### 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>
240 lines
7 KiB
Python
240 lines
7 KiB
Python
from arcade_evals import (
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EvalRubric,
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EvalSuite,
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ExpectedToolCall,
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NumericCritic,
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SimilarityCritic,
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tool_eval,
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)
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from arcade_tdk import ToolCatalog
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import arcade_search
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from arcade_search.tools import search_google
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# Evaluation rubric
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rubric = EvalRubric(
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fail_threshold=0.8,
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warn_threshold=0.9,
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)
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catalog = ToolCatalog()
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# Register the Google Search tool
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catalog.add_module(arcade_search)
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@tool_eval()
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def google_search_eval_suite() -> EvalSuite:
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"""Create an evaluation suite for the Google Search tool."""
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suite = EvalSuite(
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name="Google Search Tool Evaluation",
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system_message="You are an AI assistant that can perform web searches using the provided tools.",
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catalog=catalog,
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rubric=rubric,
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)
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# Simple search query with default results
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suite.add_case(
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name="Simple search query with default results",
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user_message="Search for 'Climate change effects on polar bears' on Google.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "Climate change effects on polar bears",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search query with specific number of results
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suite.add_case(
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name="Search query with specific number of results",
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user_message="Find the top 3 articles about quantum computing.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "articles about quantum computing",
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"n_results": 3,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=0.7),
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NumericCritic(
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critic_field="n_results",
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weight=0.3,
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value_range=(1, 100),
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),
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],
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)
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# Search query with 'n' results specified in words
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suite.add_case(
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name="Search query with 'n' results specified in words",
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user_message="Give me five recipes for vegan lasagna.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "recipes for vegan lasagna",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=0.7),
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NumericCritic(
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critic_field="n_results",
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weight=0.3,
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value_range=(1, 100),
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),
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],
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)
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# Ambiguous number of results
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suite.add_case(
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name="Ambiguous number of results",
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user_message="Find articles about climate change impacts 10.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "articles about climate change impacts 10",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search query with multiple instructions
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suite.add_case(
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name="Search query with multiple instructions",
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user_message="Search for the latest news on electric cars, and tell me about Tesla's new model.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "latest news on electric cars",
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"n_results": 5,
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},
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),
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "Tesla's new model",
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"n_results": 5,
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},
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),
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search with stop words and filler words
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suite.add_case(
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name="Search with stop words and filler words",
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user_message="Could you please search for the best ways to learn French?",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "best ways to learn French",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# No clear query given
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suite.add_case(
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name="No clear query given",
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user_message="Find it for me.",
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expected_tool_calls=[],
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critics=[],
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)
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# Search query with special characters
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suite.add_case(
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name="Search query with special characters",
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user_message="Find me '@OpenAI's latest research papers'",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "@OpenAI's latest research papers",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search query with complex instructions
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suite.add_case(
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name="Search query with complex instructions",
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user_message="I need information about the impact of deforestation in the Amazon over the past decade.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "impact of deforestation in the Amazon over the past decade",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search query in a different language
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suite.add_case(
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name="Search query in a different language",
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user_message="Busca información sobre la economía de España.",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "economía de España",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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# Search query with numeric data
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suite.add_case(
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name="Search query with numeric data",
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user_message="What was the population of Japan in 2020?",
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expected_tool_calls=[
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ExpectedToolCall(
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func=search_google,
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args={
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"query": "population of Japan in 2020",
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"n_results": 5,
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="query", weight=1.0),
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],
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)
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return suite
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