Gemini

# Recommended: Install with Google AI integration
pip install "honeyhive[openinference-google-ai]>=1.0.0rc0"

# Alternative: Manual installation
pip install "honeyhive>=1.0.0rc0" openinference-instrumentation-google-generativeai google-generativeai>=0.3.0

from honeyhive import HoneyHiveTracer
from openinference.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor
import google.generativeai as genai
import os

# Step 1: Initialize HoneyHive tracer first
tracer = HoneyHiveTracer.init(
    project="your-project"  # Or set HH_PROJECT environment variable
)  # Uses HH_API_KEY from environment

# Step 2: Initialize instrumentor with tracer_provider
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

# Configure Google AI
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Now all calls are automatically traced!
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Hello!")
print(response.text)
# ✨ Automatically traced!

from google import genai
from honeyhive import HoneyHiveTracer, trace
import os

# Initialize tracer
HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    project="my-project"
)

@trace
def generate_response(query: str) -> str:
    """Generate response using Gemini."""
    client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
    
    response = client.models.generate_content(
        model="gemini-2.0-flash",
        contents=query
    )
    
    return response.text

# All calls within @trace are captured
result = generate_response("The opposite of hot is")
print(result)

# Recommended: Install with Traceloop Google AI integration
pip install "honeyhive[traceloop-google-ai]>=1.0.0rc0"

# Alternative: Manual installation
pip install "honeyhive>=1.0.0rc0" opentelemetry-instrumentation-google-generativeai google-generativeai>=0.3.0

from honeyhive import HoneyHiveTracer
from opentelemetry.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor
import google.generativeai as genai
import os

# Step 1: Initialize HoneyHive tracer first
tracer = HoneyHiveTracer.init(project="your-project")

# Step 2: Initialize Traceloop instrumentor with tracer_provider
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

# Configure Google AI
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# All calls traced with enhanced metrics!
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Hello!")

from honeyhive import HoneyHiveTracer, trace, enrich_span
from openinference.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor
import google.generativeai as genai
import os

# Initialize tracer and instrumentor
tracer = HoneyHiveTracer.init(
    api_key=os.getenv("HH_API_KEY"),
    project="your-project",
    source="production"
)

instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

@trace
def analyze_with_gemini(content: str) -> dict:
    """Multi-model analysis with Gemini."""
    
    # Add business context
    enrich_span({
        "use_case": "content_analysis",
        "content_length": len(content)
    })
    
    # Quick analysis with Gemini Pro
    pro_model = genai.GenerativeModel('gemini-pro')
    summary = pro_model.generate_content(
        f"Summarize this in one sentence: {content}"
    )
    
    # Detailed analysis
    detailed = pro_model.generate_content(
        f"Provide detailed analysis: {content}"
    )
    
    enrich_span({
        "models_used": ["gemini-pro"],
        "status": "success"
    })
    
    return {
        "summary": summary.text,
        "analysis": detailed.text
    }

from honeyhive import HoneyHiveTracer, trace, enrich_span
from openinference.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor
import google.generativeai as genai
import PIL.Image
import os

tracer = HoneyHiveTracer.init(project="vision-demo")
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

@trace
def analyze_image(image_path: str, question: str) -> str:
    """Analyze image using Gemini Pro Vision."""
    
    enrich_span({
        "task": "image_analysis",
        "image_path": image_path
    })
    
    # Load image
    img = PIL.Image.open(image_path)
    
    # Use vision model
    model = genai.GenerativeModel('gemini-pro-vision')
    response = model.generate_content([question, img])
    
    enrich_span({
        "model": "gemini-pro-vision",
        "has_response": bool(response.text)
    })
    
    return response.text

# Example usage
result = analyze_image("photo.jpg", "What's in this image?")

from honeyhive import HoneyHiveTracer, trace
from openinference.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor
import google.generativeai as genai
import os

tracer = HoneyHiveTracer.init(project="function-calling-demo")
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Define tools
def get_weather(location: str) -> dict:
    """Get weather for a location."""
    # Simulated weather data
    return {"temp": "72°F", "conditions": "Sunny"}

weather_tool = genai.protos.Tool(
    function_declarations=[
        genai.protos.FunctionDeclaration(
            name="get_weather",
            description="Get weather for a location",
            parameters=genai.protos.Schema(
                type=genai.protos.Type.OBJECT,
                properties={
                    "location": genai.protos.Schema(
                        type=genai.protos.Type.STRING
                    )
                },
                required=["location"]
            )
        )
    ]
)

@trace
def weather_assistant(query: str) -> str:
    """Assistant with weather tool."""
    model = genai.GenerativeModel(
        'gemini-pro',
        tools=[weather_tool]
    )
    
    chat = model.start_chat()
    response = chat.send_message(query)
    
    # Handle function calls
    if response.candidates[0].content.parts[0].function_call:
        fc = response.candidates[0].content.parts[0].function_call
        if fc.name == "get_weather":
            weather = get_weather(fc.args["location"])
            response = chat.send_message(
                genai.protos.Content(
                    parts=[genai.protos.Part(
                        function_response=genai.protos.FunctionResponse(
                            name="get_weather",
                            response=weather
                        )
                    )]
                )
            )
    
    return response.text

result = weather_assistant("What's the weather in Paris?")

# HoneyHive configuration
export HH_API_KEY="your-honeyhive-api-key"
export HH_PROJECT="your-project"
export HH_SOURCE="production"

# Google AI configuration
export GOOGLE_API_KEY="your-google-ai-api-key"
# Or for newer SDK
export GEMINI_API_KEY="your-gemini-api-key"

# ✅ Correct
tracer = HoneyHiveTracer.init(project="my-project")
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument(tracer_provider=tracer.provider)

# ❌ Wrong - instrumentor before tracer
instrumentor = GoogleGenerativeAIInstrumentor()
instrumentor.instrument()  # No tracer_provider!
tracer = HoneyHiveTracer.init(project="my-project")

# For instrumentors, use google-generativeai
pip install google-generativeai>=0.3.0

# For manual tracing with @trace, you can use google-genai
pip install google-genai>=1.13.0

# For OpenInference
pip install "honeyhive[openinference-google-ai]>=1.0.0rc0"

# For Traceloop
pip install "honeyhive[traceloop-google-ai]>=1.0.0rc0"

# Before (OpenInference)
from openinference.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor

# After (Traceloop) - just change the import
from opentelemetry.instrumentation.google_generativeai import GoogleGenerativeAIInstrumentor

# Rest of the code stays the same!