Webscout
1.0.0
Google、duckduckgo、phind.com、アクセスAIモデル、YouTubeビデオの転写、一時的な電子メールと電話番号の生成、テキストへのスピーチの使用、WebAI（ターミナルGPTおよびオープンインタープリター）を活用し、オフラインLLMSなどを検索し、さらに詳しく調べてください！
rawdog機能を使用して、端末内でPythonスクリプトを直接実行します。 pip install - U webscout python - m webscout - - help| 指示 | 説明 |
|---|---|
| python -m webscout Answers -Kテキスト | CLI関数webscoutを使用して回答検索を実行します。 |
| python -m webscout images -kテキスト | WebScoutを使用して画像検索を実行するCLI関数。 |
| python -m webscoutマップ-kテキスト | WebScoutを使用してマップ検索を実行するCLI関数。 |
| python -m webscout news -kテキスト | WebScoutを使用してニュース検索を実行するCLI機能。 |
| python -m webscoutの提案-kテキスト | WebScoutを使用して提案検索を実行するCLI関数。 |
| python -m webscoutテキスト-kテキスト | WebScoutを使用してテキスト検索を実行するCLI関数。 |
| python -m webscout translate -kテキスト | WebScoutを使用して翻訳を実行するCLI関数。 |
| python -m webscoutバージョン | プログラムのバージョンを印刷および返すコマンドラインインターフェイスコマンド。 |
| python -m webscoutビデオ-kテキスト | CLI関数DuckDuckgo APIを使用してビデオ検索を実行します。 |
トップに行きます
xa-ar for Arabia
xa-en for Arabia (en)
ar-es for Argentina
au-en for Australia
at-de for Austria
be-fr for Belgium (fr)
be-nl for Belgium (nl)
br-pt for Brazil
bg-bg for Bulgaria
ca-en for Canada
ca-fr for Canada (fr)
ct-ca for Catalan
cl-es for Chile
cn-zh for China
co-es for Colombia
hr-hr for Croatia
cz-cs for Czech Republic
dk-da for Denmark
ee-et for Estonia
fi-fi for Finland
fr-fr for France
de-de for Germany
gr-el for Greece
hk-tzh for Hong Kong
hu-hu for Hungary
in-en for India
id-id for Indonesia
id-en for Indonesia (en)
ie-en for Ireland
il-he for Israel
it-it for Italy
jp-jp for Japan
kr-kr for Korea
lv-lv for Latvia
lt-lt for Lithuania
xl-es for Latin America
my-ms for Malaysia
my-en for Malaysia (en)
mx-es for Mexico
nl-nl for Netherlands
nz-en for New Zealand
no-no for Norway
pe-es for Peru
ph-en for Philippines
ph-tl for Philippines (tl)
pl-pl for Poland
pt-pt for Portugal
ro-ro for Romania
ru-ru for Russia
sg-en for Singapore
sk-sk for Slovak Republic
sl-sl for Slovenia
za-en for South Africa
es-es for Spain
se-sv for Sweden
ch-de for Switzerland (de)
ch-fr for Switzerland (fr)
ch-it for Switzerland (it)
tw-tzh for Taiwan
th-th for Thailand
tr-tr for Turkey
ua-uk for Ukraine
uk-en for United Kingdom
us-en for United States
ue-es for United States (es)
ve-es for Venezuela
vn-vi for Vietnam
wt-wt for No region
トップに行きます
from os import rename , getcwd
from webscout import YTdownloader
def download_audio ( video_id ):
youtube_link = video_id
handler = YTdownloader . Handler ( query = youtube_link )
for third_query_data in handler . run ( format = 'mp3' , quality = '128kbps' , limit = 1 ):
audio_path = handler . save ( third_query_data , dir = getcwd ())
rename ( audio_path , "audio.mp3" )
def download_video ( video_id ):
youtube_link = video_id
handler = YTdownloader . Handler ( query = youtube_link )
for third_query_data in handler . run ( format = 'mp4' , quality = 'auto' , limit = 1 ):
video_path = handler . save ( third_query_data , dir = getcwd ())
rename ( video_path , "video.mp4" )
if __name__ == "__main__" :
# download_audio("https://www.youtube.com/watch?v=c0tMvzB0OKw")
download_video ( "https://www.youtube.com/watch?v=c0tMvzB0OKw" ) from webscout import weather as w
weather = w . get ( "Qazigund" )
w . print_weather ( weather ) from webscout import weather_ascii as w
weather = w . get ( "Qazigund" )
print ( weather ) import json
import asyncio
from webscout import VNEngine
from webscout import TempMail
async def main ():
vn = VNEngine ()
countries = vn . get_online_countries ()
if countries :
country = countries [ 0 ][ 'country' ]
numbers = vn . get_country_numbers ( country )
if numbers :
number = numbers [ 0 ][ 'full_number' ]
inbox = vn . get_number_inbox ( country , number )
# Serialize inbox data to JSON string
json_data = json . dumps ( inbox , ensure_ascii = False , indent = 4 )
# Print with UTF-8 encoding
print ( json_data )
async with TempMail () as client :
domains = await client . get_domains ()
print ( "Available Domains:" , domains )
email_response = await client . create_email ( alias = "testuser" )
print ( "Created Email:" , email_response )
messages = await client . get_messages ( email_response . email )
print ( "Messages:" , messages )
await client . delete_email ( email_response . email , email_response . token )
print ( "Email Deleted" )
if __name__ == "__main__" :
asyncio . run ( main ())WebScoutのtranscriber関数は、YouTubeビデオを転写する便利なツールです。
例：
from webscout import YTTranscriber
yt = YTTranscriber ()
from rich import print
video_url = input ( "Enter the YouTube video URL: " )
transcript = yt . get_transcript ( video_url , languages = None )
print ( transcript ) from webscout import GoogleS
from rich import print
searcher = GoogleS ()
results = searcher . search ( "HelpingAI-9B" , max_results = 20 , extract_text = False , max_text_length = 200 )
for result in results :
print ( result ) from webscout import BingS
from rich import print
searcher = BingS ()
results = searcher . search ( "HelpingAI-9B" , max_results = 20 , extract_webpage_text = True , max_extract_characters = 1000 )
for result in results :
print ( result )WEBSとAsyncWEBSクラスは、duckduckgo.comから検索結果を取得するために使用されます。
AsyncWEBSクラスを使用するには、Pythonのasyncioライブラリを使用して非同期操作を実行できます。
WEBSまたはAsyncWEBSクラスのインスタンスを初期化するには、次のオプションの引数を提供できます。
例 - ウェブ：
from webscout import WEBS
R = WEBS (). text ( "python programming" , max_results = 5 )
print ( R )例-ASYNCWEBS：
import asyncio
import logging
import sys
from itertools import chain
from random import shuffle
import requests
from webscout import AsyncWEBS
# If you have proxies, define them here
proxies = None
if sys . platform . lower (). startswith ( "win" ):
asyncio . set_event_loop_policy ( asyncio . WindowsSelectorEventLoopPolicy ())
def get_words ():
word_site = "https://www.mit.edu/~ecprice/wordlist.10000"
resp = requests . get ( word_site )
words = resp . text . splitlines ()
return words
async def aget_results ( word ):
async with AsyncWEBS ( proxies = proxies ) as WEBS :
results = await WEBS . text ( word , max_results = None )
return results
async def main ():
words = get_words ()
shuffle ( words )
tasks = [ aget_results ( word ) for word in words [: 10 ]]
results = await asyncio . gather ( * tasks )
print ( f"Done" )
for r in chain . from_iterable ( results ):
print ( r )
logging . basicConfig ( level = logging . DEBUG )
await main ()重要な注意： WEBSおよびAsyncWEBSクラスは、常にコンテキストマネージャーとして（ステートメント付き）使用する必要があります。これにより、Context ManagerがHTTPクライアント接続の開閉を自動的に処理するため、適切なリソース管理とクリーンアップが保証されます。
例外：
WebscoutE ：APIリクエスト中に一般的な例外がある場合に提起されます。 text() - duckduckgo.comによるテキスト検索 from webscout import WEBS
# Text search for 'live free or die' using DuckDuckGo.com
with WEBS () as WEBS :
for r in WEBS . text ( 'live free or die' , region = 'wt-wt' , safesearch = 'off' , timelimit = 'y' , max_results = 10 ):
print ( r )
for r in WEBS . text ( 'live free or die' , region = 'wt-wt' , safesearch = 'off' , timelimit = 'y' , max_results = 10 ):
print ( r )answers() - duckduckgo.comによるインスタント回答 from webscout import WEBS
# Instant answers for the query "sun" using DuckDuckGo.com
with WEBS () as WEBS :
for r in WEBS . answers ( "sun" ):
print ( r )images() - duckduckgo.comによる画像検索 from webscout import WEBS
# Image search for the keyword 'butterfly' using DuckDuckGo.com
with WEBS () as WEBS :
keywords = 'butterfly'
WEBS_images_gen = WEBS . images (
keywords ,
region = "wt-wt" ,
safesearch = "off" ,
size = None ,
type_image = None ,
layout = None ,
license_image = None ,
max_results = 10 ,
)
for r in WEBS_images_gen :
print ( r )videos() - duckduckgo.comによるビデオ検索 from webscout import WEBS
# Video search for the keyword 'tesla' using DuckDuckGo.com
with WEBS () as WEBS :
keywords = 'tesla'
WEBS_videos_gen = WEBS . videos (
keywords ,
region = "wt-wt" ,
safesearch = "off" ,
timelimit = "w" ,
resolution = "high" ,
duration = "medium" ,
max_results = 10 ,
)
for r in WEBS_videos_gen :
print ( r )news() - DuckDuckgo.comによるニュース検索 from webscout import WEBS
import datetime
def fetch_news ( keywords , timelimit ):
news_list = []
with WEBS () as webs_instance :
WEBS_news_gen = webs_instance . news (
keywords ,
region = "wt-wt" ,
safesearch = "off" ,
timelimit = timelimit ,
max_results = 20
)
for r in WEBS_news_gen :
# Convert the date to a human-readable format using datetime
r [ 'date' ] = datetime . datetime . fromisoformat ( r [ 'date' ]). strftime ( '%B %d, %Y' )
news_list . append ( r )
return news_list
def _format_headlines ( news_list , max_headlines : int = 100 ):
headlines = []
for idx , news_item in enumerate ( news_list ):
if idx >= max_headlines :
break
new_headline = f" { idx + 1 } . { news_item [ 'title' ]. strip () } "
new_headline += f"(URL: { news_item [ 'url' ]. strip () } ) "
new_headline += f" { news_item [ 'body' ]. strip () } "
new_headline += " n "
headlines . append ( new_headline )
headlines = " n " . join ( headlines )
return headlines
# Example usage
keywords = 'latest AI news'
timelimit = 'd'
news_list = fetch_news ( keywords , timelimit )
# Format and print the headlines
formatted_headlines = _format_headlines ( news_list )
print ( formatted_headlines )maps() - duckduckgo.comによるマップ検索 from webscout import WEBS
# Map search for the keyword 'school' in 'anantnag' using DuckDuckGo.com
with WEBS () as WEBS :
for r in WEBS . maps ( "school" , place = "anantnag" , max_results = 50 ):
print ( r )translate() - duckduckgo.comによる翻訳 from webscout import WEBS
# Translation of the keyword 'school' to German ('hi') using DuckDuckGo.com
with WEBS () as WEBS :
keywords = 'school'
r = WEBS . translate ( keywords , to = "hi" )
print ( r )suggestions() - duckduckgo.comによる提案 from webscout import WEBS
# Suggestions for the keyword 'fly' using DuckDuckGo.com
with WEBS () as WEBS :
for r in WEBS . suggestions ( "fly" ):
print ( r )positionlanguage文学評論家注：一部の「行為」は、プロンプトを使用するときに特定の値に置き換える必要があるpositionやlanguageなどのプレースホルダーを使用します。
すべてのTTIプロバイダーには同じ使用コードがあります。インポートを変更するだけです。
from webscout import DeepInfraImager
bot = DeepInfraImager ()
resp = bot . generate ( "AI-generated image - webscout" , 1 )
print ( bot . save ( resp )) from webscout import Voicepods
voicepods = Voicepods ()
text = "Hello, this is a test of the Voicepods text-to-speech"
print ( "Generating audio..." )
audio_file = voicepods . tts ( text )
print ( "Playing audio..." )
voicepods . play_audio ( audio_file )Duckchat -LLMとチャット from webscout import WEBS as w
R = w (). chat ( "Who are you" , model = 'gpt-4o-mini' ) # mixtral-8x7b, llama-3.1-70b, claude-3-haiku, gpt-4o-mini
print ( R )PhindSearchを使用して検索します from webscout import PhindSearch
# Create an instance of the PHIND class
ph = PhindSearch ()
# Define a prompt to send to the AI
prompt = "write a essay on phind"
# Use the 'ask' method to send the prompt and receive a response
response = ph . ask ( prompt )
# Extract and print the message from the response
message = ph . get_message ( response )
print ( message )Phindv2の使用：
from webscout import Phindv2
# Create an instance of the PHIND class
ph = Phindv2 ()
# Define a prompt to send to the AI
prompt = ""
# Use the 'ask' method to send the prompt and receive a response
response = ph . ask ( prompt )
# Extract and print the message from the response
message = ph . get_message ( response )
print ( message )Gemini - Google Geminiで検索 import webscout
from webscout import GEMINI
from rich import print
COOKIE_FILE = "cookies.json"
# Optional: Provide proxy details if needed
PROXIES = {}
# Initialize GEMINI with cookie file and optional proxies
gemini = GEMINI ( cookie_file = COOKIE_FILE , proxy = PROXIES )
# Ask a question and print the response
response = gemini . chat ( "websearch about HelpingAI and who is its developer" )
print ( response )YEPCHAT from webscout import YEPCHAT
ai = YEPCHAT ( Tools = False )
response = ai . chat ( input ( ">>> " ))
for chunk in response :
print ( chunk , end = "" , flush = True )
#---------------Tool Call-------------
from rich import print
from webscout import YEPCHAT
def get_current_time ():
import datetime
return f"The current time is { datetime . datetime . now (). strftime ( '%H:%M:%S' ) } "
def get_weather ( location : str ) -> str :
return f"The weather in { location } is sunny."
ai = YEPCHAT ( Tools = True ) # Set Tools=True to use tools in the chat.
ai . tool_registry . register_tool ( "get_current_time" , get_current_time , "Gets the current time." )
ai . tool_registry . register_tool (
"get_weather" ,
get_weather ,
"Gets the weather for a given location." ,
parameters = {
"type" : "object" ,
"properties" : {
"location" : { "type" : "string" , "description" : "The city and state, or zip code" }
},
"required" : [ "location" ],
},
)
response = ai . chat ( input ( ">>> " ))
for chunk in response :
print ( chunk , end = "" , flush = True )BlackBoxブラックボックスとの検索/チャット from webscout import BLACKBOXAI
from rich import print
ai = BLACKBOXAI (
is_conversation = True ,
max_tokens = 800 ,
timeout = 30 ,
intro = None ,
filepath = None ,
update_file = True ,
proxies = {},
history_offset = 10250 ,
act = None ,
model = None # You can specify a model if needed
)
# Start an infinite loop for continuous interaction
while True :
# Define a prompt to send to the AI
prompt = input ( "Enter your prompt: " )
# Check if the user wants to exit the loop
if prompt . lower () == "exit" :
break
# Use the 'chat' method to send the prompt and receive a response
r = ai . chat ( prompt )
print ( r )PERPLEXITY - 困惑を伴う検索 from webscout import Perplexity
from rich import print
perplexity = Perplexity ()
# Stream the response
response = perplexity . chat ( input ( ">>> " ))
for chunk in response :
print ( chunk , end = "" , flush = True )
perplexity . close ()Meta AIメタAIとのチャット from webscout import Meta
from rich import print
# **For unauthenticated usage**
meta_ai = Meta ()
# Simple text prompt
response = meta_ai . chat ( "What is the capital of France?" )
print ( response )
# Streaming response
for chunk in meta_ai . chat ( "Tell me a story about a cat." ):
print ( chunk , end = "" , flush = True )
# **For authenticated usage (including image generation)**
fb_email = "[email protected]"
fb_password = "qwertfdsa"
meta_ai = Meta ( fb_email = fb_email , fb_password = fb_password )
# Text prompt with web search
response = meta_ai . ask ( "what is currently happning in bangladesh in aug 2024" )
print ( response [ "message" ]) # Access the text message
print ( "Sources:" , response [ "sources" ]) # Access sources (if ```python
any )
# Image generation
response = meta_ai . ask ( "Create an image of a cat wearing a hat." )
print ( response [ "message" ]) # Print the text message from the response
for media in response [ "media" ]:
print ( media [ "url" ]) # Access image URLsKOBOLDAI from webscout import KOBOLDAI
# Instantiate the KOBOLDAI class with default parameters
koboldai = KOBOLDAI ()
# Define a prompt to send to the AI
prompt = "What is the capital of France?"
# Use the 'ask' method to get a response from the AI
response = koboldai . ask ( prompt )
# Extract and print the message from the response
message = koboldai . get_message ( response )
print ( message )Reka - レカとチャット from webscout import REKA
a = REKA ( is_conversation = True , max_tokens = 8000 , timeout = 30 , api_key = "" )
prompt = "tell me about india"
response_str = a . chat ( prompt )
print ( response_str )Cohere Cohereとチャットします from webscout import Cohere
a = Cohere ( is_conversation = True , max_tokens = 8000 , timeout = 30 , api_key = "" )
prompt = "tell me about india"
response_str = a . chat ( prompt )
print ( response_str )DeepSeek -Deepseekとチャット from webscout import DeepSeek
from rich import print
ai = DeepSeek (
is_conversation = True ,
api_key = 'cookie' ,
max_tokens = 800 ,
timeout = 30 ,
intro = None ,
filepath = None ,
update_file = True ,
proxies = {},
history_offset = 10250 ,
act = None ,
model = "deepseek_chat"
)
# Define a prompt to send to the AI
prompt = "Tell me about india"
# Use the 'chat' method to send the prompt and receive a response
r = ai . chat ( prompt )
print ( r )Deepinfra from webscout import DeepInfra
ai = DeepInfra (
is_conversation = True ,
model = "Qwen/Qwen2-72B-Instruct" ,
max_tokens = 800 ,
timeout = 30 ,
intro = None ,
filepath = None ,
update_file = True ,
proxies = {},
history_offset = 10250 ,
act = None ,
)
prompt = "what is meaning of life"
response = ai . ask ( prompt )
# Extract and print the message from the response
message = ai . get_message ( response )
print ( message )GROQ from webscout import GROQ
ai = GROQ ( api_key = "" )
response = ai . chat ( "What is the meaning of life?" )
print ( response )
#----------------------TOOL CALL------------------
from webscout import GROQ # Adjust import based on your project structure
from webscout import WEBS
import json
# Initialize the GROQ client
client = GROQ ( api_key = "" )
MODEL = 'llama3-groq-70b-8192-tool-use-preview'
# Function to evaluate a mathematical expression
def calculate ( expression ):
"""Evaluate a mathematical expression"""
try :
result = eval ( expression )
return json . dumps ({ "result" : result })
except Exception as e :
return json . dumps ({ "error" : str ( e )})
# Function to perform a text search using DuckDuckGo.com
def search ( query ):
"""Perform a text search using DuckDuckGo.com"""
try :
results = WEBS (). text ( query , max_results = 5 )
return json . dumps ({ "results" : results })
except Exception as e :
return json . dumps ({ "error" : str ( e )})
# Add the functions to the provider
client . add_function ( "calculate" , calculate )
client . add_function ( "search" , search )
# Define the tools
tools = [
{
"type" : "function" ,
"function" : {
"name" : "calculate" ,
"description" : "Evaluate a mathematical expression" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"expression" : {
"type" : "string" ,
"description" : "The mathematical expression to evaluate" ,
}
},
"required" : [ "expression" ],
},
}
},
{
"type" : "function" ,
"function" : {
"name" : "search" ,
"description" : "Perform a text search using DuckDuckGo.com and Yep.com" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"query" : {
"type" : "string" ,
"description" : "The search query to execute" ,
}
},
"required" : [ "query" ],
},
}
}
]
user_prompt_calculate = "What is 25 * 4 + 10?"
response_calculate = client . chat ( user_prompt_calculate , tools = tools )
print ( response_calculate )
user_prompt_search = "Find information on HelpingAI and who is its developer"
response_search = client . chat ( user_prompt_search , tools = tools )
print ( response_search )LLama 70b -MetaのLlama 3 70bとチャット from webscout import LLAMA
llama = LLAMA ()
r = llama . chat ( "What is the meaning of life?" )
print ( r )AndiSearch from webscout import AndiSearch
a = AndiSearch ()
print ( a . chat ( "HelpingAI-9B" )) import json
import logging
from webscout import Julius , WEBS
from webscout . Agents . functioncall import FunctionCallingAgent
from rich import print
class FunctionExecutor :
def __init__ ( self , llama ):
self . llama = llama
def execute_web_search ( self , arguments ):
query = arguments . get ( "query" )
if not query :
return "Please provide a search query."
with WEBS () as webs :
search_results = webs . text ( query , max_results = 5 )
prompt = (
f"Based on the following search results: n n { search_results } n n "
f"Question: { query } n n "
"Please provide a comprehensive answer to the question based on the search results above. "
"Include relevant webpage URLs in your answer when appropriate. "
"If the search results don't contain relevant information, please state that and provide the best answer you can based on your general knowledge."
)
return self . llama . chat ( prompt )
def execute_general_ai ( self , arguments ):
question = arguments . get ( "question" )
if not question :
return "Please provide a question."
return self . llama . chat ( question )
def execute_UserDetail ( self , arguments ):
name = arguments . get ( "name" )
age = arguments . get ( "age" )
return f"User details - Name: { name } , Age: { age } "
def main ():
tools = [
{
"type" : "function" ,
"function" : {
"name" : "UserDetail" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"name" : { "title" : "Name" , "type" : "string" },
"age" : { "title" : "Age" , "type" : "integer" }
},
"required" : [ "name" , "age" ]
}
}
},
{
"type" : "function" ,
"function" : {
"name" : "web_search" ,
"description" : "Search the web for information using Google Search." ,
"parameters" : {
"type" : "object" ,
"properties" : {
"query" : {
"type" : "string" ,
"description" : "The search query to be executed."
}
},
"required" : [ "query" ]
}
}
},
{
"type" : "function" ,
"function" : {
"name" : "general_ai" ,
"description" : "Use general AI knowledge to answer the question" ,
"parameters" : {
"type" : "object" ,
"properties" : {
"question" : { "type" : "string" , "description" : "The question to answer" }
},
"required" : [ "question" ]
}
}
}
]
agent = FunctionCallingAgent ( tools = tools )
llama = Julius ()
function_executor = FunctionExecutor ( llama )
user_input = input ( ">>> " )
function_call_data = agent . function_call_handler ( user_input )
print ( f"Function Call Data: { function_call_data } " )
try :
if "error" not in function_call_data :
function_name = function_call_data . get ( "tool_name" )
arguments = function_call_data . get ( "tool_input" , {})
execute_function = getattr ( function_executor , f"execute_ { function_name } " , None )
if execute_function :
result = execute_function ( arguments )
print ( "Function Execution Result:" )
for c in result :
print ( c , end = "" , flush = True )
else :
print ( f"Unknown function: { function_name } " )
else :
print ( f"Error: { function_call_data [ 'error' ] } " )
except Exception as e :
print ( f"An error occurred: { str ( e ) } " )
if __name__ == "__main__" :
main ()コードは他のプロバイダーに似ています。
LLM from webscout . LLM import LLM
# Read the system message from the file
with open ( 'system.txt' , 'r' ) as file :
system_message = file . read ()
# Initialize the LLM class with the model name and system message
llm = LLM ( model = "microsoft/WizardLM-2-8x22B" , system_message = system_message )
while True :
# Get the user input
user_input = input ( "User: " )
# Define the messages to be sent
messages = [
{ "role" : "user" , "content" : user_input }
]
# Use the mistral_chat method to get the response
response = llm . chat ( messages )
# Print the response
print ( "AI: " , response )WebScoutは、GGUFモデルをローカルで実行できるようになりました。最小限の構成でお気に入りのモデルをダウンロードして実行できます。
例：
from webscout . Local import *
model_path = download_model ( "Qwen/Qwen2.5-0.5B-Instruct-GGUF" , "qwen2.5-0.5b-instruct-q2_k.gguf" , token = None )
model = Model ( model_path , n_gpu_layers = 0 , context_length = 2048 )
thread = Thread ( model , format = chatml )
# print(thread.send("hi")) #send a single msg to ai
# thread.interact() # interact with the model in terminal
# start webui
# webui = WebUI(thread)
# webui.start(host="0.0.0.0", port=8080, ssl=True) #Use ssl=True and make cert and key for https WebScoutのローカルRAW-DOG機能を使用すると、端末プロンプト内でPythonスクリプトを実行できます。
例：
import webscout . Local as ws
from webscout . Local . rawdog import RawDog
from webscout . Local . samplers import DefaultSampling
from webscout . Local . formats import chatml , AdvancedFormat
from webscout . Local . utils import download_model
import datetime
import sys
import os
repo_id = "YorkieOH10/granite-8b-code-instruct-Q8_0-GGUF"
filename = "granite-8b-code-instruct.Q8_0.gguf"
model_path = download_model ( repo_id , filename , token = '' )
# Load the model using the downloaded path
model = ws . Model ( model_path , n_gpu_layers = 10 )
rawdog = RawDog ()
# Create an AdvancedFormat and modify the system content
# Use a lambda to generate the prompt dynamically:
chat_format = AdvancedFormat ( chatml )
# **Pre-format the intro_prompt string:**
system_content = f"""
You are a command-line coding assistant called Rawdog that generates and auto-executes Python scripts.
A typical interaction goes like this:
1. The user gives you a natural language PROMPT.
2. You:
i. Determine what needs to be done
ii. Write a short Python SCRIPT to do it
iii. Communicate back to the user by printing to the console in that SCRIPT
3. The compiler extracts the script and then runs it using exec(). If there will be an exception raised,
it will be send back to you starting with "PREVIOUS SCRIPT EXCEPTION:".
4. In case of exception, regenerate error free script.
If you need to review script outputs before completing the task, you can print the word "CONTINUE" at the end of your SCRIPT.
This can be useful for summarizing documents or technical readouts, reading instructions before
deciding what to do, or other tasks that require multi-step reasoning.
A typical 'CONTINUE' interaction looks like this:
1. The user gives you a natural language PROMPT.
2. You:
i. Determine what needs to be done
ii. Determine that you need to see the output of some subprocess call to complete the task
iii. Write a short Python SCRIPT to print that and then print the word "CONTINUE"
3. The compiler
i. Checks and runs your SCRIPT
ii. Captures the output and appends it to the conversation as "LAST SCRIPT OUTPUT:"
iii. Finds the word "CONTINUE" and sends control back to you
4. You again:
i. Look at the original PROMPT + the "LAST SCRIPT OUTPUT:" to determine what needs to be done
ii. Write a short Python SCRIPT to do it
iii. Communicate back to the user by printing to the console in that SCRIPT
5. The compiler...
Please follow these conventions carefully:
- Decline any tasks that seem dangerous, irreversible, or that you don't understand.
- Always review the full conversation prior to answering and maintain continuity.
- If asked for information, just print the information clearly and concisely.
- If asked to do something, print a concise summary of what you've done as confirmation.
- If asked a question, respond in a friendly, conversational way. Use programmatically-generated and natural language responses as appropriate.
- If you need clarification, return a SCRIPT that prints your question. In the next interaction, continue based on the user's response.
- Assume the user would like something concise. For example rather than printing a massive table, filter or summarize it to what's likely of interest.
- Actively clean up any temporary processes or files you use.
- When looking through files, use git as available to skip files, and skip hidden files (.env, .git, etc) by default.
- You can plot anything with matplotlib.
- ALWAYS Return your SCRIPT inside of a single pair of ``` delimiters. Only the console output of the first such SCRIPT is visible to the user, so make sure that it's complete and don't bother returning anything else.
"""
chat_format . override ( 'system_content' , lambda : system_content )
thread = ws . Thread ( model , format = chat_format , sampler = DefaultSampling )
while True :
prompt = input ( ">: " )
if prompt . lower () == "q" :
break
response = thread . send ( prompt )
# Process the response using RawDog
script_output = rawdog . main ( response )
if script_output :
print ( script_output )WebScoutは、オフラインLLMSで使用するために、抱き合っているフェイスモデルをGGUF形式に変換および量子化するツールを提供します。
例：
from webscout . Extra import gguf
"""
Valid quantization methods:
"q2_k", "q3_k_l", "q3_k_m", "q3_k_s",
"q4_0", "q4_1", "q4_k_m", "q4_k_s",
"q5_0", "q5_1", "q5_k_m", "q5_k_s",
"q6_k", "q8_0"
"""
gguf . convert (
model_id = "OEvortex/HelpingAI-Lite-1.5T" , # Replace with your model ID
username = "Abhaykoul" , # Replace with your Hugging Face username
token = "hf_token_write" , # Replace with your Hugging Face token
quantization_methods = "q4_k_m" # Optional, adjust quantization methods
) WebScoutのautollamaユーティリティは、顔を抱きしめるモデルをダウンロードし、オラマ対応に自動的にダウンロードします。
from webscout . Extra import autollama
model_path = "Vortex4ai/Jarvis-0.5B"
gguf_file = "test2-q4_k_m.gguf"
autollama . main ( model_path , gguf_file ) コマンドラインの使用：
GGUF変換：
python -m webscout.Extra.gguf -m " OEvortex/HelpingAI-Lite-1.5T " -u " your_username " -t " your_hf_token " -q " q4_k_m,q5_k_m " Autollama：
python -m webscout.Extra.autollama -m " OEvortex/HelpingAI-Lite-1.5T " -g " HelpingAI-Lite-1.5T.q4_k_m.gguf " 注記：
"your_username"と"your_hf_token"実際の抱きしめている顔の資格情報に置き換えます。autollamaのmodel_pathはハグするフェイスモデルIDであり、 gguf_file GGUFファイルIDです。 WebaiターミナルGPTとオープンインタープリターpython -m webscout.webai webai --provider " phind " --rawdog貢献は大歓迎です！ WebScoutに貢献したい場合は、次の手順に従ってください。
