chore: import upstream snapshot with attribution

examples/models/oci_models.py

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+"""
+Oracle Cloud Infrastructure (OCI) Raw API Example
+
+This example demonstrates how to use OCI's Generative AI service with browser-use
+using the raw API integration (ChatOCIRaw) without Langchain dependencies.
+
+@dev You need to:
+1. Set up OCI configuration file at ~/.oci/config
+2. Have access to OCI Generative AI models in your tenancy
+3. Install the OCI Python SDK: uv add oci
+
+Requirements:
+- OCI account with Generative AI service access
+- Proper OCI configuration and authentication
+- Model deployment in your OCI compartment
+"""
+
+import asyncio
+import os
+import sys
+
+from pydantic import BaseModel
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+from browser_use import Agent
+from browser_use.llm import ChatOCIRaw
+
+
+class SearchSummary(BaseModel):
+	query: str
+	results_found: int
+	top_result_title: str
+	summary: str
+	relevance_score: float
+
+
+# Configuration examples for different providers
+compartment_id = 'ocid1.tenancy.oc1..aaaaaaaayeiis5uk2nuubznrekd6xsm56k3m4i7tyvkxmr2ftojqfkpx2ura'
+endpoint = 'https://inference.generativeai.us-chicago-1.oci.oraclecloud.com'
+
+# Example 1: Meta Llama model (uses GenericChatRequest)
+meta_model_id = 'ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyarojgfh6msa452vziycwfymle5gxdvpwwxzara53topmq'
+
+
+meta_llm = ChatOCIRaw(
+	model_id=meta_model_id,
+	service_endpoint=endpoint,
+	compartment_id=compartment_id,
+	provider='meta',  # Meta Llama model
+	temperature=0.7,
+	max_tokens=800,
+	frequency_penalty=0.0,
+	presence_penalty=0.0,
+	top_p=0.9,
+	auth_type='API_KEY',
+	auth_profile='DEFAULT',
+)
+cohere_model_id = 'ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyanrlpnq5ybfu5hnzarg7jomak3q6kyhkzjsl4qj24fyoq'
+
+# Example 2: Cohere model (uses CohereChatRequest)
+# cohere_model_id = "ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceyapnibwg42qjhwaxrlqfpreueirtwghiwvv2whsnwmnlva"
+cohere_llm = ChatOCIRaw(
+	model_id=cohere_model_id,
+	service_endpoint=endpoint,
+	compartment_id=compartment_id,
+	provider='cohere',  # Cohere model
+	temperature=1.0,
+	max_tokens=600,
+	frequency_penalty=0.0,
+	top_p=0.75,
+	top_k=0,  # Cohere-specific parameter
+	auth_type='API_KEY',
+	auth_profile='DEFAULT',
+)
+
+# Example 3: xAI model (uses GenericChatRequest)
+xai_model_id = 'ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya3bsfz4ogiuv3yc7gcnlry7gi3zzx6tnikg6jltqszm2q'
+xai_llm = ChatOCIRaw(
+	model_id=xai_model_id,
+	service_endpoint=endpoint,
+	compartment_id=compartment_id,
+	provider='xai',  # xAI model
+	temperature=1.0,
+	max_tokens=20000,
+	top_p=1.0,
+	top_k=0,
+	auth_type='API_KEY',
+	auth_profile='DEFAULT',
+)
+
+# Use Meta model by default for this example
+llm = xai_llm
+
+
+async def basic_example():
+	"""Basic example using ChatOCIRaw with a simple task."""
+	print('🔹 Basic ChatOCIRaw Example')
+	print('=' * 40)
+
+	print(f'Model: {llm.name}')
+	print(f'Provider: {llm.provider_name}')
+
+	# Create agent with a simple task
+	agent = Agent(
+		task="Go to google.com and search for 'Oracle Cloud Infrastructure pricing'",
+		llm=llm,
+	)
+
+	print("Task: Go to google.com and search for 'Oracle Cloud Infrastructure pricing'")
+
+	# Run the agent
+	try:
+		result = await agent.run(max_steps=5)
+		print('✅ Task completed successfully!')
+		print(f'Final result: {result}')
+	except Exception as e:
+		print(f'❌ Error: {e}')
+
+
+async def structured_output_example():
+	"""Example demonstrating structured output with Pydantic models."""
+	print('\n🔹 Structured Output Example')
+	print('=' * 40)
+
+	# Create agent that will return structured data
+	agent = Agent(
+		task="""Go to github.com, search for 'browser automation python', 
+                find the most popular repository, and return structured information about it""",
+		llm=llm,
+		output_format=SearchSummary,  # This will enforce structured output
+	)
+
+	print('Task: Search GitHub for browser automation and return structured data')
+
+	try:
+		result = await agent.run(max_steps=5)
+
+		if isinstance(result, SearchSummary):
+			print('✅ Structured output received!')
+			print(f'Query: {result.query}')
+			print(f'Results Found: {result.results_found}')
+			print(f'Top Result: {result.top_result_title}')
+			print(f'Summary: {result.summary}')
+			print(f'Relevance Score: {result.relevance_score}')
+		else:
+			print(f'Result: {result}')
+
+	except Exception as e:
+		print(f'❌ Error: {e}')
+
+
+async def advanced_configuration_example():
+	"""Example showing advanced configuration options."""
+	print('\n🔹 Advanced Configuration Example')
+	print('=' * 40)
+
+	print(f'Model: {llm.name}')
+	print(f'Provider: {llm.provider_name}')
+	print('Configuration: Cohere model with instance principal auth')
+
+	# Create agent with a more complex task
+	agent = Agent(
+		task="""Navigate to stackoverflow.com, search for questions about 'python web scraping' and tap search help, 
+                analyze the top 3 questions, and provide a detailed summary of common challenges""",
+		llm=llm,
+	)
+
+	print('Task: Analyze StackOverflow questions about Python web scraping')
+
+	try:
+		result = await agent.run(max_steps=8)
+		print('✅ Advanced task completed!')
+		print(f'Analysis result: {result}')
+	except Exception as e:
+		print(f'❌ Error: {e}')
+
+
+async def provider_compatibility_test():
+	"""Test different provider formats to verify compatibility."""
+	print('\n🔹 Provider Compatibility Test')
+	print('=' * 40)
+
+	providers_to_test = [('Meta', meta_llm), ('Cohere', cohere_llm), ('xAI', xai_llm)]
+
+	for provider_name, model in providers_to_test:
+		print(f'\nTesting {provider_name} model...')
+		print(f'Model ID: {model.model_id}')
+		print(f'Provider: {model.provider}')
+		print(f'Uses Cohere format: {model._uses_cohere_format()}')
+
+		# Create a simple agent to test the model
+		agent = Agent(
+			task='Go to google.com and tell me what you see',
+			llm=model,
+		)
+
+		try:
+			result = await agent.run(max_steps=3)
+			print(f'✅ {provider_name} model works correctly!')
+			print(f'Result: {str(result)[:100]}...')
+		except Exception as e:
+			print(f'❌ {provider_name} model failed: {e}')
+
+
+async def main():
+	"""Run all OCI Raw examples."""
+	print('🚀 Oracle Cloud Infrastructure (OCI) Raw API Examples')
+	print('=' * 60)
+
+	print('\n📋 Prerequisites:')
+	print('1. OCI account with Generative AI service access')
+	print('2. OCI configuration file at ~/.oci/config')
+	print('3. Model deployed in your OCI compartment')
+	print('4. Proper IAM permissions for Generative AI')
+	print('5. OCI Python SDK installed: uv add oci')
+	print('=' * 60)
+
+	print('\n⚙️ Configuration Notes:')
+	print('• Update model_id, service_endpoint, and compartment_id with your values')
+	print('• Supported providers: "meta", "cohere", "xai"')
+	print('• Auth types: "API_KEY", "INSTANCE_PRINCIPAL", "RESOURCE_PRINCIPAL"')
+	print('• Default OCI config profile: "DEFAULT"')
+	print('=' * 60)
+
+	print('\n🔧 Provider-Specific API Formats:')
+	print('• Meta/xAI models: Use GenericChatRequest with messages array')
+	print('• Cohere models: Use CohereChatRequest with single message string')
+	print('• The integration automatically detects and uses the correct format')
+	print('=' * 60)
+
+	try:
+		# Run all examples
+		await basic_example()
+		await structured_output_example()
+		await advanced_configuration_example()
+		# await provider_compatibility_test()
+
+		print('\n🎉 All examples completed successfully!')
+
+	except Exception as e:
+		print(f'\n❌ Example failed: {e}')
+		print('\n🔧 Troubleshooting:')
+		print('• Verify OCI configuration: oci setup config')
+		print('• Check model OCID and availability')
+		print('• Ensure compartment access and IAM permissions')
+		print('• Verify service endpoint URL')
+		print('• Check OCI Python SDK installation')
+		print("• Ensure you're using the correct provider name in ChatOCIRaw")
+
+
+if __name__ == '__main__':
+	asyncio.run(main())