ClickUp Operator

Metadata-Version: 2.1 Name: anthropic-tools Version: 1.0.7 Summary: Simplifies the usage of Anthropic Claude's tool use by generating the schemas and parsing the responses for you. Home-page: https://github.com/rizerphe/anthropic-tools License: MIT Keywords: nlp,anthropic,claude,claude3,claude-api,wrapper,functions,typing,docstring,docstrings,decorators,signatures,parsing Author: rizerphe Author-email: 44440399+rizerphe@users.noreply.github.com Requires-Python: >=3.8,<4.0 Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Requires-Dist: anthropic (>=0.25.6,<0.26.0) Requires-Dist: docstring-parser (>=0.15,<0.16) Requires-Dist: typing-extensions (>=4.6.3,<5.0.0) Project-URL: Documentation, https://anthropic-tools.readthedocs.io/ Description-Content-Type: text/markdown # Anthropic tools The `anthropic-tools` library simplifies the usage of Anthropics’s [tool use](https://docs.anthropic.com/claude/docs/tool-use) feature. It abstracts away the complexity of parsing function signatures and docstrings by providing developers with a clean and intuitive interface. It's a near-clone of my [openai-functions](https://openai-functions.readthedocs.io) library that does the same with OpenAI. [](https://opensource.org/licenses/MIT) [](https://badge.fury.io/py/anthropic-tools) [](https://anthropic-tools.readthedocs.io/en/latest/?badge=latest) ## Installation You can install `anthropic-tools` from PyPI using pip: ``` pip install anthropic-tools ``` ## Usage 1. Import the necessary modules and provide your API key: ```python import enum import anthropic from anthropic_tools import Conversation client = anthropic.Anthropic( api_key="<YOUR_API_KEY>", ) ``` 2. Create a `Conversation` instance: ```python conversation = Conversation(client) ``` 3. Define your tools using the `@conversation.add_tool` decorator: ```python class Unit(enum.Enum): FAHRENHEIT = "fahrenheit" CELSIUS = "celsius" @conversation.add_tool() def get_current_weather(location: str, unit: Unit = Unit.FAHRENHEIT) -> dict: """Get the current weather in a given location. Args: location (str): The city and state, e.g., San Francisco, CA unit (Unit): The unit to use, e.g., fahrenheit or celsius """ return { "location": location, "temperature": "72", "unit": unit.value, "forecast": ["sunny", "windy"], } ``` 4. Ask the AI a question: ```python response = conversation.ask("What's the weather in San Francisco?") # Should return three messages, the last one's content being something like: # The current weather in San Francisco is 72 degrees Fahrenheit and it is sunny and windy. ``` You can read more about how to use `Conversation` [here](https://anthropic-tools.readthedocs.io/en/latest/conversation.html). ## More barebones use - just schema generation and result parsing: ```python from anthropic_tools import ToolWrapper wrapper = ToolWrapper(get_current_weather) schema = wrapper.schema result = wrapper({"location": "San Francisco, CA"}) ``` Or you could use [skills](https://anthropic-tools.readthedocs.io/en/latest/skills.html). ## How it Works `anthropic-tools` takes care of the following tasks: - Parsing the function signatures (with type annotations) and docstrings. - Sending the conversation and tool descriptions to Anthropic Claude. - Deciding whether to call a tool based on the model's response. - Calling the appropriate function with the provided arguments. - Updating the conversation with the tool response. - Repeating the process until the model generates a user-facing message. This abstraction allows developers to focus on defining their functions and adding user messages without worrying about the details of tool use. ## Note Please note that `anthropic-tools` is an unofficial project not maintained by Anthropic. Use it at your discretion.