Merge pull request #54 from WorkflowAI/guillaume/update-deps

Update dependencies

Author: guillaq

README.md

@@ -105,11 +105,11 @@ class FeedbackPoint(BaseModel):
 # Model representing the structured analysis of the customer feedback call
 class CallFeedbackOutput(BaseModel):
     """Structured analysis of the customer feedback call."""
-    positive_points: List[FeedbackPoint] = Field(
+    positive_points: list[FeedbackPoint] = Field(
         default_factory=list,
         description="List of positive feedback points, each with a supporting quote."
     )
-    negative_points: List[FeedbackPoint] = Field(
+    negative_points: list[FeedbackPoint] = Field(
         default_factory=list,
         description="List of negative feedback points, each with a supporting quote."
     )
@@ -267,7 +267,6 @@ async for chunk in analyze_call_feedback(feedback_input):
 > For example, a function with the signature `async def foo() -> AsyncIterator[int]` may be called
 > `async for c in await foo():...` in certain cases...
 
-
 #### Streaming the run object
 
 Use `AsyncIterator[Run[Output]]` to get the **run** object as it is generated, which allows you, for the **last chunk**, to access the cost and duration of the run.
@@ -331,7 +330,7 @@ class PDFQuestionInput(BaseModel):
 
 class PDFAnswerOutput(BaseModel):
     answer: str = Field(description="The answer to the question based on the PDF content")
-    quotes: List[str] = Field(description="Relevant quotes from the PDF that support the answer")
+    quotes: list[str] = Field(description="Relevant quotes from the PDF that support the answer")
 
 @workflowai.agent(id="pdf-answer", model=Model.CLAUDE_3_5_SONNET_LATEST)
 async def answer_pdf_question(input: PDFQuestionInput) -> PDFAnswerOutput:
@@ -610,8 +609,8 @@ values.
 
 ```python
 class CallFeedbackOutputStrict(BaseModel):
-    positive_points: List[FeedbackPoint]
-    negative_points: List[FeedbackPoint]
+    positive_points: list[FeedbackPoint]
+    negative_points: list[FeedbackPoint]
 
 @workflowai.agent()
 async def analyze_call_feedback_strict(input: CallFeedbackInput) -> CallFeedbackOutputStrict:
@@ -628,8 +627,8 @@ except WorkflowAIError as e:
     print(e.code) # "invalid_generation" error code means that the generation did not match the schema
 
 class CallFeedbackOutputTolerant(BaseModel):
-    positive_points: List[FeedbackPoint] = Field(default_factory=list)
-    negative_points: List[FeedbackPoint] = Field(default_factory=list)
+    positive_points: list[FeedbackPoint] = Field(default_factory=list)
+    negative_points: list[FeedbackPoint] = Field(default_factory=list)
 
 @workflowai.agent()
 async def analyze_call_feedback_tolerant(input: CallFeedbackInput) -> CallFeedbackOutputTolerant:

examples/08_pdf_agent.py

@@ -1,6 +1,5 @@
 import asyncio
 import os
-from typing import List
 
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
@@ -18,7 +17,7 @@ class PDFQuestionInput(BaseModel):
 
 class PDFAnswerOutput(BaseModel):
     answer: str = Field(description="The answer to the question based on the PDF content")
-    quotes: List[str] = Field(description="Relevant quotes from the PDF that support the answer")
+    quotes: list[str] = Field(description="Relevant quotes from the PDF that support the answer")
 
 
 @workflowai.agent(id="pdf-answer", model=Model.CLAUDE_3_5_SONNET_LATEST)

examples/11_ecommerce_chatbot.py

@@ -8,7 +8,7 @@
 
 import asyncio
 from enum import Enum
-from typing import List, Optional
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -18,12 +18,14 @@
 
 class Role(str, Enum):
     """Enum representing possible message roles."""
+
     USER = "user"
     ASSISTANT = "assistant"
 
 
 class Product(BaseModel):
     """Model representing a product recommendation."""
+
     name: str = Field(
         description="Name of the product",
         examples=["Wireless Noise-Cancelling Headphones", "4K Smart TV"],
@@ -56,6 +58,7 @@ class Product(BaseModel):
 
 class Message(BaseModel):
     """Model representing a chat message."""
+
     role: Role = Field()
     content: str = Field(
         description="The content of the message",
@@ -64,28 +67,31 @@ class Message(BaseModel):
             "Based on your requirements, here are some great headphone options...",
         ],
     )
-    recommended_products: Optional[List[Product]] = Field(
+    recommended_products: Optional[list[Product]] = Field(
         default=None,
         description="Product recommendations included with this message, if any",
     )
 
 
 class AssistantMessage(Message):
     """Model representing a message from the assistant."""
+
     role: Role = Role.ASSISTANT
     content: str = ""
 
 
 class ChatbotOutput(BaseModel):
     """Output model for the chatbot response."""
+
     assistant_message: AssistantMessage = Field(
         description="The chatbot's response message",
     )
 
 
 class ChatInput(BaseModel):
     """Input model containing the user's message and conversation history."""
-    conversation_history: Optional[List[Message]] = Field(
+
+    conversation_history: Optional[list[Message]] = Field(
         default=None,
         description="Previous messages in the conversation, if any",
     )

examples/13_rag.py

@@ -12,7 +12,7 @@
 
 import asyncio
 from enum import Enum
-from typing import List, Optional
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -22,12 +22,14 @@
 
 class Role(str, Enum):
     """Enum representing possible message roles."""
+
     USER = "user"
     ASSISTANT = "assistant"
 
 
 class SearchResult(BaseModel):
     """Model representing a search result from the knowledge base."""
+
     content: str = Field(
         description="The content of the search result",
     )
@@ -38,7 +40,7 @@ class SearchResult(BaseModel):
 
 # Simulated knowledge base search tool
 # ruff: noqa: ARG001
-async def search_faq(query: str) -> List[SearchResult]:
+async def search_faq(query: str) -> list[SearchResult]:
     """
     Search the knowledge base for relevant information.
 
@@ -79,6 +81,7 @@ async def search_faq(query: str) -> List[SearchResult]:
 
 class Message(BaseModel):
     """Model representing a chat message."""
+
     role: Role
     content: str = Field(
         description="The content of the message",
@@ -87,20 +90,24 @@ class Message(BaseModel):
 
 class AssistantMessage(Message):
     """Model representing a message from the assistant."""
+
     role: Role = Role.ASSISTANT
     content: str = ""
 
 
 class ChatbotOutput(BaseModel):
     """Output model for the chatbot response."""
-    assistant_message: AssistantMessage = Field(default_factory=AssistantMessage,
+
+    assistant_message: AssistantMessage = Field(
+        default_factory=AssistantMessage,
         description="The chatbot's response message",
     )
 
 
 class ChatInput(BaseModel):
     """Input model containing the user's message and conversation history."""
-    conversation_history: Optional[List[Message]] = Field(
+
+    conversation_history: Optional[list[Message]] = Field(
         default=None,
         description="Previous messages in the conversation, if any",
     )

examples/workflows/chain_of_agents.py

@@ -16,7 +16,6 @@
 """
 
 import asyncio
-from typing import List
 
 from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
 
@@ -47,7 +46,7 @@ class WorkerOutput(BaseModel):
     findings: str = Field(
         description="Key findings and information extracted from this chunk.",
     )
-    evidence: List[str] = Field(
+    evidence: list[str] = Field(
         default_factory=list,
         description="Supporting evidence or quotes from the chunk.",
     )
@@ -70,7 +69,7 @@ class ManagerOutput(BaseModel):
 
     answer: str = Field(description="The final answer to the query.")
     reasoning: str = Field(description="Explanation of how the answer was derived.")
-    supporting_evidence: List[str] = Field(
+    supporting_evidence: list[str] = Field(
         default_factory=list,
         description="Key evidence supporting the answer.",
     )

examples/workflows/evaluator_optimizer.py

@@ -1,5 +1,5 @@
 import asyncio
-from typing import List, TypedDict
+from typing import TypedDict
 
 from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
 
@@ -45,8 +45,8 @@ class TranslationEvaluationOutput(BaseModel):
     preserves_tone: bool = Field(description="Whether the translation preserves the original tone.")
     preserves_nuance: bool = Field(description="Whether the translation preserves subtle nuances.")
     culturally_accurate: bool = Field(description="Whether the translation is culturally appropriate.")
-    specific_issues: List[str] = Field(description="List of specific issues identified.")
-    improvement_suggestions: List[str] = Field(description="List of suggested improvements.")
+    specific_issues: list[str] = Field(description="List of specific issues identified.")
+    improvement_suggestions: list[str] = Field(description="List of suggested improvements.")
 
 
 # Uses O1 for its strong analytical and evaluation capabilities
@@ -65,8 +65,8 @@ class TranslationImprovementInput(BaseModel):
     original_text: str = Field(description="The original text.")
     current_translation: str = Field(description="The current translation.")
     target_language: str = Field(description="The target language.")
-    specific_issues: List[str] = Field(description="Issues to address.")
-    improvement_suggestions: List[str] = Field(description="Suggestions for improvement.")
+    specific_issues: list[str] = Field(description="Issues to address.")
+    improvement_suggestions: list[str] = Field(description="Suggestions for improvement.")
 
 
 class TranslationImprovementOutput(BaseModel):

examples/workflows/orchestrator_worker.py

@@ -1,6 +1,6 @@
 import asyncio
 from enum import Enum
-from typing import List, TypedDict
+from typing import TypedDict
 
 from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
 
@@ -37,7 +37,7 @@ class ImplementationPlanInput(BaseModel):
 class ImplementationPlanOutput(BaseModel):
     """Output containing the implementation plan."""
 
-    files: List[FileChange] = Field(description="List of files to be changed.")
+    files: list[FileChange] = Field(description="List of files to be changed.")
     estimated_complexity: ComplexityLevel = Field(description="Estimated complexity of the implementation.")
 
 
@@ -86,7 +86,7 @@ class ImplementationChange(TypedDict):
 
 class FeatureImplementationResult(TypedDict):
     plan: ImplementationPlanOutput
-    changes: List[ImplementationChange]
+    changes: list[ImplementationChange]
 
 
 async def implement_feature(feature_request: str) -> FeatureImplementationResult:
@@ -117,7 +117,7 @@ async def implement_feature(feature_request: str) -> FeatureImplementationResult
     )
 
     # Combine results
-    changes: List[ImplementationChange] = [
+    changes: list[ImplementationChange] = [
         {
             "file": implementation_plan.files[i],
             "implementation": change,

examples/workflows/parallel_processing.py

@@ -1,6 +1,6 @@
 import asyncio
 from enum import Enum
-from typing import List, TypedDict
+from typing import TypedDict
 
 from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
 
@@ -23,9 +23,9 @@ class SecurityReviewInput(BaseModel):
 class SecurityReviewOutput(BaseModel):
     """Output from security code review."""
 
-    vulnerabilities: List[str] = Field(description="List of identified security vulnerabilities.")
+    vulnerabilities: list[str] = Field(description="List of identified security vulnerabilities.")
     risk_level: RiskLevel = Field(description="Overall security risk level.")
-    suggestions: List[str] = Field(description="Security improvement suggestions.")
+    suggestions: list[str] = Field(description="Security improvement suggestions.")
 
 
 # Uses Claude 3.5 Sonnet for its strong security analysis capabilities
@@ -47,9 +47,9 @@ class PerformanceReviewInput(BaseModel):
 class PerformanceReviewOutput(BaseModel):
     """Output from performance code review."""
 
-    issues: List[str] = Field(description="List of identified performance issues.")
+    issues: list[str] = Field(description="List of identified performance issues.")
     impact: RiskLevel = Field(description="Impact level of performance issues.")
-    optimizations: List[str] = Field(description="Performance optimization suggestions.")
+    optimizations: list[str] = Field(description="Performance optimization suggestions.")
 
 
 # Uses O1 Mini for its expertise in performance optimization
@@ -71,9 +71,9 @@ class MaintainabilityReviewInput(BaseModel):
 class MaintainabilityReviewOutput(BaseModel):
     """Output from maintainability code review."""
 
-    concerns: List[str] = Field(description="List of maintainability concerns.")
+    concerns: list[str] = Field(description="List of maintainability concerns.")
     quality_score: int = Field(description="Code quality score (1-10).", ge=1, le=10)
-    recommendations: List[str] = Field(description="Maintainability improvement recommendations.")
+    recommendations: list[str] = Field(description="Maintainability improvement recommendations.")
 
 
 # Uses Claude 3.5 Sonnet for its strong code quality and readability analysis