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ai: Introduce new API of J.A.R.V.I.S.
Browse filesSupport streaming, real-time response mode.
- assets/bin/ai +101 -18
- assets/bin/install.sh +36 -0
- docs/API.md +6 -15
assets/bin/ai
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#
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import sys
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from gradio_client import Client
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from rich.console import Console
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from rich.markdown import Markdown
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console = Console()
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jarvis = Client("hadadrjt/ai")
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args = sys.argv[1:]
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deep_search = False
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if "-d" in args:
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deep_search = True
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args.remove("-d")
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jarvis.predict(new=model, api_name="/change_model")
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console.print("[bold red]Deep Search is only available for model JARVIS: 2.1.2.[/bold red]")
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console.print("------------------------------------------------------")
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console.print("")
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deep_search = False
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if deep_search:
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deep_search=True,
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api_name="/respond_async")
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else:
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#
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import sys
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import re
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from gradio_client import Client
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from rich.console import Console, Group
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from rich.markdown import Markdown
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from rich.syntax import Syntax
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from rich.live import Live
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# Prepares the display screen to show the final output
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console = Console()
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# Creates a connection to the server
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jarvis = Client("hadadrjt/ai")
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# Defines the specific AI model to use for responding
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# Change to the model you want, see:
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# https://huggingface.co/spaces/hadadrjt/ai/blob/main/docs/API.md#multi-platform
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model = "JARVIS: 2.1.3"
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# Reads user-provided input from the command line, if available
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args = sys.argv[1:]
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# Keeps track of whether deep search mode is activated
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deep_search = False
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# Checks if the input includes "-d", which turns on deep search
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if "-d" in args:
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deep_search = True
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args.remove("-d")
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# Combines the rest of the input into one complete message
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# If nothing was typed, it defaults to a basic greeting
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input = " ".join(args) if args else "Hi!"
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# Sets the AI to the desired version before sending any messages
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jarvis.predict(new=model, api_name="/change_model")
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# Ensures deep search is only enabled for the correct AI model
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if deep_search and model != "JARVIS: 2.1.3":
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deep_search = False
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# Prepares and structures the AI’s response for display
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def layout(text):
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# Searches for blocks of code within the full response text
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code_blocks = list(re.finditer(r"\n\n```(.*?)\n\n(.*?)\n\n```\n\n\n", text, re.DOTALL))
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segments = [] # Stores parts of the final display, including both text and code
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last_end = 0 # Tracks where the previous segment ended
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# Loops through each code block found in the AI's response
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for block in code_blocks:
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# Collects any normal text before the current code block
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pre_text = text[last_end:block.start()]
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if pre_text.strip():
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# Converts plain text into styled text for easier reading
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segments.append(Markdown(prepare_markdown(pre_text.strip())))
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# Identifies the programming language used in the code block, if available
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lang = block.group(1).strip() or "text"
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# Extracts the actual code content
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code = block.group(2).rstrip()
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# Formats the code with syntax highlighting for clear presentation
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segments.append(Syntax(code, lang, theme="monokai", line_numbers=False, word_wrap=True))
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# Updates the position tracker to move past the current code block
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last_end = block.end()
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# Checks for any remaining text after the last code block
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remaining = text[last_end:]
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if remaining.strip():
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# Formats and adds this final portion of text to the display
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segments.append(Markdown(prepare_markdown(remaining.strip())))
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# Returns a complete set of styled segments ready to be shown
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return Group(*segments)
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# Adjusts special characters in the text to ensure they display correctly
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def prepare_markdown(text):
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return text.replace("•", "*")
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# Displays the AI's response in real time as it’s being received
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def response(jarvis):
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buffer = "" # Holds the entire reply as it builds up
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with Live(console=console, transient=False) as live:
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# Continuously receives and processes parts of the reply
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for partial in jarvis:
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# Extracts the latest version of the message from the AI
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text = partial[0][0][1]
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# Determines what has changed since the last update
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if text.startswith(buffer):
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delta = text[len(buffer):]
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else:
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delta = text
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# Updates the full message with any new content
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buffer = text
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# Refreshes the screen with the most recent version of the reply
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live.update(layout(buffer))
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# Ensures the final reply is printed once it’s complete
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console.print()
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# Sends the user's input to the AI and selects the appropriate method
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if deep_search:
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# Uses the deeper, slower method for more thoughtful responses
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jarvis = jarvis.submit(multi={"text": input}, deep_search=True, api_name="/respond_async")
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else:
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# Uses the standard method for quicker, more direct replies
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jarvis = jarvis.submit(multi={"text": input}, api_name="/api")
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# Create a line break before the main AI's response
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print("")
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# Begins the process of showing the AI's response on the screen
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response(jarvis)
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assets/bin/install.sh
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#!/bin/sh
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#
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# SPDX-FileCopyrightText: Hadad <hadad@linuxmail.org>
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# SPDX-License-Identifier: Apache-2.0
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#
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echo "Installing required Python packages..."
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pip install gradio_client rich --upgrade
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echo "Installation complete."
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echo ""
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echo ""
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echo "Downloading the J.A.R.V.I.S. script..."
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wget https://huggingface.co/spaces/hadadrjt/ai/raw/main/assets/bin/ai
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echo "Download complete."
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echo ""
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echo ""
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echo "Setting executable permission..."
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chmod a+x ai
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echo "Permission set."
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echo ""
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echo ""
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echo "Removing installer script..."
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rm install.sh
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echo "Done."
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echo ""
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echo ""
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echo "To send a regular message:"
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echo "./ai Your message here"
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echo ""
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echo "To use Deep Search mode:"
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echo "./ai -d Your message here"
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echo ""
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echo ""
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echo "For more details and advanced options, visit:"
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echo "https://huggingface.co/spaces/hadadrjt/ai/blob/main/docs/API.md#installations"
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echo ""
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docs/API.md
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# Make sure you have "wget", "python3" and "pip" installed.
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# This package have very small size.
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```
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#### DOWNLOAD JARVIS SCRIPT
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```bash
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# Terminal script.
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wget https://huggingface.co/spaces/hadadrjt/ai/raw/main/assets/bin/ai
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# Set permission.
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chmod a+x ai
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```
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### RUN JARVIS IN YOUR TERMINAL
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```bash
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# Example normal usage.
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./ai
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# Example with Deep Search.
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./ai -d
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```
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#### LINUX USER's
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sudo mv ai /bin/
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# Now you can run with simple command.
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ai
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```
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### MULTI PLATFORM
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```
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Edit JARVIS terminal script, and find this code:
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model = "JARVIS: 2.1.
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Choose one of the model name for the JARVIS multi platform.
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Example:
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model = "DeepSeek: V3-0324"
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Please note that the Deep Search feature is only available for JARVIS: 2.1.
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```
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# Make sure you have "wget", "python3" and "pip" installed.
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# This package have very small size.
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wget https://huggingface.co/spaces/hadadrjt/ai/raw/main/assets/bin/install.sh && chmod a+x install.sh && ./install.sh
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```
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### RUN JARVIS IN YOUR TERMINAL
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```bash
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# Example normal usage.
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./ai Your message here.
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# Example with Deep Search.
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./ai -d Your message here.
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```
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#### LINUX USER's
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sudo mv ai /bin/
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# Now you can run with simple command.
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ai Your message here.
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```
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### MULTI PLATFORM
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```
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Edit JARVIS terminal script, and find this code:
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model = "JARVIS: 2.1.3"
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Choose one of the model name for the JARVIS multi platform.
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Example:
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model = "DeepSeek: V3-0324"
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Please note that the Deep Search feature is only available for JARVIS: 2.1.3.
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```
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