Useful information and scripts for deploying an instance based Solr Cloud in 2 minutes.
Check this repo out on your Google Cloud Shell terminal.
Deploy a secure Solr instance on Google cloud:
$ ./deploy-solr.sh
import openai | |
import numpy as np | |
from openai.embeddings_utils import get_embedding | |
openai.api_key = "" | |
def gpt3_embedding(content, engine='text-similarity-ada-001'): | |
content = content.encode(encoding='ASCII',errors='ignore').decode() | |
response = openai.Embedding.create(input=content,engine=engine) | |
vector = response['data'][0]['embedding'] # this is a normal list |
// google docs app | |
var line = "{{line}}"; | |
if ("{{text}}" == "" && "{{view}}" == "") { | |
// just nothing | |
url = "https://docs.google.com/"; | |
} else if ("{{text}}" == "" && "{{view}}" != ""){ | |
// view | |
var view = "{{view}}".substring(1); // trim the | off (fix this) | |
if (view == "sheets") { |
import datetime | |
import logging | |
import os | |
import socket | |
import time | |
from random import randrange | |
import urllib.parse | |
from google.cloud import ndb |
import datetime | |
from dateutil.relativedelta import relativedelta | |
import re | |
# timewarp.py | |
# Kord Campbell | |
# Copyright 2020, Mitta Corp. | |
# All Rights Reserved. | |
# description: common name to solr timerange converter in python3 |
import datetime | |
import os | |
import requests | |
from google.cloud import ndb | |
import flask_login | |
from lib.util import random_string, random_number, generate_token, random_name | |
from lib.solr import doc_count |
{ | |
"timestamp": "{{timestamp}}", | |
"request_url": "/g", | |
"result": "success", | |
"response": [{ | |
"source_type": "spool", | |
"created": "{{created}}", | |
"updated": "{{updated}}", | |
"name": "{{name}}", | |
"upload_url": "{{upload_url}}", |
import datetime | |
import json | |
import io | |
from google.cloud import ndb | |
from google.cloud import vision | |
from google.cloud import storage | |
from flask import Blueprint, request, make_response, render_template, abort | |
import flask_login |
This script deploys Lucidwork's Fusion 4.1.1 on a Google Cloud instance. Fusion includes Solr 7.4.
Solr 7.4 provides the semantic knowledge graph relatedness() function. This function may be used to extract related topics from datasets which represent a question/answer type structure.
No video for this script exists.
TBD
This gist guide starts a server on your Google Cloud Free account. That server will be configured with Tensorflow. A script and sample notebook will guide you through trying out image recognition.
The instance takes about 10 minutes to launch.