Webscout
1.0.0
搜索使用Google，DuckDuckgo，Phind.com，訪問AI模型，轉錄YouTube視頻，生成臨時電子郵件和電話號碼，使用文本到語音，Leverage Webai（終端GPT和OPEN解釋器）以及探索離線LLMS等等！
rawdog功能直接在您的終端內執行Python腳本。 pip install - U webscout python - m webscout - - help| 命令 | 描述 |
|---|---|
| Python -M WebScout答案-K文本 | CLI功能使用WebScout執行答案搜索。 |
| Python -M WebScout圖像-K文本 | CLI功能使用WebScout執行圖像搜索。 |
| Python -M WebScout Maps -k文本 | CLI功能使用WebScout執行地圖搜索。 |
| Python -M WebScout News -K文字 | 使用WebScout執行新聞搜索的CLI功能。 |
| Python -M WebScout建議-K文本 | CLI功能使用WebScout執行建議搜索。 |
| Python -M WebScout文本-K文本 | CLI功能使用WebScout執行文本搜索。 |
| Python -M WebScout Translate -k文本 | CLI函數使用WebScout執行翻譯。 |
| Python -M WebScout版本 | 打印和返回程序版本的命令行接口命令。 |
| Python -M WebScout視頻-K文字 | 使用DuckDuckgo API執行視頻搜索的CLI功能。 |
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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
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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中的transcriber功能是轉錄YouTube視頻的方便工具。
例子：
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和AsyncWEBS課程用於從duckduckgo.com檢索搜索結果。
要使用AsyncWEBS類，您可以使用Python的asyncio庫執行異步操作。
要初始化WEBS或AsyncWEBS類別類的實例，您可以提供以下可選參數：
示例 - 網絡：
from webscout import WEBS
R = WEBS (). text ( "python programming" , max_results = 5 )
print ( R )示例 - 異步：
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 ()重要說明： WEBS和AsyncWEBS課程應始終用作上下文管理器（帶有語句）。這確保了正確的資源管理和清理，因為上下文管理器將自動處理打開和關閉HTTP客戶端連接。
例外：
WebscoutE ：在API請求期間有通用例外時提出。 text() - duckduckgo.com的文本搜索 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的即時答案 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的圖像搜索 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的視頻搜索 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的新聞搜索 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的地圖搜索 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的翻譯 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的建議 from webscout import WEBS
# Suggestions for the keyword 'fly' using DuckDuckGo.com
with WEBS () as WEBS :
for r in WEBS . suggestions ( "fly" ):
print ( r )position訪調員language文學評論家注意：使用提示時，某些“行為”使用佔位符（例如position或language ，應由特定值替換。
每個TTI提供商都有相同的用法代碼，您只需要更改導入即可。
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與LLM聊天 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使用phind.com搜索 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 )使用phindv2：
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 - 與Google Gemini一起搜索 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搜索/與BlackBox聊天 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 - 困惑搜索 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與meta ai聊天 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與Reka聊天 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與Cohere聊天 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與DeepSeek聊天 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與Meta的Llama 3 70B聊天 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 ()代碼類似於其他提供商。
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現在可以在本地運行GGUF模型。您可以使用最小的配置下載並運行自己喜歡的型號。
例子：
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的本地RAW-DOG功能使您可以在終端提示中運行Python腳本。
例子：
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提供了將擁抱面模型轉換為GGGUF格式的工具，以供離線LLMS使用。
例子：
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的autollama Utility從擁抱臉上下載了模型，然後自動使其成為Ollama準備。
from webscout . Extra import autollama
model_path = "Vortex4ai/Jarvis-0.5B"
gguf_file = "test2-q4_k_m.gguf"
autollama . main ( model_path , gguf_file ) 命令行的用法：
GGUF轉換：
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 " 筆記：
"your_username"和"your_hf_token" 。autollama中的model_path是擁抱面部模型ID， gguf_file是GGGUF文件ID。 Webai終端GPT和開放口譯員python -m webscout.webai webai --provider " phind " --rawdog歡迎捐款！如果您想為WebScout做出貢獻，請執行以下步驟：
