# Stop hallucinations with physics-based verification
curl -X POST   -H 'Content-Type: application/json'   -H 'Authorization: Bearer YOUR_API_KEY'   http://api.memories.dev/v1/fact-verify   -d '{"claim": "This embedding represents urban features accurately", "lat": 12.9716, "lon": 77.5946}'

# Response: 99.7% confidence vs 60% web search reliability

# Replace the entire web search stack with satellite truth
curl -X GET   -H 'Authorization: Bearer YOUR_API_KEY'   "http://api.memories.dev/v1/unified-truth?lat=12.9716&lon=77.5946&verify=true"

# Cryptographic proof included with every response

# Get available band presets and render modes
curl -X GET https://lbvm.vortx.ai/v2/earth-engine/presets

# Response includes true_color, false_color, vegetation_health, thermal_analysis presets

# True color satellite imagery (Sentinel-2 B4/B3/B2)
curl -X GET   "https://lbvm.vortx.ai/v2/earth-engine/embedding?lat=12.9716&lon=77.5946&band_type=optical&render_mode=true_color"

# False color infrared (Sentinel-2 B8/B4/B3) - reveals vegetation health
curl -X GET   "https://lbvm.vortx.ai/v2/earth-engine/embedding?lat=12.9716&lon=77.5946&red_band=B8&green_band=B4&blue_band=B3&render_mode=false_color"

# NDVI vegetation analysis - pure GPU acceleration
curl -X GET   "https://lbvm.vortx.ai/v2/earth-engine/embedding?lat=12.9716&lon=77.5946&band_type=ndvi&render_mode=enhanced"

Authorization: Bearer YOUR_API_KEY

# Python: AI agent for generating training datasets
from memories import MemoriesClient
import openai

client = MemoriesClient(api_key="your_key")

def genai_training_agent(location, model_type):
    # Get satellite-verified training context
    training_data = client.get_training_context(
        location=location,
        include_spectral_bands=True,
        include_physics_labels=True,
        include_embedding_validation=True
    )
    
    # Generate training dataset with physics supervision
    prompt = f"""
    Model Type: {model_type}
    
    PHYSICS-BASED TRAINING DATA:
    - Spectral Signatures: {training_data.spectral_data} (64-band LBVM)
    - Physics Labels: {training_data.physics_labels}
    - Embedding Validation: {training_data.embedding_scores}
    - Ground Truth Confidence: {training_data.confidence}% (vs 60% web labels)
    
    Generate synthetic training examples based on this physics-verified intelligence.
    """
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content

# Usage: Generate high-quality AI training data
dataset = genai_training_agent(
    "123 Training Location, Bangalore", 
    "computer-vision-foundation-model"
)

# JavaScript: AI travel planner with satellite context
const MemoriesClient = require('@memories/sdk');
const client = new MemoriesClient(process.env.MEMORIES_API_KEY);

async function aiTravelPlanner(destination, dates) {
  // Get real-time satellite intelligence
  const satelliteContext = await client.getLocationIntelligence({
    location: destination,
    dates: dates,
    include_weather_patterns: true,
    include_infrastructure: true,
    include_safety_metrics: true
  });
  
  // Stream into your LLM
  const travelPlan = await yourLLM.generatePlan({
    destination,
    dates,
    verifiedContext: satelliteContext, // Satellite-verified ground truth
    confidence: satelliteContext.confidence // Typically 99.7%
  });
  
  return {
    plan: travelPlan,
    satelliteProof: satelliteContext.blockchain_anchor,
    trustScore: satelliteContext.confidence
  };
}

# Python: RAG system with satellite-verified environmental data
from memories import MemoriesClient
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

client = MemoriesClient(api_key="your_key")

class SatelliteRAG:
    def __init__(self):
        self.embeddings = OpenAIEmbeddings()
        self.vectorstore = FAISS.from_texts([], self.embeddings)
    
    def ingest_satellite_intelligence(self, region):
        # Get 50+ years of verified satellite data
        historical_data = client.get_environmental_timeline(
            region=region,
            start_year=1970,  # Since 1970 in AI memory
            include_predictions=True  # 'What-if' futures
        )
        
        # Add to vector database with verification
        for data_point in historical_data:
            self.vectorstore.add_texts(
                [data_point.content],
                metadatas=[{
                    'confidence': data_point.confidence,
                    'satellite_proof': data_point.blockchain_anchor,
                    'date': data_point.timestamp
                }]
            )
    
    def query_with_satellite_truth(self, query):
        # Retrieve satellite-verified context
        docs = self.vectorstore.similarity_search(query, k=5)
        
        # Only use high-confidence satellite data (>95%)
        verified_docs = [d for d in docs 
                        if d.metadata['confidence'] > 0.95]
        
        return self.generate_response_with_proof(query, verified_docs)

# Python: Replace OpenAI's web search with satellite intelligence
pip install memories-sdk

# JavaScript/Node.js: Drop-in replacement for unreliable APIs
npm install @memories/sdk

# Build the most trusted AI system
from memories import MemoriesClient
client = MemoriesClient(api_key="your_key")
# 99.7% accuracy vs 60% web search reliability
intel = client.get_verified_context(lat=12.97, lon=77.59)

{
  "sentinel2_true_color": { "red": "B4", "green": "B3", "blue": "B2" },
  "sentinel2_false_color": { "red": "B8", "green": "B4", "blue": "B3" },
  "vegetation_health": { "red": "NDVI", "green": "EVI", "blue": "SAVI" },
  "thermal_analysis": { "red": "B10", "green": "B11", "blue": "B4" }
}