proxy and agent

本文探讨了Proxy和Agent的概念及其在客户服务中的应用。Proxy作为客户与服务实体间的桥梁,简化了客户发起请求的过程;Agent则代表服务实体处理来自客户的请求。

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Proxy  是客户联系到客服人员的某种具体途径。其实Proxy和Agent都有着"代理"的意思,在板书中我把Proxy画的偏向于Client和请求端,Agent在商业领域(也就是提供服务的实体)端,它们在某种程度上是有区别的。

Proxy的意图是存在于客户和服务实体之间,对客户(调用者)屏蔽细节,对底层调用能力。在这个case里,我们可以把电话机或Email做为一种Proxy,用户通过一个简单字符串(电话号码,Email)地址,以及易于上手的操作,就可以把他的信息(话音/文本)投递给客服中心,不管客服中心是在那里,哪个客服人员接受和处理的,也就是说,它通过字符串配置寻址(而不需要物理寻址),通过简单的,面向客户的接口(用户只管说电话或写Email)接受数据并进行协议转换,并调用底层(电信网,互联网)进行传输。

Proxy物理上至少部分存在客户端,只有胖客户端和瘦客户端之分,如发Email,装个Foxmail就是胖Proxy,登录mail.163.com/mail.qq.com就是瘦客户端,这没什么本质区别,总之Proxy代理了用户向Agent发出了请求,它是渠道,是工具,直接面向用户。

Agent相对Proxy来说,更有了实体气质。在英文里面,一个坐席员,一个投诉处理专员,一个销售人员,都可以称为Agent,他是代表一个大实体和客户打交道的小实体,做为一种实体,它是可以资源化的,数量有上限,有生命周期,比如每个公司的客服部每个班次只有那么多员工,这批员工根据不同的技能分组,不同组别的员工处理不同的业务投诉、咨询,受理,他们通过不同的渠道(用户通过Proxy投递信息的方式)如IVR,邮件,网站收到用户的信息,并开始(注意,是“开始”)处理。

转载于:https://www.cnblogs.com/Enjoymyprocess/archive/2010/04/11/1709724.html

### Llama Agent Framework or Proxy in IT Field In the context of artificial intelligence, particularly within AI-generated content (AIGC), frameworks and proxies related to models like LLaMA play a crucial role in enhancing functionality and accessibility[^1]. The term "Llama agent framework" refers to software systems designed to facilitate interactions with large language models such as LLaMA. These agents can perform tasks ranging from simple queries to complex dialog management. For developers looking to integrate LLaMA into applications, several libraries provide APIs that act as intermediaries between user requests and model responses. One popular choice is Hugging Face’s `transformers` library which supports fine-tuning pre-trained models including those similar to LLaMA. Below demonstrates how one might set up an environment using Python along with necessary packages: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = 'cuda' if torch.cuda.is_available() else 'cpu' model_name = "decapoda-research/llama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name).to(device) def generate_response(prompt): inputs = tokenizer(prompt, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_length=50) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response ``` This code snippet initializes a transformer-based causal language model named after LLaMA architecture on either CPU or GPU depending upon availability. It also defines a function called `generate_response`, allowing users to input prompts and receive generated text based on these inputs.
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