docs (#91)

docs/tips/index.md

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+# Tips for Structure Engineering in Python
+
+## Modular Chain of Thought
+
+This approach to "chain of thought" improves data quality but can have modular components rather than global CoT.
+
+```python hl_lines="4 5"
+from pydantic import BaseModel, Field
+
+class Role(BaseModel):
+    chain_of_thought: str = Field(..., 
+        description="Think step by step to determine the correct title")
+    title: str
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    role: Role
+```
+
+## Utilize Optional Attributes
+
+For optional fields, use Python's **`Optional`** type and set a default value to avoid the model filling in undesired defaults like `''` for strings that are missing
+
+```python hl_lines="6"
+from typing import Optional
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    role: Optional[str] = Field(default=None)
+
+```
+
+## Tips for Enumerations
+
+To prevent data misalignment, use Enums for standardized fields. Always include an "Other" option as a fallback so the model can signal uncertainty.
+
+```python hl_lines="7 12"
+from enum import Enum, auto
+
+class Role(Enum):
+    PRINCIPAL = auto()
+    TEACHER = auto()
+    STUDENT = auto()
+    OTHER = auto()
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    role: Role = Field(description="Correctly assign one of the predefined roles to the user.")
+
+```
+
+## Reiterate Long Instructions
+
+For complex attributes, it helps to reiterate the instructions in the field's description.
+
+```python hl_lines="5 11"
+class Role(BaseModel):
+    """
+    Extract the role based on the following rules ...
+    """
+    instructions: str = Field(..., description="Restate the instructions and rules to correctly determine the title.")
+    title: str
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    role: Role
+
+```
+
+## Handle Arbitrary Properties
+
+When you need to extract undefined attributes, use a list of key-value pairs.
+
+```python hl_lines="10"
+from typing import List
+
+class Property(BaseModel):
+    key: str
+    value: str
+
+class UserDetail(BaseModel):
+    age: int
+    name: str
+    properties: List[Property] = Field(..., description="Extract any other properties that might be relevant.")
+
+```
+
+## Advanced Arbitrary Properties
+
+For multiple users, aim to use consistent key names when extracting properties.
+
+```python
+class UserDetails(BaseModel):
+    """
+    Extract information for multiple users. 
+    Use consistent key names for properties across users.
+    """
+    users: List[UserDetail]
+
+```
+
+This refined guide should offer a cleaner and more organized approach to structure engineering in Python.
+
+## Defining Relationships Between Entities
+
+In cases where relationships exist between entities, it's vital to define them explicitly in the model. The following example demonstrates how to define relationships between users by incorporating an id and a friends field:
+
+```python hl_lines="2 5 8"
+class UserDetail(BaseModel):
+    id: int = Field(..., description="Unique identifier for each user.")
+    age: int
+    name: str
+    friends: List[int] = Field(..., description="Correctl and complete list of friend IDs, representing relationships between users.")
+
+class UserRelationships(BaseModel):
+    users: List[UserDetail] = Field(..., description="Collection of users, correctly capturing the relationships among them.")
+```
+
+## Reusing Components with Different Contexts
+
+You can reuse the same component for different contexts within a model. In this example, the TimeRange component is used for both work_time and leisure_time.
+
+```python hl_lines="9 10"
+class TimeRange(BaseModel):
+    start_time: int = Field(..., description="The start time in hours.")
+    end_time: int = Field(..., description="The end time in hours.")
+
+class UserDetail(BaseModel):
+    id: int = Field(..., description="Unique identifier for each user.")
+    age: int
+    name: str
+    work_time: TimeRange = Field(..., description="Time range during which the user is working.")
+    leisure_time: TimeRange = Field(..., description="Time range reserved for leisure activities.")
+```
+
+Sometimes, a component like TimeRange may require some context or additional logic to be used effectively. Employing a "chain of thought" field within the component can help in understanding or optimizing the time range allocations.
+
+```python hl_lines="2"
+class TimeRange(BaseModel):
+    chain_of_thought: str = Field(..., description="Step by step reasoning to get the correct time range")
+    start_time: int = Field(..., description="The start time in hours.")
+    end_time: int = Field(..., description="The end time in hours.")
+```

mkdocs.yml

@@ -57,6 +57,7 @@ nav:
       - 'Exact Citations': 'examples/exact_citations.md'
       - 'Automated Dataframe Extraction': "examples/autodataframe.md"
       - 'Creating multiple file programs': "examples/gpt-engineer.md"
+  - Structure Engineering Tips: 'tips/index.md'
   - API Reference:
       - 'OpenAISchema': 'openai_schema.md'
       - 'MultiTask': 'api_multitask.md'