# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


# embeddings_queue

created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833128-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan is currently working at Dassault Syst\u00e8mes as apprentice AI Research Scientist"}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833164-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "During his work/learning at Dassault Syst\u00e8mes, Ilan trains models, reads and applies articles, deploys LLM applications by optimizing inference and their size, in short a lot of research"}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da83327c-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan passions Reading, include photography, artificial intelligence, soccer, MMA, virtual photography, and programming since he was 13."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da8332a4-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan qualities are being very attentive, calm, humble, very curious, and above all, honest."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da83338a-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Concerning Data Engineering, theoretically, Ilan has a good understanding of DataLakes, Data Warehouse, Lake House, and Data Mesh."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da8333ee-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Concerning AI, Ilan has a very good command of the concepts relating to NLP, whether the basics or the more advanced recent techniques such as Transformers, MOE, RAG, quantization..."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da83340c-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan learned Machine/Deep Learning by reading and doing projects. This is why he masters advanced techniques."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833434-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan AI skills include excellent knowledge of NLP, from basic principles like TF-IDF and Word2Vec to advanced concepts such as RNN, LSTM, and the most recent: Transformers, their training, fine-tuning, and optimization of training or inference."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833452-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan aims to pursue a PhD in NLP, ideally focusing on Automated Program Repair (APR)."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833498-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "During his studies Ilan learned the fundamental principles of IT such as computer architecture, advanced algorithms, databases, machine learning etc... He was also able to develop solid skills in clean code and very important concepts in math and logic."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da8334c0-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan LinkedIn profile is https://www.linkedin.com/in/ilan-aliouchouche-752722277/."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da8334fc-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan projects can be found at https://github.com/ilanaliouchouche?tab=repositories."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833524-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's email are: ilanaitk@hotmail.com and ilan.aliouchouche@universite-paris-saclay.fr"}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833560-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has completed many computing projects, mainly personal ones, many of which concern Machine Learning (NLP)."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da83357e-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan favorite dishes are all the dishes his mother makes, especially couscous."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da8335c4-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan appreciates natural language processing in AI for its ability to humanize non-human entities and for the potential to be understood by something other than ourselves."}


created_at: 2024-03-21 09:39:11
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: da833664-e766-11ee-b31c-c0b6f9d4ff7b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's background / studies is a scientific baccalaureate then a license in computer science then currently a master's degree in Data Science"}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 756f9036-e4dc-439a-ae0f-5287dbd53179
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has a Bachelor's degree in Computer Science from Universit\u00e9 Paris-Saclay."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: a3734b12-a439-4b47-8615-8eaa448ff587
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan optimized a retrieval system in a RAG for Dassault Syst\u00e8mes using vector indexes, Matryoshka embeddings, and embedding quantization."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: ebf39034-fdc7-44c0-9357-1527c59aec72
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan developed a pipeline for detecting problematic content in influencer posts in collaboration with Professor Ammi."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 1a5ac990-4e15-4dd6-8b3f-203912388e45
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan created QuickDraw-API, an interactive game using a fine-tuned Vision Transformer, deployed with complete automation via GitHub Actions."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 172e65f3-a270-46ff-9229-46ec12eaba35
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan built a modular, CPU-friendly CV Chatbot using RAG and LLM GGUF for students."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 7641c99e-97de-4caa-9e1f-d1806188383b
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan researched and implemented a code generation paradigm using Test-Driven Development."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: b2223aa5-eaf1-4d6f-bd5a-e4df0e404dcd
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan implemented a novel auto-encoder architecture named KANBert, combining bidirectional attention mechanism (BERT) and Kolmogorov-Arnold networks."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 8a21e224-71c4-40dd-aa47-d6725c449013
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan holds a Master's degree in Data Science with high distinction (16.2/20) from Universit\u00e9 Paris-Saclay."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: f932dc71-705b-4368-a048-4ee7ac4dcd10
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's Bachelor's degree in Computer Science was awarded with distinction (13.7/20)."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: d0ed0184-200b-487e-ae58-116b769084d1
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan graduated from Lyc\u00e9e Jean-Baptiste Corot with a Scientific Baccalaureate."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: df0b38c3-d4dc-49db-a4eb-1a65a1aea2dd
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's notable high school grades include Mathematics (17/20) and Physics-Chemistry (18/20)."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 1c1cf468-77b4-468b-8225-cf92c649f1ba
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan enjoys researching, documenting, and implementing machine learning papers."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 18f17963-b5bc-4beb-87ed-4735b22b30ce
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan collaborates with Professor Ammi on content detection research to address the needs of product placement companies."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: eb4ea1c9-c558-459c-94a0-2a39b8dfcf37
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan combines abstract code structures (AST, CFG) with deep learning (LLM, RAG, GNN) in his research at Dassault Syst\u00e8mes."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 8332fb41-b2af-4feb-b1ee-7f1a0b6da2a3
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan is keen on the synergy between cybersecurity and code generation at Dassault Syst\u00e8mes."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 212ac05b-47a6-4083-9060-05c6cc8ec546
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's GitHub showcases various ML engineering projects, including games and applications."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 3426e3ed-7708-4483-bbf6-70212fda0883
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan implemented novel guard rails in code generation including a method inspired by GenQ at Dassault Syst\u00e8mes."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 16282792-be03-4add-9429-0d83cb207e79
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's work involves both theoretical and practical aspects of machine learning."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: f5f0ad62-731b-4940-af74-762376dd8613
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has a strong foundation in ensemble learning, including bagging, boosting, and stacking."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 5612c363-d356-4d0d-af73-d59994e40c68
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan is proficient in various NLP techniques, such as RAG, LLMs, generation, embeddings, LDA, and domain adaptation."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 556e54e5-f352-4a9a-8be9-d157631c6539
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has expertise in training methods, including schedulers, QLoRa, and mixed precision."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 26ce3a81-5a75-4502-bc57-7026badb1dc7
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan specializes in optimization techniques, such as quantization, knowledge distillation, and training without gradients."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 2bb92c5f-549d-4b97-8c91-7ab49f994abd
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has experience with clustering methods like Kmeans, GMM, CLARA, Hopkins, and iVAT."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 6b1b0d52-052e-46c6-b476-d011f3d04a58
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's CV Chatbot is designed to assist students in creating their own personalized agent."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 9c7dde95-0a60-4a8d-b1d2-a9ef86a0dbf9
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has developed a code generation system enhanced by Behavior-Driven Development (BDD)."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 91035e5d-479d-4a00-966e-ce8e11f747ff
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's KANBert architecture merges BERT's attention mechanism with Kolmogorov-Arnold networks."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: a5d4f7bc-5728-41db-9b37-5fc942a02866
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's research on code generation paradigms aims to improve code structure and security for companies."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: eddd4714-767b-40a5-93ed-21af28239dcb
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan is involved in a research project on problematic content detection for influencer posts."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: aa612a6d-6bfc-44bc-8d0d-5c8fcd7d7fc2
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's Vision Transformer game, QuickDraw-API, is containerized and deployed on ghcr.io."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: bc0b62db-fb72-44e3-b37f-28d00ecef5e9
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's GitHub Actions workflows automate the deployment processes of his projects."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 9f4efe5a-9394-4611-9450-2c283723c352
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan has experience in deploying machine learning models using Docker and GitHub Actions."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: efd1ab3e-c0d1-4dd7-a053-6cb855da57a9
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's QuickDraw-API project utilizes technologies such as Python, JavaScript, PyTorch, transformers, optuna, MLFlow, FastAPI, Docker, and GitHub Actions."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 4e8c263d-abc9-4c72-9b86-74a0fe646714
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's my-ai-cv project leverages langchain, Docker, and Hugging Face Spaces to create a customizable CV chatbot."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: c8bab9a5-87a1-4696-bb44-b66169e42260
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan's Code-Gen-Enhanced-by-BDD project explores a novel approach to code generation using test-driven development."}


created_at: 2024-06-30 08:34:15
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
id: 866f9513-5fb4-4b90-a3c4-6f8d0c2e2c51
encoding: FLOAT32
metadata:
{"chroma:document": "Ilan is an open person, who does not judge any person without having spoken with."}


created_at: 2024-06-30 08:44:54
topic: persistent://default/default/27e5d395-fa93-4a19-9e54-35fda006f475
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{"chroma:document": "Ilan has traveled to the following countries: France, Algeria, Greece, Spain, Germany, England, Switzerland"}


# embedding_metadata

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During his work/learning at Dassault Systèmes, Ilan trains models, reads and applies articles, deploys LLM applications by optimizing inference and their size, in short a lot of research


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Ilan passions Reading, include photography, artificial intelligence, soccer, MMA, virtual photography, and programming since he was 13.


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Concerning Data Engineering, theoretically, Ilan has a good understanding of DataLakes, Data Warehouse, Lake House, and Data Mesh.


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Concerning AI, Ilan has a very good command of the concepts relating to NLP, whether the basics or the more advanced recent techniques such as Transformers, MOE, RAG, quantization...


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Ilan learned Machine/Deep Learning by reading and doing projects. This is why he masters advanced techniques.


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Ilan AI skills include excellent knowledge of NLP, from basic principles like TF-IDF and Word2Vec to advanced concepts such as RNN, LSTM, and the most recent: Transformers, their training, fine-tuning, and optimization of training or inference.


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During his studies Ilan learned the fundamental principles of IT such as computer architecture, advanced algorithms, databases, machine learning etc... He was also able to develop solid skills in clean code and very important concepts in math and logic.


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Ilan has completed many computing projects, mainly personal ones, many of which concern Machine Learning (NLP).


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Ilan appreciates natural language processing in AI for its ability to humanize non-human entities and for the potential to be understood by something other than ourselves.


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Ilan's background / studies is a scientific baccalaureate then a license in computer science then currently a master's degree in Data Science


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Ilan optimized a retrieval system in a RAG for Dassault Systèmes using vector indexes, Matryoshka embeddings, and embedding quantization.


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Ilan developed a pipeline for detecting problematic content in influencer posts in collaboration with Professor Ammi.


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Ilan created QuickDraw-API, an interactive game using a fine-tuned Vision Transformer, deployed with complete automation via GitHub Actions.


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Ilan implemented a novel auto-encoder architecture named KANBert, combining bidirectional attention mechanism (BERT) and Kolmogorov-Arnold networks.


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Ilan holds a Master's degree in Data Science with high distinction (16.2/20) from Université Paris-Saclay.


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Ilan collaborates with Professor Ammi on content detection research to address the needs of product placement companies.


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Ilan combines abstract code structures (AST, CFG) with deep learning (LLM, RAG, GNN) in his research at Dassault Systèmes.


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Ilan implemented novel guard rails in code generation including a method inspired by GenQ at Dassault Systèmes.


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Ilan is proficient in various NLP techniques, such as RAG, LLMs, generation, embeddings, LDA, and domain adaptation.


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Ilan specializes in optimization techniques, such as quantization, knowledge distillation, and training without gradients.


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Ilan's research on code generation paradigms aims to improve code structure and security for companies.


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Ilan's QuickDraw-API project utilizes technologies such as Python, JavaScript, PyTorch, transformers, optuna, MLFlow, FastAPI, Docker, and GitHub Actions.


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Ilan's my-ai-cv project leverages langchain, Docker, and Hugging Face Spaces to create a customizable CV chatbot.


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Ilan's Code-Gen-Enhanced-by-BDD project explores a novel approach to code generation using test-driven development.


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Ilan has traveled to the following countries: France, Algeria, Greece, Spain, Germany, England, Switzerland


