├── tiny_ai_client ├── __init__.py ├── vision.py ├── gemini_.py ├── tools.py ├── ollama_.py ├── groq_.py ├── openai_.py ├── anthropic_.py └── models.py ├── requirements.txt ├── assets ├── kirk.jpg └── spock.jpg ├── scripts └── run-all-examples.sh ├── pyproject.toml ├── examples ├── ollama_.py ├── groq_.py ├── gemini_.py ├── anthropic_.py └── openai_.py ├── .github └── workflows │ └── pythonpublish.yml ├── .gitignore ├── README.md ├── LICENSE └── poetry.lock /tiny_ai_client/__init__.py: -------------------------------------------------------------------------------- 1 | from .models import AI, AsyncAI 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pydantic==2.7.3 2 | openai==1.31.0 3 | anthropic==0.28.0 4 | -------------------------------------------------------------------------------- /assets/kirk.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piEsposito/tiny-ai-client/HEAD/assets/kirk.jpg -------------------------------------------------------------------------------- /assets/spock.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/piEsposito/tiny-ai-client/HEAD/assets/spock.jpg -------------------------------------------------------------------------------- /scripts/run-all-examples.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | for file in examples/*.py; do 4 | echo "Running $file" 5 | 6 | poetry run python $file 7 | done 8 | -------------------------------------------------------------------------------- /tiny_ai_client/vision.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | import base64 3 | from io import BytesIO 4 | 5 | from PIL import Image as PIL_Image 6 | 7 | 8 | def encode_pil_image(image: PIL_Image.Image) -> str: 9 | buffered = BytesIO() 10 | image.save(buffered, format="JPEG") 11 | return base64.b64encode(buffered.getvalue()).decode("utf-8") 12 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "tiny-ai-client" 3 | version = "0.0.14" 4 | description = "Tiny AI client for LLMs. As simple as it gets." 5 | authors = ["piEsposito "] 6 | license = "Apache 2.0" 7 | readme = "README.md" 8 | 9 | [tool.poetry.dependencies] 10 | python = ">=3.9" 11 | pydantic = "2.7.3" 12 | openai = "1.58.1" 13 | anthropic = "0.42.0" 14 | pillow = "11" 15 | groq = "0.13.1" 16 | python-dotenv = "^1.0.1" 17 | aiohttp = "^3.10.3" 18 | 19 | 20 | [tool.poetry.group.gemini] 21 | optional = false 22 | 23 | [tool.poetry.group.gemini.dependencies] 24 | 25 | [build-system] 26 | requires = ["poetry-core"] 27 | build-backend = "poetry.core.masonry.api" 28 | -------------------------------------------------------------------------------- /tiny_ai_client/gemini_.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import os 4 | from typing import Callable, Dict, List, Union 5 | 6 | from openai import AsyncOpenAI, OpenAI 7 | 8 | from tiny_ai_client.openai_ import OpenAIClientWrapper 9 | from tiny_ai_client.tools import function_to_json 10 | 11 | 12 | class GeminiClientWrapper(OpenAIClientWrapper): 13 | def __init__(self, model_name: str, tools: List[Union[Callable, Dict]]): 14 | assert ( 15 | "googleapis.com" in os.environ["OPENAI_BASE_URL"] 16 | ), "OPENAI_BASE_URL must be set to inside googleapis.com" 17 | self.model_name = model_name 18 | self.client = OpenAI() 19 | self.async_client = AsyncOpenAI() 20 | self.tools = tools 21 | self.tools_json = [function_to_json(tool) for tool in tools] 22 | -------------------------------------------------------------------------------- /examples/ollama_.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | 3 | from tiny_ai_client import AI, AsyncAI 4 | 5 | 6 | async def async_ai_main(): 7 | print("### ASYNC AI ###") 8 | ai = AsyncAI( 9 | model_name="ollama:llama3", 10 | system="You are Spock, from Star Trek.", 11 | max_new_tokens=128, 12 | model_server_url="http://localhost:11434/api/chat", 13 | ) 14 | response = await ai("What is the meaning of life?") 15 | print(f"{response=}") 16 | response = await ai("Did it work?") 17 | print(f"{response=}") 18 | print(f"{ai.chat=}") 19 | 20 | 21 | def main(): 22 | print("### SYNC AI ###") 23 | ai = AI( 24 | model_name="ollama:llama3", 25 | system="You are Spock, from Star Trek.", 26 | max_new_tokens=128, 27 | model_server_url="http://localhost:11434/api/chat", 28 | # tools=[get_current_weather], 29 | ) 30 | response = ai("What is the meaning of life?") 31 | print(f"{response=}") 32 | response = ai("Did it work?") 33 | print(f"{response=}") 34 | print(f"{ai.chat=}") 35 | 36 | 37 | if __name__ == "__main__": 38 | main() 39 | asyncio.run(async_ai_main()) 40 | -------------------------------------------------------------------------------- /.github/workflows/pythonpublish.yml: -------------------------------------------------------------------------------- 1 | # This workflows will upload a Python Package using Twine when a release is created 2 | # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries 3 | 4 | name: Upload Python Package 5 | 6 | on: 7 | release: 8 | types: [created] 9 | 10 | jobs: 11 | pypi-publish: 12 | name: Upload release to PyPI 13 | runs-on: ubuntu-latest 14 | environment: 15 | name: pypi 16 | url: https://pypi.org/p/tiny-ai-client 17 | permissions: 18 | id-token: write 19 | 20 | steps: 21 | - uses: actions/checkout@v2 22 | with: 23 | repository: piEsposito/tiny-ai-client 24 | token: ${{ secrets.GH_PAT }} 25 | - name: Set up Python 26 | uses: actions/setup-python@v2 27 | with: 28 | python-version: '3.x' 29 | - name: Install dependencies 30 | run: | 31 | python -m pip install --upgrade pip poetry 32 | pip install setuptools wheel twine 33 | - name: Build 34 | run: | 35 | poetry build 36 | - name: Publish package distributions to PyPI 37 | uses: pypa/gh-action-pypi-publish@release/v1 38 | -------------------------------------------------------------------------------- /tiny_ai_client/tools.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | from typing import get_type_hints 3 | 4 | from pydantic import BaseModel 5 | 6 | 7 | def function_to_json(func, parameters_key="parameters") -> dict: 8 | func_name = func.__name__ 9 | func_desc = func.__doc__.strip().split("\n")[0] if func.__doc__ else "" 10 | 11 | hints = get_type_hints(func) 12 | 13 | # Extract the BaseModel from the function's parameters 14 | param_name, param_type = next(iter(hints.items())) 15 | if not issubclass(param_type, BaseModel): 16 | raise ValueError("The function's parameter must be a Pydantic BaseModel") 17 | 18 | model_schema = param_type.schema() 19 | 20 | func_json = { 21 | "type": "function", 22 | "function": { 23 | "name": func_name, 24 | "description": func_desc, 25 | parameters_key: { 26 | "type": "object", 27 | "properties": model_schema["properties"], 28 | "required": model_schema.get("required", True), 29 | }, 30 | }, 31 | } 32 | return func_json 33 | 34 | 35 | def json_to_function_input(func, json_input: dict): 36 | hints = get_type_hints(func) 37 | param_name, param_type = next(iter(hints.items())) 38 | return param_type(**json_input) 39 | -------------------------------------------------------------------------------- /examples/groq_.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | 3 | from PIL import Image as PIL_Image 4 | from pydantic import BaseModel, Field 5 | 6 | from tiny_ai_client import AI, AsyncAI 7 | 8 | 9 | class WeatherParams(BaseModel): 10 | location: str = Field(..., description="The city and state, e.g. San Francisco, CA") 11 | unit: str = Field( 12 | "celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] 13 | ) 14 | 15 | 16 | def get_current_weather(weather: WeatherParams): 17 | """ 18 | Get the current weather in a given location 19 | """ 20 | return { 21 | "abc": f"Getting the current weather in {weather.location} in {weather.unit}." 22 | } 23 | 24 | 25 | def get_images(): 26 | return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] 27 | 28 | 29 | async def async_ai_main(): 30 | print("### ASYNC AI ###") 31 | ai = AsyncAI( 32 | model_name="groq:llama3-70b-8192", 33 | system="You are Spock, from Star Trek.", 34 | max_new_tokens=128, 35 | # tools=[get_current_weather], 36 | ) 37 | response = await ai("What is the meaning of life?") 38 | print(f"{response=}") 39 | response = await ai("Please get the current weather in celsius for San Francisco.") 40 | print(f"{response=}") 41 | response = await ai("Did it work?") 42 | print(f"{response=}") 43 | print(f"{ai.chat=}") 44 | 45 | print("\n### ASYNC AI STREAMING ###") 46 | async for chunk in ai.astream("Tell me a short story about a brave astronaut."): 47 | print(chunk, end="", flush=True) 48 | print("\n") 49 | 50 | 51 | def main(): 52 | print("### SYNC AI ###") 53 | ai = AI( 54 | model_name="groq:llama3-70b-8192", 55 | system="You are Spock, from Star Trek.", 56 | max_new_tokens=128, 57 | # tools=[get_current_weather], 58 | ) 59 | response = ai("What is the meaning of life?") 60 | print(f"{response=}") 61 | response = ai("Please get the current weather in celsius for San Francisco.") 62 | print(f"{response=}") 63 | response = ai("Did it work?") 64 | print(f"{response=}") 65 | print(f"{ai.chat=}") 66 | 67 | print("\n### SYNC AI STREAMING ###") 68 | for chunk in ai.stream("Tell me a short story about a brave astronaut."): 69 | print(chunk, end="", flush=True) 70 | print("\n") 71 | 72 | 73 | if __name__ == "__main__": 74 | from dotenv import load_dotenv 75 | 76 | load_dotenv() 77 | main() 78 | asyncio.run(async_ai_main()) 79 | -------------------------------------------------------------------------------- /examples/gemini_.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | 3 | from PIL import Image as PIL_Image 4 | from pydantic import BaseModel, Field 5 | 6 | from tiny_ai_client import AI, AsyncAI 7 | 8 | 9 | class WeatherParams(BaseModel): 10 | location: str = Field(..., description="The city and state, e.g. San Francisco, CA") 11 | unit: str = Field( 12 | "celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] 13 | ) 14 | 15 | 16 | def get_current_weather(weather: WeatherParams): 17 | """ 18 | Get the current weather in a given location 19 | """ 20 | return { 21 | "abc": f"Getting the current weather in {weather.location} in {weather.unit}." 22 | } 23 | 24 | 25 | def get_images(): 26 | return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] 27 | 28 | 29 | def main(): 30 | print("### SYNC AI ###") 31 | ai = AI( 32 | model_name="gemini-1.5-flash", 33 | system="You are Spock, from Star Trek.", 34 | max_new_tokens=128, 35 | tools=[], 36 | ) 37 | response = ai("How are you?") 38 | print(f"{response=}") 39 | response = ai("Who is on the images?", images=get_images()) 40 | print(f"{response=}") 41 | # print(f"{ai.chat=}") 42 | 43 | print("\n### SYNC AI STREAMING ###") 44 | for chunk in ai.stream("Tell me a short story about a brave astronaut."): 45 | print(chunk, end="", flush=True) 46 | print("\n") 47 | 48 | 49 | async def async_ai_main(): 50 | print("### ASYNC AI ###") 51 | ai = AsyncAI( 52 | model_name="gemini-1.5-flash", 53 | system="You are Spock, from Star Trek.", 54 | max_new_tokens=128, 55 | tools=[], 56 | ) 57 | response = await ai("How are you?") 58 | print(f"{response=}") 59 | response = await ai("Who is on the images?", images=get_images()) 60 | print(f"{response=}") 61 | # print(f"{ai.chat=}") 62 | 63 | print("\n### ASYNC AI STREAMING ###") 64 | async for chunk in ai.astream("Tell me a short story about a brave astronaut."): 65 | print(chunk, end="", flush=True) 66 | print("\n") 67 | 68 | 69 | if __name__ == "__main__": 70 | import os 71 | 72 | from dotenv import load_dotenv 73 | 74 | load_dotenv() 75 | if os.environ.get("GOOGLE_API_KEY"): 76 | os.environ["OPENAI_API_KEY"] = os.environ["GOOGLE_API_KEY"] 77 | if os.environ.get("GOOGLE_OPENAI_BASE_URL"): 78 | os.environ["OPENAI_BASE_URL"] = os.environ["GOOGLE_OPENAI_BASE_URL"] 79 | main() 80 | asyncio.run(async_ai_main()) 81 | -------------------------------------------------------------------------------- /examples/anthropic_.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | 3 | from PIL import Image as PIL_Image 4 | from pydantic import BaseModel, Field 5 | 6 | from tiny_ai_client import AI, AsyncAI 7 | 8 | 9 | class WeatherParams(BaseModel): 10 | location: str = Field(..., description="The city and state, e.g. San Francisco, CA") 11 | unit: str = Field( 12 | "celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] 13 | ) 14 | 15 | 16 | def get_current_weather(weather: WeatherParams): 17 | """ 18 | Get the current weather in a given location 19 | """ 20 | return { 21 | "abc": f"Getting the current weather in {weather.location} in {weather.unit}." 22 | } 23 | 24 | 25 | def get_images(): 26 | return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] 27 | 28 | 29 | async def async_ai_main(): 30 | print("### ASYNC AI ###") 31 | ai = AsyncAI( 32 | model_name="claude-3-sonnet-20240229", 33 | system="You are a helpful assistant capable of using tools.", 34 | max_new_tokens=1024, 35 | tools=[get_current_weather], 36 | ) 37 | response = await ai("Please get the current weather in celsius for San Francisco.") 38 | print(f"{response=}") 39 | response = await ai("Did it work?") 40 | print(f"{response=}") 41 | response = await ai("Who is on the images?", images=get_images()) 42 | print(f"{response=}") 43 | print(f"{ai.chat=}") 44 | 45 | print("\n### ASYNC AI STREAMING ###") 46 | async for chunk in ai.astream("Tell me a short story about a brave astronaut."): 47 | print(chunk, end="", flush=True) 48 | print("\n") 49 | 50 | 51 | def main(): 52 | print("### SYNC AI ###") 53 | ai = AI( 54 | model_name="claude-3-sonnet-20240229", 55 | system="You are a helpful assistant capable of using tools.", 56 | max_new_tokens=1024, 57 | tools=[get_current_weather], 58 | ) 59 | response = ai("Please get the current weather in celsius for San Francisco.") 60 | print(f"{response=}") 61 | response = ai("Did it work?") 62 | print(f"{response=}") 63 | response = ai("Who is on the images?", images=get_images()) 64 | print(f"{response=}") 65 | print(f"{ai.chat=}") 66 | 67 | print("\n### SYNC AI STREAMING ###") 68 | for chunk in ai.stream("Tell me a short story about a brave astronaut."): 69 | print(chunk, end="", flush=True) 70 | print("\n") 71 | 72 | 73 | if __name__ == "__main__": 74 | from dotenv import load_dotenv 75 | 76 | load_dotenv() 77 | main() 78 | asyncio.run(async_ai_main()) 79 | -------------------------------------------------------------------------------- /examples/openai_.py: -------------------------------------------------------------------------------- 1 | import asyncio 2 | 3 | from PIL import Image as PIL_Image 4 | from pydantic import BaseModel, Field 5 | 6 | from tiny_ai_client import AI, AsyncAI 7 | 8 | 9 | class WeatherParams(BaseModel): 10 | location: str = Field(..., description="The city and state, e.g. San Francisco, CA") 11 | unit: str = Field( 12 | "celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] 13 | ) 14 | 15 | 16 | def get_current_weather(weather: WeatherParams): 17 | """ 18 | Get the current weather in a given location 19 | """ 20 | return { 21 | "abc": f"Getting the current weather in {weather.location} in {weather.unit}." 22 | } 23 | 24 | 25 | def get_images(): 26 | return [PIL_Image.open("assets/kirk.jpg"), PIL_Image.open("assets/spock.jpg")] 27 | 28 | 29 | async def async_ai_main(): 30 | print("### ASYNC AI ###") 31 | ai = AsyncAI( 32 | model_name="gpt-4o", 33 | system="You are Spock, from Star Trek.", 34 | max_new_tokens=128, 35 | tools=[get_current_weather], 36 | ) 37 | response = await ai("What is the meaning of life?") 38 | print(f"{response=}") 39 | response = await ai("Please get the current weather in celsius for San Francisco.") 40 | print(f"{response=}") 41 | response = await ai("Did it work?") 42 | print(f"{response=}") 43 | response = await ai("Who is on the images?", images=get_images()) 44 | print(f"{response=}") 45 | print(f"{ai.chat=}") 46 | 47 | print("\n### ASYNC AI STREAMING ###") 48 | async for chunk in ai.astream("Tell me a short story about a brave astronaut."): 49 | print(chunk, end="", flush=True) 50 | print("\n") 51 | 52 | 53 | def main(): 54 | print("### SYNC AI ###") 55 | ai = AI( 56 | model_name="gpt-4o", 57 | system="You are Spock, from Star Trek.", 58 | max_new_tokens=128, 59 | tools=[get_current_weather], 60 | ) 61 | response = ai("What is the meaning of life?") 62 | print(f"{response=}") 63 | response = ai("Please get the current weather in celsius for San Francisco.") 64 | print(f"{response=}") 65 | response = ai("Did it work?") 66 | print(f"{response=}") 67 | response = ai("Who is on the images?", images=get_images()) 68 | print(f"{response=}") 69 | print(f"{ai.chat=}") 70 | 71 | print("\n### SYNC AI STREAMING ###") 72 | for chunk in ai.stream("Tell me a short story about a brave astronaut."): 73 | print(chunk, end="", flush=True) 74 | print("\n") 75 | 76 | 77 | if __name__ == "__main__": 78 | from dotenv import load_dotenv 79 | 80 | load_dotenv() 81 | main() 82 | asyncio.run(async_ai_main()) 83 | -------------------------------------------------------------------------------- /tiny_ai_client/ollama_.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | from typing import Any, Callable, Dict, List, Union 3 | 4 | import aiohttp 5 | import requests 6 | 7 | from tiny_ai_client.models import LLMClientWrapper, Message 8 | 9 | 10 | class OllamaClientWrapper(LLMClientWrapper): 11 | def __init__( 12 | self, 13 | model_name: str, 14 | tools: List[Union[Callable, Dict]], 15 | url: str = "http://localhost:11434/api/chat", 16 | ): 17 | self.model_name = model_name.split("ollama:")[1] 18 | self.url = url 19 | if tools: 20 | raise ValueError("Ollama does not support tools") 21 | 22 | def build_model_input(self, messages: List["Message"]) -> Any: 23 | input_messages = [] 24 | for message in messages: 25 | if message.tool_call: 26 | raise ValueError("Ollama does not support tool calls") 27 | else: 28 | if message.text is not None: 29 | content = message.text 30 | if message.images: 31 | raise ValueError("Ollama does not support images") 32 | model_input_message = { 33 | "role": message.role, 34 | "content": content, 35 | } 36 | input_messages.append(model_input_message) 37 | model_input = { 38 | "messages": input_messages, 39 | "model": self.model_name, 40 | "stream": False, 41 | } 42 | return model_input 43 | 44 | def call_llm_provider( 45 | self, 46 | model_input: Any, 47 | temperature: int | None, 48 | max_new_tokens: int | None, 49 | timeout: int, 50 | ) -> str: 51 | kwargs = {} 52 | if temperature is not None: 53 | kwargs["temperature"] = temperature 54 | if max_new_tokens is not None: 55 | kwargs["num_ctx"] = max_new_tokens 56 | model_input["options"] = kwargs 57 | response = requests.post(self.url, json=model_input, timeout=timeout) 58 | response = response.json() 59 | chat_response = response["message"]["content"] 60 | if chat_response is not None: 61 | return Message(text=chat_response, role="assistant") 62 | 63 | async def async_call_llm_provider( 64 | self, 65 | model_input: Any, 66 | temperature: int | None, 67 | max_new_tokens: int | None, 68 | timeout: int, 69 | ) -> str: 70 | kwargs = {} 71 | if temperature is not None: 72 | kwargs["temperature"] = temperature 73 | if max_new_tokens is not None: 74 | kwargs["num_ctx"] = max_new_tokens 75 | model_input["options"] = kwargs 76 | async with aiohttp.ClientSession() as session: 77 | async with session.post( 78 | self.url, json=model_input, timeout=timeout 79 | ) as response: 80 | response_data = await response.json() 81 | chat_response = response_data["message"]["content"] 82 | if chat_response is not None: 83 | return Message(text=chat_response.strip(), role="assistant") 84 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/latest/usage/project/#working-with-version-control 110 | .pdm.toml 111 | .pdm-python 112 | .pdm-build/ 113 | 114 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 115 | __pypackages__/ 116 | 117 | # Celery stuff 118 | celerybeat-schedule 119 | celerybeat.pid 120 | 121 | # SageMath parsed files 122 | *.sage.py 123 | 124 | # Environments 125 | .env 126 | .venv 127 | env/ 128 | venv/ 129 | ENV/ 130 | env.bak/ 131 | venv.bak/ 132 | 133 | # Spyder project settings 134 | .spyderproject 135 | .spyproject 136 | 137 | # Rope project settings 138 | .ropeproject 139 | 140 | # mkdocs documentation 141 | /site 142 | 143 | # mypy 144 | .mypy_cache/ 145 | .dmypy.json 146 | dmypy.json 147 | 148 | # Pyre type checker 149 | .pyre/ 150 | 151 | # pytype static type analyzer 152 | .pytype/ 153 | 154 | # Cython debug symbols 155 | cython_debug/ 156 | 157 | # PyCharm 158 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 159 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 160 | # and can be added to the global gitignore or merged into this file. For a more nuclear 161 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 162 | #.idea/ 163 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Tiny AI Client 2 | 3 | Inspired by [tinygrad](https://github.com/tinygrad/tinygrad) and [simpleaichat](https://github.com/minimaxir/simpleaichat/tree/main/simpleaichat), `tiny-ai-client` is the easiest way to use and switch LLMs with vision and tool usage support. It works because it is `tiny`, `simple` and most importantly `fun` to develop. 4 | 5 | I want to change LLMs with ease, while knowing what is happening under the hood. Langchain is cool, but became bloated, complicated there is just too much chaos going on. I want to keep it simple, easy to understand and easy to use. If you want to use a LLM and have an API key, you should not need to read a 1000 lines of code and write `response.choices[0].message.content` to get the response. 6 | 7 | Simple and tiny, that's the goal. 8 | 9 | Features: 10 | 11 | - OpenAI 12 | - Anthropic 13 | - Async 14 | - Tool usage 15 | - Structured output 16 | - Vision 17 | - PyPI package `tiny-ai-client` 18 | - Gemini (vision, no tools) 19 | - Ollama (text, no vision, no tools) (you can also pass a custom model_server_url to AI/AsyncAI) 20 | - To use it, `model_name='ollama:llama3'` or your model name. 21 | - Groq (text, tools, no vision) 22 | - To use it `model_name='groq:llama-70b-8192'` or your model name as in Groq docs. 23 | 24 | Roadmap: 25 | 26 | - Gemini tools 27 | 28 | ## Simple 29 | 30 | `tiny-ai-client` is simple and intuitive: 31 | 32 | - Do you want set your model? Just pass the model name. 33 | - Do you want to change your model? Just change the model name. 34 | - Want to send a message? `msg: str = ai("hello")` and say goodbye to parsing a complex json. 35 | - Do you want to use a tool? Just pass the tool as a function 36 | - Type hint it with a single argument that inherits from `pydantic.BaseModel` and just pass the callable. `AI` will call it and get its results to you if the model wants to. 37 | - Want to use vision? Just pass a `PIL.Image.Image`. 38 | - Video? Just pass a list of `PIL.Image.Image`. 39 | 40 | ## Tiny 41 | 42 | - `tiny-ai-client` is very small, its core logic is < 250 lines of code (including comments and docstrings) and ideally won't pass 500. It is and always will be easy to understand, tweak and use. 43 | - The core logic is in `tiny_ai_client/models.py` 44 | - Vision utils are in `tiny_ai_client/vision.py` 45 | - Tool usage utils are in `tiny_ai_client/tools.py` 46 | - The interfaces are implemented by subclassing `tiny_ai_client.models.LLMClientWrapper` binding it to a specific LLM provider. This logic might get bigger, but it is isolated in a single file and does not affect the core logic. 47 | 48 | ## Usage 49 | 50 | ```bash 51 | pip install tiny-ai-client 52 | ``` 53 | 54 | To test, set the following environment variables: 55 | 56 | - OPENAI_API_KEY 57 | - ANTHROPIC_API_KEY 58 | - GROQ_API_KEY 59 | - GOOGLE_API_KEY 60 | 61 | Then 62 | 63 | To run all examples: 64 | 65 | ```bash 66 | ./scripts/run-all-examples.sh 67 | ``` 68 | 69 | For OpenAI: 70 | 71 | ```python 72 | from tiny_ai_client import AI, AsyncAI 73 | 74 | ai = AI( 75 | model_name="gpt-4o", system="You are Spock, from Star Trek.", max_new_tokens=128 76 | ) 77 | response = ai("What is the meaning of life?") 78 | 79 | ai = AsyncAI( 80 | model_name="gpt-4o", system="You are Spock, from Star Trek.", max_new_tokens=128 81 | ) 82 | response = await ai("What is the meaning of life?") 83 | ``` 84 | 85 | For Anthropic: 86 | 87 | ```python 88 | from tiny_ai_client import AI, AsyncAI 89 | 90 | ai = AI( 91 | model_name="claude-3-haiku-20240307", system="You are Spock, from Star Trek.", max_new_tokens=128 92 | ) 93 | response = ai("What is the meaning of life?") 94 | 95 | ai = AsyncAI( 96 | model_name="claude-3-haiku-20240307", system="You are Spock, from Star Trek.", max_new_tokens=128 97 | ) 98 | response = await ai("What is the meaning of life?") 99 | ``` 100 | 101 | We also support tool usage for both. You can pass as many functions you want as type-hinted functions with a single argument that inherits from `pydantic.BaseModel`. `AI` will call the function and get its results to you. 102 | 103 | ```python 104 | from pydantic import BaseModel, Field 105 | 106 | from tiny_ai_client import AI, AsyncAI 107 | 108 | 109 | class WeatherParams(BaseModel): 110 | location: str = Field(..., description="The city and state, e.g. San Francisco, CA") 111 | unit: str = Field( 112 | "celsius", description="Temperature unit", enum=["celsius", "fahrenheit"] 113 | ) 114 | 115 | 116 | def get_current_weather(weather: WeatherParams): 117 | """ 118 | Get the current weather in a given location 119 | """ 120 | return f"Getting the current weather in {weather.location} in {weather.unit}." 121 | 122 | ai = AI( 123 | model_name="gpt-4o", 124 | system="You are Spock, from Star Trek.", 125 | max_new_tokens=32, 126 | tools=[get_current_weather], 127 | ) 128 | response = ai("What is the meaning of life?") 129 | print(f"{response=}") 130 | response = ai("Please get the current weather in celsius for San Francisco.") 131 | print(f"{response=}") 132 | response = ai("Did it work?") 133 | print(f"{response=}") 134 | ``` 135 | 136 | And vision. Pass a list of `PIL.Image.Image` (or a single one) and we will handle the rest. 137 | 138 | ```python 139 | from tiny_ai_client import AI, AsyncAI 140 | from PIL import Image 141 | 142 | ai = AI( 143 | model_name="gpt-4o", 144 | system="You are Spock, from Star Trek.", 145 | max_new_tokens=32, 146 | ) 147 | 148 | response = ai( 149 | "Who is on the images?", 150 | images[ 151 | Image.open("assets/kirk.jpg"), 152 | Image.open("assets/spock.jpg") 153 | ] 154 | ) 155 | print(f"{response=}") 156 | 157 | ``` 158 | -------------------------------------------------------------------------------- /tiny_ai_client/groq_.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | import json 3 | from typing import Any, AsyncGenerator, Callable, Dict, Generator, List, Union 4 | 5 | from groq import AsyncGroq, Groq 6 | 7 | from tiny_ai_client.models import LLMClientWrapper, Message, ToolCall 8 | from tiny_ai_client.tools import function_to_json 9 | 10 | 11 | class GroqClientWrapper(LLMClientWrapper): 12 | def __init__(self, model_name: str, tools: List[Union[Callable, Dict]]): 13 | self.model_name = model_name.split("groq:")[1] 14 | self.client = Groq() 15 | self.async_client = AsyncGroq() 16 | self.tools = tools 17 | self.tools_json = [function_to_json(tool) for tool in tools] 18 | 19 | def build_model_input(self, messages: List["Message"]) -> Any: 20 | input_messages = [] 21 | for message in messages: 22 | if message.tool_call is not None: 23 | tool_call_in = { 24 | "role": "assistant", 25 | "tool_calls": [ 26 | { 27 | "id": message.tool_call.id_, 28 | "function": { 29 | "name": message.tool_call.name, 30 | "arguments": json.dumps(message.tool_call.parameters), 31 | }, 32 | "type": "function", 33 | } 34 | ], 35 | } 36 | tool_call_response = { 37 | "tool_call_id": message.tool_call.id_, 38 | "role": "tool", 39 | "name": message.tool_call.name, 40 | "content": str(message.tool_call.result), 41 | } 42 | input_messages.extend([tool_call_in, tool_call_response]) 43 | continue 44 | else: 45 | content = None 46 | assert message.text is not None, "Message text cannot be None for Groq" 47 | content = message.text 48 | if message.images: 49 | raise ValueError("Groq does not support images yet.") 50 | model_input_message = { 51 | "role": message.role, 52 | "content": content, 53 | } 54 | input_messages.append(model_input_message) 55 | return input_messages 56 | 57 | def call_llm_provider( 58 | self, 59 | model_input: Any, 60 | temperature: int | None, 61 | max_new_tokens: int | None, 62 | timeout: int, 63 | ) -> str: 64 | kwargs = {} 65 | if temperature is not None: 66 | kwargs["temperature"] = temperature 67 | if max_new_tokens is not None: 68 | kwargs["max_tokens"] = max_new_tokens 69 | if self.tools_json: 70 | kwargs["tools"] = self.tools_json 71 | response = self.client.with_options(timeout=timeout).chat.completions.create( 72 | model=self.model_name, 73 | messages=model_input, 74 | **kwargs, 75 | ) 76 | chat_response = response.choices[0].message 77 | if chat_response.content is not None: 78 | return Message(text=chat_response.content.strip(), role="assistant") 79 | if chat_response.tool_calls: 80 | return Message( 81 | role="assistant", 82 | text=None, 83 | tool_call=ToolCall( 84 | id_=chat_response.tool_calls[0].id, 85 | parameters=json.loads( 86 | chat_response.tool_calls[0].function.arguments 87 | ), 88 | name=chat_response.tool_calls[0].function.name, 89 | ), 90 | ) 91 | 92 | async def async_call_llm_provider( 93 | self, 94 | model_input: Any, 95 | temperature: int | None, 96 | max_new_tokens: int | None, 97 | timeout: int, 98 | ) -> str: 99 | kwargs = {} 100 | if temperature is not None: 101 | kwargs["temperature"] = temperature 102 | if max_new_tokens is not None: 103 | kwargs["max_tokens"] = max_new_tokens 104 | if self.tools_json: 105 | kwargs["tools"] = self.tools_json 106 | response = await self.async_client.with_options( 107 | timeout=timeout 108 | ).chat.completions.create( 109 | model=self.model_name, 110 | messages=model_input, 111 | **kwargs, 112 | ) 113 | chat_response = response.choices[0].message 114 | if chat_response.content is not None: 115 | return Message(text=chat_response.content.strip(), role="assistant") 116 | if chat_response.tool_calls: 117 | return Message( 118 | role="assistant", 119 | text=None, 120 | tool_call=ToolCall( 121 | id_=chat_response.tool_calls[0].id, 122 | parameters=json.loads( 123 | chat_response.tool_calls[0].function.arguments 124 | ), 125 | name=chat_response.tool_calls[0].function.name, 126 | ), 127 | ) 128 | 129 | def stream( 130 | self, 131 | max_new_tokens: int | None, 132 | temperature: int | None, 133 | timeout: int, 134 | chat: List["Message"], 135 | ) -> Generator[str, None, None]: 136 | kwargs = {} 137 | if temperature is not None: 138 | kwargs["temperature"] = temperature 139 | if max_new_tokens is not None: 140 | kwargs["max_tokens"] = max_new_tokens 141 | 142 | model_input = self.build_model_input(chat) 143 | 144 | stream = self.client.with_options(timeout=timeout).chat.completions.create( 145 | model=self.model_name, 146 | messages=model_input, 147 | stream=True, 148 | **kwargs, 149 | ) 150 | 151 | for chunk in stream: 152 | if chunk.choices[0].delta.content is not None: 153 | yield chunk.choices[0].delta.content 154 | 155 | async def astream( 156 | self, 157 | max_new_tokens: int | None, 158 | temperature: int | None, 159 | timeout: int, 160 | chat: List["Message"], 161 | ) -> AsyncGenerator[str, None]: 162 | kwargs = {} 163 | if temperature is not None: 164 | kwargs["temperature"] = temperature 165 | if max_new_tokens is not None: 166 | kwargs["max_tokens"] = max_new_tokens 167 | 168 | model_input = self.build_model_input(chat) 169 | 170 | stream = await self.async_client.with_options( 171 | timeout=timeout 172 | ).chat.completions.create( 173 | model=self.model_name, 174 | messages=model_input, 175 | stream=True, 176 | **kwargs, 177 | ) 178 | 179 | async for chunk in stream: 180 | if chunk.choices[0].delta.content is not None: 181 | yield chunk.choices[0].delta.content 182 | -------------------------------------------------------------------------------- /tiny_ai_client/openai_.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import json 4 | from typing import Any, AsyncGenerator, Callable, Dict, Generator, List, Union 5 | 6 | from openai import AsyncOpenAI, OpenAI 7 | 8 | from tiny_ai_client.models import LLMClientWrapper, Message, ToolCall 9 | from tiny_ai_client.tools import function_to_json 10 | from tiny_ai_client.vision import encode_pil_image 11 | 12 | 13 | class OpenAIClientWrapper(LLMClientWrapper): 14 | def __init__(self, model_name: str, tools: List[Union[Callable, Dict]]): 15 | self.model_name = model_name 16 | self.client = OpenAI() 17 | self.async_client = AsyncOpenAI() 18 | self.tools = tools 19 | self.tools_json = [function_to_json(tool) for tool in tools] 20 | 21 | def build_model_input(self, messages: List["Message"]) -> Any: 22 | input_messages = [] 23 | for message in messages: 24 | if message.tool_call: 25 | tool_call_in = { 26 | "role": "assistant", 27 | "tool_calls": [ 28 | { 29 | "id": message.tool_call.id_, 30 | "function": { 31 | "name": message.tool_call.name, 32 | "arguments": json.dumps(message.tool_call.parameters), 33 | }, 34 | "type": "function", 35 | } 36 | ], 37 | } 38 | tool_call_response = { 39 | "tool_call_id": message.tool_call.id_, 40 | "role": "tool", 41 | "name": message.tool_call.name, 42 | "content": str(message.tool_call.result), 43 | } 44 | input_messages.extend([tool_call_in, tool_call_response]) 45 | continue 46 | else: 47 | content = [] 48 | if message.text is not None: 49 | content.append({"type": "text", "text": message.text}) 50 | if message.images: 51 | for image in message.images: 52 | content.append( 53 | { 54 | "type": "image_url", 55 | "image_url": { 56 | "url": f"data:image/jpeg;base64,{encode_pil_image(image)}" 57 | }, 58 | } 59 | ) 60 | model_input_message = { 61 | "role": message.role, 62 | "content": content, 63 | } 64 | input_messages.append(model_input_message) 65 | return input_messages 66 | 67 | def call_llm_provider( 68 | self, 69 | model_input: Any, 70 | temperature: int | None, 71 | max_new_tokens: int | None, 72 | timeout: int, 73 | ) -> str: 74 | kwargs = {} 75 | if temperature is not None: 76 | kwargs["temperature"] = temperature 77 | if max_new_tokens is not None: 78 | kwargs["max_completion_tokens"] = max_new_tokens 79 | if self.tools_json: 80 | kwargs["tools"] = self.tools_json 81 | response = self.client.with_options(timeout=timeout).chat.completions.create( 82 | model=self.model_name, 83 | messages=model_input, 84 | **kwargs, 85 | ) 86 | chat_response = response.choices[0].message 87 | if chat_response.content is not None: 88 | return Message(text=chat_response.content.strip(), role="assistant") 89 | if chat_response.tool_calls: 90 | return Message( 91 | role="assistant", 92 | text=None, 93 | tool_call=ToolCall( 94 | id_=chat_response.tool_calls[0].id, 95 | parameters=json.loads( 96 | chat_response.tool_calls[0].function.arguments 97 | ), 98 | name=chat_response.tool_calls[0].function.name, 99 | ), 100 | ) 101 | 102 | def stream( 103 | self, 104 | max_new_tokens: int | None, 105 | temperature: int | None, 106 | timeout: int, 107 | chat: List["Message"], 108 | ) -> Generator[str, None, None]: 109 | kwargs = {} 110 | if temperature is not None: 111 | kwargs["temperature"] = temperature 112 | if max_new_tokens is not None: 113 | kwargs["max_completion_tokens"] = max_new_tokens 114 | if self.tools_json: 115 | kwargs["tools"] = self.tools_json 116 | 117 | model_input = self.build_model_input(chat) 118 | 119 | stream = self.client.with_options(timeout=timeout).chat.completions.create( 120 | model=self.model_name, 121 | messages=model_input, 122 | stream=True, 123 | **kwargs, 124 | ) 125 | 126 | for chunk in stream: 127 | if chunk.choices[0].delta.content is not None: 128 | yield chunk.choices[0].delta.content 129 | 130 | async def async_call_llm_provider( 131 | self, 132 | model_input: Any, 133 | temperature: int | None, 134 | max_new_tokens: int | None, 135 | timeout: int, 136 | ) -> str: 137 | kwargs = {} 138 | if temperature is not None: 139 | kwargs["temperature"] = temperature 140 | if max_new_tokens is not None: 141 | kwargs["max_completion_tokens"] = max_new_tokens 142 | if self.tools_json: 143 | kwargs["tools"] = self.tools_json 144 | response = await self.async_client.with_options( 145 | timeout=timeout 146 | ).chat.completions.create( 147 | model=self.model_name, 148 | messages=model_input, 149 | **kwargs, 150 | ) 151 | chat_response = response.choices[0].message 152 | if chat_response.content is not None: 153 | return Message(text=chat_response.content.strip(), role="assistant") 154 | if chat_response.tool_calls: 155 | return Message( 156 | role="assistant", 157 | text=None, 158 | tool_call=ToolCall( 159 | id_=chat_response.tool_calls[0].id, 160 | parameters=json.loads( 161 | chat_response.tool_calls[0].function.arguments 162 | ), 163 | name=chat_response.tool_calls[0].function.name, 164 | ), 165 | ) 166 | 167 | async def astream( 168 | self, 169 | max_new_tokens: int | None, 170 | temperature: int | None, 171 | timeout: int, 172 | chat: List["Message"], 173 | ) -> AsyncGenerator[str, None]: 174 | kwargs = {} 175 | if temperature is not None: 176 | kwargs["temperature"] = temperature 177 | if max_new_tokens is not None: 178 | kwargs["max_completion_tokens"] = max_new_tokens 179 | if self.tools_json: 180 | kwargs["tools"] = self.tools_json 181 | 182 | model_input = self.build_model_input(chat) 183 | 184 | stream = await self.async_client.with_options( 185 | timeout=timeout 186 | ).chat.completions.create( 187 | model=self.model_name, 188 | messages=model_input, 189 | stream=True, 190 | **kwargs, 191 | ) 192 | 193 | async for chunk in stream: 194 | if chunk.choices[0].delta.content is not None: 195 | yield chunk.choices[0].delta.content 196 | -------------------------------------------------------------------------------- /tiny_ai_client/anthropic_.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | import os 3 | from typing import Any, Callable, Dict, List, Union, Generator, AsyncGenerator 4 | 5 | from anthropic import Anthropic, AsyncAnthropic 6 | 7 | from tiny_ai_client.models import LLMClientWrapper, Message, ToolCall 8 | from tiny_ai_client.tools import function_to_json 9 | from tiny_ai_client.vision import encode_pil_image 10 | 11 | 12 | class AnthropicClientWrapper(LLMClientWrapper): 13 | def __init__(self, model_name: str, tools: List[Union[Callable, Dict]]): 14 | self.model_name = model_name 15 | self.client = Anthropic( 16 | api_key=os.environ.get("ANTHROPIC_API_KEY"), 17 | ) 18 | self.async_client = AsyncAnthropic( 19 | api_key=os.environ.get("ANTHROPIC_API_KEY"), 20 | ) 21 | self.tools = tools 22 | self.tools_json = [ 23 | function_to_json(tool, "input_schema")["function"] for tool in tools 24 | ] 25 | 26 | def build_model_input(self, messages: List["Message"]) -> Any: 27 | input_messages = [] 28 | system = None 29 | for message in messages: 30 | if message.role == "system": 31 | system = message.text 32 | continue 33 | elif message.tool_call: 34 | content_in = [ 35 | { 36 | "type": "tool_use", 37 | "name": message.tool_call.name, 38 | "id": message.tool_call.id_, 39 | "input": message.tool_call.parameters, 40 | } 41 | ] 42 | content_out = [ 43 | { 44 | "type": "tool_result", 45 | "tool_use_id": message.tool_call.id_, 46 | "content": [ 47 | {"type": "text", "text": str(message.tool_call.result)} 48 | ], 49 | } 50 | ] 51 | input_messages.append( 52 | { 53 | "role": "assistant", 54 | "content": content_in, 55 | } 56 | ) 57 | input_messages.append( 58 | { 59 | "role": "user", 60 | "content": content_out, 61 | } 62 | ) 63 | input_messages.append( 64 | { 65 | "role": "assistant", 66 | "content": "Acknowledged.", 67 | } 68 | ) 69 | continue 70 | else: 71 | content = [] 72 | role = message.role 73 | if message.images: 74 | for image in message.images: 75 | content.append( 76 | { 77 | "type": "image", 78 | "source": { 79 | "type": "base64", 80 | "media_type": "image/jpeg", 81 | "data": encode_pil_image(image), 82 | }, 83 | } 84 | ) 85 | if message.text is not None: 86 | content.append( 87 | { 88 | "type": "text", 89 | "text": message.text, 90 | } 91 | ) 92 | model_input_message = { 93 | "role": role, 94 | "content": content, 95 | } 96 | input_messages.append(model_input_message) 97 | return input_messages, system 98 | 99 | def call_llm_provider( 100 | self, 101 | model_input: Any, 102 | temperature: int | None, 103 | max_new_tokens: int | None, 104 | timeout: int, 105 | ) -> str: 106 | kwargs = {} 107 | input_messages, system = model_input 108 | if temperature is not None: 109 | kwargs["temperature"] = temperature 110 | if max_new_tokens is not None: 111 | kwargs["max_tokens"] = max_new_tokens 112 | if system is not None: 113 | kwargs["system"] = system 114 | if self.tools_json: 115 | kwargs["tools"] = self.tools_json 116 | response = self.client.with_options(timeout=timeout).messages.create( 117 | model=self.model_name, 118 | messages=input_messages, 119 | **kwargs, 120 | ) 121 | response_content = response.content[0] 122 | if response_content.type == "text": 123 | return Message(text=response_content.text, role="assistant") 124 | 125 | if response_content.type == "tool_use": 126 | print(response_content) 127 | return Message( 128 | role="assistant", 129 | text=None, 130 | tool_call=ToolCall( 131 | id_=response_content.id, 132 | parameters=response_content.input, 133 | name=response_content.name, 134 | ), 135 | ) 136 | 137 | def stream( 138 | self, 139 | max_new_tokens: int | None, 140 | temperature: int | None, 141 | timeout: int, 142 | chat: List["Message"], 143 | ) -> Generator[str, None, None]: 144 | kwargs = {} 145 | input_messages, system = self.build_model_input(chat) 146 | if temperature is not None: 147 | kwargs["temperature"] = temperature 148 | if max_new_tokens is not None: 149 | kwargs["max_tokens"] = max_new_tokens 150 | if system is not None: 151 | kwargs["system"] = system 152 | if self.tools_json: 153 | kwargs["tools"] = self.tools_json 154 | 155 | with self.client.with_options(timeout=timeout).messages.stream( 156 | model=self.model_name, 157 | messages=input_messages, 158 | **kwargs, 159 | ) as stream: 160 | for text in stream.text_stream: 161 | yield text 162 | 163 | async def async_call_llm_provider( 164 | self, 165 | model_input: Any, 166 | temperature: int | None, 167 | max_new_tokens: int | None, 168 | timeout: int, 169 | ) -> str: 170 | kwargs = {} 171 | input_messages, system = model_input 172 | if temperature is not None: 173 | kwargs["temperature"] = temperature 174 | if max_new_tokens is not None: 175 | kwargs["max_tokens"] = max_new_tokens 176 | if system is not None: 177 | kwargs["system"] = system 178 | if self.tools_json: 179 | kwargs["tools"] = self.tools_json 180 | response = await self.async_client.with_options( 181 | timeout=timeout 182 | ).messages.create( 183 | model=self.model_name, 184 | messages=input_messages, 185 | **kwargs, 186 | ) 187 | response_content = response.content[0] 188 | if response_content.type == "text": 189 | return Message(text=response_content.text, role="assistant") 190 | 191 | if response_content.type == "tool_use": 192 | return Message( 193 | role="assistant", 194 | text=None, 195 | tool_call=ToolCall( 196 | id_=response_content.id, 197 | parameters=response_content.input, 198 | name=response_content.name, 199 | ), 200 | ) 201 | 202 | async def astream( 203 | self, 204 | max_new_tokens: int | None, 205 | temperature: int | None, 206 | timeout: int, 207 | chat: List["Message"], 208 | ) -> AsyncGenerator[str, None]: 209 | kwargs = {} 210 | input_messages, system = self.build_model_input(chat) 211 | if temperature is not None: 212 | kwargs["temperature"] = temperature 213 | if max_new_tokens is not None: 214 | kwargs["max_tokens"] = max_new_tokens 215 | if system is not None: 216 | kwargs["system"] = system 217 | if self.tools_json: 218 | kwargs["tools"] = self.tools_json 219 | 220 | async with self.async_client.with_options(timeout=timeout).messages.stream( 221 | model=self.model_name, 222 | messages=input_messages, 223 | **kwargs, 224 | ) as stream: 225 | async for text in stream.text_stream: 226 | yield text 227 | -------------------------------------------------------------------------------- /tiny_ai_client/models.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from typing import Any, AsyncGenerator, Callable, Dict, Generator, List, Union 4 | 5 | from PIL import Image as PIL_Image 6 | from pydantic import BaseModel 7 | 8 | from tiny_ai_client.tools import json_to_function_input 9 | 10 | 11 | class AI: 12 | def __init__( 13 | self, 14 | model_name: str, 15 | system: str | None = None, 16 | temperature: int = 1, 17 | max_new_tokens: int | None = None, 18 | timeout: int = 30, 19 | tools: List[Union[Callable, Dict]] | None = None, 20 | chat: List["Message"] | None = None, 21 | model_server_url: str | None = None, 22 | ): 23 | self.model_server_url = model_server_url 24 | # llm sampling parameters 25 | self.temperature: int = temperature 26 | self.max_new_tokens: int | None = max_new_tokens 27 | self.timeout: int = timeout 28 | self.chat: List[Message] = chat or [] 29 | if system: 30 | self.chat.append(Message(text=system, role="system")) 31 | 32 | tools = tools or [] 33 | self.tools = tools 34 | self.tools_dict = {tool.__name__: tool for tool in self.tools} 35 | 36 | self.model_name: str = model_name 37 | self.system: str = system 38 | 39 | self.client_wrapper: LLMClientWrapper = self.get_llm_client_wrapper( 40 | model_name=model_name, tools=self.tools 41 | ) 42 | 43 | def __call__( 44 | self, 45 | message: str | None = None, 46 | max_new_tokens: int | None = None, 47 | temperature: int | None = 1, 48 | timeout: int | None = None, 49 | images: List[PIL_Image.Image] | PIL_Image.Image | None = None, 50 | ) -> str: 51 | max_new_tokens = max_new_tokens or self.max_new_tokens 52 | temperature = temperature or self.temperature 53 | timeout = timeout or self.timeout 54 | message = message or "" 55 | if isinstance(images, PIL_Image.Image): 56 | images = [images] 57 | self.chat.append(Message(text=message, role="user", images=images)) 58 | response_msg: "Message" = self.client_wrapper( 59 | max_new_tokens=max_new_tokens, 60 | temperature=temperature, 61 | chat=self.chat, 62 | timeout=timeout, 63 | ) 64 | self.chat.append(response_msg) 65 | if response_msg.tool_call: 66 | func = self.tools_dict[response_msg.tool_call.name] 67 | tool_input = json_to_function_input(func, response_msg.tool_call.parameters) 68 | print(f"{tool_input=}") 69 | tool_result = func(tool_input) 70 | response_msg.tool_call.result = tool_result 71 | return response_msg.text or ( 72 | response_msg.tool_call.result if response_msg.tool_call else "" 73 | ) 74 | 75 | def stream( 76 | self, 77 | message: str | None = None, 78 | max_new_tokens: int | None = None, 79 | temperature: int | None = 1, 80 | timeout: int | None = None, 81 | images: List[PIL_Image.Image] | PIL_Image.Image | None = None, 82 | ) -> Generator[str, None, None]: 83 | max_new_tokens = max_new_tokens or self.max_new_tokens 84 | temperature = temperature or self.temperature 85 | timeout = timeout or self.timeout 86 | message = message or "" 87 | if isinstance(images, PIL_Image.Image): 88 | images = [images] 89 | self.chat.append(Message(text=message, role="user", images=images)) 90 | text = "" 91 | for chunk in self.client_wrapper.stream( 92 | max_new_tokens=max_new_tokens, 93 | temperature=temperature, 94 | chat=self.chat, 95 | timeout=timeout, 96 | ): 97 | text += chunk 98 | yield chunk 99 | # After streaming, update the chat history 100 | self.chat.append( 101 | Message( 102 | text=text, 103 | role="assistant", 104 | ) 105 | ) 106 | 107 | def get_llm_client_wrapper( 108 | self, model_name: str, tools: List[Union[Callable, Dict]] 109 | ) -> "LLMClientWrapper": 110 | if model_name.startswith("ollama:"): 111 | from tiny_ai_client.ollama_ import OllamaClientWrapper 112 | 113 | kwargs = {} 114 | if self.model_server_url: 115 | kwargs["url"] = self.model_server_url 116 | return OllamaClientWrapper(model_name, tools, **kwargs) 117 | 118 | if model_name.startswith("groq:"): 119 | from tiny_ai_client.groq_ import GroqClientWrapper 120 | 121 | return GroqClientWrapper(model_name, tools) 122 | 123 | if "gpt" in model_name or "o1" in model_name: 124 | from tiny_ai_client.openai_ import OpenAIClientWrapper 125 | 126 | return OpenAIClientWrapper(model_name, tools) 127 | if "claude" in model_name: 128 | from tiny_ai_client.anthropic_ import AnthropicClientWrapper 129 | 130 | return AnthropicClientWrapper(model_name, tools) 131 | 132 | if "gemini" in model_name: 133 | from tiny_ai_client.gemini_ import GeminiClientWrapper 134 | 135 | return GeminiClientWrapper(model_name, tools) 136 | 137 | raise NotImplementedError(f"{model_name=} not supported") 138 | 139 | @property 140 | def model_name(self) -> str: 141 | return self._model_name 142 | 143 | @model_name.setter 144 | def model_name(self, value: str) -> None: 145 | self.client_wrapper = self.get_llm_client_wrapper(value, self.tools) 146 | 147 | 148 | class AsyncAI(AI): 149 | async def __call__( 150 | self, 151 | message: str | None = None, 152 | max_new_tokens: int | None = None, 153 | temperature: int | None = 1, 154 | timeout: int | None = None, 155 | images: List[PIL_Image.Image] | PIL_Image.Image | None = None, 156 | ) -> str: 157 | max_new_tokens = max_new_tokens or self.max_new_tokens 158 | temperature = temperature or self.temperature 159 | timeout = timeout or self.timeout 160 | message = message or "" 161 | if isinstance(images, PIL_Image.Image): 162 | images = [images] 163 | self.chat.append(Message(text=message, role="user", images=images)) 164 | response_msg: "Message" = await self.client_wrapper.acall( 165 | max_new_tokens=max_new_tokens, 166 | temperature=temperature, 167 | chat=self.chat, 168 | timeout=timeout, 169 | ) 170 | self.chat.append(response_msg) 171 | if response_msg.tool_call: 172 | func = self.tools_dict[response_msg.tool_call.name] 173 | tool_input = json_to_function_input(func, response_msg.tool_call.parameters) 174 | tool_result = func(tool_input) 175 | response_msg.tool_call.result = tool_result 176 | return response_msg.text or ( 177 | response_msg.tool_call.result if response_msg.tool_call else "" 178 | ) 179 | 180 | async def astream( 181 | self, 182 | message: str | None = None, 183 | max_new_tokens: int | None = None, 184 | temperature: int | None = 1, 185 | timeout: int | None = None, 186 | images: List[PIL_Image.Image] | PIL_Image.Image | None = None, 187 | ) -> AsyncGenerator[str, None]: 188 | max_new_tokens = max_new_tokens or self.max_new_tokens 189 | temperature = temperature or self.temperature 190 | timeout = timeout or self.timeout 191 | message = message or "" 192 | if isinstance(images, PIL_Image.Image): 193 | images = [images] 194 | self.chat.append(Message(text=message, role="user", images=images)) 195 | text = "" 196 | async for chunk in self.client_wrapper.astream( 197 | max_new_tokens=max_new_tokens, 198 | temperature=temperature, 199 | chat=self.chat, 200 | timeout=timeout, 201 | ): 202 | text += chunk 203 | yield chunk 204 | self.chat.append(Message(text=text, role="assistant")) 205 | 206 | 207 | class ToolCall(BaseModel): 208 | id_: str 209 | parameters: Dict[str, Any] 210 | result: str | None = None 211 | name: str 212 | 213 | def format(self) -> str: 214 | return f"Called {self.name} with ({self.parameters}) and got {self.result}" 215 | 216 | 217 | class Message(BaseModel): 218 | text: str | None = None 219 | role: str 220 | tool_call: ToolCall | None = None 221 | images: List[PIL_Image.Image] | None = None 222 | 223 | class Config: 224 | arbitrary_types_allowed = True 225 | 226 | 227 | class LLMClientWrapper: 228 | def build_model_input(self, messages: List["Message"]) -> Any: 229 | raise NotImplementedError 230 | 231 | def call_llm_provider( 232 | self, 233 | model_input: Any, 234 | temperature: int | None, 235 | max_new_tokens: int | None, 236 | timeout: int, 237 | ) -> "Message": 238 | raise NotImplementedError 239 | 240 | def __call__( 241 | self, 242 | max_new_tokens: int | None, 243 | temperature: int | None, 244 | timeout: int, 245 | chat: List["Message"], 246 | ) -> "Message": 247 | model_input = self.build_model_input(chat) 248 | return self.call_llm_provider( 249 | model_input, 250 | max_new_tokens=max_new_tokens, 251 | temperature=temperature, 252 | timeout=timeout, 253 | ) 254 | 255 | def stream( 256 | self, 257 | max_new_tokens: int | None, 258 | temperature: int | None, 259 | timeout: int, 260 | chat: List["Message"], 261 | ) -> Generator[str, None, None]: 262 | raise NotImplementedError 263 | 264 | async def async_call_llm_provider( 265 | self, 266 | model_input: Any, 267 | temperature: int | None, 268 | max_new_tokens: int | None, 269 | timeout: int, 270 | ) -> "Message": 271 | raise NotImplementedError 272 | 273 | async def acall( 274 | self, 275 | max_new_tokens: int | None, 276 | temperature: int | None, 277 | timeout: int, 278 | chat: List["Message"], 279 | ) -> "Message": 280 | model_input = self.build_model_input(chat) 281 | return await self.async_call_llm_provider( 282 | model_input, 283 | max_new_tokens=max_new_tokens, 284 | temperature=temperature, 285 | timeout=timeout, 286 | ) 287 | 288 | async def astream( 289 | self, 290 | max_new_tokens: int | None, 291 | temperature: int | None, 292 | timeout: int, 293 | chat: List["Message"], 294 | ) -> AsyncGenerator[str, None]: 295 | raise NotImplementedError 296 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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