Wednesday, June 24, 2026

Priorities

Migrate Git repositories, and it's CICD pipeline from GitLab to GitHub — DA-1353

Work Log

GitLab to GitHub Migration - DA-1353

Today, I migrated the following repositories:

Repository	Jira Ticket	GitLab	GitHub
Gargantua	DA-1353	https://gitlab.com/allofresh/data/gargantua	https://github.com/allofresh/gargantua

Find the details of this migration in this page.

LLM Inference -- Embedding Function

Situation

This is a continuation of creating an LLM inference live demo.

In this section, I'd like to show you a way to convert raw data into a vectors or embeddings. To achieve this, I already have the following things:

Task

Create a Python script to vectorize the following information, and store it into the vector database (ChromaDB).

Action

I port forwarded to the ChromaDB vector database on localhost at port 8000.

kubectl port-forward chroma-596665dd8f-xv2t8 8000:8000 -n chroma

I port forwarded to the embedded service on localhost at port 8080.

kubectl port-forward server-5559dd68f4-jkpwq 8080:8080 -n embed-server

I created a python scrip to store texts into the greeting collection in the ChromaDB database. See the details below:

import chromadb
import httpx

from typing import Any, Dict

from chromadb import Documents, EmbeddingFunction
from chromadb.utils.embedding_functions import register_embedding_function

CHROMA_HOST = "localhost"
CHROMA_PORT = 8000
EMBED_SERVER_URL = "http://localhost:8080"

@register_embedding_function
class RemoteEmbeddingFunction(EmbeddingFunction):

    def __init__(self):
        self.url = EMBED_SERVER_URL

    def __call__(self, texts: Documents):
        resp = httpx.post(
            f"{self.url}/v1/embeddings",
            json=dict(input=list(texts), model="all-MiniLM-L6-v2"),
            timeout=30
        )
        resp.raise_for_status()
        data = sorted(resp.json()["data"], key=lambda d: d["index"])
        return [d["embedding"] for d in data]

    @staticmethod
    def name():
        return "embed-server"

    def get_config(self):
        return dict(url=self.url)

    @staticmethod
    def build_from_config(config: Dict[str, Any]):
        return RemoteEmbeddingFunction()

if __name__ == "__main__":
    chroma_client = chromadb.HttpClient(host=CHROMA_HOST, port=CHROMA_PORT)
    collection = chroma_client.get_or_create_collection(
        name="greetings",
        embedding_function=RemoteEmbeddingFunction(),
    )

    collection.upsert(
        ids=[
            "id1",
            "id2",
            "id3"
        ],
        documents=[
            "hai!",
            "greetings!",
            "hola!"
        ]
    )

    results = collection.get(include=["documents", "embeddings"])
    for doc_id, document, embeddings in zip(results["ids"], results["documents"], results["embeddings"]):
        print(f"{doc_id}: {document}")
        print(f"embeddings: {embeddings}")

Result

As a result, I successfully transformed the texts into embeddings or vectors, and then store it into the ChromaDB database. See the details below:

click here to see the output

/Users/ilyasahsan/Documents/personal/repositories/kubernetes/.venv/bin/python /Users/ilyasahsan/Documents/personal/repositories/kubernetes/chat-server/images/backend/populate.py 
id1: hai!
embeddings: [-1.11144446e-01  2.94786540e-02  1.38537810e-02 -4.78725880e-02
 -2.24278680e-02  5.94940630e-02  1.12092310e-01 -5.79963920e-02
 -3.83616280e-02 -3.24583460e-02  7.08877400e-02 -5.62409050e-02
  7.26752300e-02 -3.70103140e-03  1.17759430e-02 -1.63233060e-02
 -8.13809560e-02 -2.65393820e-02 -3.52271870e-02  3.20056740e-04
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  1.39549690e-02  6.78600100e-02 -2.84722060e-02 -6.78828130e-03
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 -7.59442200e-03 -1.90405400e-02  8.16529540e-02 -4.04433940e-02
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  1.78665090e-01  4.28608730e-02 -5.28776900e-02  5.56241100e-02]
id2: greetings!
embeddings: [-8.54396900e-02  1.01567246e-01  5.77587000e-02  5.00192600e-02
 -1.79142710e-02 -6.41134700e-02  1.31150640e-02 -1.90565100e-02
 -5.52052900e-02 -3.06557960e-02 -5.20573800e-02  5.97937030e-02
  2.97810680e-02  1.23004060e-02 -1.32516440e-02 -1.73422080e-02
 -1.90504680e-02  4.13085300e-02  2.35316530e-02  7.02978450e-02
 -1.91540810e-03  2.96737940e-02 -1.20978080e-02  5.77184220e-02
 -3.56382130e-02  3.81987060e-02  3.80875060e-02  6.75429400e-02
 -1.04167720e-02 -1.20007070e-01 -8.26485600e-02  1.21050686e-01
  4.18354980e-02  1.46730530e-02 -1.43723970e-02  6.59302100e-02
 -2.40190100e-03 -4.54624070e-02  5.42337100e-02  2.11203050e-03
 -3.66120340e-02  2.86116840e-02 -1.20063730e-03  1.86419540e-02
  7.71013700e-02 -2.39346220e-03  4.86581800e-03  4.25505640e-02
  8.52202000e-02  5.97737540e-02 -7.96402500e-02 -6.66353000e-02
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id3: hola!
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Process finished with exit code 0

Blockers

N/A

Carry-overs

Create a live demo for LLM Inference

Reflection

N/A