from llama_index.llms.cometapi import CometLLMimport osos.environ["COMETAPI_KEY"] = "<COMETAPI_KEY>"api_key = os.getenv("COMETAPI_KEY")
使用环境变量比在脚本中硬编码凭证更安全。
3
初始化模型并发起补全调用
from llama_index.core.llms import ChatMessagellm = CometLLM( api_key=api_key, max_tokens=256, context_window=4096, model="your-model-id",)# Chat callmessages = [ ChatMessage(role="system", content="You are a helpful assistant"), ChatMessage(role="user", content="Say 'Hi' only!"),]resp = llm.chat(messages)print(resp)# Completion callresp = llm.complete("Who is Kaiming He?")print(resp)
4
启用流式输出
使用 stream_chat 或 stream_complete 获取实时分块输出:
# Streaming chatmessage = ChatMessage(role="user", content="Tell me what ResNet is")for chunk in llm.stream_chat([message]): print(chunk.delta, end="")# Streaming completionfor chunk in llm.stream_complete("Tell me about Large Language Models"): print(chunk.delta, end="")