frutik

Performance implications of medium size values and TOAST in Postgres and how to mitigate them - https://pganalyze.com/blog/5mins-postgres-TOAST-performance
The Surprising Impact of Medium-Size Texts on PostgreSQL Performance. Why TOAST is the best thing since sliced bread  - https://hakibenita.com/sql-medium-text-performance
Speed up your queries by avoiding to hit the TOAST - https://medium.com/@walttonm/speed-up-your-queries-by-avoiding-to-hit-the-toast-a71feaaeacc2

frutik

from mlx_lm import load, generate

model, tokenizer = load('Qwen/Qwen2-7B-Instruct-MLX', tokenizer_config={"eos_token": "<|im_end|>"})

prompt = "Why people call putin khuilo."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(

frutik

CREATE OR REPLACE FUNCTION hamming_distance(
 A0 bigint, A1 bigint, A2 bigint, A3 bigint,
 B0 bigint, B1 bigint, B2 bigint, B3 bigint
)
RETURNS integer AS $$
BEGIN
RETURN 
    bits_count(A0 # B0) + 
    bits_count(A1 # B1) +
    bits_count(A2 # B2) + 

frutik

SELECT
    c.relname AS index_name,
    pg_size_pretty(pg_relation_size(c.oid)) AS index_size,
    pg_size_pretty(pg_total_relation_size(c.oid) - pg_relation_size(c.oid)) AS index_bloat_size,
    ROUND((pg_total_relation_size(c.oid) - pg_relation_size(c.oid)) / pg_relation_size(c.oid)::numeric * 100, 2) AS bloat_percentage
FROM
    pg_class c
JOIN
    pg_namespace n ON c.relnamespace = n.oid
WHERE

frutik

import onnxruntime as ort
from transformers import AutoTokenizer

session = ort.InferenceSession('./bge-small-en/model.onnx')
tokenizer = AutoTokenizer.from_pretrained("./bge-small-en")

inputs = tokenizer("hello world.", padding="longest", return_tensors="np")

inputs_onnx = {key: ort.OrtValue.ortvalue_from_numpy(value) for key, value in inputs.items()}

frutik

https://brandur.org/fragments/postgres-partitioning-2022
https://pganalyze.com/blog/postgresql-partitioning-django
https://django-postgres-extra.readthedocs.io/en/master/table_partitioning.html
https://hevodata.com/learn/postgresql-partitions/
https://www.2ndquadrant.com/en/blog/postgresql-12-foreign-keys-and-partitioned-tables/
https://www.postgresql.org/docs/current/ddl-partitioning.html#DDL-PARTITIONING-DECLARATIVE-LIMITATIONS

frutik

def divide_chunks(l, n):
    for i in range(0, len(l), n):
        yield l[i:i + n]

a = 'i.strip() for i in a.split('.') if i.strip()]
c = list(divide_chunks(b, 3))
d = ['. '.join(i + ['']).strip() for i in c]
y = '\n\n'.join(d)
print(y)

frutik

kubectl get --raw "/apis/metrics.k8s.io/v1beta1/nodes" -v=8 | python -m json.tool

frutik

Must have

- Google Analytics / Tag manager / Funnels
- Set up and maintain pipelintes for for importing Analytical data to Data lake of your choice
- Defining KPI, Metrics that will help us to understand our visitors better.


Nice to have

- BigQuery

frutik

https://medium.com/@kelvin.lu.au/compare-pdf-question-answering-with-openai-and-google-vertexai-46638d62327b
https://medium.com/@kelvin.lu.au/what-we-need-to-know-before-adopting-a-vector-database-85e137570fbb
https://medium.com/@kelvin.lu.au/disadvantages-of-rag-5024692f2c53

https://medium.com/@Ratnaparkhi/how-the-search-technology-is-evolving-88607f5efb9e