### Development Setup and Testing

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/README.md

Provides commands for cloning the HawkinsDB repository, installing development dependencies, and running tests. This setup is for developers contributing to the project.

```bash
git clone https://github.com/your-username/hawkinsdb.git
cd hawkinsdb
pip install -e ".[dev]"
pytest tests/
```

--------------------------------

### Install HawkinsDB

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/README.md

Installs the HawkinsDB package. Basic installation includes core features, while `[all]` installs all available features. Specific features like ConceptNet tools can also be installed separately.

```bash
pip install hawkinsdb
pip install hawkinsdb[all]
pip install hawkinsdb[conceptnet]
```

--------------------------------

### Quick Start: Initialize and Store Data

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/README.md

Demonstrates initializing HawkinsDB and an LLMInterface, then storing entity data with properties and relationships. This example shows how to add structured knowledge to the memory layer.

```python
from hawkinsdb import HawkinsDB, LLMInterface

db = HawkinsDB()
llm = LLMInterface(db)

db.add_entity({
    "column": "Semantic",
    "name": "Coffee Cup",
    "properties": {
        "type": "Container",
        "material": "Ceramic",
        "capacity": "350ml"
    },
    "relationships": {
        "used_for": ["Drinking Coffee", "Hot Beverages"],
        "found_in": ["Kitchen", "Coffee Shop"]
    }
})

response = llm.query("What can you tell me about the coffee cup?")
print(response)
```

--------------------------------

### Relationship Discovery: IsA Relationships

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Example of discovering 'IsA' relationships for an entity. A 'Laptop' entity is added, and then `enrich_relationships` is used to find and add its 'IsA' connections.

```python
# Example of IsA relationship discovery
computer_data = {
    "name": "Laptop",
    "column": "Semantic",
    "properties": {
        "type": "Device",
        "manufacturer": "Generic"
    }
}
db.add_entity(computer_data)
enricher.enrich_relationships(db, "Laptop", relationship_types=["IsA"])
```

--------------------------------

### Initialize LLM Interface

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Demonstrates how to initialize the LLMInterface with a HawkinsDB instance, with options for auto-enrichment and custom configuration like confidence thresholds and validation.

```python
from hawkinsdb import HawkinsDB, LLMInterface

# Initialize database and LLM interface with auto-enrichment
db = HawkinsDB()
llm = LLMInterface(db, auto_enrich=True)

# Or initialize without auto-enrichment for more control
llm_manual = LLMInterface(db, auto_enrich=False)

# Configure additional settings (optional)
llm = LLMInterface(
    db,
    auto_enrich=True,
    confidence_threshold=0.7,  # Minimum confidence for accepting properties
    max_enrichment_depth=2,    # Maximum depth for ConceptNet enrichment
    validate_properties=True    # Enable strict property validation
)
```

--------------------------------

### Enriching a Technical Concept (Laptop)

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Example of adding and enriching a technical concept, 'Laptop', with its properties like type and category.

```python
# Add and enrich a technical concept
# computer_data = {
#     "name": "Laptop",
#     "column": "Semantic",
#     "properties": {
#         "type": "Computer",
#         "category": "Device"
#     }
# }
# db.add_entity(computer_data)
# enricher.enrich_entity(db, "Laptop", "Computer")
```

--------------------------------

### Confidence Scoring Example

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Shows how to retrieve and inspect confidence scores associated with properties of entities, which is useful for understanding the certainty of the inferred data.

```python
# Query with metadata to see confidence scores
response = llm.query_entity(
    "Speed_of_Light",
    include_metadata=True
)

# Check confidence scores for properties
for prop, value in response["data"]["Semantic"]["properties"].items():
    print(f"{prop}: {value[0]['confidence']}")
```

--------------------------------

### Input Formatting Best Practices

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Illustrates the difference between good and bad input formatting for natural language descriptions when adding entities, emphasizing clarity and specificity.

```python
# Good: Clear, specific descriptions
result = llm.add_from_text("""
    A MacBook Pro is a high-end laptop computer made by Apple.
    It features:
    - Retina display
    - M1 or M2 processor
    - Up to 32GB RAM
    Location: Office desk
""")

# Bad: Vague or ambiguous descriptions
result = llm.add_from_text("It's a computer that does stuff")
```

--------------------------------

### Property Validation Example

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Demonstrates how the LLM Interface automatically validates properties during entity creation, ensuring correct types and formats based on inferred or provided schemas.

```python
# The LLM interface automatically validates properties
result = llm.add_from_text("""
    The speed of light is approximately 299,792,458 meters per second.
    It is a fundamental physical constant represented by 'c'.
""")

# Properties are validated and properly typed
print(result["entity_data"]["properties"])
```

--------------------------------

### Install HawkinsDB using pip

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Installs the HawkinsDB package using pip, the Python package installer. This command downloads and installs the latest version of HawkinsDB and its dependencies.

```bash
pip install hawkinsdb
```

--------------------------------

### Custom Entity Processing

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Provides an example of subclassing the LLMInterface to implement custom logic for processing properties, allowing for extended or modified data handling.

```python
from hawkinsdb.llm_interface import LLMInterface

class CustomLLMInterface(LLMInterface):
    def _process_properties(self, properties):
        """Custom property processing"""
        processed = super()._process_properties(properties)
        # Add custom processing logic
        return processed
```

--------------------------------

### Query Formulation Best Practices

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Highlights best practices for formulating natural language queries, contrasting specific and focused questions with vague or compound ones for better results.

```python
# Good: Specific, focused questions
response = llm.query("What is the processor type in the MacBook Pro?")

# Bad: Vague or compound questions
response = llm.query("Tell me about computers and what they do")
```

--------------------------------

### Enriching an Abstract Concept (Happiness)

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Example of adding and enriching an abstract concept, 'Happiness', with its properties like type and category.

```python
# Add and enrich an abstract concept
# concept_data = {
#     "name": "Happiness",
#     "column": "Semantic",
#     "properties": {
#         "type": "Emotion",
#         "category": "Feeling"
#     }
# }
# db.add_entity(concept_data)
# enricher.enrich_entity(db, "Happiness", "Emotion")
```

--------------------------------

### Building a Question-Answering System

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Provides a function to build a simple question-answering system. It takes a list of questions, queries the LLM for each question using `llm.query`, and prints the question and the corresponding answer if successful, or an error message otherwise.

```python
# Build a simple QA system
def answer_questions(questions):
    for question in questions:
        response = llm.query(question)
        if response["success"]:
            print(f"Q: {question}")
            print(f"A: {response['response']}")
        else:
            print(f"Could not answer: {question}")

# Example usage
questions = [
    "What programming languages are in the database?",
    "What is Python used for?",
    "Compare JavaScript and Java"
]
answer_questions(questions)
```

--------------------------------

### Batch Enrichment of Entities

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Provides an example of enriching multiple related entities in a batch process. This is an efficient way to update data for a list of similar entities.

```python
# Enrich multiple related entities
# Assuming 'enricher' is an instance of ConceptNetEnricher
# and 'db' is the database connection
# entities = ["Dog", "Cat", "Hamster"]
# entity_type = "Pet"

# for entity in entities:
#     enricher.enrich_entity(db, entity, entity_type)
```

--------------------------------

### Context-Aware Enrichment

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Illustrates enabling context-aware enrichment by specifying a domain. This helps in retrieving more relevant information for entities within a particular field.

```python
# Context-aware enrichment
enricher = ConceptNetEnricher(
    context_aware=True,
    domain="technology"
)
enricher.enrich_entity(db, "Smartphone", "Device")
```

--------------------------------

### Enriching a Natural Concept (Oak Tree)

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Example of adding and enriching a natural concept, 'Oak_Tree', with its properties like type and category.

```python
# Add and enrich a natural concept
# tree_data = {
#     "name": "Oak_Tree",
#     "column": "Semantic",
#     "properties": {
#         "type": "Tree",
#         "category": "Plant"
#     }
# }
# db.add_entity(tree_data)
# enricher.enrich_entity(db, "Oak_Tree", "Tree")
```

--------------------------------

### Automated System Documentation Generation

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Details a function for generating automated documentation from a system description. It first adds the description to the system and then queries for key aspects like components, features, and requirements, returning them in a structured dictionary.

```python
# Generate structured documentation from text
def document_system(description):
    # Add system description
    result = llm.add_from_text(description)
    if not result["success"]:
        return False
        
    # Query for important aspects
    components = llm.query("What are the main components?")
    features = llm.query("What are the key features?")
    requirements = llm.query("What are the system requirements?")
    
    return {
        "components": components["response"],
        "features": features["response"],
        "requirements": requirements["response"]
    }
```

--------------------------------

### Knowledge Base Population with Multiple Entities

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Illustrates how to populate a knowledge base by adding multiple related entities from a list of descriptions. It iterates through each description, adds it to the system using `llm.add_from_text`, and prints the name of the added entity.

```python
# Add multiple related entities
descriptions = [
    "Python is a high-level programming language known for its readability",
    "JavaScript is a programming language used primarily for web development",
    "Java is a widely-used object-oriented programming language"
]

for desc in descriptions:
    result = llm.add_from_text(desc)
    if result["success"]:
        print(f"Added programming language: {result['entity_name']}")
```

--------------------------------

### Direct Entity Enrichment with ConceptNet

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Demonstrates how to initialize HawkinsDB and ConceptNetEnricher, add a basic entity, and then enrich it using ConceptNet. It also shows how to query the enriched entity to view its properties and relationships.

```python
from hawkinsdb import HawkinsDB, ConceptNetEnricher

# Initialize
db = HawkinsDB()
enricher = ConceptNetEnricher()

# Add basic entity
entity_data = {
    "name": "Dog",
    "column": "Semantic",
    "properties": {
        "type": "Animal",
        "category": "Pet"
    }
}
db.add_entity(entity_data)

# Enrich the entity
enriched_result = enricher.enrich_entity(db, "Dog", "Animal")
print(f"Enrichment status: {enriched_result}")

# Query enriched entity
enriched_dog = db.query_frames("Dog")
print("Enriched properties:", enriched_dog["Semantic"].properties)
print("Enriched relationships:", enriched_dog["Semantic"].relationships)
```

--------------------------------

### LLMInterface API Reference

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Provides an overview of the `LLMInterface` methods, including `add_entity`, `add_from_text`, `query`, and `query_entity`. It details the purpose of each method, its parameters, and expected return types, serving as a reference for developers interacting with the LLM.

```apidoc
LLMInterface:
  add_entity(entity_json: Union[str, Dict]) -> Dict[str, Any]: Add entity from structured data
  add_from_text(text: str) -> Dict[str, Any]: Add entity from natural language text
  query(question: str) -> Dict[str, Any]: Answer questions about entities
  query_entity(name: str, include_metadata: bool = False) -> Dict[str, Any]: Query specific entity details
```

--------------------------------

### Add Entities from Text

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Shows how to add new entities to the database using natural language descriptions. The system automatically processes the text, infers properties, and can enrich the data.

```python
# Add entity using natural language
result = llm.add_from_text("""
    A Tesla Model 3 is an electric car manufactured by Tesla. 
    It has autopilot capabilities, a glass roof, and typically comes 
    in various colors including red, white, and black.
""")

if result["success"]:
    print(f"Added entity: {result['entity_name']}")
    print(f"Enriched: {result['enriched']}")
```

--------------------------------

### Query with Natural Language

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Illustrates how to query the database using natural language questions. It covers general questions about entities and retrieving specific entity details with metadata.

```python
# Ask questions about stored entities
response = llm.query("What features does the Tesla Model 3 have?")
print(f"Answer: {response['response']}")

# Query specific entity details
details = llm.query_entity("Tesla_Model_3", include_metadata=True)
print(f"Entity details: {details}")
```

--------------------------------

### Enrichment with Multiple Sources

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Shows how to configure the ConceptNetEnricher to validate data from multiple sources and require a minimum number of sources for enrichment. This enhances data reliability.

```python
# Enrichment with multiple sources
enricher = ConceptNetEnricher(
    validate_sources=True,
    min_sources=2
)
enricher.enrich_entity(db, "Computer", "Device")
```

--------------------------------

### Property Inference: Physical Characteristics

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Example of enriching an entity with its physical attributes using ConceptNet. It adds a 'Car' entity and then uses `enrich_properties` to add physical characteristics.

```python
# Example of physical characteristics enrichment
car_data = {
    "name": "Car",
    "column": "Semantic",
    "properties": {"type": "Vehicle"}
}
db.add_entity(car_data)
enricher.enrich_properties(db, "Car", ["physical_attributes"])
```

--------------------------------

### Property Inference: Typical Locations

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Illustrates location-based enrichment. A 'Hammer' entity is added, and then `enrich_properties` is called to discover and add its typical locations.

```python
# Location-based enrichment
tool_data = {
    "name": "Hammer",
    "column": "Semantic",
    "properties": {"type": "Tool"}
}
db.add_entity(tool_data)
enricher.enrich_properties(db, "Hammer", ["locations"])
```

--------------------------------

### Relationship Discovery: UsedFor Relationships

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Illustrates discovering utility ('UsedFor') relationships for an entity. A 'Screwdriver' entity is added, and then `enrich_relationships` is used to find and add its 'UsedFor' connections.

```python
# Discovering utility relationships
tool_data = {
    "name": "Screwdriver",
    "column": "Semantic",
    "properties": {"type": "Tool"}
}
db.add_entity(tool_data)
enricher.enrich_relationships(db, "Screwdriver", relationship_types=["UsedFor"])
```

--------------------------------

### Selective Property Enrichment

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Demonstrates enriching only specific properties of an entity, such as 'parts', 'capabilities', and 'location'. This is useful for targeted data augmentation.

```python
# Enrich specific properties
# Assuming 'enricher' is an instance of ConceptNetEnricher
# and 'db' is the database connection
# enricher.enrich_properties(
#     db,
#     entity_name="Car",
#     properties=["parts", "capabilities", "location"]
# )
```

--------------------------------

### Automatic Entity Enrichment via LLM Interface

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Shows how to initialize LLMInterface with auto-enrichment enabled and add an entity from text. It then verifies the enrichment by querying the entity and printing its enhanced properties and relationships.

```python
from hawkinsdb import HawkinsDB, LLMInterface

# Initialize with auto-enrichment
db = HawkinsDB()
llm = LLMInterface(db, auto_enrich=True)

# Add entity with automatic enrichment
result = llm.add_from_text(
    "A golden retriever is a friendly dog breed known for its golden coat"
)

# Verify enrichment
if result["success"]:
    entity_name = result["entity_name"]
    enriched_data = db.query_frames(entity_name)
    
    # Print enriched properties
    semantic_frame = enriched_data.get("Semantic")
    if semantic_frame:
        print("Enriched properties:", semantic_frame.properties)
        print("Added relationships:", semantic_frame.relationships)
```

--------------------------------

### Error Handling during Enrichment

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Shows a robust try-except block for handling potential errors during the entity enrichment process and verifying the enrichment results.

```python
try:
    # Assuming 'enricher' is an instance of ConceptNetEnricher
    # and 'db' is the database connection
    # enriched = enricher.enrich_entity(db, entity_name, entity_type)
    # if enriched:
    #     print("Successfully enriched entity")
        
    #     # Verify enrichment
    #     result = db.query_frames(entity_name)
    #     if result:
    #         semantic_frame = result.get("Semantic")
    #         if semantic_frame:
    #             print("Enriched properties:", semantic_frame.properties)
    #             print("Enriched relationships:", semantic_frame.relationships)
    # else:
    #     print("No enrichment data found")
        
# except Exception as e:
#     print(f"Error during enrichment: {str(e)}")
```

--------------------------------

### Relationship Discovery: HasA Relationships

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Demonstrates discovering part-whole ('HasA') relationships for an entity. A 'Car' entity is added, and then `enrich_relationships` is used to find and add its 'HasA' connections.

```python
# Discovering part-whole relationships
car_data = {
    "name": "Car",
    "column": "Semantic",
    "properties": {"type": "Vehicle"}
}
db.add_entity(car_data)
enricher.enrich_relationships(db, "Car", relationship_types=["HasA"])
```

--------------------------------

### Error Handling for Database Operations

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Provides an example of how to implement error handling for database operations in HawkinsDB, catching specific ValueErrors and general exceptions.

```python
try:
    result = db.add_entity(entity_data)
except ValueError as e:
    print(f"Invalid data: {str(e)}")
except Exception as e:
    print(f"Storage error: {str(e)}")
```

--------------------------------

### Relationship Discovery: CapableOf Relationships

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Shows how to find capability ('CapableOf') relationships for an entity. A 'Robot' entity is added, and then `enrich_relationships` is used to discover and add its 'CapableOf' connections.

```python
# Finding capability relationships
robot_data = {
    "name": "Robot",
    "column": "Semantic",
    "properties": {"type": "Machine"}
}
db.add_entity(robot_data)
enricher.enrich_relationships(db, "Robot", relationship_types=["CapableOf"])
```

--------------------------------

### Property Inference: Common Behaviors

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Demonstrates enriching an entity with behavioral information. A 'Cat' entity is added, and then `enrich_properties` is used to add its common behaviors.

```python
# Enriching with behavior information
animal_data = {
    "name": "Cat",
    "column": "Semantic",
    "properties": {"type": "Pet"}
}
db.add_entity(animal_data)
enricher.enrich_properties(db, "Cat", ["behaviors"])
```

--------------------------------

### Property Inference: Related Concepts

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Shows how to enrich an entity by discovering related concepts using ConceptNet. An 'Apple' entity is added, and then `enrich_properties` is used to find and add related concepts.

```python
# Concept relationship enrichment
fruit_data = {
    "name": "Apple",
    "column": "Semantic",
    "properties": {"type": "Fruit"}
}
db.add_entity(fruit_data)
enricher.enrich_properties(db, "Apple", ["related_concepts"])
```

--------------------------------

### Define Semantic Memory Data Structure

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Defines the structure for semantic memory data, including properties and relationships. This example represents knowledge about 'Photosynthesis'.

```python
semantic_data = {
    "name": "Photosynthesis",
    "column": "Semantic",
    "properties": {
        "type": "biological_process",
        "location": "plant_cells",
        "components": ["chlorophyll", "sunlight", "water", "carbon_dioxide"],
        "products": ["glucose", "oxygen"]
    },
    "relationships": {
        "occurs_in": ["plants", "algae"],
        "requires": ["light_energy", "chloroplasts"],
        "produces": ["chemical_energy", "organic_compounds"]
    }
}
```

--------------------------------

### Custom Confidence-Based Filtering

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/conceptnet_guide.md

Demonstrates how to create a custom enricher that filters ConceptNet relations based on a minimum confidence threshold. This allows for more control over the quality of enriched data.

```python
from hawkinsdb import ConceptNetEnricher

class CustomEnricher(ConceptNetEnricher):
    def __init__(self):
        super().__init__()
        self.min_confidence = 0.7  # Set minimum confidence threshold
        
    def filter_relations(self, relations):
        """Custom filtering of ConceptNet relations"""
        return [r for r in relations if r.weight >= self.min_confidence]
```

--------------------------------

### Define Episodic Memory Data Structure

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Defines the structure for episodic memory data, including timestamp, duration, location, and outcome. This example represents a memory of a 'first_python_project'.

```python
episodic_data = {
    "name": "first_python_project",
    "column": "Episodic",
    "properties": {
        "timestamp": time.time(),
        "duration": "2 hours",
        "location": "home_office",
        "outcome": "successful"
    },
    "relationships": {
        "involves": ["Python_Language"],
        "followed_by": ["code_review"]
    }
}
```

--------------------------------

### Error Handling for LLM Operations

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/llm_interface_guide.md

Demonstrates how to handle potential errors during LLM operations, specifically when adding text and checking the success status of the operation. It includes checks for enrichment and prints informative messages for both success and failure scenarios.

```python
try:
    result = llm.add_from_text(text_description)
    if result["success"]:
        print(f"Added: {result['entity_name']}")
        if result["enriched"]:
            print("Entity was enriched with ConceptNet data")
    else:
        print(f"Error: {result['message']}")
except Exception as e:
    print(f"Error processing text: {str(e)}")
```

--------------------------------

### Configure SQLite Storage

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Demonstrates how to initialize HawkinsDB and configure it to use SQLite as the storage backend, including setting a custom storage path.

```python
from hawkinsdb import HawkinsDB

# Initialize database
db = HawkinsDB()

# Enable SQLite storage
db.config.set_storage_backend('sqlite')

# Optionally configure SQLite path (default: ./hawkins_memory.db)
db.config.set_storage_path('path/to/your/database.db')
```

--------------------------------

### Initialize LLM Interface with Auto-Enrichment and Query

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Initializes the LLMInterface with auto-enrichment enabled and demonstrates converting natural language text into structured data and querying the database using natural language.

```python
from hawkinsdb import HawkinsDB, LLMInterface

# Initialize
db = HawkinsDB()
llm = LLMInterface(
    db,
    auto_enrich=True,
    confidence_threshold=0.7,
    max_enrichment_depth=2
)

# Convert natural language to structured data
result = llm.add_from_text("""
    The respiratory system is responsible for taking in oxygen 
    and releasing carbon dioxide. Key organs include the lungs, 
    trachea, and diaphragm.
""")
print(f"Added entity: {result['entity_name']}")

# Complex querying with context
response = llm.query(
    "What are the main components of the respiratory system?",
)
print(f"Response: {response}")
```

--------------------------------

### Initialize HawkinsDB with SQLite and LLM Interface

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Initializes HawkinsDB with SQLite storage and creates an LLMInterface for natural language interaction. This sets up the database connection and the language model interface.

```python
from hawkinsdb import HawkinsDB , LLMInterface

# Initialize with SQLite storage
db = HawkinsDB(storage_type="sqlite", db_path="memory.db")
llm = LLMInterface(db)
```

--------------------------------

### Configure HawkinsDB with Custom SQLite Path and Error Handling

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Demonstrates initializing HawkinsDB with a custom SQLite database path and includes basic operations with error handling. It shows how to add and query entities, and how to handle potential storage errors.

```python
# Initialize with custom SQLite path
db = HawkinsDB(
    storage_type="sqlite",
    db_path="custom_path/memory.db"
)

# Basic operations
try:
    # Add entity
    result = db.add_entity(entity_data)
    
    # Query data
    frames = db.query_frames("entity_name")
    
    # Cleanup
    db.cleanup()  # Close connections
except Exception as e:
    print(f"Storage error: {str(e)}")
```

--------------------------------

### Cleanup HawkinsDB Connections

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Illustrates the proper way to close database connections and free resources managed by HawkinsDB.

```python
db.cleanup()
```

--------------------------------

### Query Data from HawkinsDB

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Illustrates how to retrieve data from HawkinsDB, including fetching frames for a specific entity and listing all available entities.

```python
# Get all frames for an entity
frames = db.query_frames("Tesla Model 3")

# List all entities
entities = db.list_entities()
```

--------------------------------

### Execute Custom SQLite Queries

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Shows how to execute custom SQL queries directly against the SQLite backend using the get_storage_backend interface.

```python
from hawkinsdb.storage import get_storage_backend

storage = get_storage_backend('sqlite')
storage.execute_query("SELECT * FROM entities WHERE name LIKE ?", ("%Tesla%",))
```

--------------------------------

### Bulk Add Entities

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Demonstrates how to efficiently add multiple entities to HawkinsDB by iterating through a list of entity data.

```python
entities = [
    {"name": "Entity1", "properties": {...}},
    {"name": "Entity2", "properties": {...}}
]

for entity in entities:
    db.add_entity(entity)
```

--------------------------------

### Query and List Entities in HawkinsDB

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Shows how to query a specific entity and list all entities stored in HawkinsDB.  `query_frames` retrieves information about a specific entity, while `list_entities` returns a list of all entities in the database.

```python
# Query specific entity
cat_info = db.query_frames("cat")

# List all entities
entities = db.list_entities()
```

--------------------------------

### Integrate ConceptNet for Knowledge Enrichment

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Demonstrates integrating ConceptNet to enrich entities in HawkinsDB. It initializes the ConceptNetEnricher, adds an entity, and then enriches it using ConceptNet.

```python
from hawkinsdb import ConceptNetEnricher

# Initialize
db = HawkinsDB()
enricher = ConceptNetEnricher()

# Add and enrich entity
entity_data = {
    "name": "Dog",
    "column": "Semantic",
    "properties": {
        "type": "Animal",
        "category": "Pet"
    }
}
db.add_entity(entity_data)
enriched_result = enricher.enrich_entity(db, "Dog", "Animal")
```

--------------------------------

### Add Entity to HawkinsDB

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/sqlite_backend.md

Shows how to add a new entity with its properties and relationships to the HawkinsDB using the add_entity method.

```python
entity_data = {
    "name": "Tesla Model 3",
    "properties": {
        "color": "red",
        "year": 2023
    },
    "relationships": {
        "located_in": ["garage"]
    }
}

result = db.add_entity(entity_data)
```

--------------------------------

### Error Handling with ValidationError

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Illustrates how to handle potential `ValidationError` exceptions when adding entities to the database, along with general exception handling for other errors. It shows how to check the success status of an add operation and print relevant messages.

```python
from hawkinsdb import ValidationError

try:
    # Add entity with validation
    result = db.add_entity({
        "name": "Test",
        "column": "Semantic",
        "properties": {
            "age": "42"  # Will be converted to integer
        }
    })
    
    if result["success"]:
        print(f"Added: {result['entity_name']}")
    else:
        print(f"Error: {result['message']}")
        
except ValidationError as e:
    print(f"Validation error: {str(e)}")
except Exception as e:
    print(f"General error: {str(e)}")
```

--------------------------------

### Add Semantic and Episodic Memories to HawkinsDB

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Demonstrates adding semantic and episodic memories to HawkinsDB. Semantic memory stores conceptual knowledge, while episodic memory stores event-based information with timestamps.

```python
# Add semantic memory
semantic_memory = {
    "name": "cat",
    "column": "Semantic",
    "properties": {
        "type": "animal",
        "size": "medium",
        "characteristics": ["furry", "agile", "carnivorous"]
    },
    "relationships": {
        "habitat": ["homes", "outdoors"],
        "behavior": ["hunting", "sleeping", "grooming"]
    }
}

result = db.add_entity(semantic_memory)
response = llm.query(
    "Explain the behaviours of cat",
)

# Add episodic memory
import time

episodic_memory = {
    "name": "cat_observation",
    "column": "Episodic",
    "properties": {
        "timestamp": time.time(),
        "action": "Observed cat behavior",
        "location": "Garden",
        "details": "Cat was chasing a butterfly"
    },
    "relationships": {
        "relates_to": ["cat"],
        "observed_by": ["human"]
    }
}

result = db.add_entity(episodic_memory)
```

--------------------------------

### Custom Entity Enrichment

Source: https://github.com/harishsg993010/hawkinsdb/blob/main/docs/README.md

Demonstrates how to create and use a custom enricher by extending the ConceptNetEnricher class. It filters relations based on a minimum confidence score and applies this custom logic to enrich an entity.

```python
class CustomEnricher(ConceptNetEnricher):
    def __init__(self):
        super().__init__()
        self.min_confidence = 0.7
        
    def filter_relations(self, relations):
        return [r for r in relations if r.weight >= self.min_confidence]

# Use custom enricher
custom_enricher = CustomEnricher()
custom_enricher.enrich_entity(db, "Dog", "Animal")
```

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