing the random sample.
45 | """
46 | sampled_dataset = dataset.shuffle(seed=42).select(range(sample_size))
47 | logger.info(f"Number of samples in the sampled dataset: {len(sampled_dataset)}")
48 |
49 | return sampled_dataset
50 |
--------------------------------------------------------------------------------
/arduino-agent/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
When CrewAI meets Arduino
4 | CrewAI + Llama3 + Ollama team up to program my Arduino UNO
5 |
6 |
7 | ---
8 |
9 | ## Description
10 |
11 | A funny project where we'll connecting [crewAI] with an Arduino. The final
12 | crew consists of [two agents](agents.py).
13 |
14 | 1️⃣ Sketch Programmer Agent
15 |
16 | It receives a circuit description and its expected behaviour (e.g. light on and off a LED repeatedly) generating,
17 | in return, a sketch file (the script containing the code for sending instructions to Arduino).
18 |
19 | 2️⃣ Web Researcher
20 |
21 | Takes the generated sketch file, compiles it and uploads the instructions into the Arduino. To achieve its goal, this agent uses a [custom tool](tools%2Farduino.py)
22 | for the compilation and uploading.
23 |
24 |
25 |
26 |
27 |
28 |
29 |
30 | ## Usage
31 |
32 | There are a couple of prerequisites for running this project:
33 |
34 | 1️⃣ Arduino board
35 |
36 | Since this project seeks to connect crewAI and Arduino ... you should
37 | have an Arduino 😅. In my case I was using an [Arduino UNO](https://store.arduino.cc/products/arduino-uno-rev3).
38 |
39 | 2️⃣ Arduino CLI
40 |
41 | Apart from the Arduino board, you'll also need to install the Arduino CLI. This is
42 | the CLI used by the custom tool for compiling and uploading the instructions to the Arduino.
43 |
44 | Check the installation guide [here](https://arduino.github.io/arduino-cli/0.35/installation/).
45 |
46 | 3️⃣ Project dependencies
47 |
48 | Finally, you need to install the project dependencies defined in the
49 | requirements file.
50 |
51 | ```
52 | pip install -r requirements.txt
53 | ```
54 |
55 | And, to run the kickoff the crew.
56 |
57 | ```
58 | python main.py
59 | ```
60 |
--------------------------------------------------------------------------------
/linkedin-influencer-agent/tools/linkedin.py:
--------------------------------------------------------------------------------
1 | import os
2 | import time
3 |
4 | from crewai_tools import tool
5 | from selenium import webdriver
6 | from selenium.webdriver.common.keys import Keys
7 |
8 | from tools.utils import get_linkedin_posts
9 |
10 |
11 | class LinkedinToolException(Exception):
12 | def __init__(self):
13 | super().__init__("You need to set the LINKEDIN_EMAIL and LINKEDIN_PASSWORD env variables")
14 |
15 |
16 | def scrape_linkedin_posts_fn() -> str:
17 | """
18 | A tool that can be used to scrape LinkedIn posts
19 | """
20 | linkedin_username = os.environ.get("LINKEDIN_EMAIL")
21 | linkedin_password = os.environ.get("LINKEDIN_PASSWORD")
22 | linkedin_profile_name = os.environ.get("LINKEDIN_PROFILE_NAME")
23 |
24 | if not (linkedin_username and linkedin_password):
25 | raise LinkedinToolException()
26 |
27 | browser = webdriver.Chrome()
28 | browser.get("https://www.linkedin.com/login")
29 |
30 | username_input = browser.find_element("id", "username")
31 | password_input = browser.find_element("id", "password")
32 | username_input.send_keys(linkedin_username)
33 | password_input.send_keys(linkedin_password)
34 | password_input.send_keys(Keys.RETURN)
35 |
36 | time.sleep(3)
37 |
38 | browser.get(f"https://www.linkedin.com/in/{linkedin_profile_name}/recent-activity/all/")
39 |
40 | for _ in range(2):
41 | browser.execute_script("window.scrollTo(0, document.body.scrollHeight);")
42 | time.sleep(2)
43 |
44 | posts = get_linkedin_posts(browser.page_source)
45 | browser.quit()
46 |
47 | # We'll just return 2 of the latest posts, since it should be enough for the LLM to get the overall style
48 | return str(posts[:2])
49 |
50 |
51 | @tool("ScrapeLinkedinPosts")
52 | def scrape_linkedin_posts_tool() -> str:
53 | """
54 | A tool that can be used to scrape LinkedIn posts
55 | """
56 | return scrape_linkedin_posts_fn()
57 |
--------------------------------------------------------------------------------
/vector-twin/src/vector_twin/models.py:
--------------------------------------------------------------------------------
1 | import logging
2 | from functools import lru_cache
3 | from uuid import uuid4
4 |
5 | import torch
6 | from facenet_pytorch import MTCNN, InceptionResnetV1 # type: ignore
7 |
8 | logger = logging.getLogger(__name__)
9 |
10 |
11 | @lru_cache(maxsize=1)
12 | def initialize_models() -> tuple[torch.device, MTCNN, InceptionResnetV1]:
13 | """Initializes and returns the required ML models and device for face recognition.
14 |
15 | This function sets up the device (CPU/GPU), initializes the MTCNN model for face detection,
16 | and loads a pre-trained InceptionResnetV1 model for generating face embeddings.
17 |
18 | Returns:
19 | tuple:
20 | device (torch.device): The torch device (CPU/GPU) to use for computations
21 | mtcnn (MTCNN): Initialized MTCNN model for face detection
22 | resnet (InceptionResnetV1): Pre-trained InceptionResnetV1 model for embeddings
23 | """
24 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
25 | mtcnn = MTCNN(image_size=160, margin=0, device=device)
26 | resnet = InceptionResnetV1(pretrained='vggface2').eval().to(device)
27 | return device, mtcnn, resnet
28 |
29 |
30 | def process_single_image(img, device: torch.device, mtcnn: MTCNN, resnet: InceptionResnetV1) -> None:
31 | """Process a single image and generates its embedding.
32 |
33 | Args:
34 | img: The celebrity image
35 | device: Torch device to use
36 | mtcnn: MTCNN model for face detection
37 | resnet: ResNet model for embedding generation
38 | """
39 | try:
40 | img_cropped = mtcnn(img)
41 | img_embedding = resnet(
42 | img_cropped.unsqueeze(0).to(device)
43 | ).squeeze(0).cpu().detach().numpy().tolist()
44 |
45 | return img_embedding
46 |
47 | except Exception as e:
48 | logger.warning(f"Failed to process image: {str(e)}")
49 |
--------------------------------------------------------------------------------
/vision-rag-complex-pdf/infrastructure/python_scripts/create_runpod.py:
--------------------------------------------------------------------------------
1 | import runpod
2 | import os
3 | import sys
4 | from dotenv import load_dotenv
5 | from pathlib import Path
6 |
7 | load_dotenv(".env")
8 |
9 | api_key = os.environ.get('RUNPOD_API_KEY')
10 |
11 | if not api_key:
12 | print("Error: RUNPOD_API_KEY not found in .env.")
13 | sys.exit(1)
14 |
15 |
16 | if not api_key.startswith('rpa_') or len(api_key) < 40:
17 | print("Invalid api key.")
18 | sys.exit(1)
19 |
20 | runpod.api_key = api_key.strip()
21 |
22 | ssh_public_key_path = Path.home() / ".ssh" / "id_ed25519.pub"
23 |
24 | if not ssh_public_key_path.exists():
25 | print("Not public SSH key found.")
26 | sys.exit(1)
27 |
28 | with open(ssh_public_key_path, 'r') as f:
29 | public_key = f.read().strip()
30 |
31 | # Pod configuration
32 |
33 | pod_config = {
34 | "name": "rtx6000-pytorch-pod",
35 | "image_name": "runpod/pytorch:2.1.0-py3.10-cuda11.8.0-devel-ubuntu22.04",
36 | "gpu_type_id": "NVIDIA RTX 6000 Ada Generation",
37 | "cloud_type": "ALL",
38 | "support_public_ip": True,
39 | "start_ssh": True,
40 | "container_disk_in_gb": 100,
41 | "volume_in_gb": 100,
42 | "min_vcpu_count": 1,
43 | "min_memory_in_gb": 1,
44 | "ports": "22/tcp",
45 | "volume_mount_path": "/workspace",
46 | "env": {"PUBLIC_KEY": public_key}
47 | }
48 |
49 | try:
50 |
51 | print("Creating pod...")
52 |
53 | response = runpod.create_pod(**pod_config)
54 |
55 | pod = None
56 |
57 | if isinstance(response, dict):
58 | pod = response
59 | elif isinstance(response, list) and len(response) > 0:
60 | pod = response[0]
61 |
62 | if pod and 'id' in pod:
63 | print(f"Created pod: {pod['id']}")
64 | else:
65 | print("Pod couldn't be created.")
66 | print(f"Response: {response}")
67 | sys.exit(1)
68 |
69 | except Exception as e:
70 | print(f"Error creating the pod: {str(e)}")
71 | sys.exit(1)
--------------------------------------------------------------------------------
/linkedin-influencer-agent/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
Automating LinkedIn Post
4 | crewAI automates my LinkedIn Posts
5 |
6 |
7 | ---
8 |
9 | > Looking for a more detailed explanation of this repository? You might be interested in the [YouTube video](https://www.youtube.com/watch?v=oIb5JqZ5ylA&ab_channel=TheNeuralMaze)! 😁
10 |
11 |
12 | ## Description
13 |
14 | This repository contains a crewAI application for generating LinkedIn posts automatically.
15 | The crew consists of [three agents](agents.py):
16 |
17 | 1️⃣ LinkedIn Scraper Ninja
18 |
19 | It uses a [Selenium custom tool](tools%2Flinkedin.py) to scrape my LinkedIn profile. I need to scrape my
20 | posts since I want some examples for the last agent to emulate my writing style. This tool needs some env variables,
21 | defined in an `.env` file (you can see an example [here](.env.example)).
22 |
23 | 2️⃣ Web Researcher
24 |
25 | It fetches relevant information about a given topic. In my case, I chose the recent release of Llama3 by Meta AI, but
26 | you can choose whatever you want (you'll need to modify the Tasks and Agents of course ...)
27 |
28 | 3️⃣ Influencer Agent
29 |
30 | This agent has to deal with the information gathered by the two previous agents and write a high quality and engaging
31 | LinkedIn post emulating my writing style.
32 |
33 |
34 |
35 |
36 |
37 |
38 |
39 | ## Usage
40 |
41 | First of all, install the necessary dependencies.
42 |
43 | ```shell
44 | pip install -r requirements.txt
45 | ```
46 |
47 | After all the dependencies are installed, run the `main.py`.
48 |
49 | > Keep in mind that you need to have all the necessary env variables in your `.env` file for this to work. Also, if you
50 | want to change the topic of your LinkedIn post, you'll need to modify the Agents and Tasks.
51 |
52 | ```shell
53 | python3 main.py
54 | ```
55 |
--------------------------------------------------------------------------------