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# Integrations

## Overview

This directory contains integrations with external services and APIs.

## Available Integrations

### 1. Google Gemini API

**File**: `gemini_experimental.py`

Integration with Google's Gemini API for advanced reasoning:

- Multi-modal understanding
- Advanced reasoning capabilities
- Natural language processing
- Code generation

**Setup**:

```bash
# Set API key in .env
GOOGLE_API_KEY=your_gemini_api_key_here
```

**Usage**:

```python
from integrations.gemini_experimental import GeminiReasoner
reasoner = GeminiReasoner(api_key="your_key")
result = reasoner.reason_about_query("What is the relationship between X and Y?")
```

### 2. Advanced Execution

**File**: `main_execution_advanced.py`

Advanced execution script with all integrations enabled:

- LangGraph reasoning
- RAG system
- Multi-agent coordination
- Gemini API integration

**Run**:

```bash
python integrations/main_execution_advanced.py
```

## Adding New Integrations

### Template Structure

```python
#!/usr/bin/env python3
"""
Integration with [Service Name]
"""
import os
from dotenv import load_dotenv
class ServiceIntegration:
    def __init__(self, api_key=None):
        load_dotenv()
        self.api_key = api_key or os.getenv('SERVICE_API_KEY')
        if not self.api_key:
            raise ValueError("API key not found")
    def connect(self):
        """Establish connection to service"""
        pass
    def query(self, query_text):
        """Query the service"""
        pass
    def disconnect(self):
        """Close connection"""
        pass
# Example usage
if __name__ == "__main__":
    integration = ServiceIntegration()
    result = integration.query("test query")
    print(result)
```

## Supported Services

### AI/ML Services

- **Google Gemini**: Advanced reasoning and generation
- **OpenAI GPT**: Language understanding and generation
- **Anthropic Claude**: Reasoning and analysis
- **Hugging Face**: Model hosting and inference

### Vector Databases

- **ChromaDB**: Vector storage and retrieval
- **FAISS**: Similarity search
- **Pinecone**: Managed vector database
- **Weaviate**: Vector search engine

### Graph Databases

- **Neo4j**: Graph database for knowledge graphs
- **ArangoDB**: Multi-model database
- **JanusGraph**: Distributed graph database

### Monitoring & Logging

- **Weights & Biases**: Experiment tracking
- **MLflow**: Model versioning and tracking
- **TensorBoard**: Visualization and monitoring

## Configuration

### Environment Variables

Create a `.env` file in the project root:

```bash
# AI Services
GOOGLE_API_KEY=your_gemini_key
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key
HUGGINGFACE_API_KEY=your_hf_key
# Vector Databases
CHROMA_PERSIST_DIRECTORY=./chroma_db
PINECONE_API_KEY=your_pinecone_key
PINECONE_ENVIRONMENT=us-west1-gcp
# Graph Databases
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_password
# Monitoring
WANDB_API_KEY=your_wandb_key
MLFLOW_TRACKING_URI=http://localhost:5000
```

## Integration Testing

Test integrations before use:

```python
# Test Gemini integration
python -c "from integrations.gemini_experimental import GeminiReasoner; print('Gemini OK')"
# Test all integrations
python integrations/test_integrations.py
```

## Error Handling

Handle integration errors gracefully:

```python
try:
    from integrations.gemini_experimental import GeminiReasoner
    reasoner = GeminiReasoner()
except ImportError:
    print("Gemini integration not available")
    reasoner = None
except ValueError as e:
    print(f"Configuration error: {e}")
    reasoner = None
```

## Rate Limiting

Be aware of API rate limits:

```python
import time
from functools import wraps
def rate_limit(calls_per_minute=60):
    min_interval = 60.0 / calls_per_minute
    last_called = [0.0]
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            left_to_wait = min_interval - elapsed
            if left_to_wait > 0:
                time.sleep(left_to_wait)
            result = func(*args, **kwargs)
            last_called[0] = time.time()
            return result
        return wrapper
    return decorator
@rate_limit(calls_per_minute=10)
def call_api(query):
    # API call here
    pass
```

## Caching

Cache API responses to reduce costs:

```python
from functools import lru_cache
import hashlib
@lru_cache(maxsize=1000)
def cached_api_call(query):
    # API call here
    return result
```

## Best Practices

1. **API Keys**: Never commit API keys to version control
2. **Error Handling**: Always handle API errors gracefully
3. **Rate Limiting**: Respect API rate limits
4. **Caching**: Cache responses when possible
5. **Logging**: Log all API calls for debugging
6. **Timeouts**: Set appropriate timeouts for API calls
7. **Retries**: Implement retry logic for failed requests

## Troubleshooting

### Issue: API Key Not Found

**Solution**: Ensure `.env` file exists and contains the required keys

### Issue: Connection Timeout

**Solution**: Check network connectivity and increase timeout values

### Issue: Rate Limit Exceeded

**Solution**: Implement rate limiting and backoff strategies

## Documentation

For detailed integration documentation, see:

- [Gemini API Docs](https://ai.google.dev/docs)
- [OpenAI API Docs](https://platform.openai.com/docs)
- [Anthropic API Docs](https://docs.anthropic.com)
- [ChromaDB Docs](https://docs.trychroma.com)
- [Neo4j Docs](https://neo4j.com/docs)

---

**Note**: Some integrations may require additional setup or paid subscriptions.

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	# Integrations

	## Overview

	This directory contains integrations with external services and APIs.

	## Available Integrations

	### 1. Google Gemini API

	File: `gemini_experimental.py`

	Integration with Google's Gemini API for advanced reasoning:

	- Multi-modal understanding
	- Advanced reasoning capabilities
	- Natural language processing
	- Code generation

	Setup:

	```bash
	# Set API key in .env
	GOOGLE_API_KEY=your_gemini_api_key_here
	```

	Usage:

	```python
	from integrations.gemini_experimental import GeminiReasoner

	reasoner = GeminiReasoner(api_key="your_key")
	result = reasoner.reason_about_query("What is the relationship between X and Y?")
	```

	### 2. Advanced Execution

	File: `main_execution_advanced.py`

	Advanced execution script with all integrations enabled:

	- LangGraph reasoning
	- RAG system
	- Multi-agent coordination
	- Gemini API integration

	Run:

	```bash
	python integrations/main_execution_advanced.py
	```

	## Adding New Integrations

	### Template Structure

	```python
	#!/usr/bin/env python3
	"""
	Integration with [Service Name]
	"""

	import os
	from dotenv import load_dotenv

	class ServiceIntegration:
	def __init__(self, api_key=None):
	load_dotenv()
	self.api_key = api_key or os.getenv('SERVICE_API_KEY')
	if not self.api_key:
	raise ValueError("API key not found")

	def connect(self):
	"""Establish connection to service"""
	pass

	def query(self, query_text):
	"""Query the service"""
	pass

	def disconnect(self):
	"""Close connection"""
	pass

	# Example usage
	if __name__ == "__main__":
	integration = ServiceIntegration()
	result = integration.query("test query")
	print(result)
	```

	## Supported Services

	### AI/ML Services

	- Google Gemini: Advanced reasoning and generation
	- OpenAI GPT: Language understanding and generation
	- Anthropic Claude: Reasoning and analysis
	- Hugging Face: Model hosting and inference

	### Vector Databases

	- ChromaDB: Vector storage and retrieval
	- FAISS: Similarity search
	- Pinecone: Managed vector database
	- Weaviate: Vector search engine

	### Graph Databases

	- Neo4j: Graph database for knowledge graphs
	- ArangoDB: Multi-model database
	- JanusGraph: Distributed graph database

	### Monitoring & Logging

	- Weights & Biases: Experiment tracking
	- MLflow: Model versioning and tracking
	- TensorBoard: Visualization and monitoring

	## Configuration

	### Environment Variables

	Create a `.env` file in the project root:

	```bash
	# AI Services
	GOOGLE_API_KEY=your_gemini_key
	OPENAI_API_KEY=your_openai_key
	ANTHROPIC_API_KEY=your_anthropic_key
	HUGGINGFACE_API_KEY=your_hf_key

	# Vector Databases
	CHROMA_PERSIST_DIRECTORY=./chroma_db
	PINECONE_API_KEY=your_pinecone_key
	PINECONE_ENVIRONMENT=us-west1-gcp

	# Graph Databases
	NEO4J_URI=bolt://localhost:7687
	NEO4J_USER=neo4j
	NEO4J_PASSWORD=your_password

	# Monitoring
	WANDB_API_KEY=your_wandb_key
	MLFLOW_TRACKING_URI=http://localhost:5000
	```

	## Integration Testing

	Test integrations before use:

	```python
	# Test Gemini integration
	python -c "from integrations.gemini_experimental import GeminiReasoner; print('Gemini OK')"

	# Test all integrations
	python integrations/test_integrations.py
	```

	## Error Handling

	Handle integration errors gracefully:

	```python
	try:
	from integrations.gemini_experimental import GeminiReasoner
	reasoner = GeminiReasoner()
	except ImportError:
	print("Gemini integration not available")
	reasoner = None
	except ValueError as e:
	print(f"Configuration error: {e}")
	reasoner = None
	```

	## Rate Limiting

	Be aware of API rate limits:

	```python
	import time
	from functools import wraps

	def rate_limit(calls_per_minute=60):
	min_interval = 60.0 / calls_per_minute
	last_called = [0.0]

	def decorator(func):
	@wraps(func)
	def wrapper(args, *kwargs):
	elapsed = time.time() - last_called[0]
	left_to_wait = min_interval - elapsed
	if left_to_wait > 0:
	time.sleep(left_to_wait)
	result = func(args, *kwargs)
	last_called[0] = time.time()
	return result
	return wrapper
	return decorator

	@rate_limit(calls_per_minute=10)
	def call_api(query):
	# API call here
	pass
	```

	## Caching

	Cache API responses to reduce costs:

	```python
	from functools import lru_cache
	import hashlib

	@lru_cache(maxsize=1000)
	def cached_api_call(query):
	# API call here
	return result
	```

	## Best Practices

	1. API Keys: Never commit API keys to version control
	2. Error Handling: Always handle API errors gracefully
	3. Rate Limiting: Respect API rate limits
	4. Caching: Cache responses when possible
	5. Logging: Log all API calls for debugging
	6. Timeouts: Set appropriate timeouts for API calls
	7. Retries: Implement retry logic for failed requests

	## Troubleshooting

	### Issue: API Key Not Found

	Solution: Ensure `.env` file exists and contains the required keys

	### Issue: Connection Timeout

	Solution: Check network connectivity and increase timeout values

	### Issue: Rate Limit Exceeded

	Solution: Implement rate limiting and backoff strategies

	## Documentation

	For detailed integration documentation, see:

	- [Gemini API Docs](https://ai.google.dev/docs)
	- [OpenAI API Docs](https://platform.openai.com/docs)
	- [Anthropic API Docs](https://docs.anthropic.com)
	- [ChromaDB Docs](https://docs.trychroma.com)
	- [Neo4j Docs](https://neo4j.com/docs)

	---

	Note: Some integrations may require additional setup or paid subscriptions.