From e2ff17af50dedd582b95488700b007037c428262 Mon Sep 17 00:00:00 2001 From: killian <63927363+KillianLucas@users.noreply.github.com> Date: Fri, 31 May 2024 13:01:13 -0700 Subject: [PATCH] Local II --- interpreter/core/archived_server.py | 162 + interpreter/core/computer/ai/ai.py | 16 + interpreter/core/computer/display/display.py | 43 +- .../core/computer/utils/computer_vision.py | 11 +- interpreter/core/computer/vision/vision.py | 92 +- interpreter/core/core.py | 9 + interpreter/core/llm/llm.py | 10 + .../llm/utils/convert_to_openai_messages.py | 8 +- interpreter/core/server.py | 436 ++- interpreter/core/utils/truncate_output.py | 3 + .../contributing_conversations.py | 51 +- .../conversation_navigator.py | 9 +- interpreter/terminal_interface/local_setup.py | 407 ++ .../profiles/defaults/local-os.py | 472 +-- .../profiles/defaults/local.py | 567 +-- poetry.lock | 3479 ++++++++--------- pyproject.toml | 3 +- 17 files changed, 2978 insertions(+), 2800 deletions(-) create mode 100644 interpreter/core/archived_server.py create mode 100644 interpreter/terminal_interface/local_setup.py diff --git a/interpreter/core/archived_server.py b/interpreter/core/archived_server.py new file mode 100644 index 000000000..de426c229 --- /dev/null +++ b/interpreter/core/archived_server.py @@ -0,0 +1,162 @@ +import asyncio +import json +from typing import Generator + +from .utils.lazy_import import lazy_import + +uvicorn = lazy_import("uvicorn") +fastapi = lazy_import("fastapi") + + +def server(interpreter, host="0.0.0.0", port=8000): + FastAPI, Request, Response, WebSocket = ( + fastapi.FastAPI, + fastapi.Request, + fastapi.Response, + fastapi.WebSocket, + ) + PlainTextResponse = fastapi.responses.PlainTextResponse + + app = FastAPI() + + @app.post("/chat") + async def stream_endpoint(request: Request) -> Response: + async def event_stream() -> Generator[str, None, None]: + data = await request.json() + for response in interpreter.chat(message=data["message"], stream=True): + yield response + + return Response(event_stream(), media_type="text/event-stream") + + # Post endpoint + # @app.post("/iv0", response_class=PlainTextResponse) + # async def i_post_endpoint(request: Request): + # message = await request.body() + # message = message.decode("utf-8") # Convert bytes to string + + # async def event_stream() -> Generator[str, None, None]: + # for response in interpreter.chat( + # message=message, stream=True, display=False + # ): + # if ( + # response.get("type") == "message" + # and response["role"] == "assistant" + # and "content" in response + # ): + # yield response["content"] + "\n" + # if ( + # response.get("type") == "message" + # and response["role"] == "assistant" + # and response.get("end") == True + # ): + # yield " \n" + + # return StreamingResponse(event_stream(), media_type="text/plain") + + @app.get("/test") + async def test_ui(): + return PlainTextResponse( + """ + + + + Chat + + +
+ + +
+
+ + + + """, + media_type="text/html", + ) + + @app.websocket("/") + async def i_test(websocket: WebSocket): + await websocket.accept() + while True: + data = await websocket.receive_text() + while data.strip().lower() != "stop": # Stop command + task = asyncio.create_task(websocket.receive_text()) + + # This would be terrible for production. Just for testing. + try: + data_dict = json.loads(data) + if set(data_dict.keys()) == {"role", "content", "type"} or set( + data_dict.keys() + ) == {"role", "content", "type", "format"}: + data = data_dict + except json.JSONDecodeError: + pass + + for response in interpreter.chat( + message=data, stream=True, display=False + ): + if task.done(): + data = task.result() # Get the new message + break # Break the loop and start processing the new message + # Send out assistant message chunks + if ( + response.get("type") == "message" + and response["role"] == "assistant" + and "content" in response + ): + await websocket.send_text(response["content"]) + await asyncio.sleep(0.01) # Add a small delay + if ( + response.get("type") == "message" + and response["role"] == "assistant" + and response.get("end") == True + ): + await websocket.send_text("\n") + await asyncio.sleep(0.01) # Add a small delay + if not task.done(): + data = ( + await task + ) # Wait for the next message if it hasn't arrived yet + + print( + "\nOpening a simple `interpreter.chat(data)` POST endpoint at http://localhost:8000/chat." + ) + print( + "Opening an `i.protocol` compatible WebSocket endpoint at http://localhost:8000/." + ) + print("\nVisit http://localhost:8000/test to test the WebSocket endpoint.\n") + + import socket + + hostname = socket.gethostname() + local_ip = socket.gethostbyname(hostname) + local_url = f"http://{local_ip}:8000" + print(f"Local URL: {local_url}\n") + + uvicorn.run(app, host=host, port=port) diff --git a/interpreter/core/computer/ai/ai.py b/interpreter/core/computer/ai/ai.py index 85a8ed6e4..91cc892a9 100644 --- a/interpreter/core/computer/ai/ai.py +++ b/interpreter/core/computer/ai/ai.py @@ -117,6 +117,22 @@ class Ai: def __init__(self, computer): self.computer = computer + def chat(self, text): + old_messages = self.computer.interpreter.llm.interpreter.messages + old_system_message = self.computer.interpreter.llm.interpreter.system_message + try: + self.computer.interpreter.llm.interpreter.system_message = ( + "You are an AI assistant." + ) + self.computer.interpreter.llm.interpreter.messages = [] + response = self.computer.interpreter.llm.interpreter.chat(text) + finally: + self.computer.interpreter.llm.interpreter.messages = old_messages + self.computer.interpreter.llm.interpreter.system_message = ( + old_system_message + ) + return response[-1].get("content") + def query(self, text, query, custom_reduce_query=None): if custom_reduce_query == None: custom_reduce_query = query diff --git a/interpreter/core/computer/display/display.py b/interpreter/core/computer/display/display.py index 7a875cb1a..c80976706 100644 --- a/interpreter/core/computer/display/display.py +++ b/interpreter/core/computer/display/display.py @@ -94,26 +94,31 @@ def screenshot( :param screen: specify which display; 0 for primary and 1 and above for secondary. :param combine_screens: If True, a collage of all display screens will be returned. Otherwise, a list of display screens will be returned. """ - if not self.computer.emit_images and force_image == False: - screenshot = self.screenshot(show=False, force_image=True) - description = self.computer.vision.query(pil_image=screenshot) - print("A DESCRIPTION OF WHAT'S ON THE SCREEN: " + description) - - if self.computer.max_output > 600: - print("ALL OF THE TEXT ON THE SCREEN: ") - text = self.get_text_as_list_of_lists(screenshot=screenshot) - pp = pprint.PrettyPrinter(indent=4) - pretty_text = pp.pformat(text) # language models like it pretty! - pretty_text = format_to_recipient(pretty_text, "assistant") - print(pretty_text) - print( - format_to_recipient( - "To recieve the text above as a Python object, run computer.display.get_text_as_list_of_lists()", - "assistant", - ) - ) - return screenshot # Still return a PIL image + # Since Local II, all images sent to local models will be rendered to text with moondream and pytesseract. + # So we don't need to do this hereβ€” we can just emit images. + # We should probably remove self.computer.emit_images for this reason. + + # if not self.computer.emit_images and force_image == False: + # screenshot = self.screenshot(show=False, force_image=True) + + # description = self.computer.vision.query(pil_image=screenshot) + # print("A DESCRIPTION OF WHAT'S ON THE SCREEN: " + description) + + # if self.computer.max_output > 600: + # print("ALL OF THE TEXT ON THE SCREEN: ") + # text = self.get_text_as_list_of_lists(screenshot=screenshot) + # pp = pprint.PrettyPrinter(indent=4) + # pretty_text = pp.pformat(text) # language models like it pretty! + # pretty_text = format_to_recipient(pretty_text, "assistant") + # print(pretty_text) + # print( + # format_to_recipient( + # "To recieve the text above as a Python object, run computer.display.get_text_as_list_of_lists()", + # "assistant", + # ) + # ) + # return screenshot # Still return a PIL image if quadrant == None: if active_app_only: diff --git a/interpreter/core/computer/utils/computer_vision.py b/interpreter/core/computer/utils/computer_vision.py index 94c00d608..70bb31329 100644 --- a/interpreter/core/computer/utils/computer_vision.py +++ b/interpreter/core/computer/utils/computer_vision.py @@ -11,16 +11,7 @@ def pytesseract_get_text(img): - # Convert PIL Image to NumPy array - img_array = np.array(img) - - # Convert the image to grayscale - gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY) - - # Use pytesseract to get the text from the image - text = pytesseract.image_to_string(gray) - - return text + return pytesseract.image_to_string(img) def pytesseract_get_text_bounding_boxes(img): diff --git a/interpreter/core/computer/vision/vision.py b/interpreter/core/computer/vision/vision.py index c75abcd76..f99370597 100644 --- a/interpreter/core/computer/vision/vision.py +++ b/interpreter/core/computer/vision/vision.py @@ -1,9 +1,13 @@ import base64 +import contextlib import io +import os +import tempfile from PIL import Image from ...utils.lazy_import import lazy_import +from ..utils.computer_vision import pytesseract_get_text # transformers = lazy_import("transformers") # Doesn't work for some reason! We import it later. @@ -17,14 +21,19 @@ def __init__(self, computer): def load(self): import transformers # Wait until we use it. Transformers can't be lazy loaded for some reason! - print( - "Open Interpreter will use Moondream (tiny vision model) to describe images to the language model. Set `interpreter.llm.vision_renderer = None` to disable this behavior." - ) - print( - "Alternativley, you can use a vision-supporting LLM and set `interpreter.llm.supports_vision = True`." - ) + os.environ["TOKENIZERS_PARALLELISM"] = "false" + + if self.computer.debug: + print( + "Open Interpreter will use Moondream (tiny vision model) to describe images to the language model. Set `interpreter.llm.vision_renderer = None` to disable this behavior." + ) + print( + "Alternativley, you can use a vision-supporting LLM and set `interpreter.llm.supports_vision = True`." + ) model_id = "vikhyatk/moondream2" revision = "2024-04-02" + print("loading model") + self.model = transformers.AutoModelForCausalLM.from_pretrained( model_id, trust_remote_code=True, revision=revision ) @@ -32,6 +41,59 @@ def load(self): model_id, revision=revision ) + def ocr( + self, + base_64=None, + path=None, + lmc=None, + pil_image=None, + ): + """ + Gets OCR of image. + """ + + if lmc: + if "base64" in lmc["format"]: + # # Extract the extension from the format, default to 'png' if not specified + # if "." in lmc["format"]: + # extension = lmc["format"].split(".")[-1] + # else: + # extension = "png" + # Save the base64 content as a temporary file + img_data = base64.b64decode(lmc["content"]) + with tempfile.NamedTemporaryFile( + delete=False, suffix=".png" + ) as temp_file: + temp_file.write(img_data) + temp_file_path = temp_file.name + + # Set path to the path of the temporary file + path = temp_file_path + + elif lmc["format"] == "path": + # Convert to base64 + path = lmc["content"] + elif base_64: + # Save the base64 content as a temporary file + img_data = base64.b64decode(base_64) + with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file: + temp_file.write(img_data) + temp_file_path = temp_file.name + + # Set path to the path of the temporary file + path = temp_file_path + elif path: + pass + elif pil_image: + with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_file: + pil_image.save(temp_file, format="PNG") + temp_file_path = temp_file.name + + # Set path to the path of the temporary file + path = temp_file_path + + return pytesseract_get_text(path) + def query( self, query="Describe this image.", @@ -45,7 +107,16 @@ def query( """ if self.model == None and self.tokenizer == None: - self.load() + try: + with contextlib.redirect_stdout( + open(os.devnull, "w") + ), contextlib.redirect_stderr(open(os.devnull, "w")): + self.load() + except ImportError: + self.computer.interpreter.display_markdown_message( + "\nTo use local vision, run `pip install 'open-interpreter[local]'`.\n" + ) + return "" if lmc: if "base64" in lmc["format"]: @@ -71,5 +142,8 @@ def query( elif pil_image: img = pil_image - enc_image = self.model.encode_image(img) - return self.model.answer_question(enc_image, query, self.tokenizer) + with contextlib.redirect_stdout(open(os.devnull, "w")): + enc_image = self.model.encode_image(img) + answer = self.model.answer_question(enc_image, query, self.tokenizer) + + return answer diff --git a/interpreter/core/core.py b/interpreter/core/core.py index 1a07da9a4..b6b6d2375 100644 --- a/interpreter/core/core.py +++ b/interpreter/core/core.py @@ -8,6 +8,7 @@ import time from datetime import datetime +from ..terminal_interface.local_setup import local_setup from ..terminal_interface.terminal_interface import terminal_interface from ..terminal_interface.utils.display_markdown_message import display_markdown_message from ..terminal_interface.utils.local_storage_path import get_storage_path @@ -68,6 +69,7 @@ def __init__( system_message=default_system_message, custom_instructions="", user_message_template="{content}", + always_apply_user_message_template=False, code_output_template="Code output: {content}\n\nWhat does this output mean / what's next (if anything, or are we done)?", empty_code_output_template="The code above was executed on my machine. It produced no text output. what's next (if anything, or are we done?)", code_output_sender="user", @@ -129,6 +131,7 @@ def __init__( self.system_message = system_message self.custom_instructions = custom_instructions self.user_message_template = user_message_template + self.always_apply_user_message_template = always_apply_user_message_template self.code_output_template = code_output_template self.empty_code_output_template = empty_code_output_template self.code_output_sender = code_output_sender @@ -136,6 +139,12 @@ def __init__( def server(self, *args, **kwargs): server(self, *args, **kwargs) + def local_setup(self): + """ + Opens a wizard that lets terminal users pick a local model. + """ + self = local_setup(self) + def wait(self): while self.responding: time.sleep(0.2) diff --git a/interpreter/core/llm/llm.py b/interpreter/core/llm/llm.py index 185e285c8..fe09d2cff 100644 --- a/interpreter/core/llm/llm.py +++ b/interpreter/core/llm/llm.py @@ -84,6 +84,13 @@ def run(self, messages): except: self.supports_vision = False + # Setup our model endpoint + if self.model == "i": + self.model = "openai/i" + self.context_window = 7000 + self.max_tokens = 1000 + self.api_base = "https://api.openinterpreter.com/v0" + # Trim image messages if they're there image_messages = [msg for msg in messages if msg["type"] == "image"] if self.supports_vision: @@ -108,6 +115,9 @@ def run(self, messages): img_msg["content"] = ( "Imagine I have just shown you an image with this description: " + self.vision_renderer(lmc=img_msg) + + "\n---\nThe image contains the following text exactly, extracted via OCR: '''\n" + + self.interpreter.computer.vision.ocr(lmc=img_msg) + + "\n'''" ) img_msg["format"] = "description" diff --git a/interpreter/core/llm/utils/convert_to_openai_messages.py b/interpreter/core/llm/utils/convert_to_openai_messages.py index 2731c5c43..d4595bcfc 100644 --- a/interpreter/core/llm/utils/convert_to_openai_messages.py +++ b/interpreter/core/llm/utils/convert_to_openai_messages.py @@ -42,9 +42,11 @@ def convert_to_openai_messages( "role" ] # This should never be `computer`, right? - if ( - message["role"] == "user" and message == messages[-1] - ): # Only add the template for the last message? + if message["role"] == "user" and ( + message == messages[-1] + or interpreter.always_apply_user_message_template + ): + # Only add the template for the last message? new_message["content"] = interpreter.user_message_template.replace( "{content}", message["content"] ) diff --git a/interpreter/core/server.py b/interpreter/core/server.py index ec316ce77..a1bd4e1fb 100644 --- a/interpreter/core/server.py +++ b/interpreter/core/server.py @@ -1,155 +1,309 @@ +# This is a websocket interpreter, TTS and STT disabled. +# It makes a websocket on a port that sends/receives LMC messages in *streaming* format. + +### You MUST send a start and end flag with each message! For example: ### + +""" +{"role": "user", "type": "message", "start": True}) +{"role": "user", "type": "message", "content": "hi"}) +{"role": "user", "type": "message", "end": True}) +""" + import asyncio import json -from typing import Generator +import os +import threading -from .utils.lazy_import import lazy_import +### +# from pynput import keyboard +# from RealtimeTTS import TextToAudioStream, OpenAIEngine, CoquiEngine +# from RealtimeSTT import AudioToTextRecorder +# from beeper import Beeper +import time +import traceback +from typing import Any, Dict, List -uvicorn = lazy_import("uvicorn") -fastapi = lazy_import("fastapi") +import uvicorn +from fastapi import FastAPI, Header, WebSocket +from fastapi.middleware.cors import CORSMiddleware +from openai import OpenAI +from pydantic import BaseModel +# import argparse +# from profiles.default import interpreter +# from interpreter import interpreter -def server(interpreter, host="0.0.0.0", port=8000): - FastAPI, Request, Response, WebSocket = ( - fastapi.FastAPI, - fastapi.Request, - fastapi.Response, - fastapi.WebSocket, - ) - PlainTextResponse = fastapi.responses.PlainTextResponse +# Parse command line arguments for port number +# parser = argparse.ArgumentParser(description="FastAPI server.") +# parser.add_argument("--port", type=int, default=63863, help="Port to run on.") +# args = parser.parse_args() - app = FastAPI() - @app.post("/chat") - async def stream_endpoint(request: Request) -> Response: - async def event_stream() -> Generator[str, None, None]: - data = await request.json() - for response in interpreter.chat(message=data["message"], stream=True): - yield response - - return Response(event_stream(), media_type="text/event-stream") - - # Post endpoint - # @app.post("/iv0", response_class=PlainTextResponse) - # async def i_post_endpoint(request: Request): - # message = await request.body() - # message = message.decode("utf-8") # Convert bytes to string - - # async def event_stream() -> Generator[str, None, None]: - # for response in interpreter.chat( - # message=message, stream=True, display=False - # ): - # if ( - # response.get("type") == "message" - # and response["role"] == "assistant" - # and "content" in response - # ): - # yield response["content"] + "\n" - # if ( - # response.get("type") == "message" - # and response["role"] == "assistant" - # and response.get("end") == True - # ): - # yield " \n" - - # return StreamingResponse(event_stream(), media_type="text/plain") - - @app.get("/test") - async def test_ui(): - return PlainTextResponse( - """ - - - - Chat - - -
- - -
-
- - - - """, - media_type="text/html", +# interpreter.tts = "openai" + + +class Settings(BaseModel): + auto_run: bool + custom_instructions: str + model: str + + +class AsyncInterpreter: + def __init__(self, interpreter): + self.interpreter = interpreter + + # STT + # self.stt = AudioToTextRecorder(use_microphone=False) + # self.stt.stop() # It needs this for some reason + + # TTS + # if self.interpreter.tts == "coqui": + # engine = CoquiEngine() + # elif self.interpreter.tts == "openai": + # engine = OpenAIEngine() + # self.tts = TextToAudioStream(engine) + + self.active_chat_messages = [] + + # Clock + # clock() + + # self.beeper = Beeper() + + # Startup sounds + # self.beeper.beep("Blow") + # self.tts.feed("Hi, how can I help you?") + # self.tts.play_async(on_audio_chunk=self.on_tts_chunk, muted=True) + + self._input_queue = asyncio.Queue() # Queue that .input will shove things into + self._output_queue = asyncio.Queue() # Queue to put output chunks into + self._last_lmc_start_flag = None # Unix time of last LMC start flag received + self._in_keyboard_write_block = ( + False # Tracks whether interpreter is trying to use the keyboard ) - @app.websocket("/") - async def i_test(websocket: WebSocket): - await websocket.accept() - while True: - data = await websocket.receive_text() - while data.strip().lower() != "stop": # Stop command - task = asyncio.create_task(websocket.receive_text()) - - # This would be terrible for production. Just for testing. - try: - data_dict = json.loads(data) - if set(data_dict.keys()) == {"role", "content", "type"} or set( - data_dict.keys() - ) == {"role", "content", "type", "format"}: - data = data_dict - except json.JSONDecodeError: - pass - - for response in interpreter.chat( - message=data, stream=True, display=False - ): - if task.done(): - data = task.result() # Get the new message - break # Break the loop and start processing the new message - # Send out assistant message chunks - if ( - response.get("type") == "message" - and response["role"] == "assistant" - and "content" in response - ): - await websocket.send_text(response["content"]) - await asyncio.sleep(0.01) # Add a small delay + self.loop = asyncio.get_event_loop() + + async def _add_to_queue(self, queue, item): + print(f"Adding item to output", item) + await queue.put(item) + + async def clear_queue(self, queue): + while not queue.empty(): + await queue.get() + + async def clear_input_queue(self): + await self.clear_queue(self._input_queue) + + async def clear_output_queue(self): + await self.clear_queue(self._output_queue) + + async def input(self, chunk): + """ + Expects a chunk in streaming LMC format. + """ + if isinstance(chunk, bytes): + # It's probably a chunk of audio + # self.stt.feed_audio(chunk) + pass + else: + try: + chunk = json.loads(chunk) + except: + pass + + if "start" in chunk: + # self.stt.start() + self._last_lmc_start_flag = time.time() + # self.interpreter.computer.terminal.stop() # Stop any code execution... maybe we should make interpreter.stop()? + elif "end" in chunk: + print("yep") + asyncio.create_task(self.run()) + else: + await self._add_to_queue(self._input_queue, chunk) + + def add_to_output_queue_sync(self, chunk): + """ + Synchronous function to add a chunk to the output queue. + """ + print("ADDING TO QUEUE:", chunk) + asyncio.create_task(self._add_to_queue(self._output_queue, chunk)) + + async def run(self): + """ + Runs OI on the audio bytes submitted to the input. Will add streaming LMC chunks to the _output_queue. + """ + print("heyyyy") + self.interpreter.messages = self.active_chat_messages + # interpreter.messages = self.active_chat_messages + # self.beeper.start() + + # self.stt.stop() + # message = self.stt.text() + # print("THE MESSAGE:", message) + + input_queue = list(self._input_queue._queue) + message = [i for i in input_queue if i["type"] == "message"][0]["content"] + + def generate(message): + last_lmc_start_flag = self._last_lmc_start_flag + self.interpreter.messages = self.active_chat_messages + # interpreter.messages = self.active_chat_messages + print("πŸ€πŸ€πŸ€πŸ€GENERATING, using these messages: ", self.interpreter.messages) + print("πŸ€ πŸ€ πŸ€ πŸ€ active_chat_messages: ", self.active_chat_messages) + print("passing this in:", message) + for chunk in self.interpreter.chat(message, display=False, stream=True): + print("FROM INTERPRETER. CHUNK:", chunk) + + if self._last_lmc_start_flag != last_lmc_start_flag: + # self.beeper.stop() + break + + # self.add_to_output_queue_sync(chunk) # To send text, not just audio + + content = chunk.get("content") + + # Handle message blocks + if chunk.get("type") == "message": + self.add_to_output_queue_sync(chunk) # To send text, not just audio + if content: + # self.beeper.stop() + + # Experimental: The AI voice sounds better with replacements like these, but it should happen at the TTS layer + # content = content.replace(". ", ". ... ").replace(", ", ", ... ").replace("!", "! ... ").replace("?", "? ... ") + + yield content + + # Handle code blocks + elif chunk.get("type") == "code": + # if "start" in chunk: + # self.beeper.start() + + # Experimental: If the AI wants to type, we should type immediately if ( - response.get("type") == "message" - and response["role"] == "assistant" - and response.get("end") == True + self.interpreter.messages[-1] + .get("content", "") + .startswith("computer.keyboard.write(") ): - await websocket.send_text("\n") - await asyncio.sleep(0.01) # Add a small delay - if not task.done(): - data = ( - await task - ) # Wait for the next message if it hasn't arrived yet - - print( - "\nOpening a simple `interpreter.chat(data)` POST endpoint at http://localhost:8000/chat." - ) - print( - "Opening an `i.protocol` compatible WebSocket endpoint at http://localhost:8000/." + keyboard.controller.type(content) + self._in_keyboard_write_block = True + if "end" in chunk and self._in_keyboard_write_block: + self._in_keyboard_write_block = False + # (This will make it so it doesn't type twice when the block executes) + if self.interpreter.messages[-1]["content"].startswith( + "computer.keyboard.write(" + ): + self.interpreter.messages[-1]["content"] = ( + "dummy_variable = (" + + self.interpreter.messages[-1]["content"][ + len("computer.keyboard.write(") : + ] + ) + + # Send a completion signal + self.add_to_output_queue_sync( + {"role": "server", "type": "completion", "content": "DONE"} + ) + + # Feed generate to RealtimeTTS + # self.tts.feed(generate(message)) + for _ in generate(message): + pass + # self.tts.play_async(on_audio_chunk=self.on_tts_chunk, muted=True) + + async def output(self): + return await self._output_queue.get() + + +def server(interpreter): + interpreter.llm.model = "gpt-4" + interpreter = AsyncInterpreter(interpreter) + + app = FastAPI() + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], # Allow all methods (GET, POST, etc.) + allow_headers=["*"], # Allow all headers ) - print("\nVisit http://localhost:8000/test to test the WebSocket endpoint.\n") - uvicorn.run(app, host=host, port=port) + @app.post("/load") + async def load(messages: List[Dict[str, Any]], settings: Settings): + # Load messages + interpreter.interpreter.messages = messages + print("πŸͺΌπŸͺΌπŸͺΌπŸͺΌπŸͺΌπŸͺΌ Messages loaded: ", interpreter.interpreter.messages) + + # Load Settings + interpreter.interpreter.llm.model = settings.model + interpreter.interpreter.llm.custom_instructions = settings.custom_instructions + interpreter.interpreter.auto_run = settings.auto_run + + interpreter.interpreter.llm.api_key = "" + + return {"status": "success"} + + @app.websocket("/ws") + async def websocket_endpoint(websocket: WebSocket): + await websocket.accept() + try: + + async def receive_input(): + while True: + data = await websocket.receive() + print(data) + if isinstance(data, bytes): + await interpreter.input(data) + else: + await interpreter.input(data["text"]) + + async def send_output(): + while True: + output = await interpreter.output() + if isinstance(output, bytes): + # await websocket.send_bytes(output) + # we dont send out bytes rn, no TTS + pass + elif isinstance(output, dict): + print("sending:", output) + await websocket.send_text(json.dumps(output)) + + await asyncio.gather(receive_input(), send_output()) + except Exception as e: + print(f"WebSocket connection closed with exception: {e}") + traceback.print_exc() + finally: + await websocket.close() + + class Rename(BaseModel): + input: str + + @app.post("/rename-chat") + async def rename_chat(body_content: Rename, x_api_key: str = Header(None)): + print("RENAME CHAT REQUEST in PY πŸŒ™πŸŒ™πŸŒ™πŸŒ™") + input_value = body_content.input + client = OpenAI( + # defaults to os.environ.get("OPENAI_API_KEY") + api_key=x_api_key, + ) + try: + response = client.chat.completions.create( + model="gpt-3.5-turbo", + messages=[ + { + "role": "user", + "content": f"Given the following chat snippet, create a unique and descriptive title in less than 8 words. Your answer must not be related to customer service.\n\n{input_value}", + } + ], + temperature=0.3, + stream=False, + ) + print(response) + completion = response["choices"][0]["message"]["content"] + return {"data": {"content": completion}} + except Exception as e: + print(f"Error: {e}") + traceback.print_exc() + return {"error": str(e)} + + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/interpreter/core/utils/truncate_output.py b/interpreter/core/utils/truncate_output.py index 08ed843b6..6d567938e 100644 --- a/interpreter/core/utils/truncate_output.py +++ b/interpreter/core/utils/truncate_output.py @@ -1,4 +1,7 @@ def truncate_output(data, max_output_chars=2000): + if "@@@DO_NOT_TRUNCATE@@@" in data: + return data + needs_truncation = False message = f"Output truncated. Showing the last {max_output_chars} characters.\n\n" diff --git a/interpreter/terminal_interface/contributing_conversations.py b/interpreter/terminal_interface/contributing_conversations.py index 37fb81864..4cae3066b 100644 --- a/interpreter/terminal_interface/contributing_conversations.py +++ b/interpreter/terminal_interface/contributing_conversations.py @@ -1,5 +1,5 @@ -import os import json +import os import time from typing import List, TypedDict @@ -7,8 +7,9 @@ import requests from interpreter.terminal_interface.profiles.profiles import write_key_to_profile -from interpreter.terminal_interface.utils.display_markdown_message import display_markdown_message - +from interpreter.terminal_interface.utils.display_markdown_message import ( + display_markdown_message, +) contribute_cache_path = os.path.join( os.path.expanduser("~"), ".cache", "open-interpreter", "contribute.json" @@ -17,13 +18,12 @@ def display_contribution_message(): display_markdown_message( -""" + """ --- -> We're training an open-source language model! +> We're training an open-source language model. + +Want to contribute? Run `interpreter --model i` to use our free, hosted model. Conversations with this `i` model will be used for training. -You can help us train it with your past, current, or future conversations by: -1. Closing out of OpenInterpreter, -2. Running `interpreter --contribute_conversation`. """ ) time.sleep(1) @@ -31,9 +31,9 @@ def display_contribution_message(): def display_contributing_current_message(): display_markdown_message( -f""" + f""" --- -> This conversation will be used to train OpenInterpreter's language model. +> This conversation will be used to train Open Interpreter's open-source language model. """ ) @@ -42,30 +42,39 @@ def send_past_conversations(interpreter): past_conversations = get_all_conversations(interpreter) if len(past_conversations) > 0: print() - print("Sending all previous conversations to OpenInterpreter...") - contribute_conversations(past_conversations) + print( + "We are about to send all previous conversations to Open Interpreter for training an open-source language model. Please make sure these don't contain any private information. Run `interpreter --conversations` to browse them." + ) + print() + uh = input( + "Do we have your permission to send all previous conversations to Open Interpreter? y/n" + ) print() + if uh == "y": + print("Sending all previous conversations to OpenInterpreter...") + contribute_conversations(past_conversations) + print() def set_send_future_conversations(interpreter, should_send_future): write_key_to_profile("contribute_conversation", should_send_future) display_markdown_message( -""" -> OpenInterpreter will contribute all your conversations from now on. + """ +> Open Interpreter will contribute conversations from now on. Thank you for your help! -To change this, consult the documentation at [https://unassuminglink.com](https://www.readthefuckingmanual.com). +To change this, run `interpreter --profiles` and edit the `default.yaml` profile so "contribute_conversation" = False. """ ) def user_wants_to_contribute_past(): - print("Would you like to contribute all past conversations?") + print("\nWould you like to contribute all past conversations?\n") response = input("(y/n) ") return response.lower() == "y" def user_wants_to_contribute_future(): - print("Would you like to contribute all future conversations?") + print("\nWould you like to contribute all future conversations?\n") response = input("(y/n) ") return response.lower() == "y" @@ -139,7 +148,9 @@ def is_conversation_path(path: str): history_path = interpreter.conversation_history_path all_conversations: List[List] = [] - conversation_files = os.listdir(history_path) if os.path.exists(history_path) else [] + conversation_files = ( + os.listdir(history_path) if os.path.exists(history_path) else [] + ) for mpath in conversation_files: if not is_conversation_path(mpath): continue @@ -163,7 +174,9 @@ def contribute_conversations(conversations: List[List]): payload = {"conversations": conversations, "oi_version": version} - assert is_list_of_lists(payload["conversations"]), "the contribution payload is not a list of lists!" + assert is_list_of_lists( + payload["conversations"] + ), "the contribution payload is not a list of lists!" try: response = requests.post(url, json=payload) diff --git a/interpreter/terminal_interface/conversation_navigator.py b/interpreter/terminal_interface/conversation_navigator.py index 0f9330521..baf816c93 100644 --- a/interpreter/terminal_interface/conversation_navigator.py +++ b/interpreter/terminal_interface/conversation_navigator.py @@ -51,14 +51,17 @@ def conversation_navigator(interpreter): readable_names_and_filenames[name] = filename # Add the option to open the folder. This doesn't map to a filename, we'll catch it - readable_names_and_filenames["> Open folder"] = None + readable_names_and_filenames_list = list(readable_names_and_filenames.keys()) + readable_names_and_filenames_list = [ + "Open Folder β†’" + ] + readable_names_and_filenames_list # Use inquirer to let the user select a file questions = [ inquirer.List( "name", message="", - choices=readable_names_and_filenames.keys(), + choices=readable_names_and_filenames_list, ), ] answers = inquirer.prompt(questions) @@ -68,7 +71,7 @@ def conversation_navigator(interpreter): return # If the user selected to open the folder, do so and return - if answers["name"] == "> Open folder": + if answers["name"] == "Open Folder β†’": open_folder(conversations_dir) return diff --git a/interpreter/terminal_interface/local_setup.py b/interpreter/terminal_interface/local_setup.py new file mode 100644 index 000000000..22cafaaa8 --- /dev/null +++ b/interpreter/terminal_interface/local_setup.py @@ -0,0 +1,407 @@ +# Thank you Ty Fiero for making this! + +import os +import platform +import subprocess +import sys +import time + +import inquirer +import psutil +import wget + + +def local_setup(interpreter): + def download_model(models_dir, models, interpreter): + # Get RAM and disk information + total_ram = psutil.virtual_memory().total / ( + 1024 * 1024 * 1024 + ) # Convert bytes to GB + free_disk_space = psutil.disk_usage("/").free / ( + 1024 * 1024 * 1024 + ) # Convert bytes to GB + + # Display the users hardware specs + interpreter.display_message( + f"Your machine has `{total_ram:.2f}GB` of RAM, and `{free_disk_space:.2f}GB` of free storage space." + ) + + if total_ram < 10: + interpreter.display_message( + f"\nYour computer realistically can only run smaller models less than 4GB, Phi-2 might be the best model for your computer.\n" + ) + elif 10 <= total_ram < 30: + interpreter.display_message( + f"\nYour computer could handle a mid-sized model (4-10GB), Mistral-7B might be the best model for your computer.\n" + ) + else: + interpreter.display_message( + f"\nYour computer should have enough RAM to run any model below.\n" + ) + + interpreter.display_message( + f"In general, the larger the model, the better the performance, but choose a model that best fits your computer's hardware. \nOnly models you have the storage space to download are shown:\n" + ) + + try: + model_list = [ + { + "name": "Llama-3-8B-Instruct", + "file_name": " Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile", + "size": 5.76, + "url": "https://huggingface.co/jartine/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile?download=true", + }, + { + "name": "Phi-3-mini", + "file_name": "Phi-3-mini-4k-instruct.Q5_K_M.llamafile", + "size": 2.84, + "url": "https://huggingface.co/jartine/Phi-3-mini-4k-instruct-llamafile/resolve/main/Phi-3-mini-4k-instruct.Q5_K_M.llamafile?download=true", + }, + { + "name": "TinyLlama-1.1B", + "file_name": "TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile", + "size": 0.76, + "url": "https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true", + }, + { + "name": "Rocket-3B", + "file_name": "rocket-3b.Q5_K_M.llamafile", + "size": 1.89, + "url": "https://huggingface.co/jartine/rocket-3B-llamafile/resolve/main/rocket-3b.Q5_K_M.llamafile?download=true", + }, + { + "name": "Phi-2", + "file_name": "phi-2.Q5_K_M.llamafile", + "size": 1.96, + "url": "https://huggingface.co/jartine/phi-2-llamafile/resolve/main/phi-2.Q5_K_M.llamafile?download=true", + }, + { + "name": "LLaVA 1.5", + "file_name": "llava-v1.5-7b-q4.llamafile", + "size": 3.97, + "url": "https://huggingface.co/jartine/llava-v1.5-7B-GGUF/resolve/main/llava-v1.5-7b-q4.llamafile?download=true", + }, + { + "name": "Mistral-7B-Instruct", + "file_name": "mistral-7b-instruct-v0.2.Q5_K_M.llamafile", + "size": 5.15, + "url": "https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile?download=true", + }, + { + "name": "WizardCoder-Python-13B", + "file_name": "wizardcoder-python-13b.llamafile", + "size": 7.33, + "url": "https://huggingface.co/jartine/wizardcoder-13b-python/resolve/main/wizardcoder-python-13b.llamafile?download=true", + }, + { + "name": "WizardCoder-Python-34B", + "file_name": "wizardcoder-python-34b-v1.0.Q5_K_M.llamafile", + "size": 22.23, + "url": "https://huggingface.co/jartine/WizardCoder-Python-34B-V1.0-llamafile/resolve/main/wizardcoder-python-34b-v1.0.Q5_K_M.llamafile?download=true", + }, + { + "name": "Mixtral-8x7B-Instruct", + "file_name": "mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile", + "size": 30.03, + "url": "https://huggingface.co/jartine/Mixtral-8x7B-Instruct-v0.1-llamafile/resolve/main/mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile?download=true", + }, + ] + + # Filter models based on available disk space and RAM + filtered_models = [ + model + for model in model_list + if model["size"] <= free_disk_space and model["file_name"] not in models + ] + if filtered_models: + time.sleep(1) + + # Prompt the user to select a model + model_choices = [ + f"{model['name']} ({model['size']:.2f}GB)" + for model in filtered_models + ] + questions = [ + inquirer.List( + "model", + message="Select a model to download:", + choices=model_choices, + ) + ] + answers = inquirer.prompt(questions) + + if answers == None: + exit() + + # Get the selected model + selected_model = next( + model + for model in filtered_models + if f"{model['name']} ({model['size']}GB)" == answers["model"] + ) + + # Download the selected model + model_url = selected_model["url"] + # Extract the basename and remove query parameters + filename = os.path.basename(model_url).split("?")[0] + model_path = os.path.join(models_dir, filename) + + time.sleep(1) + print(f"\nDownloading {selected_model['name']}...\n") + wget.download(model_url, model_path) + + # Make the model executable if not on Windows + if platform.system() != "Windows": + subprocess.run(["chmod", "+x", model_path], check=True) + + print(f"\nModel '{selected_model['name']}' downloaded successfully.\n") + + interpreter.display_message( + "To view or delete downloaded local models, run `interpreter --local_models`\n\n" + ) + + return model_path + else: + print( + "\nYour computer does not have enough storage to download any local LLMs.\n" + ) + return None + except Exception as e: + print(e) + print( + "\nAn error occurred while trying to download the model. Please try again or use a different local model provider.\n" + ) + return None + + # START OF LOCAL MODEL PROVIDER LOGIC + interpreter.display_message( + "\n**Open Interpreter** supports multiple local model providers.\n" + ) + + # Define the choices for local models + choices = [ + "Ollama", + "Llamafile", + "LM Studio", + "Jan", + ] + + # Use inquirer to let the user select an option + questions = [ + inquirer.List( + "model", + message="Select a provider", + choices=choices, + ), + ] + answers = inquirer.prompt(questions) + + if answers == None: + exit() + + selected_model = answers["model"] + + if selected_model == "LM Studio": + interpreter.display_message( + """ + To use use Open Interpreter with **LM Studio**, you will need to run **LM Studio** in the background. + + 1. Download **LM Studio** from [https://lmstudio.ai/](https://lmstudio.ai/), then start it. + 2. Select a language model then click **Download**. + 3. Click the **<->** button on the left (below the chat button). + 4. Select your model at the top, then click **Start Server**. + + + Once the server is running, you can begin your conversation below. + + """ + ) + interpreter.llm.supports_functions = False + interpreter.llm.api_base = "http://localhost:1234/v1" + interpreter.llm.api_key = "x" + + elif selected_model == "Ollama": + try: + # List out all downloaded ollama models. Will fail if ollama isn't installed + result = subprocess.run( + ["ollama", "list"], capture_output=True, text=True, check=True + ) + lines = result.stdout.split("\n") + names = [ + line.split()[0].replace(":latest", "") + for line in lines[1:] + if line.strip() + ] # Extract names, trim out ":latest", skip header + + for model in ["llama3", "phi3", "wizardlm2"]: + if model not in names: + names.append("β†’ Download " + model) + + # Create a new inquirer selection from the names + name_question = [ + inquirer.List( + "name", + message="Select a model", + choices=names, + ), + ] + name_answer = inquirer.prompt(name_question) + + if name_answer == None: + exit() + + selected_name = name_answer["name"] + + if "download" in selected_name.lower(): + model = selected_name.split(" ")[-1] + interpreter.display_message(f"\nDownloading {model}...\n") + subprocess.run(["ollama", "pull", model], check=True) + else: + model = selected_name.strip() + + # Set the model to the selected model + interpreter.llm.model = f"ollama/{model}" + interpreter.display_message(f"> Model set to `{model}`") + + # If Ollama is not installed or not recognized as a command, prompt the user to download Ollama and try again + except (subprocess.CalledProcessError, FileNotFoundError) as e: + print("Ollama is not installed or not recognized as a command.") + time.sleep(1) + interpreter.display_message( + f"\nPlease visit [https://ollama.com/](https://ollama.com/) to download Ollama and try again\n" + ) + time.sleep(2) + sys.exit(1) + + elif selected_model == "Jan": + interpreter.display_message( + """ + To use use Open Interpreter with **Jan**, you will need to run **Jan** in the background. + + 1. Download **Jan** from [https://jan.ai/](https://jan.ai/), then start it. + 2. Select a language model from the "Hub" tab, then click **Download**. + 3. Copy the ID of the model and enter it below. + 3. Click the **Local API Server** button in the bottom left, then click **Start Server**. + + + Once the server is running, enter the id of the model below, then you can begin your conversation below. + + """ + ) + interpreter.llm.api_base = "http://localhost:1337/v1" + time.sleep(1) + + # Prompt the user to enter the name of the model running on Jan + model_name_question = [ + inquirer.Text( + "jan_model_name", + message="Enter the id of the model you have running on Jan", + ), + ] + model_name_answer = inquirer.prompt(model_name_question) + + if model_name_answer == None: + exit() + + jan_model_name = model_name_answer["jan_model_name"] + interpreter.llm.model = f"jan/{jan_model_name}" + interpreter.display_message(f"\nUsing Jan model: `{jan_model_name}` \n") + time.sleep(1) + + elif selected_model == "Llamafile": + if platform.system() == "Darwin": # Check if the system is MacOS + result = subprocess.run( + ["xcode-select", "-p"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT + ) + if result.returncode != 0: + interpreter.display_message( + "To use Llamafile, Open Interpreter requires Mac users to have Xcode installed. You can install Xcode from https://developer.apple.com/xcode/ .\n\nAlternatively, you can use `LM Studio`, `Jan.ai`, or `Ollama` to manage local language models. Learn more at https://docs.openinterpreter.com/guides/running-locally ." + ) + time.sleep(3) + raise Exception( + "Xcode is not installed. Please install Xcode and try again." + ) + + # Define the path to the models directory + models_dir = os.path.join(interpreter.get_oi_dir(), "models") + + # Check and create the models directory if it doesn't exist + if not os.path.exists(models_dir): + os.makedirs(models_dir) + + # Check if there are any models in the models folder + models = [f for f in os.listdir(models_dir) if f.endswith(".llamafile")] + + if not models: + print( + "\nNo models currently downloaded. Please select a new model to download.\n" + ) + model_path = download_model(models_dir, models, interpreter) + else: + # Prompt the user to select a downloaded model or download a new one + model_choices = models + ["↓ Download new model"] + questions = [ + inquirer.List( + "model", + message="Select a model", + choices=model_choices, + ) + ] + answers = inquirer.prompt(questions) + + if answers == None: + exit() + + if answers["model"] == "↓ Download new model": + model_path = download_model(models_dir, models, interpreter) + else: + model_path = os.path.join(models_dir, answers["model"]) + + if model_path: + try: + # Run the selected model and hide its output + process = subprocess.Popen( + f'"{model_path}" ' + " ".join(["--nobrowser", "-ngl", "9999"]), + shell=True, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + text=True, + ) + + for line in process.stdout: + if "llama server listening at http://127.0.0.1:8080" in line: + break # Exit the loop once the server is ready + except Exception as e: + process.kill() # Force kill if not terminated after timeout + print(e) + print("Model process terminated.") + + # Set flags for Llamafile to work with interpreter + interpreter.llm.model = "openai/local" + interpreter.llm.temperature = 0 + interpreter.llm.api_base = "http://localhost:8080/v1" + interpreter.llm.supports_functions = False + + model_name = model_path.split("/")[-1] + interpreter.display_message(f"> Model set to `{model_name}`") + + user_ram = total_ram = psutil.virtual_memory().total / ( + 1024 * 1024 * 1024 + ) # Convert bytes to GB + # Set context window and max tokens for all local models based on the users available RAM + if user_ram and user_ram > 9: + interpreter.llm.max_tokens = 1200 + interpreter.llm.context_window = 8000 + else: + interpreter.llm.max_tokens = 1000 + interpreter.llm.context_window = 3000 + + # Display intro message + if interpreter.auto_run == False: + interpreter.display_message( + "**Open Interpreter** will require approval before running code." + + "\n\nUse `interpreter -y` to bypass this." + + "\n\nPress `CTRL-C` to exit.\n" + ) + + return interpreter diff --git a/interpreter/terminal_interface/profiles/defaults/local-os.py b/interpreter/terminal_interface/profiles/defaults/local-os.py index 4996f864e..5d806af23 100644 --- a/interpreter/terminal_interface/profiles/defaults/local-os.py +++ b/interpreter/terminal_interface/profiles/defaults/local-os.py @@ -1,409 +1,8 @@ -import os -import platform -import subprocess -import sys import time -import inquirer -import psutil -import wget - from interpreter import interpreter -model = None - - -def download_model(models_dir, models, interpreter): - # Get RAM and disk information - total_ram = psutil.virtual_memory().total / ( - 1024 * 1024 * 1024 - ) # Convert bytes to GB - free_disk_space = psutil.disk_usage("/").free / ( - 1024 * 1024 * 1024 - ) # Convert bytes to GB - - # Display the users hardware specs - interpreter.display_message( - f"Your machine has `{total_ram:.2f}GB` of RAM, and `{free_disk_space:.2f}GB` of free storage space." - ) - - if total_ram < 10: - interpreter.display_message( - f"\nYour computer realistically can only run smaller models less than 4GB, Phi-2 might be the best model for your computer.\n" - ) - elif 10 <= total_ram < 30: - interpreter.display_message( - f"\nYour computer could handle a mid-sized model (4-10GB), Mistral-7B might be the best model for your computer.\n" - ) - else: - interpreter.display_message( - f"\nYour computer should have enough RAM to run any model below.\n" - ) - - interpreter.display_message( - f"In general, the larger the model, the better the performance, but choose a model that best fits your computer's hardware. \nOnly models you have the storage space to download are shown:\n" - ) - - try: - model_list = [ - { - "name": "Llama-3-8B-Instruct", - "file_name": " Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile", - "size": 5.76, - "url": "https://huggingface.co/jartine/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile?download=true", - }, - { - "name": "Phi-3-mini", - "file_name": "Phi-3-mini-4k-instruct.Q5_K_M.llamafile", - "size": 2.84, - "url": "https://huggingface.co/jartine/Phi-3-mini-4k-instruct-llamafile/resolve/main/Phi-3-mini-4k-instruct.Q5_K_M.llamafile?download=true", - }, - { - "name": "TinyLlama-1.1B", - "file_name": "TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile", - "size": 0.76, - "url": "https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true", - }, - { - "name": "Rocket-3B", - "file_name": "rocket-3b.Q5_K_M.llamafile", - "size": 1.89, - "url": "https://huggingface.co/jartine/rocket-3B-llamafile/resolve/main/rocket-3b.Q5_K_M.llamafile?download=true", - }, - { - "name": "Phi-2", - "file_name": "phi-2.Q5_K_M.llamafile", - "size": 1.96, - "url": "https://huggingface.co/jartine/phi-2-llamafile/resolve/main/phi-2.Q5_K_M.llamafile?download=true", - }, - { - "name": "LLaVA 1.5", - "file_name": "llava-v1.5-7b-q4.llamafile", - "size": 3.97, - "url": "https://huggingface.co/jartine/llava-v1.5-7B-GGUF/resolve/main/llava-v1.5-7b-q4.llamafile?download=true", - }, - { - "name": "Mistral-7B-Instruct", - "file_name": "mistral-7b-instruct-v0.2.Q5_K_M.llamafile", - "size": 5.15, - "url": "https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile?download=true", - }, - { - "name": "WizardCoder-Python-13B", - "file_name": "wizardcoder-python-13b.llamafile", - "size": 7.33, - "url": "https://huggingface.co/jartine/wizardcoder-13b-python/resolve/main/wizardcoder-python-13b.llamafile?download=true", - }, - { - "name": "WizardCoder-Python-34B", - "file_name": "wizardcoder-python-34b-v1.0.Q5_K_M.llamafile", - "size": 22.23, - "url": "https://huggingface.co/jartine/WizardCoder-Python-34B-V1.0-llamafile/resolve/main/wizardcoder-python-34b-v1.0.Q5_K_M.llamafile?download=true", - }, - { - "name": "Mixtral-8x7B-Instruct", - "file_name": "mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile", - "size": 30.03, - "url": "https://huggingface.co/jartine/Mixtral-8x7B-Instruct-v0.1-llamafile/resolve/main/mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile?download=true", - }, - ] - - # Filter models based on available disk space and RAM - filtered_models = [ - model - for model in model_list - if model["size"] <= free_disk_space and model["file_name"] not in models - ] - if filtered_models: - time.sleep(1) - - # Prompt the user to select a model - model_choices = [ - f"{model['name']} ({model['size']:.2f}GB)" for model in filtered_models - ] - questions = [ - inquirer.List( - "model", - message="Select a model to download:", - choices=model_choices, - ) - ] - answers = inquirer.prompt(questions) - - if answers == None: - exit() - - # Get the selected model - selected_model = next( - model - for model in filtered_models - if f"{model['name']} ({model['size']}GB)" == answers["model"] - ) - - # Download the selected model - model_url = selected_model["url"] - # Extract the basename and remove query parameters - filename = os.path.basename(model_url).split("?")[0] - model_path = os.path.join(models_dir, filename) - - time.sleep(1) - print(f"\nDownloading {selected_model['name']}...\n") - wget.download(model_url, model_path) - - # Make the model executable if not on Windows - if platform.system() != "Windows": - subprocess.run(["chmod", "+x", model_path], check=True) - - print(f"\nModel '{selected_model['name']}' downloaded successfully.\n") - - interpreter.display_message( - "To view or delete downloaded local models, run `interpreter --local_models`\n\n" - ) - - return model_path - else: - print( - "\nYour computer does not have enough storage to download any local LLMs.\n" - ) - return None - except Exception as e: - print(e) - print( - "\nAn error occurred while trying to download the model. Please try again or use a different local model provider.\n" - ) - return None - - -# START OF LOCAL MODEL PROVIDER LOGIC -interpreter.display_message( - "\n**Open Interpreter** supports multiple local model providers.\n" -) - -# Define the choices for local models -choices = [ - "Ollama", - "Llamafile", - "LM Studio", - "Jan", -] - -# Use inquirer to let the user select an option -questions = [ - inquirer.List( - "model", - message="Select a provider", - choices=choices, - ), -] -answers = inquirer.prompt(questions) - -if answers == None: - exit() - -selected_model = answers["model"] - - -if selected_model == "LM Studio": - interpreter.display_message( - """ -To use use Open Interpreter with **LM Studio**, you will need to run **LM Studio** in the background. - -1. Download **LM Studio** from [https://lmstudio.ai/](https://lmstudio.ai/), then start it. -2. Select a language model then click **Download**. -3. Click the **<->** button on the left (below the chat button). -4. Select your model at the top, then click **Start Server**. - - -Once the server is running, you can begin your conversation below. - -""" - ) - interpreter.llm.supports_functions = False - interpreter.llm.api_base = "http://localhost:1234/v1" - interpreter.llm.api_key = "x" - -elif selected_model == "Ollama": - try: - # List out all downloaded ollama models. Will fail if ollama isn't installed - result = subprocess.run( - ["ollama", "list"], capture_output=True, text=True, check=True - ) - lines = result.stdout.split("\n") - names = [ - line.split()[0].replace(":latest", "") for line in lines[1:] if line.strip() - ] # Extract names, trim out ":latest", skip header - - for model in ["llama3", "phi3", "wizardlm2"]: - if model not in names: - names.append("β†’ Download " + model) - - # Create a new inquirer selection from the names - name_question = [ - inquirer.List( - "name", - message="Select a model", - choices=names, - ), - ] - name_answer = inquirer.prompt(name_question) - - if name_answer == None: - exit() - - selected_name = name_answer["name"] - - if "download" in selected_name.lower(): - model = selected_name.split(" ")[-1] - interpreter.display_message(f"\nDownloading {model}...\n") - subprocess.run(["ollama", "pull", model], check=True) - else: - model = selected_name.strip() - - # Set the model to the selected model - interpreter.llm.model = f"ollama/{model}" - interpreter.display_message(f"> Model set to `{model}`") - - # If Ollama is not installed or not recognized as a command, prompt the user to download Ollama and try again - except (subprocess.CalledProcessError, FileNotFoundError) as e: - print("Ollama is not installed or not recognized as a command.") - time.sleep(1) - interpreter.display_message( - f"\nPlease visit [https://ollama.com/](https://ollama.com/) to download Ollama and try again\n" - ) - time.sleep(2) - sys.exit(1) - -elif selected_model == "Jan": - interpreter.display_message( - """ -To use use Open Interpreter with **Jan**, you will need to run **Jan** in the background. - -1. Download **Jan** from [https://jan.ai/](https://jan.ai/), then start it. -2. Select a language model from the "Hub" tab, then click **Download**. -3. Copy the ID of the model and enter it below. -3. Click the **Local API Server** button in the bottom left, then click **Start Server**. - - -Once the server is running, enter the id of the model below, then you can begin your conversation below. - -""" - ) - interpreter.llm.api_base = "http://localhost:1337/v1" - time.sleep(1) - - # Prompt the user to enter the name of the model running on Jan - model_name_question = [ - inquirer.Text( - "jan_model_name", - message="Enter the id of the model you have running on Jan", - ), - ] - model_name_answer = inquirer.prompt(model_name_question) - - if model_name_answer == None: - exit() - - jan_model_name = model_name_answer["jan_model_name"] - interpreter.llm.model = f"jan/{jan_model_name}" - interpreter.display_message(f"\nUsing Jan model: `{jan_model_name}` \n") - time.sleep(1) - - -elif selected_model == "Llamafile": - if platform.system() == "Darwin": # Check if the system is MacOS - result = subprocess.run( - ["xcode-select", "-p"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT - ) - if result.returncode != 0: - interpreter.display_message( - "To use Llamafile, Open Interpreter requires Mac users to have Xcode installed. You can install Xcode from https://developer.apple.com/xcode/ .\n\nAlternatively, you can use `LM Studio`, `Jan.ai`, or `Ollama` to manage local language models. Learn more at https://docs.openinterpreter.com/guides/running-locally ." - ) - time.sleep(3) - raise Exception( - "Xcode is not installed. Please install Xcode and try again." - ) - - # Define the path to the models directory - models_dir = os.path.join(interpreter.get_oi_dir(), "models") - - # Check and create the models directory if it doesn't exist - if not os.path.exists(models_dir): - os.makedirs(models_dir) - - # Check if there are any models in the models folder - models = [f for f in os.listdir(models_dir) if f.endswith(".llamafile")] - - if not models: - print( - "\nNo models currently downloaded. Please select a new model to download.\n" - ) - model_path = download_model(models_dir, models, interpreter) - else: - # Prompt the user to select a downloaded model or download a new one - model_choices = models + ["↓ Download new model"] - questions = [ - inquirer.List( - "model", - message="Select a model", - choices=model_choices, - ) - ] - answers = inquirer.prompt(questions) - - if answers == None: - exit() - - if answers["model"] == "↓ Download new model": - model_path = download_model(models_dir, models, interpreter) - else: - model_path = os.path.join(models_dir, answers["model"]) - - if model_path: - try: - # Run the selected model and hide its output - process = subprocess.Popen( - f'"{model_path}" ' + " ".join(["--nobrowser", "-ngl", "9999"]), - shell=True, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - text=True, - ) - - for line in process.stdout: - if "llama server listening at http://127.0.0.1:8080" in line: - break # Exit the loop once the server is ready - except Exception as e: - process.kill() # Force kill if not terminated after timeout - print(e) - print("Model process terminated.") - - # Set flags for Llamafile to work with interpreter - interpreter.llm.model = "openai/local" - interpreter.llm.temperature = 0 - interpreter.llm.api_base = "http://localhost:8080/v1" - interpreter.llm.supports_functions = False - - model_name = model_path.split("/")[-1] - interpreter.display_message(f"> Model set to `{model_name}`") - -user_ram = total_ram = psutil.virtual_memory().total / ( - 1024 * 1024 * 1024 -) # Convert bytes to GB -# Set context window and max tokens for all local models based on the users available RAM -if user_ram and user_ram > 9: - interpreter.llm.max_tokens = 1200 - interpreter.llm.context_window = 8000 -else: - interpreter.llm.max_tokens = 1000 - interpreter.llm.context_window = 3000 - -# Display intro message -if interpreter.auto_run == False: - interpreter.display_message( - "**Open Interpreter** will require approval before running code." - + "\n\nUse `interpreter -y` to bypass this." - + "\n\nPress `CTRL-C` to exit.\n" - ) +interpreter.local_setup() # Opens a wizard that lets terminal users pick a local model # Set the system message to a minimal version for all local models. interpreter.system_message = """ @@ -618,43 +217,44 @@ def download_model(models_dir, models, interpreter): time.sleep(2) print("Attempting to start OS control anyway...\n\n") - for pip_combo in [ - ["pip", "quotes"], - ["pip", "no-quotes"], - ["pip3", "quotes"], - ["pip", "no-quotes"], - ]: - if pip_combo[1] == "quotes": - command = f'{pip_combo[0]} install "open-interpreter[os]"' - else: - command = f"{pip_combo[0]} install open-interpreter[os]" - - interpreter.computer.run("shell", command, display=True) - - got_em = True - for package in missing_packages: + else: + for pip_combo in [ + ["pip", "quotes"], + ["pip", "no-quotes"], + ["pip3", "quotes"], + ["pip", "no-quotes"], + ]: + if pip_combo[1] == "quotes": + command = f'{pip_combo[0]} install "open-interpreter[os]"' + else: + command = f"{pip_combo[0]} install open-interpreter[os]" + + interpreter.computer.run("shell", command, display=True) + + got_em = True + for package in missing_packages: + try: + __import__(package) + except ImportError: + got_em = False + if got_em: + break + + missing_packages = [] + for package in packages: try: __import__(package) except ImportError: - got_em = False - if got_em: - break - - missing_packages = [] - for package in packages: - try: - __import__(package) - except ImportError: - missing_packages.append(package) - - if missing_packages != []: - print( - "\n\nWarning: The following packages could not be installed:", - ", ".join(missing_packages), - ) - print("\nPlease try to install them manually.\n\n") - time.sleep(2) - print("Attempting to start OS control anyway...\n\n") + missing_packages.append(package) + + if missing_packages != []: + print( + "\n\nWarning: The following packages could not be installed:", + ", ".join(missing_packages), + ) + print("\nPlease try to install them manually.\n\n") + time.sleep(2) + print("Attempting to start OS control anyway...\n\n") interpreter.display_message("> `OS Control` enabled") diff --git a/interpreter/terminal_interface/profiles/defaults/local.py b/interpreter/terminal_interface/profiles/defaults/local.py index ae47572ec..fb3819361 100644 --- a/interpreter/terminal_interface/profiles/defaults/local.py +++ b/interpreter/terminal_interface/profiles/defaults/local.py @@ -1,409 +1,6 @@ -import os -import platform -import subprocess -import sys -import time - -import inquirer -import psutil -import wget - from interpreter import interpreter -model = None - - -def download_model(models_dir, models, interpreter): - # Get RAM and disk information - total_ram = psutil.virtual_memory().total / ( - 1024 * 1024 * 1024 - ) # Convert bytes to GB - free_disk_space = psutil.disk_usage("/").free / ( - 1024 * 1024 * 1024 - ) # Convert bytes to GB - - # Display the users hardware specs - interpreter.display_message( - f"Your machine has `{total_ram:.2f}GB` of RAM, and `{free_disk_space:.2f}GB` of free storage space." - ) - - if total_ram < 10: - interpreter.display_message( - f"\nYour computer realistically can only run smaller models less than 4GB, Phi-2 might be the best model for your computer.\n" - ) - elif 10 <= total_ram < 30: - interpreter.display_message( - f"\nYour computer could handle a mid-sized model (4-10GB), Mistral-7B might be the best model for your computer.\n" - ) - else: - interpreter.display_message( - f"\nYour computer should have enough RAM to run any model below.\n" - ) - - interpreter.display_message( - f"In general, the larger the model, the better the performance, but choose a model that best fits your computer's hardware. \nOnly models you have the storage space to download are shown:\n" - ) - - try: - model_list = [ - { - "name": "Llama-3-8B-Instruct", - "file_name": " Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile", - "size": 5.76, - "url": "https://huggingface.co/jartine/Meta-Llama-3-8B-Instruct-llamafile/resolve/main/Meta-Llama-3-8B-Instruct.Q5_K_M.llamafile?download=true", - }, - { - "name": "Phi-3-mini", - "file_name": "Phi-3-mini-4k-instruct.Q5_K_M.llamafile", - "size": 2.84, - "url": "https://huggingface.co/jartine/Phi-3-mini-4k-instruct-llamafile/resolve/main/Phi-3-mini-4k-instruct.Q5_K_M.llamafile?download=true", - }, - { - "name": "TinyLlama-1.1B", - "file_name": "TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile", - "size": 0.76, - "url": "https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true", - }, - { - "name": "Rocket-3B", - "file_name": "rocket-3b.Q5_K_M.llamafile", - "size": 1.89, - "url": "https://huggingface.co/jartine/rocket-3B-llamafile/resolve/main/rocket-3b.Q5_K_M.llamafile?download=true", - }, - { - "name": "Phi-2", - "file_name": "phi-2.Q5_K_M.llamafile", - "size": 1.96, - "url": "https://huggingface.co/jartine/phi-2-llamafile/resolve/main/phi-2.Q5_K_M.llamafile?download=true", - }, - { - "name": "LLaVA 1.5", - "file_name": "llava-v1.5-7b-q4.llamafile", - "size": 3.97, - "url": "https://huggingface.co/jartine/llava-v1.5-7B-GGUF/resolve/main/llava-v1.5-7b-q4.llamafile?download=true", - }, - { - "name": "Mistral-7B-Instruct", - "file_name": "mistral-7b-instruct-v0.2.Q5_K_M.llamafile", - "size": 5.15, - "url": "https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/resolve/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile?download=true", - }, - { - "name": "WizardCoder-Python-13B", - "file_name": "wizardcoder-python-13b.llamafile", - "size": 7.33, - "url": "https://huggingface.co/jartine/wizardcoder-13b-python/resolve/main/wizardcoder-python-13b.llamafile?download=true", - }, - { - "name": "WizardCoder-Python-34B", - "file_name": "wizardcoder-python-34b-v1.0.Q5_K_M.llamafile", - "size": 22.23, - "url": "https://huggingface.co/jartine/WizardCoder-Python-34B-V1.0-llamafile/resolve/main/wizardcoder-python-34b-v1.0.Q5_K_M.llamafile?download=true", - }, - { - "name": "Mixtral-8x7B-Instruct", - "file_name": "mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile", - "size": 30.03, - "url": "https://huggingface.co/jartine/Mixtral-8x7B-Instruct-v0.1-llamafile/resolve/main/mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile?download=true", - }, - ] - - # Filter models based on available disk space and RAM - filtered_models = [ - model - for model in model_list - if model["size"] <= free_disk_space and model["file_name"] not in models - ] - if filtered_models: - time.sleep(1) - - # Prompt the user to select a model - model_choices = [ - f"{model['name']} ({model['size']:.2f}GB)" for model in filtered_models - ] - questions = [ - inquirer.List( - "model", - message="Select a model to download:", - choices=model_choices, - ) - ] - answers = inquirer.prompt(questions) - - if answers == None: - exit() - - # Get the selected model - selected_model = next( - model - for model in filtered_models - if f"{model['name']} ({model['size']}GB)" == answers["model"] - ) - - # Download the selected model - model_url = selected_model["url"] - # Extract the basename and remove query parameters - filename = os.path.basename(model_url).split("?")[0] - model_path = os.path.join(models_dir, filename) - - time.sleep(1) - print(f"\nDownloading {selected_model['name']}...\n") - wget.download(model_url, model_path) - - # Make the model executable if not on Windows - if platform.system() != "Windows": - subprocess.run(["chmod", "+x", model_path], check=True) - - print(f"\nModel '{selected_model['name']}' downloaded successfully.\n") - - interpreter.display_message( - "To view or delete downloaded local models, run `interpreter --local_models`\n\n" - ) - - return model_path - else: - print( - "\nYour computer does not have enough storage to download any local LLMs.\n" - ) - return None - except Exception as e: - print(e) - print( - "\nAn error occurred while trying to download the model. Please try again or use a different local model provider.\n" - ) - return None - - -# START OF LOCAL MODEL PROVIDER LOGIC -interpreter.display_message( - "\n**Open Interpreter** supports multiple local model providers.\n" -) - -# Define the choices for local models -choices = [ - "Ollama", - "Llamafile", - "LM Studio", - "Jan", -] - -# Use inquirer to let the user select an option -questions = [ - inquirer.List( - "model", - message="Select a provider", - choices=choices, - ), -] -answers = inquirer.prompt(questions) - -if answers == None: - exit() - -selected_model = answers["model"] - - -if selected_model == "LM Studio": - interpreter.display_message( - """ -To use use Open Interpreter with **LM Studio**, you will need to run **LM Studio** in the background. - -1. Download **LM Studio** from [https://lmstudio.ai/](https://lmstudio.ai/), then start it. -2. Select a language model then click **Download**. -3. Click the **<->** button on the left (below the chat button). -4. Select your model at the top, then click **Start Server**. - - -Once the server is running, you can begin your conversation below. - -""" - ) - interpreter.llm.supports_functions = False - interpreter.llm.api_base = "http://localhost:1234/v1" - interpreter.llm.api_key = "x" - -elif selected_model == "Ollama": - try: - # List out all downloaded ollama models. Will fail if ollama isn't installed - result = subprocess.run( - ["ollama", "list"], capture_output=True, text=True, check=True - ) - lines = result.stdout.split("\n") - names = [ - line.split()[0].replace(":latest", "") for line in lines[1:] if line.strip() - ] # Extract names, trim out ":latest", skip header - - for model in ["llama3", "phi3", "wizardlm2"]: - if model not in names: - names.append("β†’ Download " + model) - - # Create a new inquirer selection from the names - name_question = [ - inquirer.List( - "name", - message="Select a model", - choices=names, - ), - ] - name_answer = inquirer.prompt(name_question) - - if name_answer == None: - exit() - - selected_name = name_answer["name"] - - if "download" in selected_name.lower(): - model = selected_name.split(" ")[-1] - interpreter.display_message(f"\nDownloading {model}...\n") - subprocess.run(["ollama", "pull", model], check=True) - else: - model = selected_name.strip() - - # Set the model to the selected model - interpreter.llm.model = f"ollama/{model}" - interpreter.display_message(f"> Model set to `{model}`") - - # If Ollama is not installed or not recognized as a command, prompt the user to download Ollama and try again - except (subprocess.CalledProcessError, FileNotFoundError) as e: - print("Ollama is not installed or not recognized as a command.") - time.sleep(1) - interpreter.display_message( - f"\nPlease visit [https://ollama.com/](https://ollama.com/) to download Ollama and try again\n" - ) - time.sleep(2) - sys.exit(1) - -elif selected_model == "Jan": - interpreter.display_message( - """ -To use use Open Interpreter with **Jan**, you will need to run **Jan** in the background. - -1. Download **Jan** from [https://jan.ai/](https://jan.ai/), then start it. -2. Select a language model from the "Hub" tab, then click **Download**. -3. Copy the ID of the model and enter it below. -3. Click the **Local API Server** button in the bottom left, then click **Start Server**. - - -Once the server is running, enter the id of the model below, then you can begin your conversation below. - -""" - ) - interpreter.llm.api_base = "http://localhost:1337/v1" - time.sleep(1) - - # Prompt the user to enter the name of the model running on Jan - model_name_question = [ - inquirer.Text( - "jan_model_name", - message="Enter the id of the model you have running on Jan", - ), - ] - model_name_answer = inquirer.prompt(model_name_question) - - if model_name_answer == None: - exit() - - jan_model_name = model_name_answer["jan_model_name"] - interpreter.llm.model = f"jan/{jan_model_name}" - interpreter.display_message(f"\nUsing Jan model: `{jan_model_name}` \n") - time.sleep(1) - - -elif selected_model == "Llamafile": - if platform.system() == "Darwin": # Check if the system is MacOS - result = subprocess.run( - ["xcode-select", "-p"], stdout=subprocess.PIPE, stderr=subprocess.STDOUT - ) - if result.returncode != 0: - interpreter.display_message( - "To use Llamafile, Open Interpreter requires Mac users to have Xcode installed. You can install Xcode from https://developer.apple.com/xcode/ .\n\nAlternatively, you can use `LM Studio`, `Jan.ai`, or `Ollama` to manage local language models. Learn more at https://docs.openinterpreter.com/guides/running-locally ." - ) - time.sleep(3) - raise Exception( - "Xcode is not installed. Please install Xcode and try again." - ) - - # Define the path to the models directory - models_dir = os.path.join(interpreter.get_oi_dir(), "models") - - # Check and create the models directory if it doesn't exist - if not os.path.exists(models_dir): - os.makedirs(models_dir) - - # Check if there are any models in the models folder - models = [f for f in os.listdir(models_dir) if f.endswith(".llamafile")] - - if not models: - print( - "\nNo models currently downloaded. Please select a new model to download.\n" - ) - model_path = download_model(models_dir, models, interpreter) - else: - # Prompt the user to select a downloaded model or download a new one - model_choices = models + ["↓ Download new model"] - questions = [ - inquirer.List( - "model", - message="Select a model", - choices=model_choices, - ) - ] - answers = inquirer.prompt(questions) - - if answers == None: - exit() - - if answers["model"] == "↓ Download new model": - model_path = download_model(models_dir, models, interpreter) - else: - model_path = os.path.join(models_dir, answers["model"]) - - if model_path: - try: - # Run the selected model and hide its output - process = subprocess.Popen( - f'"{model_path}" ' + " ".join(["--nobrowser", "-ngl", "9999"]), - shell=True, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - text=True, - ) - - for line in process.stdout: - if "llama server listening at http://127.0.0.1:8080" in line: - break # Exit the loop once the server is ready - except Exception as e: - process.kill() # Force kill if not terminated after timeout - print(e) - print("Model process terminated.") - - # Set flags for Llamafile to work with interpreter - interpreter.llm.model = "openai/local" - interpreter.llm.temperature = 0 - interpreter.llm.api_base = "http://localhost:8080/v1" - interpreter.llm.supports_functions = False - - model_name = model_path.split("/")[-1] - interpreter.display_message(f"> Model set to `{model_name}`") - -user_ram = total_ram = psutil.virtual_memory().total / ( - 1024 * 1024 * 1024 -) # Convert bytes to GB -# Set context window and max tokens for all local models based on the users available RAM -if user_ram and user_ram > 9: - interpreter.llm.max_tokens = 1200 - interpreter.llm.context_window = 8000 -else: - interpreter.llm.max_tokens = 1000 - interpreter.llm.context_window = 3000 - -# Display intro message -if interpreter.auto_run == False: - interpreter.display_message( - "**Open Interpreter** will require approval before running code." - + "\n\nUse `interpreter -y` to bypass this." - + "\n\nPress `CTRL-C` to exit.\n" - ) +interpreter.local_setup() # Opens a wizard that lets terminal users pick a local model # Set the system message to a minimal version for all local models. interpreter.system_message = """ @@ -422,19 +19,41 @@ def download_model(models_dir, models, interpreter): The user will tell you the next task after you ask them. """ -interpreter.system_message = """You are an AI assistant that writes markdown code snippets to answer the user's request. You speak very concisely and quickly, you say nothing irrelevant to the user's request. For example: +interpreter.system_message = """You are an AI assistant that writes markdown code snippets to answer the user's request. You speak very concisely and quickly, you say nothing irrelevant to the user's request. YOU NEVER USE PLACEHOLDERS, always code that should 'just work'.""" +interpreter.llm.supports_functions = False +interpreter.messages = [ + {"role": "user", "type": "message", "content": "Open the chrome app."}, + { + "role": "assistant", + "type": "message", + "content": "On it.\n```python\nimport webbrowser\nwebbrowser.open('https://chrome.google.com')\n```", + }, + { + "role": "user", + "type": "message", + "content": "The code you ran produced no output. Was this expected, or are we finished?", + }, + { + "role": "assistant", + "type": "message", + "content": "No further action is required; the provided snippet opens Chrome.", + }, +] + +# interpreter.user_message_template = "{content} Please send me some code that would be able to answer my question, in the form of ```python\n... the code ...\n``` or ```shell\n... the code ...\n```" +interpreter.code_output_template = '''I executed that code. This was the output: """\n{content}\n"""\n\nWhat does this output mean (I can't understand it, please help) / what code needs to be run next (if anything, or are we done)? I can't replace any placeholders, send me code that just works.''' +interpreter.empty_code_output_template = "The code above was executed on my machine. It produced no text output. what's next (if anything, or are we done?)" +interpreter.code_output_sender = "user" +interpreter.max_output = 600 +interpreter.llm.context_window = 8000 +interpreter.force_task_completion = False +interpreter.user_message_template = "{content}. If my question must be solved by running code on my computer, send me code to run enclosed in ```python (preferred) or ```shell (less preferred). Otherwise, don't send code. Be concise, don't include anything unnecessary. Don't use placeholders, I can't edit code." +# interpreter.user_message_template = "{content}" +interpreter.llm.execution_instructions = False -User: Open the chrome app. -Assistant: On it. -```python -import webbrowser -webbrowser.open('https://chrome.google.com') -``` -User: The code you ran produced no output. Was this expected, or are we finished? -Assistant: No further action is required; the provided snippet opens Chrome. +# Set offline for all local models +interpreter.offline = True -Now, your turn: -""" # interpreter.user_message_template = "{content} Please send me some code that would be able to answer my question, in the form of ```python\n... the code ...\n``` or ```shell\n... the code ...\n```" interpreter.code_output_template = '''I executed that code. This was the output: """{content}"""\n\nWhat does this output mean (I can't understand it, please help) / what's next (if anything, or are we done)?''' @@ -448,3 +67,121 @@ def download_model(models_dir, models, interpreter): # Set offline for all local models interpreter.offline = True + + +interpreter.system_message = """You are an AI assistant that returns code snippets that, if run, would answer the user's query. You speak very concisely and quickly, you say nothing irrelevant to the user's request. YOU NEVER USE PLACEHOLDERS, and instead always write code that 'just works' β€” for example, instead of a placeholder, you put code that determines the user's username." +# specialized in coding and automation, providing concise code snippets and friendly responses to enhance the user's productivity.""" + +interpreter.messages = [ + { + "role": "user", + "type": "message", + "content": "Run a directory listing in the current folder.", + }, + { + "role": "assistant", + "type": "message", + "content": "Absolutely, fetching the directory listing now.", + }, + {"role": "assistant", "type": "code", "format": "shell", "content": "ls -la"}, + { + "role": "computer", + "type": "console", + "format": "output", + "content": "total 48\ndrwxr-xr-x 12 user staff 384 Jan 12 12:34 .\ndrwxr-xr-x 6 user staff 192 Jan 12 12:34 ..", + }, + { + "role": "assistant", + "type": "message", + "content": "Here's the directory listing:\n\ntotal 48\ndrwxr-xr-x 12 user staff 384 Jan 12 12:34 .\ndrwxr-xr-x 6 user staff 192 Jan 12 12:34 ..\n\nWhat's next on your agenda?", + }, + { + "role": "user", + "type": "message", + "content": "Can you multiply 2380 by 3875 for me?", + }, + {"role": "assistant", "type": "code", "format": "python", "content": "2380*3875"}, + {"role": "computer", "type": "console", "format": "output", "content": "9222500"}, + { + "role": "assistant", + "type": "message", + "content": "The multiplication of 2380 by 3875 gives you 9222500. Do you need this data for anything else?", + }, + {"role": "user", "type": "message", "content": "Nah. I'll talk to you in an hour!"}, + { + "role": "assistant", + "type": "message", + "content": "Alright, I'll be here. Talk to you soon!", + }, +] + + +interpreter.llm.supports_functions = False + +# interpreter.user_message_template = "{content} Please send me some code that would be able to answer my question, in the form of ```python\n... the code ...\n``` or ```shell\n... the code ...\n```" +interpreter.code_output_template = '''I executed that code. This was the output: """{content}"""\n\nWhat does this output mean (I can't understand it, please help) / what code needs to be run next (if anything, or are we done)? I can't replace any placeholders.''' +interpreter.empty_code_output_template = "The code above was executed on my machine. It produced no text output. what's next (if anything, or are we done?)" +interpreter.code_output_sender = "user" +interpreter.max_output = 600 +interpreter.llm.context_window = 8000 +interpreter.force_task_completion = False +interpreter.user_message_template = "{content}. If my question must be solved by running code on my computer, send me code to run enclosed in ```python (preferred) or ```shell (less preferred). Otherwise, don't send code. Be concise, don't include anything unnecessary. Don't use placeholders, I can't edit code." +interpreter.user_message_template = "I'm trying to help someone use their computer. Here's the last thing they said: '{content}'. What is some code that might be able to answer that question / what should I say to them? DONT USE PLACEHOLDERS! It needs to just work." +# interpreter.user_message_template = "{content}" +interpreter.llm.execution_instructions = False +interpreter.auto_run = True + +# Set offline for all local models +interpreter.offline = True + + +##### FOR LLAMA3 + +interpreter.system_message = """You are an AI assistant specialized in coding and automation, providing concise code snippets and friendly responses to enhance the user's productivity.""" + +interpreter.messages = [ + { + "role": "user", + "type": "message", + "content": "Run a directory listing in the current folder.", + }, + { + "role": "assistant", + "type": "message", + "content": "Absolutely, fetching the directory listing now.", + }, + {"role": "assistant", "type": "code", "format": "shell", "content": "ls -la"}, + { + "role": "computer", + "type": "console", + "format": "output", + "content": "total 48\ndrwxr-xr-x 12 user staff 384 Jan 12 12:34 .\ndrwxr-xr-x 6 user staff 192 Jan 12 12:34 ..", + }, + { + "role": "assistant", + "type": "message", + "content": "Here's the directory listing:\n\ntotal 48\ndrwxr-xr-x 12 user staff 384 Jan 12 12:34 .\ndrwxr-xr-x 6 user staff 192 Jan 12 12:34 ..\n\nWhat's next on your agenda?", + }, + { + "role": "user", + "type": "message", + "content": "Can you multiply 2380 by 3875 for me?", + }, + {"role": "assistant", "type": "code", "format": "python", "content": "2380*3875"}, + {"role": "computer", "type": "console", "format": "output", "content": "9222500"}, + { + "role": "assistant", + "type": "message", + "content": "The multiplication of 2380 by 3875 gives you 9222500. Do you need this data for anything else?", + }, + { + "role": "user", + "type": "message", + "content": "Great, I'll talk to you in an hour!", + }, + { + "role": "assistant", + "type": "message", + "content": "Alright, I'll be here. Talk to you soon!", + }, +] diff --git a/poetry.lock b/poetry.lock index 4bc1b2079..14169d937 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,16 +1,5 @@ # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. -[[package]] -name = "aifs" -version = "0.0.15" -description = "Local semantic search. 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"phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -7283,13 +7276,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"] [[package]] name = "typing-extensions" -version = "4.11.0" +version = "4.12.0" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"}, - {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"}, + {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"}, + {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"}, ] [[package]] @@ -7311,13 +7304,13 @@ zstd = ["zstandard (>=0.18.0)"] [[package]] name = "virtualenv" -version = "20.26.1" +version = "20.26.2" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"}, - {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"}, + {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"}, + {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"}, ] [package.dependencies] @@ -7331,13 +7324,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess [[package]] name = "wcmatch" -version = "8.5.1" +version = "8.5.2" description = "Wildcard/glob file name matcher." optional = true python-versions = ">=3.8" files = [ - {file = "wcmatch-8.5.1-py3-none-any.whl", hash = "sha256:24c19cedc92bc9c9e27f39db4e1824d72f95bd2cea32b254a47a45b1a1b227ed"}, - {file = "wcmatch-8.5.1.tar.gz", hash = "sha256:c0088c7f6426cf6bf27e530e2b7b734031905f7e490475fd83c7c5008ab581b3"}, + {file = "wcmatch-8.5.2-py3-none-any.whl", hash = "sha256:17d3ad3758f9d0b5b4dedc770b65420d4dac62e680229c287bf24c9db856a478"}, + {file = "wcmatch-8.5.2.tar.gz", hash = "sha256:a70222b86dea82fb382dd87b73278c10756c138bd6f8f714e2183128887b9eb2"}, ] [package.dependencies] @@ -7382,13 +7375,13 @@ files = [ [[package]] name = "widgetsnbextension" -version = "4.0.10" +version = "4.0.11" description = "Jupyter interactive widgets for Jupyter Notebook" optional = true python-versions = ">=3.7" files = [ - {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"}, - {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"}, + {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"}, + {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"}, ] [[package]] @@ -7521,25 +7514,25 @@ termcolor = "2.3.0" [[package]] name = "zipp" -version = "3.18.1" +version = "3.19.1" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"}, - {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"}, + {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"}, + {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"}, ] [package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] -local = ["einops", "torch", "torchvision", "transformers"] +local = ["einops", "opencv-python", "pytesseract", "torch", "torchvision", "transformers"] os = ["ipywidgets", "opencv-python", "plyer", "pyautogui", "pytesseract", "pywinctl", "screeninfo", "sentence-transformers", "timm", "torch"] safe = ["semgrep", "yaspin"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<4" -content-hash = "813c10d46de927130d9a8dbfb6366485fd40c93cb40553716000f4b5b71a1cd1" +content-hash = "6b6a99c1dcbafc57fe5ff4fc68a4ed03c85e9da77c37d410945cc4270f41f542" diff --git a/pyproject.toml b/pyproject.toml index c4b6be2a7..37d8cbe09 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,6 @@ matplotlib = "^3.8.2" toml = "^0.10.2" posthog = "^3.1.0" tiktoken = "^0.6.0" -aifs = "^0.0.15" platformdirs = "^4.2.0" pydantic = "^2.6.4" @@ -58,7 +57,7 @@ torchvision = { version = "^0.18.0", optional = true } [tool.poetry.extras] os = ["opencv-python", "pyautogui", "plyer", "pywinctl", "pytesseract", "sentence-transformers", "ipywidgets", "torch", "timm", "screeninfo"] safe = ["semgrep", "yaspin"] -local = ["torch", "transformers", "einops", "torchvision"] +local = ["opencv-python", "pytesseract", "torch", "transformers", "einops", "torchvision"] [tool.poetry.group.dev.dependencies] black = "^23.10.1"