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docs: update Google GenAI SDK examples to use new client API #147

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252 changes: 160 additions & 92 deletions docs/usage/http-transport/integrations/genai-compatible.md
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Original file line number Diff line number Diff line change
Expand Up @@ -27,24 +27,23 @@ Bifrost provides **100% Google GenAI API compatibility** with enhanced features:
### **Python (Google GenAI SDK)**

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions

# Before - Direct Google GenAI
genai.configure(
api_key="your-google-api-key",
transport="rest"
)
client = genai.Client(api_key="your-google-api-key")

# After - Via Bifrost
genai.configure(
client = genai.Client(
api_key="your-google-api-key",
transport="rest",
client_options={"api_endpoint": "http://localhost:8080/genai"} # Only change this
http_options=HttpOptions(base_url="http://localhost:8080/genai") # Only change this
)

# Everything else stays the same
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Hello!")
response = client.models.generate_content(
model="gemini-pro",
contents="Hello!"
)
```

### **JavaScript (Google GenAI SDK)**
Expand Down Expand Up @@ -249,89 +248,93 @@ curl -X POST http://localhost:8080/genai/v1beta/models/gemini-pro:generateConten
### **System Instructions**

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions, GenerateContentConfig

genai.configure(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
client = genai.Client(
api_key="your-google-api-key",
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

model = genai.GenerativeModel(
'gemini-pro',
system_instruction="You are a helpful assistant that answers questions about geography."
response = client.models.generate_content(
model="gemini-pro",
contents="What is the capital of France?",
config=GenerateContentConfig(
system_instruction="You are a helpful assistant that answers questions about geography."
)
)

response = model.generate_content("What is the capital of France?")
```

### **Generation Configuration**

```python
import google.generativeai as genai

genai.configure(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
)
from google import genai
from google.genai.types import HttpOptions, GenerateContentConfig

generation_config = genai.types.GenerationConfig(
candidate_count=1,
max_output_tokens=1000,
temperature=0.7,
top_p=0.8,
top_k=40,
stop_sequences=["END"]
client = genai.Client(
api_key="your-google-api-key",
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(
"Tell me a story",
generation_config=generation_config
response = client.models.generate_content(
model="gemini-pro",
contents="Tell me a story",
config=GenerateContentConfig(
candidate_count=1,
max_output_tokens=1000,
temperature=0.7,
top_p=0.8,
top_k=40,
stop_sequences=["END"]
)
)
```

### **Safety Settings**

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions, GenerateContentConfig, SafetySetting

genai.configure(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
client = genai.Client(
api_key="your-google-api-key",
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

safety_settings = [
{
"category": "HARM_CATEGORY_HARASSMENT",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
},
{
"category": "HARM_CATEGORY_HATE_SPEECH",
"threshold": "BLOCK_MEDIUM_AND_ABOVE"
}
SafetySetting(
category="HARM_CATEGORY_HARASSMENT",
threshold="BLOCK_MEDIUM_AND_ABOVE"
),
SafetySetting(
category="HARM_CATEGORY_HATE_SPEECH",
threshold="BLOCK_MEDIUM_AND_ABOVE"
)
]

model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(
"Your content here",
safety_settings=safety_settings
response = client.models.generate_content(
model="gemini-pro",
contents="Your content here",
config=GenerateContentConfig(safety_settings=safety_settings)
)
```

### **Error Handling**

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions
from google.api_core import exceptions

genai.configure(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
)

try:
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content("Hello!")
client = genai.Client(
api_key=google_api_key,
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)
response = client.models.generate_content(
model="gemini-pro",
contents="Hello!"
)
except exceptions.InvalidArgument as e:
print(f"Invalid argument: {e}")
except exceptions.PermissionDenied as e:
Expand All @@ -349,23 +352,73 @@ except Exception as e:
MCP tools are automatically available in GenAI-compatible requests:

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions, Tool, FunctionDeclaration, Schema

genai.configure(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
client = genai.Client(
api_key="your-google-api-key",
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

# No tool definitions needed - MCP tools auto-discovered
model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(
"List the files in the current directory and tell me about the project structure"
# Define tools if needed (or use auto-discovered MCP tools)
tools = [
Tool(function_declarations=[
FunctionDeclaration(
name="list_files",
description="List files in a directory",
parameters=Schema(
type="OBJECT",
properties={
"path": Schema(type="STRING", description="Directory path")
},
required=["path"]
)
)
])
]

response = client.models.generate_content(
model="gemini-pro",
contents="List the files in the current directory and tell me about the project structure",
config=GenerateContentConfig(tools=tools)
)

# Response may include automatic function calls
# Check for function calls in response
if response.candidates[0].content.parts[0].function_call:
function_call = response.candidates[0].content.parts[0].function_call
print(f"Called MCP tool: {function_call.name}")
print(f"Called tool: {function_call.name}")
```

### **Multi-provider Support**

Use multiple providers with Google GenAI SDK format by prefixing model names:

```python
from google import genai
from google.genai.types import HttpOptions

client = genai.Client(
api_key="dummy", # API keys configured in Bifrost
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

# Google models (default)
response1 = client.models.generate_content(
model="gemini-pro",
contents="Hello!"
)

# OpenAI models via GenAI SDK
response2 = client.models.generate_content(
model="openai/gpt-4o-mini",
contents="Hello!"
)

# Anthropic models via GenAI SDK
response3 = client.models.generate_content(
model="anthropic/claude-3-sonnet-20240229",
contents="Hello!"
)
```

### **Multi-provider Fallbacks**
Expand Down Expand Up @@ -428,28 +481,32 @@ Multiple Google Cloud projects automatically load balanced:
Test your existing Google GenAI code with Bifrost:

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions

def test_bifrost_compatibility():
# Test with Bifrost
genai.configure(
bifrost_client = genai.Client(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)
bifrost_model = genai.GenerativeModel('gemini-pro')

# Test with direct Google GenAI (for comparison)
genai.configure(
api_key=google_api_key,
client_options={} # Reset to default
google_client = genai.Client(
api_key=google_api_key
)
google_model = genai.GenerativeModel('gemini-pro')

test_prompt = "Hello, test!"

# Both should work identically
bifrost_response = bifrost_model.generate_content(test_prompt)
google_response = google_model.generate_content(test_prompt)
bifrost_response = bifrost_client.models.generate_content(
model="gemini-pro",
contents=test_prompt
)
google_response = google_client.models.generate_content(
model="gemini-pro",
contents=test_prompt
)

# Compare response structure
assert bifrost_response.candidates[0].content.parts[0].text is not None
Expand All @@ -463,23 +520,34 @@ test_bifrost_compatibility()
### **Function Calling Testing**

```python
import google.generativeai as genai
from google import genai
from google.genai.types import HttpOptions, Tool, FunctionDeclaration, Schema

def test_function_calling():
genai.configure(
client = genai.Client(
api_key=google_api_key,
client_options={"api_endpoint": "http://localhost:8080/genai"}
http_options=HttpOptions(base_url="http://localhost:8080/genai")
)

# Define a test function
def get_time():
"""Get current time"""
return "2024-01-01 12:00:00"
# Define a test tool
tools = [
Tool(function_declarations=[
FunctionDeclaration(
name="get_time",
description="Get current time",
parameters=Schema(
type="OBJECT",
properties={},
required=[]
)
)
])
]

model = genai.GenerativeModel('gemini-pro')
response = model.generate_content(
"What time is it?",
tools=[get_time]
response = client.models.generate_content(
model="gemini-pro",
contents="What time is it?",
config=GenerateContentConfig(tools=tools)
)

# Should include function call
Expand All @@ -498,7 +566,7 @@ test_function_calling()
Use multiple providers with Google GenAI SDK format by prefixing model names:

```python
import google.generativeai as genai
from google import genai

genai.configure(
api_key="dummy", # API keys configured in Bifrost
Expand Down