Gemini 2.5 Pro Multimodal Agent Architecture: Visual QA and Knowledge Graph Integration

class KnowledgeGraphStore:
    """Persistent knowledge graph for agent context management"""
    
    def __init__(self):
        self.nodes: Dict[str, KnowledgeGraphNode] = {}
        self.edges: List[KnowledgeGraphEdge] = []
        self.adjacency: Dict[str, List[str]] = {}
    
    def add_node(self, node: KnowledgeGraphNode) -> None:
        """Add a node to the knowledge graph"""
        self.nodes[node.id] = node
        if node.id not in self.adjacency:
            self.adjacency[node.id] = []
    
    def add_edge(self, edge: KnowledgeGraphEdge) -> None:
        """Connect two nodes with a relationship"""
        if edge.source not in self.nodes:
            raise ValueError(f"Source node {edge.source} not found")
        if edge.target not in self.nodes:
            raise ValueError(f"Target node {edge.target} not found")
        
        self.edges.append(edge)
        self.adjacency[edge.source].append(edge.target)
    
    def query_by_relation(self, node_id: str, relation: str) -> List[KnowledgeGraphNode]:
        """Find connected nodes through specific relationship"""
        related_ids = [
            e.target for e in self.edges 
            if e.source == node_id and e.relation == relation
        ]
        return [self.nodes[nid] for nid in related_ids if nid in self.nodes]
    
    def get_context_for_query(self, query: str, max_hops: int = 2) -> str:
        """Generate contextual prompt from knowledge graph"""
        context_parts = ["Available knowledge context:"]
        
        for node_id, node in list(self.nodes.items())[:20]:
            context_parts.append(f"- {node.label}: {node.properties}")
        
        return "\n".join(context_parts) if context_parts else "No prior context available"

class MultimodalAgent:
    """Production multimodal agent with visual QA and knowledge graph"""
    
    def __init__(self, api_key: str):
        self.client = HolySheepMultimodalClient(api_key)
        self.knowledge_graph = KnowledgeGraphStore()
        self.conversation_history: List[Dict] = []
    
    async def process_visual_query(
        self,
        image_data: bytes,
        query: str,
        entity_name: Optional[str] = None
    ) -> Dict[str, Any]:
        """Process visual question with knowledge graph integration"""
        
        # Step 1: Analyze image using Gemini 2.5 Pro
        visual_analysis = await self.client.analyze_image(
            image_data=image_data,
            prompt=f"""Analyze this image thoroughly. Extract:
            1. Key objects and their positions
            2. Text content (OCR)
            3. Scene context and setting
            4. Any specific entities visible
            
            Query: {query}"""
        )
        
        # Step 2: Retrieve relevant knowledge graph context
        graph_context = ""
        if entity_name:
            entity_node = self.knowledge_graph.nodes.get(entity_name)
            if entity_node:
                related_nodes = self.knowledge_graph.query_by_relation(
                    entity_name, 
                    "related_to"
                )
                graph_context = self.knowledge_graph.get_context_for_query(entity_name)
        
        # Step 3: Generate comprehensive response
        system_prompt = """You are an expert visual analysis assistant with access to a knowledge graph.
        Combine visual observations with existing knowledge to provide accurate, context-aware answers.
        When knowledge graph data is available, cross-reference it with visual evidence."""
        
        final_response = await self.client.generate_content(
            prompt=f"""Based on the visual analysis and knowledge context, answer the user's question.

Visual Analysis:
{visual_analysis}

Knowledge Graph Context:
{graph_context}

User Question: {query}

Provide a detailed, accurate response that synthesizes visual evidence with stored knowledge.""",
            system_instruction=system_prompt
        )
        
        # Step 4: Store extracted knowledge in graph
        extracted_entities = await self._extract_entities_from_response(final_response)
        for entity in extracted_entities:
            self.knowledge_graph.add_node(KnowledgeGraphNode(
                id=entity["id"],
                label=entity["label"],
                properties=entity.get("properties", {})
            ))
        
        return {
            "visual_analysis": visual_analysis,
            "graph_context": graph_context,
            "response": final_response["choices"][0]["message"]["content"],
            "entities_extracted": len(extracted_entities)
        }
    
    async def _extract_entities_from_response(self, response: Dict) -> List[Dict]:
        """Extract named entities from model response for graph storage"""
        content = response["choices"][0]["message"]["content"]
        
        extraction_prompt = await self.client.generate_content(
            prompt=f"""Extract all named entities (people, places, organizations, products) 
            from this text as JSON array with id, label, and properties:
            
            {content}"""
        )
        
        try:
            return json.loads(extraction_prompt["choices"][0]["message"]["content"])
        except json.JSONDecodeError:
            return []

# Migration Verification Script
Run this to validate your HolySheep AI credentials and connectivity

import asyncio
import httpx

async def verify_holy_sheep_connection(api_key: str) -> dict:
    """Verify HolySheep API connectivity and model availability"""
    
    base_url = "https://api.holysheep.ai/v1"
    
    async with httpx.AsyncClient(timeout=30.0) as client:
        # Test 1: Verify API key validity
        response = await client.get(
            f"{base_url}/models",
            headers={"Authorization": f"Bearer {api_key}"}
        )
        
        if response.status_code != 200:
            return {
                "status": "error",
                "message": f"Authentication failed: {response.status_code}",
                "details": response.text
            }
        
        available_models = response.json()
        
        # Test 2: Verify Gemini 2.5 Pro availability
        gemini_models = [
            m for m in available_models.get("data", []) 
            if "gemini" in m.get("id", "").lower()
        ]
        
        # Test 3: Measure latency
        latencies = []
        for _ in range(5):
            start = asyncio.get_event_loop().time()
            await client.post(
                f"{base_url}/chat/completions",
                headers={"Authorization": f"Bearer {api_key}"},
                json={
                    "model": "gemini-2.5-pro-preview-05-06",
                    "messages": [{"role": "user", "content": "Ping"}],
                    "max_tokens": 10
                }
            )
            latencies.append((asyncio.get_event_loop().time() - start) * 1000)
        
        return {
            "status": "success",
            "api_valid": True,
            "gemini_models": gemini_models,
            "average_latency_ms": sum(latencies) / len(latencies),
            "all_latencies_ms": latencies
        }

Usage
if __name__ == "__main__":
    result = asyncio.run(verify_holy_sheep_connection("YOUR_HOLYSHEEP_API_KEY"))
    print(json.dumps(result, indent=2))

# Emergency Rollback Configuration
Execute this if migration causes production issues

ROLLBACK_CONFIG = {
    "primary_provider": "holy_sheep",  # Current production
    "fallback_provider": "official_gemini",  # Rollback target
    "fallback_endpoint": "https://generativelanguage.googleapis.com/v1beta",
    "health_check_timeout": 10,
    "traffic_restore_threshold": 0.99,  # 99% health score required
    "auto_rollback_enabled": True,
    "rollback_triggers": [
        "error_rate_above_5_percent",
        "p99_latency_above_2000ms",
        "authentication_errors_above_1_percent"
    ]
}

def execute_rollback():
    """Emergency rollback to official Gemini API"""
    import os
    os.environ["AI_PROVIDER"] = "official_gemini"
    os.environ["API_BASE_URL"] = ROLLBACK_CONFIG["fallback_endpoint"]
    # Notify operations team
    # Restore traffic routing
    # Log rollback event
    return {"status": "rollback_initiated", "target": "official_gemini"}

# INCORRECT (causes 401 error)
headers = {"Authorization": "YOUR_HOLYSHEEP_API_KEY"}  # Missing Bearer prefix

CORRECT (authenticates successfully)
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

Alternative: Use httpx auth parameter
from httpx import Auth
class BearerAuth(Auth):
    def __init__(self, token: str):
        self.token = token
    def auth_flow(self, request):
        request.headers["Authorization"] = f"Bearer {self.token}"
        yield request

Verify key format before making requests
def validate_api_key(key: str) -> bool:
    return len(key) == 32 and key.replace("-", "").isalnum()

# INCORRECT (causes MIME type error)
{
    "inline_data": {
        "mime_type": "image/png",  # Wrong type!
        "data": base64.b64encode(image_bytes).decode()
    }
}

CORRECT (auto-detect MIME type)
import imghdr

def detect_image_mime_type(image_bytes: bytes) -> str:
    mime_type = imghdr.what(None, h=image_bytes[:32])
    mime_map = {
        'jpeg': 'image/jpeg',
        'png': 'image/png',
        'gif': 'image/gif',
        'webp': 'image/webp'
    }
    return mime_map.get(mime_type, 'image/jpeg')  # Safe default

Properly formatted image content
{
    "inline_data": {
        "mime_type": detect_image_mime_type(image_bytes),
        "data": base64.b64encode(image_bytes).decode('utf-8')
    }
}

# INCORRECT (crashes on malformed JSON)
content = response["choices"][0]["message"]["content"]
data = json.loads(content)  # JSONDecodeError if markdown present

CORRECT (robust parsing with multiple fallbacks)
import re

def extract_json_from_response(response_text: str) -> dict:
    """Extract and parse JSON from model response with markdown support"""
    
    # Try direct parsing first
    try:
        return json.loads(response_text)
    except json.JSONDecodeError:
        pass
    
    # Try extracting from code blocks
    json_patterns = [
        r'``json\s*(\{.*?\})\s*``',  # Markdown JSON blocks
        r'``\s*(\{.*?\})\s*``',      # Generic code blocks
        r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}',  # Nested JSON (limited)
    ]
    
    for pattern in json_patterns:
        matches = re.findall(pattern, response_text, re.DOTALL)
        for match in matches:
            try:
                return json.loads(match)
            except json.JSONDecodeError:
                continue
    
    # Fallback: Return raw text wrapped in dict
    return {"raw_response": response_text, "parse_error": True}

Safe response handling
content = response["choices"][0]["message"]["content"]
data = extract_json_from_response(content)