SambaNova Cloud와 Weave로 확장 가능한 리서치 에이전트 구축 방법

gh repo clone wandb/sambanova-webinar

from openai import OpenAI

client = OpenAI(
    base_url="https://api.sambanova.ai/v1", 
    api_key="YOUR SAMBANOVA CLOUD API KEY"
)

import weave
@weave.op(name='initialize_agent')
async def initialize_agent_runtime(
    redis_client: SecureRedisService,
    api_keys: APIKeys,
    user_id: str,
    conversation_id: str,
    websocket_manager: WebSocketInterface
) -> SingleThreadedAgentRuntime:
    """
    Initializes the agent runtime with the required agents and tools.

    Returns:
        SingleThreadedAgentRuntime: The initialized runtime for managing agents.
    """

import weave
class CustomLLM(LLM):
    @weave.op()
    def call(
        self,
        messages: Union[str, List[Dict[str, str]]],
        tools: Optional[List[dict]] = None,
        callbacks: Optional[List[Any]] = None,
        available_functions: Optional[Dict[str, Any]] = None,
    ) -> str:
        """
        High-level llm call method that:
        1) Accepts either a string or a list of messages
        2) Converts string input to the required message format
        3) Calls litellm.completion
        4) Handles function/tool calls if any
        5) Returns the final text response or tool result
        """