Lama lamoboos223

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"

Arduino code

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

  pinMode(7, INPUT);

}

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

install on linux

OLLAMA

curl -fsSL https://ollama.com/install.sh | sh

Open WebUI (docker image)

https://github.com/open-webui/open-webui#:~:text=For%20CPU%20Only%3A%20If%20you%27re%20not%20using%20a%20GPU%2C%20use%20this%20command%20instead%3A

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 '{

Build Project From Source

poetry install --extras all

Activate Virtual Environment (MacOS)

source /Users//Library/Caches/pypoetry/virtualenvs//bin/activate

import warnings
from fastapi import FastAPI
from langchain_experimental.llms.ollama_functions import OllamaFunctions
from pydantic import BaseModel
from typing import List

warnings.filterwarnings('ignore')

app = FastAPI()

	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 ...

	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


	'''
	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})`