diff --git a/docs/api-reference.md b/docs/api-reference.md
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-# API Reference
-
-Complete reference for Augini's core APIs.
-
-## DataEngineer
-
-The `DataEngineer` class provides data transformation and feature engineering capabilities.
-
-### Methods
-
-#### transform()
-```python
-def transform(
-    data: pd.DataFrame,
-    target_columns: Optional[List[str]] = None,
-    **kwargs
-) -> pd.DataFrame
-```
-
-Transform input data and generate new features.
-
-**Parameters:**
-- `data` (pd.DataFrame): Input dataset
-- `target_columns` (List[str], optional): Columns to generate
-- `**kwargs`: Additional transformation options
-
-**Returns:**
-- pd.DataFrame: Transformed dataset
-
-## DataAnalyzer
-
-The `DataAnalyzer` class provides data analysis and insight generation capabilities.
-
-### Methods
-
-#### analyze()
-```python
-def analyze(
-    data: pd.DataFrame,
-    analysis_type: str = 'statistical',
-    **kwargs
-) -> Dict[str, Any]
-```
-
-Analyze input data and generate insights.
-
-**Parameters:**
-- `data` (pd.DataFrame): Input dataset
-- `analysis_type` (str): Type of analysis ('statistical', 'trends', etc.)
-- `**kwargs`: Additional analysis options
-
-**Returns:**
-- Dict[str, Any]: Analysis results
-
-## Configuration
-
-The `AuginiConfig` class manages configuration settings.
-
-### Methods
-
-#### from_env()
-```python
-@classmethod
-def from_env(cls) -> 'AuginiConfig'
-```
-
-Load configuration from environment variables.
-
-#### from_file()
-```python
-@classmethod
-def from_file(
-    cls,
-    file_path: str
-) -> 'AuginiConfig'
-```
-
-Load configuration from a YAML/JSON file.
-
-### Configuration Options
-
-Key configuration parameters:
-
-```yaml
-# API Settings
-api_key: str  # Your API key
-model: str    # Model to use (default: gpt-4-turbo-preview)
-
-# Processing Settings
-batch_size: int     # Batch size for processing
-temperature: float  # Model temperature
-
-# Debug Settings
-debug: bool      # Enable debug mode
-log_level: str   # Logging level
-``` 
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diff --git a/docs/chat.md b/docs/chat.md
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-# Chat Interface
-
-The Chat interface provides an interactive way to analyze and understand your data through natural language conversations.
-
-## Key Features
-
-- Natural language data analysis
-- Context-aware responses
-- Memory of previous interactions
-- Custom analysis queries
-- Interactive visualizations
-
-## Basic Usage
-
-```python
-from augini import Chat
-import pandas as pd
-
-# Initialize chat interface
-chat = Chat(config={
-    'api_key': 'your-api-key',
-    'model': 'gpt-4-turbo-preview'
-})
-
-# Load your data
-data = pd.read_csv('your_data.csv')
-
-# Start chatting about your data
-response = chat.ask(
-    data,
-    "What are the main trends in this dataset?"
-)
-```
-
-## Advanced Features
-
-### Context Management
-
-```python
-# Enable context awareness
-chat = Chat(config={'enable_memory': True})
-
-# Chat maintains context across questions
-response1 = chat.ask(data, "What's the average age?")
-response2 = chat.ask(data, "How does it correlate with income?")
-```
-
-### Custom Analysis
-
-```python
-# Perform specific analyses through chat
-analysis = chat.ask(
-    data,
-    "Create a cohort analysis based on signup date",
-    output_format='dataframe'
-)
-```
-
-### Visualization Requests
-
-```python
-# Generate visualizations
-plot = chat.ask(
-    data,
-    "Show me a trend plot of sales over time",
-    output_format='plot'
-)
-```
-
-## Configuration Options
-
-```python
-config = {
-    # Chat settings
-    'enable_memory': True,
-    'memory_window': 10,  # Remember last 10 interactions
-    
-    # Response settings
-    'response_format': 'markdown',
-    'include_visualizations': True,
-    
-    # Model settings
-    'temperature': 0.7,
-    'max_tokens': 1000
-}
-```
-
-## Best Practices
-
-1. Ask clear, specific questions
-2. Use context to build complex analyses
-3. Specify output formats when needed
-4. Review and validate generated insights
-5. Save important analyses for future reference 
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