### Overview Major restructuring from monolithic `arcade-ai` package to modular library architecture with standardized uv-based dependency management.  ### New Package Structure - **`arcade-tdk`** - Lightweight toolkit development kit (core decorators, auth) - **`arcade-core`** - Core execution engine and catalog functionality - **`arcade-serve`** - FastAPI/MCP server components - **`arcade-ai`** - Meta package that includes CLI functionality. Optionally include evals via the `evals` extra. Optionally include all packages via the `all` extra. ### Key Benefits - **Lighter Dependencies**: Toolkits now depend only on `arcade-tdk` (~2 deps) vs full `arcade-ai` (~30+ deps) - **Faster Builds**: uv provides 10-100x faster dependency resolution and installation - **Better Modularity**: Clear separation of concerns, consumers import only what they need - **Standard Tooling**: Eliminates custom poetry scripts, uses standard Python packaging ### Migration Impact - All 20 toolkits converted from poetry → uv with `arcade-tdk` dependencies plus `arcade-ai[evals]` and `arcade-serve` dev dependencies. When developing locally, devs should install toolkits via `make install-local`. - Modern Python 3.10+ type hints throughout - Standardized build system with hatchling backend - Enhanced Makefile with robust toolkit management commands - Removed `arcade dev` CLI command - Reduce the number of files created by `arcade new` and add an option to not generate a tests and evals folder. This foundation enables faster development cycles and cleaner dependency chains for the growing toolkit ecosystem. ### Todo After this PR is merged - [ ] Post-merge workflow(s) (release & publish containers, etc) - [ ] Release order plan. @EricGustin suggests releasing in the following order: 1. `arcade-core` version 0.1.0 2. `arcade-serve` version 0.1.0 and `arcade-tdk` version 0.1.0 3. `arcade-ai` version 2.0.0 4. Patch release for all toolkits (all changes in toolkits are internal refactors) - [ ] [Update docs](https://github.com/ArcadeAI/docs/pull/318) --------- Co-authored-by: Eric Gustin <eric@arcade.dev> Co-authored-by: Eric Gustin <34000337+EricGustin@users.noreply.github.com>
96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
import asyncio
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from typing import Annotated
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from arcade_tdk import ToolContext, tool
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from arcade_tdk.auth import Google
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from arcade_google.constants import DEFAULT_SEARCH_CONTACTS_LIMIT
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from arcade_google.utils import build_people_service, search_contacts
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async def _warmup_cache(service) -> None: # type: ignore[no-untyped-def]
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"""
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Warm-up the search cache for contacts by sending a request with an empty query.
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This ensures that the lazy cache is updated for both primary contacts and other contacts.
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This is unfortunately a real thing: https://developers.google.com/people/v1/contacts#search_the_users_contacts
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"""
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service.people().searchContacts(query="", pageSize=1, readMask="names,emailAddresses").execute()
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await asyncio.sleep(3) # TODO experiment with this value
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@tool(requires_auth=Google(scopes=["https://www.googleapis.com/auth/contacts.readonly"]))
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async def search_contacts_by_email(
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context: ToolContext,
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email: Annotated[str, "The email address to search for"],
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limit: Annotated[
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int | None,
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"The maximum number of contacts to return (30 is the max allowed by Google API)",
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] = DEFAULT_SEARCH_CONTACTS_LIMIT,
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) -> Annotated[dict, "A dictionary containing the list of matching contacts"]:
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"""
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Search the user's contacts in Google Contacts by email address.
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"""
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service = build_people_service(context.get_auth_token_or_empty())
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# Warm-up the cache before performing search.
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# TODO: Ideally we should warmup only if this user (or google domain?) hasn't warmed up recently
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await _warmup_cache(service)
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return {"contacts": search_contacts(service, email, limit)}
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@tool(requires_auth=Google(scopes=["https://www.googleapis.com/auth/contacts.readonly"]))
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async def search_contacts_by_name(
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context: ToolContext,
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name: Annotated[str, "The full name to search for"],
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limit: Annotated[
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int | None,
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"The maximum number of contacts to return (30 is the max allowed by Google API)",
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] = DEFAULT_SEARCH_CONTACTS_LIMIT,
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) -> Annotated[dict, "A dictionary containing the list of matching contacts"]:
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"""
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Search the user's contacts in Google Contacts by name.
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"""
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service = build_people_service(context.get_auth_token_or_empty())
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# Warm-up the cache before performing search.
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# TODO: Ideally we should warmup only if this user (or google domain?) hasn't warmed up recently
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await _warmup_cache(service)
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return {"contacts": search_contacts(service, name, limit)}
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@tool(requires_auth=Google(scopes=["https://www.googleapis.com/auth/contacts"]))
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async def create_contact(
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context: ToolContext,
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given_name: Annotated[str, "The given name of the contact"],
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family_name: Annotated[str | None, "The optional family name of the contact"],
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email: Annotated[str | None, "The optional email address of the contact"],
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) -> Annotated[dict, "A dictionary containing the details of the created contact"]:
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"""
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Create a new contact record in Google Contacts.
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Examples:
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```
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create_contact(given_name="Alice")
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create_contact(given_name="Alice", family_name="Smith")
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create_contact(given_name="Alice", email="alice@example.com")
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```
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"""
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# Build the People API service
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service = build_people_service(context.get_auth_token_or_empty())
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# Construct the person payload with the specified names
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name_body = {"givenName": given_name}
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if family_name:
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name_body["familyName"] = family_name
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contact_body = {"names": [name_body]}
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if email:
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contact_body["emailAddresses"] = [{"value": email, "type": "work"}]
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# Create the contact. The personFields parameter specifies what information
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# should be returned. Here, we return names and emailAddresses.
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created_contact = (
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service.people()
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.createContact(body=contact_body, personFields="names,emailAddresses")
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.execute()
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)
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return {"contact": created_contact}
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