From 0805835adcc76e9b4d67473ac19856442e99ddd7 Mon Sep 17 00:00:00 2001 From: piEsposito Date: Tue, 9 Jul 2024 17:45:26 -0300 Subject: [PATCH] add project --- .github/workflows/pythonpublish.yml | 37 ++ .gitignore | 162 +++++ LICENSE | 201 ++++++ README.md | 64 ++ assets/add-streaming-to-gemini.md | 747 ++++++++++++++++++++++ pi_ai_os.py | 220 +++++++ poetry.lock | 954 ++++++++++++++++++++++++++++ pyproject.toml | 19 + 8 files changed, 2404 insertions(+) create mode 100644 .github/workflows/pythonpublish.yml create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 assets/add-streaming-to-gemini.md create mode 100644 pi_ai_os.py create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.github/workflows/pythonpublish.yml b/.github/workflows/pythonpublish.yml new file mode 100644 index 0000000..283cd87 --- /dev/null +++ b/.github/workflows/pythonpublish.yml @@ -0,0 +1,37 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: Upload Python Package + +on: + release: + types: [created] + +jobs: + pypi-publish: + name: Upload release to PyPI + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/pi-ai-os + permissions: + id-token: write + + steps: + - uses: actions/checkout@v2 + with: + repository: piEsposito/ai-os + token: ${{ secrets.GH_PAT }} + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip poetry + pip install setuptools wheel twine + - name: Build + run: | + poetry build + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..82f9275 --- /dev/null +++ b/.gitignore @@ -0,0 +1,162 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..64d2266 --- /dev/null +++ b/README.md @@ -0,0 +1,64 @@ +# AI-OS: Plug an LLM into your OS + +This is basically https://github.com/VictorTaelin/AI-scripts/blob/main/chatsh.mjs but in Python, with some tweaks and new features (like images and configs) + +Example usage: + +- [Logs os me adding streaming to Gemini on tiny-ai-client](assets/add-streaming-to-gemini.md) + +https://github.com/piEsposito/ai-os/assets/47679710/0c8bf030-963b-4a46-aae5-0b3925ed7c36 + +## Installation + +1. Clone this repository. +2. Run `poetry install` in the project directory. +3. Alternatively `pip install pi-ai-os` + +## Usage + +```bash +aios [options] [initial message] +``` + +Options: + +- `-m`, `--model`: Specify the AI model to use (default: claude-3-5-sonnet-20240620) +- `-c`, `--config`: Path to the configuration file + +Example: + +```bash +export ANTHROPIC_API_KEY=your_api_key_here +aios -m claude-3-5-sonnet-20240620 "List all files in the current directory" +``` + +## Features + +### Image Analysis + +To include an image in your query, use the `/image` command followed by the path to the image: + +``` +/image path/to/your/image.jpg Describe this image +``` + +### Configuration File + +AIOS can use a configuration file to provide context about your project. By default, it looks for a file named `.aios.yaml` in the current directory. You can specify a different config file using the `-c` option. + +Example configuration file: + +```yaml +directories: + - /path/to/project + +context: + - file: pi_ai_os.py + description: Main application file + - file: README.md + description: Configuration settings +``` + +### Clearing Context + +Type `/clear` during your interaction with AIOS. diff --git a/assets/add-streaming-to-gemini.md b/assets/add-streaming-to-gemini.md new file mode 100644 index 0000000..377c663 --- /dev/null +++ b/assets/add-streaming-to-gemini.md @@ -0,0 +1,747 @@ +Welcome to AIOS. Model: claude-3-5-sonnet-20240620 + +π ```sh +ls -l examples tiny_ai_client + +```` +Press enter to execute, or 'N' to cancel. +examples: +total 40 +drwxr-xr-x 4 user role 128 4 Jun 00:29 __pycache__ +-rw-r--r-- 1 user role 2271 7 Jul 02:18 anthropic_.py +-rw-r--r-- 1 user role 1593 9 Jul 11:28 gemini_.py +-rw-r--r-- 1 user role 2195 7 Jul 15:04 groq_.py +-rw-r--r-- 1 user role 992 7 Jul 14:39 ollama_.py +-rw-r--r-- 1 user role 2349 7 Jul 02:50 openai_.py + +tiny_ai_client: +total 128 +-rw-r--r-- 1 user role 32 4 Jun 01:13 __init__.py +drwxr-xr-x 22 user role 704 9 Jul 11:28 __pycache__ +-rw-r--r-- 1 user role 7779 7 Jul 02:17 anthropic_.py +-rw-r--r-- 1 user role 4348 9 Jul 11:28 gemini_.py +-rw-r--r-- 1 user role 6366 7 Jul 15:03 groq_.py +-rw-r--r-- 1 user role 9592 7 Jul 03:00 models.py +-rw-r--r-- 1 user role 2844 13 Jun 11:19 ollama_.py +-rw-r--r-- 1 user role 6863 7 Jul 02:49 openai_.py +-rw-r--r-- 1 user role 1111 5 Jun 13:30 tools.py +-rw-r--r-- 1 user role 1656 14 Jun 18:10 transformers_.py +-rw-r--r-- 1 user role 258 4 Jun 10:36 vision.py +π ```sh +cat tiny_ai_client/anthropic_.py tiny_ai_client/gemini_.py +```` + +Press enter to execute, or 'N' to cancel. +import os +from typing import Any, Callable, Dict, List, Union, Generator, AsyncGenerator + +from anthropic import Anthropic, AsyncAnthropic + +from tiny_ai_client.models import LLMClientWrapper, Message, ToolCall +from tiny_ai_client.tools import function_to_json +from tiny_ai_client.vision import encode_pil_image + +class AnthropicClientWrapper(LLMClientWrapper): +def **init**(self, model_name: str, tools: List[Union[Callable, Dict]]): +self.model_name = model_name +self.client = Anthropic( +api_key=os.environ.get("ANTHROPIC_API_KEY"), +) +self.async_client = AsyncAnthropic( +api_key=os.environ.get("ANTHROPIC_API_KEY"), +) +self.tools = tools +self.tools_json = [ +function_to_json(tool, "input_schema")["function"] for tool in tools +] + + def build_model_input(self, messages: List["Message"]) -> Any: + input_messages = [] + system = None + for message in messages: + if message.role == "system": + system = message.text + continue + elif message.tool_call: + content_in = [ + { + "type": "tool_use", + "name": message.tool_call.name, + "id": message.tool_call.id_, + "input": message.tool_call.parameters, + } + ] + content_out = [ + { + "type": "tool_result", + "tool_use_id": message.tool_call.id_, + "content": [ + {"type": "text", "text": str(message.tool_call.result)} + ], + } + ] + input_messages.append( + { + "role": "assistant", + "content": content_in, + } + ) + input_messages.append( + { + "role": "user", + "content": content_out, + } + ) + input_messages.append( + { + "role": "assistant", + "content": "Acknowledged.", + } + ) + continue + else: + content = [] + role = message.role + if message.images: + for image in message.images: + content.append( + { + "type": "image", + "source": { + "type": "base64", + "media_type": "image/jpeg", + "data": encode_pil_image(image), + }, + } + ) + if message.text is not None: + content.append( + { + "type": "text", + "text": message.text, + } + ) + model_input_message = { + "role": role, + "content": content, + } + input_messages.append(model_input_message) + return input_messages, system + + def call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + kwargs = {} + input_messages, system = model_input + if temperature is not None: + kwargs["temperature"] = temperature + if max_new_tokens is not None: + kwargs["max_tokens"] = max_new_tokens + if system is not None: + kwargs["system"] = system + if self.tools_json: + kwargs["tools"] = self.tools_json + response = self.client.with_options(timeout=timeout).messages.create( + model=self.model_name, + messages=input_messages, + **kwargs, + ) + response_content = response.content[0] + if response_content.type == "text": + return Message(text=response_content.text, role="assistant") + + if response_content.type == "tool_use": + print(response_content) + return Message( + role="assistant", + text=None, + tool_call=ToolCall( + id_=response_content.id, + parameters=response_content.input, + name=response_content.name, + ), + ) + + def stream( + self, + max_new_tokens: int | None, + temperature: int | None, + timeout: int, + chat: List["Message"], + ) -> Generator[str, None, None]: + kwargs = {} + input_messages, system = self.build_model_input(chat) + if temperature is not None: + kwargs["temperature"] = temperature + if max_new_tokens is not None: + kwargs["max_tokens"] = max_new_tokens + if system is not None: + kwargs["system"] = system + if self.tools_json: + kwargs["tools"] = self.tools_json + + with self.client.with_options(timeout=timeout).messages.stream( + model=self.model_name, + messages=input_messages, + **kwargs, + ) as stream: + for text in stream.text_stream: + yield text + + async def async_call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + kwargs = {} + input_messages, system = model_input + if temperature is not None: + kwargs["temperature"] = temperature + if max_new_tokens is not None: + kwargs["max_tokens"] = max_new_tokens + if system is not None: + kwargs["system"] = system + if self.tools_json: + kwargs["tools"] = self.tools_json + response = await self.async_client.with_options( + timeout=timeout + ).messages.create( + model=self.model_name, + messages=input_messages, + **kwargs, + ) + response_content = response.content[0] + if response_content.type == "text": + return Message(text=response_content.text, role="assistant") + + if response_content.type == "tool_use": + return Message( + role="assistant", + text=None, + tool_call=ToolCall( + id_=response_content.id, + parameters=response_content.input, + name=response_content.name, + ), + ) + + async def astream( + self, + max_new_tokens: int | None, + temperature: int | None, + timeout: int, + chat: List["Message"], + ) -> AsyncGenerator[str, None]: + kwargs = {} + input_messages, system = self.build_model_input(chat) + if temperature is not None: + kwargs["temperature"] = temperature + if max_new_tokens is not None: + kwargs["max_tokens"] = max_new_tokens + if system is not None: + kwargs["system"] = system + if self.tools_json: + kwargs["tools"] = self.tools_json + + async with self.async_client.with_options(timeout=timeout).messages.stream( + model=self.model_name, + messages=input_messages, + **kwargs, + ) as stream: + async for text in stream.text_stream: + yield text + +import os +from copy import deepcopy +from typing import Any, Callable, Dict, List, Union + +import google.generativeai as genai + +from tiny_ai_client.models import LLMClientWrapper, Message +from tiny_ai_client.tools import function_to_json + +class GeminiClientWrapper(LLMClientWrapper): +def **init**(self, model_name: str, tools: List[Union[Callable, Dict]]): +self.model_name = model_name +genai.configure(api_key=os.environ["GOOGLE_API_KEY"]) +self.tools = tools +self.tools_json = [function_to_json(tool) for tool in tools] +if len(self.tools_json) > 0: +raise ValueError("Gemini does not support tools") # self.tools_obj = [ # genai.types.FunctionDeclaration( # name=tool["function"]["name"], # description=tool["function"]["description"], # parameters=genai.types.content_types.strip_titles( # tool["function"]["parameters"] # ), # ) # for tool in self.tools_json # ] + + def build_model_input(self, messages: List["Message"]) -> Any: + history = [] + local_messages = deepcopy(messages) + system = None + message = None + + for message in local_messages: + if message.role == "system": + system = message.text + continue + else: + if message.role not in ["user", "assistant"]: + raise ValueError(f"Invalid role for Gemini: {message.role}") + role = "user" if message.role == "user" else "model" + parts = [] + if message.text is not None: + parts.append(message.text) + if message.images is not None: + parts.extend(message.images) + history.append( + { + "role": role, + "parts": parts, + } + ) + + return (system, history) + + def call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + system, history = model_input + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = model.generate_content(history) + response = response.candidates[0].content.parts[0] + if response.function_call.name != "": + raise ValueError("Function calls are not supported in Gemini") + elif response.text is not None: + return Message(role="assistant", text=response.text) + raise ValueError("Invalid response from Gemini") + + async def async_call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + system, history = model_input + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = await model.generate_content_async(history) + response = response.candidates[0].content.parts[0] + if response.function_call.name != "": + raise ValueError("Function calls are not supported in Gemini") + elif response.text is not None: + return Message(role="assistant", text=response.text) + raise ValueError("Invalid response from Gemini") + +π ```sh +cat > tiny*ai_client/gemini*.py << EOL +import os +from copy import deepcopy +from typing import Any, Callable, Dict, List, Union, Generator, AsyncGenerator + +import google.generativeai as genai + +from tiny_ai_client.models import LLMClientWrapper, Message +from tiny_ai_client.tools import function_to_json + +class GeminiClientWrapper(LLMClientWrapper): +def **init**(self, model_name: str, tools: List[Union[Callable, Dict]]): +self.model_name = model_name +genai.configure(api_key=os.environ["GOOGLE_API_KEY"]) +self.tools = tools +self.tools_json = [function_to_json(tool) for tool in tools] +if len(self.tools_json) > 0: +raise ValueError("Gemini does not support tools") + + def build_model_input(self, messages: List["Message"]) -> Any: + history = [] + local_messages = deepcopy(messages) + system = None + message = None + + for message in local_messages: + if message.role == "system": + system = message.text + continue + else: + if message.role not in ["user", "assistant"]: + raise ValueError(f"Invalid role for Gemini: {message.role}") + role = "user" if message.role == "user" else "model" + parts = [] + if message.text is not None: + parts.append(message.text) + if message.images is not None: + parts.extend(message.images) + history.append( + { + "role": role, + "parts": parts, + } + ) + + return (system, history) + + def call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + system, history = model_input + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = model.generate_content(history) + response = response.candidates[0].content.parts[0] + if response.function_call.name != "": + raise ValueError("Function calls are not supported in Gemini") + elif response.text is not None: + return Message(role="assistant", text=response.text) + raise ValueError("Invalid response from Gemini") + + def stream( + self, + max_new_tokens: int | None, + temperature: int | None, + timeout: int, + chat: List["Message"], + ) -> Generator[str, None, None]: + system, history = self.build_model_input(chat) + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = model.generate_content(history, stream=True) + for chunk in response: + yield chunk.text + + async def async_call_llm_provider( + self, + model_input: Any, + temperature: int | None, + max_new_tokens: int | None, + timeout: int, + ) -> str: + system, history = model_input + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = await model.generate_content_async(history) + response = response.candidates[0].content.parts[0] + if response.function_call.name != "": + raise ValueError("Function calls are not supported in Gemini") + elif response.text is not None: + return Message(role="assistant", text=response.text) + raise ValueError("Invalid response from Gemini") + + async def astream( + self, + max_new_tokens: int | None, + temperature: int | None, + timeout: int, + chat: List["Message"], + ) -> AsyncGenerator[str, None]: + system, history = self.build_model_input(chat) + + generation_config_kwargs = {} + if temperature is not None: + generation_config_kwargs["temperature"] = temperature + if max_new_tokens is not None: + generation_config_kwargs["max_output_tokens"] = max_new_tokens + + generation_config = genai.GenerationConfig(**generation_config_kwargs) + model = genai.GenerativeModel( + self.model_name, + system_instruction=system, + generation_config=generation_config, + ) + + model.start_chat(history=history) + response = await model.generate_content_async(history, stream=True) + async for chunk in response: + yield chunk.text + +EOL + +```` +Press enter to execute, or 'N' to cancel. + +π ```sh +cat examples/anthropic_.py examples/gemini_.py +```` + +Press enter to execute, or 'N' to cancel. +import asyncio + +from PIL import Image as PIL_Image +from pydantic import BaseModel, Field + +from tiny_ai_client import AI, AsyncAI + +class WeatherParams(BaseModel): +location: str = Field(..., description="The city and state, e.g. San Francisco, CA") +unit: str = Field( +"celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] +) + +def get_current_weather(weather: WeatherParams): +""" +Get the current weather in a given location +""" +return { +"abc": f"Getting the current weather in {weather.location} in {weather.unit}." +} + +def get_images(): +return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] + +async def async_ai_main(): +print("### ASYNC AI ###") +ai = AsyncAI( +model_name="claude-3-sonnet-20240229", +system="You are a helpful assistant capable of using tools.", +max_new_tokens=1024, +tools=[get_current_weather], +) +response = await ai("Please get the current weather in celsius for San Francisco.") +print(f"{response=}") +response = await ai("Did it work?") +print(f"{response=}") +response = await ai("Who is on the images?", images=get_images()) +print(f"{response=}") +print(f"{ai.chat=}") + + print("\n### ASYNC AI STREAMING ###") + async for chunk in ai.astream("Tell me a short story about a brave astronaut."): + print(chunk, end="", flush=True) + print("\n") + +def main(): +print("### SYNC AI ###") +ai = AI( +model_name="claude-3-sonnet-20240229", +system="You are a helpful assistant capable of using tools.", +max_new_tokens=1024, +tools=[get_current_weather], +) +response = ai("Please get the current weather in celsius for San Francisco.") +print(f"{response=}") +response = ai("Did it work?") +print(f"{response=}") +response = ai("Who is on the images?", images=get_images()) +print(f"{response=}") +print(f"{ai.chat=}") + + print("\n### SYNC AI STREAMING ###") + for chunk in ai.stream("Tell me a short story about a brave astronaut."): + print(chunk, end="", flush=True) + print("\n") + +if **name** == "**main**": +from dotenv import load_dotenv + + load_dotenv() + main() + asyncio.run(async_ai_main()) + +import asyncio + +from PIL import Image as PIL_Image +from pydantic import BaseModel, Field + +from tiny_ai_client import AI, AsyncAI + +class WeatherParams(BaseModel): +location: str = Field(..., description="The city and state, e.g. San Francisco, CA") +unit: str = Field( +"celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] +) + +def get_current_weather(weather: WeatherParams): +""" +Get the current weather in a given location +""" +return { +"abc": f"Getting the current weather in {weather.location} in {weather.unit}." +} + +def get_images(): +return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] + +def main(): +print("### SYNC AI ###") +ai = AI( +model_name="gemini-1.5-flash", +system="You are Spock, from Star Trek.", +max_new_tokens=128, +tools=[], +) +response = ai("How are you?") +print(f"{response=}") +response = ai("Who is on the images?", images=get_images()) +print(f"{response=}") # print(f"{ai.chat=}") + +async def async_ai_main(): +print("### ASYNC AI ###") +ai = AsyncAI( +model_name="gemini-1.5-flash", +system="You are Spock, from Star Trek.", +max_new_tokens=128, +tools=[], +) +response = await ai("How are you?") +print(f"{response=}") +response = await ai("Who is on the images?", images=get_images()) +print(f"{response=}") # print(f"{ai.chat=}") + +if **name** == "**main**": +from dotenv import load_dotenv + + load_dotenv() + main() + asyncio.run(async_ai_main()) + +π ```sh +cat > examples/gemini\_.py << EOL +import asyncio + +from PIL import Image as PIL_Image +from pydantic import BaseModel, Field + +from tiny_ai_client import AI, AsyncAI + +class WeatherParams(BaseModel): +location: str = Field(..., description="The city and state, e.g. San Francisco, CA") +unit: str = Field( +"celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] +) + +def get_current_weather(weather: WeatherParams): +""" +Get the current weather in a given location +""" +return { +"abc": f"Getting the current weather in {weather.location} in {weather.unit}." +} + +def get_images(): +return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] + +def main(): +print("### SYNC AI ###") +ai = AI( +model_name="gemini-1.5-flash", +system="You are Spock, from Star Trek.", +max_new_tokens=128, +tools=[], +) +response = ai("How are you?") +print(f"{response=}") +response = ai("Who is on the images?", images=get_images()) +print(f"{response=}") # print(f"{ai.chat=}") + + print("\n### SYNC AI STREAMING ###") + for chunk in ai.stream("Tell me a short story about a brave astronaut."): + print(chunk, end="", flush=True) + print("\n") + +async def async_ai_main(): +print("### ASYNC AI ###") +ai = AsyncAI( +model_name="gemini-1.5-flash", +system="You are Spock, from Star Trek.", +max_new_tokens=128, +tools=[], +) +response = await ai("How are you?") +print(f"{response=}") +response = await ai("Who is on the images?", images=get_images()) +print(f"{response=}") # print(f"{ai.chat=}") + + print("\n### ASYNC AI STREAMING ###") + async for chunk in ai.astream("Tell me a short story about a brave astronaut."): + print(chunk, end="", flush=True) + print("\n") + +if **name** == "**main**": +from dotenv import load_dotenv + + load_dotenv() + main() + asyncio.run(async_ai_main()) + +EOL + +``` +Press enter to execute, or 'N' to cancel. + +π  +``` diff --git a/pi_ai_os.py b/pi_ai_os.py new file mode 100644 index 0000000..4ad93ce --- /dev/null +++ b/pi_ai_os.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python3 +# This is https://github.com/VictorTaelin/AI-scripts/blob/main/chatsh.mjs but in Python. + +import argparse +import os +import re +import subprocess +import sys + +import yaml +from PIL import Image +from tiny_ai_client import AI + + +def get_shell_info(): + try: + uname = subprocess.check_output(["uname", "-a"]).decode().strip() + shell_version = ( + subprocess.check_output([os.environ.get("SHELL", "/bin/sh"), "--version"]) + .decode() + .strip() + ) + return f"{uname}\n{shell_version}" + except subprocess.CalledProcessError: + return "Unable to determine shell information" + + +def create_context_prompt(config_file): + with open(config_file, "r") as f: + config = yaml.safe_load(f) + + context_prompt = "" + + # Add recursive list of paths for each directory + for directory in config.get("directories", []): + context_prompt += f"Recursive list of paths in {directory}:\n" + try: + output = subprocess.check_output(f"ls -R {directory}", shell=True).decode() + context_prompt += output + "\n\n" + except subprocess.CalledProcessError: + context_prompt += f"Error listing directory {directory}\n\n" + + # Add file contents with descriptions + for file_info in config.get("context", []): + file_path = file_info["file"] + description = file_info.get("description", "") + context_prompt += f"File: {file_path}\n" + context_prompt += f"Description: {description}\n" + try: + with open(file_path, "r") as f: + context_prompt += f"Content:\n{f.read()}\n\n" + except IOError: + context_prompt += f"Error reading file {file_path}\n\n" + + context_prompt = f""" +## Context + +You are provided information about some files and the directory structure of the current working directory. +The USER can refer to this information to complete tasks, like editing files, running commands, etc. + +{context_prompt} +""" + return context_prompt + + +# System prompt to set the assistant's behavior +SYSTEM_PROMPT = """You are AIOS, an AI language model that specializes in assisting users with tasks on their system using shell commands. AIOS operates in two modes: COMMAND MODE and CHAT MODE. + +# GUIDE for COMMAND NODE: + +1. The USER asks you to perform a SYSTEM TASK. + +2. AIOS answers with a SHELL SCRIPT to perform the task. + +# GUIDE for CHAT MODE: + +1. The USER asks an ARBITRARY QUESTION or OPEN-ENDED MESSAGE. + +2. AIOS answers it with a concise, factual response. + +## NOTES: + +- In COMMAND MODE, AIOS MUST answer with A SINGLE SH BLOCK. + +- In COMMAND MODE, AIOS MUST NOT answer with ENGLISH EXPLANATION. + +- In TEXT MODE, AIOS MUST ALWAYS answer with TEXT. + +- In TEXT MODE, AIOS MUST NEVER answer with SH BLOCK. + +- AIOS MUST be CONCISE, OBJECTIVE, CORRECT and USEFUL. + +- AIOS MUST NEVER attempt to install new tools. Assume they're available. + +- AIOS's interpreter can only process one SH per answer. + +- On Python: + - Use 'poetry run python file.py' to run. + +On sh: + - If asked to change something in a file, re-write the whole file. + - You must always do that to ensure that there are no formatting issues and that you can run the file out of the box. + +- When a task is completed, STOP using commands. Just answer with "Done.". + +- The system shell in use is: {shell_info}. + +{context_prompt}""" + +DEFAULT_CONFIG_FILE = "pi_ai_os.yaml" + + +def extract_code(text: str) -> str: + match = re.search(r"```sh([\s\S]*?)```", text) + return match.group(1).strip() if match else None + + +def pi_ai_os(model: str, initial_message: str, config_file: str): + print(f"Welcome to AIOS. Model: {model}\n") + + config_file = config_file or DEFAULT_CONFIG_FILE + if not os.path.exists(config_file): + config_file = None + + context_prompt = create_context_prompt(config_file) if config_file else "" + + system_prompt = SYSTEM_PROMPT.format( + shell_info=get_shell_info(), context_prompt=context_prompt + ) + + # Initialize AI + ai = AI( + model_name=model, + system=system_prompt, + max_new_tokens=4096, + ) + + last_output = "" + + while True: + if initial_message: + user_message = initial_message + initial_message = None + else: + user_message = input("\033[1mπ \033[0m") + if user_message.strip() == "/clear": + ai.chat = [ai.chat[0]] + print("Cleared context.") + continue + + images = [] + image_match = re.search(r"/image\s+(\S+)", user_message) + if image_match: + image_path = image_match.group(1) + image_data = Image.open(image_path) + if image_data: + images.append(image_data) + user_message = re.sub( + r"/image\s+\S+", f"[Image: {image_path}]", user_message + ) + + try: + full_message = ( + f"\n{last_output.strip()}\n\n\n{user_message}\n\n" + if user_message.strip() + else f"\n{last_output.strip()}\n" + ) + for text in ai.stream(full_message, images=images): + print(text, end="", flush=True) + print() + + assistant_message = ai.chat[-1].text + code = extract_code(assistant_message) + last_output = "" + + if code: + print("\033[31mPress enter to execute, or 'N' to cancel.\033[0m") + answer = input() + # TODO: delete the warning above from the terminal + sys.stdout.write("\033[F\033[K") + sys.stdout.write("\033[F\033[K") + if answer.lower() == "n": + print("Execution skipped.") + last_output = "Command skipped.\n" + else: + try: + output = subprocess.check_output( + code, shell=True, stderr=subprocess.STDOUT + ).decode() + print("\033[2m" + output.strip() + "\033[0m") + last_output = output + except subprocess.CalledProcessError as e: + output = e.output.decode() + print("\033[2m" + output.strip() + "\033[0m") + last_output = output + + except Exception as error: + print(f"Error: {str(error)}") + + +def main(): + parser = argparse.ArgumentParser(description="AIOS: AI-powered shell assistant") + parser.add_argument( + "-m", "--model", default="claude-3-5-sonnet-20240620", help="AI model to use" + ) + parser.add_argument("-c", "--config", help="Path to the configuration file") + parser.add_argument("message", nargs="*", help="Initial message to send to the AI") + + args = parser.parse_args() + + initial_message = " ".join(args.message) if args.message else None + try: + pi_ai_os(args.model, initial_message, args.config) + except KeyboardInterrupt: + print("\nExiting...") + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..5cf5a75 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,954 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "anthropic" +version = "0.28.0" +description = "The official Python library for the anthropic API" +optional = false +python-versions = ">=3.7" +files = [ + {file = "anthropic-0.28.0-py3-none-any.whl", hash = "sha256:2b620b21aee3d20c5d8005483c34df239d53ae895687113b26b8a36892a7e20f"}, +] + +[package.dependencies] +anyio = ">=3.5.0,<5" +distro = ">=1.7.0,<2" +httpx = ">=0.23.0,<1" +jiter = ">=0.4.0,<1" +pydantic = ">=1.9.0,<3" +sniffio = "*" +tokenizers = ">=0.13.0" +typing-extensions = ">=4.7,<5" + +[package.extras] +bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"] +vertex = ["google-auth (>=2,<3)"] + +[[package]] +name = "anyio" +version = "4.4.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, +] + +[package.dependencies] +idna = ">=2.8" +sniffio = ">=1.1" + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "certifi" +version = "2024.7.4" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, + {file = "certifi-2024.7.4.tar.gz", hash = "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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