Skip to content

Instantly share code, notes, and snippets.

enable notification for a room

note that %21RotqTsIrBvXaPAbhiC%3Atwkl.chat is the room_id after url encoding it using this tool

curl --location --request PUT 'http://localhost:8008/_matrix/client/r0/pushrules/global/room/%21RotqTsIrBvXaPAbhiC%3Atwkl.chat' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer <user_token>' \
--data '{
'''
Steps for making a RAG system in Langchain framework
1. Prepare the data (knowledge base)
2. Use the Loader object to load the data in the proper format
3. Chunk the data into appropriate size
4. Create Embedding and Retriever
4.1. Use Embedding model like BAAI/bge-base-en-v1.5 from HuggingFace
4.2. Create a Vector database like FAISS (Facebook AI Similarity Search). And the Retriever is a an object from the DB class. E.g. `retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})`
apt install python3 python3-pip wget curl unzip portaudio19-dev virtualenv libespeak1 libespeak-dev -y
cd /opt
wget https://github.com/lamoboos223/demo-faster-whisper/archive/refs/heads/master.zip
unzip master.zip
rm master.zip
cd demo-faster-whisper-master
virtualenv myenv
source myenv/bin/activate
python3 -m pip install -r requirements.txt
python3 __init__.py

install on unix like systems

sh <(curl -L https://nixos.org/nix/install) --darwin-use-unencrypted-nix-store-volume
# restart terminal session
nix --version

create nix shell

@lamoboos223
lamoboos223 / esp8266_output_to_arduino_uno.md
Last active April 1, 2024 03:17
connect esp8266 to output a value to arduino uno where arduino reads this value and do some action

Arduino code

void setup() {
  // put your setup code here, to run once:
  Serial.begin(9600);   // Initialize the serial communication

  pinMode(7, INPUT);

}
from flask import Flask, request
from graphene import ObjectType, String, Schema, List, Field
'''
Pre-requesities: python3 -m pip install graphene flask
USAGE: open Postman and create new QraphQL request then paste this url: http://localhost:5000/graphql and these Queries ...
pip install jaeger-client
from jaeger_client import Tracer, SpanContext, Config

# Replace these with your actual configuration
SERVICE_NAME = "your-application-name"
COLLECTOR_HOST = "localhost"
import pika
import json

# Connect to RabbitMQ
connection = pika.BlockingConnection(
    pika.ConnectionParameters('localhost', 5672)
)
channel = connection.channel()
docker pull docker.elastic.co/elasticsearch/elasticsearch:7.17.15
docker pull docker.elastic.co/kibana/kibana:7.17.15
docker run -d --name elasticsearch -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" docker.elastic.co/elasticsearch/elasticsearch:7.17.15
docker run -d --name kibana --link elasticsearch:elasticsearch -p 5601:5601 docker.elastic.co/kibana/kibana:7.17.15