Cdaprod

#------------------------------------------------------------------#
#- Clear-GlobalWindowsCache                                        #
#------------------------------------------------------------------#
Function Clear-GlobalWindowsCache {
    Remove-CacheFiles 'C:\Windows\Temp' 
    Remove-CacheFiles "C:\`$Recycle.Bin"
    Remove-CacheFiles "C:\Windows\Prefetch"
    C:\Windows\System32\rundll32.exe InetCpl.cpl, ClearMyTracksByProcess 255
    C:\Windows\System32\rundll32.exe InetCpl.cpl, ClearMyTracksByProcess 4351
}

Cdaprod

ghcr (GitHub Container Registry) quickstart

CLI

To push container images to ghcr, you need peronal access token (PAT) - see how to create PAT

1. Get PAT (personal access token)

Personal Settings > Developer settings > Personal access tokens

Cdaprod

Enter this in the search box along with your search terms:

Get all gists from the user santisbon.
user:santisbon

Find all gists with a .yml extension.
extension:yml

Find all gists with HTML files.
language:html

Cdaprod

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import requests
from bs4 import BeautifulSoup
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import json

RESULTS_PER_QUESTION = 3

Cdaprod

from langchain.chains.openai_functions import create_structured_output_runnable
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel, Field
 
class Insight(BaseModel):
    insight: str = Field(description="""insight""")

chat_model = ChatOpenAI(model_name="gpt-4-1106-preview")

Cdaprod

from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatAnthropic
from langchain.schema.output_parser import StrOutputParser


#### ROUTER
# This is the router - responsible for chosing what to do
chain = PromptTemplate.from_template("""Given the user question below, classify it as either being about `weather` or `other`.
                                     
Do not respond with more than one word.

Cdaprod

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI

llm = OpenAI(temperature=0, model="gpt-3.5-turbo-instruct")

from metaphor_python import Metaphor

client = Metaphor("")

Cdaprod

from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.runnable import RunnableMap
from langchain.schema import format_document
from typing import AsyncGenerator

Cdaprod

from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel, Field
from langchain.document_loaders import UnstructuredURLLoader
from langchain.chains.openai_functions import create_extraction_chain_pydantic

class LLMItem(BaseModel):
    title: str = Field(description="The simple and concise title of the product")
    description: str = Field(description="The description of the product")

def main():

Cdaprod

from langchain.document_loaders import YoutubeLoader
from langchain.indexes import VectorstoreIndexCreator
urls = [
    ("https://www.youtube.com/watch?v=fP6vRNkNEt0", "Prompt Injection"),
    ("https://www.youtube.com/watch?v=qWv2vyOX0tk", "Low Code-No Code"),
    ("https://www.youtube.com/watch?v=k8GNCCs16F4", "Agents In Production"),
    ("https://www.youtube.com/watch?v=1gRlCjy18m4", "Agents"),
    ("https://www.youtube.com/watch?v=fLn-WqliEQU", "Output Parsing"),
    ("https://www.youtube.com/watch?v=ywT-5yKDtDg", "Document QA"),
    ("https://www.youtube.com/watch?v=GrCFyyyAxCU", "SQL"),