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LlamaEdge

LlamaEdge 允许您通过本地或聊天服务与 GGUF 格式的大型语言模型进行聊天。

  • LlamaEdgeChatService 为开发者提供了一个与 OpenAI API 兼容的服务，可通过 HTTP 请求与大型语言模型进行交互。

  • LlamaEdgeChatLocal 使开发人员能够本地与大型语言模型聊天（即将推出）。

LlamaEdgeChatService 和 LlamaEdgeChatLocal 都运行在由 WasmEdge Runtime 驱动的基础设施上，它为大型语言模型推理任务提供了一个轻量级且可移植的 WebAssembly 容器环境。

通过API服务聊天​

LlamaEdgeChatService 适用于 llama-api-server。按照 llama-api-server 快速入门 中的步骤，您可以托管自己的 API 服务，从而可以在任何有网络的设备上与任何您喜欢的模型进行聊天。

from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
from langchain_core.messages import HumanMessage, SystemMessage

在非流模式下与大型语言模型聊天​

# service url
service_url = "https://b008-54-186-154-209.ngrok-free.app"

# create wasm-chat service instance
chat = LlamaEdgeChatService(service_url=service_url)

# create message sequence
system_message = SystemMessage(content="You are an AI assistant")
user_message = HumanMessage(content="What is the capital of France?")
messages = [system_message, user_message]

# chat with wasm-chat service
response = chat.invoke(messages)

print(f"[Bot] {response.content}")
[Bot] Hello! The capital of France is Paris.

以流模式与大型语言模型聊天​

# service url
service_url = "https://b008-54-186-154-209.ngrok-free.app"

# create wasm-chat service instance
chat = LlamaEdgeChatService(service_url=service_url, streaming=True)

# create message sequence
system_message = SystemMessage(content="You are an AI assistant")
user_message = HumanMessage(content="What is the capital of Norway?")
messages = [
    system_message,
    user_message,
]

output = ""
for chunk in chat.stream(messages):
    # print(chunk.content, end="", flush=True)
    output += chunk.content

print(f"[Bot] {output}")
[Bot]   Hello! I'm happy to help you with your question. The capital of Norway is Oslo.