import{s as es,n as ts,o as ns}from"../chunks/scheduler.65852ee5.js";import{S as Ms,i as as,g as a,s as n,r as o,A as ps,h as p,f as e,c as M,j as ls,u as c,x as y,k as ss,y as ys,a as t,v as i,d as j,t as U,w as J}from"../chunks/index.aa74147d.js";import{D as os,C as q}from"../chunks/DocNotebookDropdown.479f4286.js";import{H as P,E as cs}from"../chunks/EditOnGithub.4eda6a96.js";function is(Gl){let T,O,D,ll,w,sl,r,el,m,Wl='<em>Authored by: <a href="https://huggingface.co/m-ric" rel="nofollow">Aymeric Roucher</a></em>',tl,u,zl='<p>This tutorial builds upon agent knowledge: to know more about agents, you can start with <a href="agents">this introductory notebook</a></p>',nl,I,vl='<a href="https://huggingface.co/docs/transformers/en/agents" rel="nofollow">Transformers Agents</a> is a library to build agents, using an LLM to power it in the <code>llm_engine</code> argument. This argument was designed to leave the user maximal freedom to choose any LLM.',Ml,d,Xl="Let’s see how to build this <code>llm_engine</code> from the APIs of a few leading providers.",al,C,pl,h,Ql="Transformers agents provides a built-in <code>HfEngine</code> class that lets you use any model on the Hub via the Serverless API or your own dedicated Endpoint. This is the preferred way to use HF agents.",yl,b,ol,A,cl,f,il,g,Sl=`['unicodedata', 're', 'math', 'collections', 'queue', 'itertools', 'random', 'time', 'stat', 'statistics']
`,jl,Z,$l="The <code>llm_engine</code> initialization arg of the agent could be a simple callable such as:",Ul,_,Jl,B,Rl="This callable is the heart of the llm engine. It should respect these requirements:",Tl,N,Yl='<li>takes as input a list of messages in <a href="https://huggingface.co/docs/transformers/main/en/chat_templating" rel="nofollow">chat template</a> format and outputs a <code>str</code>.</li> <li>accepts a <code>stop_sequences</code> argument where the agent system will pass it sequences where it should stop generation.</li>',wl,E,Hl="Let’s take a closer look at the code for the <code>HfEngine</code> that we used:",rl,V,ml,k,xl="Here the engine is not a function, but a class with a <code>__call__</code> method, which adds the possibility to store attributes such as the client.",ul,G,Ll=`We also use <code>get_clean_message_list()</code> utility to concatenate successive messages to the same role
This method takes a <code>role_conversions</code> arg to convert the range of roles supported in Transformers Agents to only the ones accepted by your LLM.`,Il,W,Fl="This recipe can be adapted for any LLM! Let’s look at other examples.",dl,z,Cl,v,ql=`Using the above recipe, you can use any LLM inference source as your <code>llm_engine</code>.
Just keep in mind the two main constraints:`,hl,X,Pl='<li><code>llm_engine</code> is a callable that takes as input a list of messages in <a href="https://huggingface.co/docs/transformers/main/en/chat_templating" rel="nofollow">chat template</a> format and outputs a <code>str</code>.</li> <li>It accepts a <code>stop_sequences</code> argument.</li>',bl,Q,Al,S,fl,$,gl,R,Zl,Y,_l,H,Dl="Go on and implement your <code>llm_engine</code> for <code>transformers.agents</code> with your own LLM inference provider!",Bl,x,Kl="Then to use this shiny new <code>llm_engine</code>, check out these use cases:",Nl,L,Ol='<li><a href="agent_rag">Agentic RAG: turbocharge your RAG with query reformulation and self-query</a></li> <li><a href="agent_text_to_sql">Agent for text-to-SQL with automatic error correction</a></li>',El,F,Vl,K,kl;return w=new os({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Google Colab",value:"https://colab.research.google.com/github/huggingface/cookbook/blob/milvus_refinements/notebooks/en/agent_change_llm.ipynb"}]}}),r=new P({props:{title:"Create a Transformers Agent from any LLM inference provider",local:"create-a-transformers-agent-from-any-llm-inference-provider",headingTag:"h1"}}),C=new P({props:{title:"HuggingFace Serverless API and Dedicated Endpoints",local:"huggingface-serverless-api-and-dedicated-endpoints",headingTag:"h2"}}),b=new q({props:{code:"IXBpcCUyMGluc3RhbGwlMjAtcSUyMG9wZW5haSUyMGFudGhyb3BpYw==",highlighted:"!pip install -q openai anthropic",wrap:!1}}),A=new q({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`,wrap:!1}}),f=new q({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.agents <span class="hljs-keyword">import</span> HfEngine, ReactCodeAgent

<span class="hljs-meta">&gt;&gt;&gt; </span>repo_id = <span class="hljs-string">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>endpoint_url = <span class="hljs-string">&quot;your_endpoint_url&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>llm_engine = HfEngine(model=repo_id)  <span class="hljs-comment"># you could use model=endpoint_url here</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>agent = ReactCodeAgent(tools=[], llm_engine=llm_engine)

<span class="hljs-meta">&gt;&gt;&gt; </span>agent.run(<span class="hljs-string">&quot;What&#x27;s the 10th Fibonacci number?&quot;</span>)`,wrap:!1}}),_=new q({props:{code:"ZGVmJTIwbGxtX2VuZ2luZShtZXNzYWdlcyUyQyUyMHN0b3Bfc2VxdWVuY2VzJTNEJTVCJTVEKSUyMC0lM0UlMjBzdHIlM0ElMEElMjAlMjAlMjAlMjByZXR1cm4lMjByZXNwb25zZShtZXNzYWdlcyk=",highlighted:`<span class="hljs-keyword">def</span> <span class="hljs-title function_">llm_engine</span>(<span class="hljs-params">messages, stop_sequences=[]</span>) -&gt; <span class="hljs-built_in">str</span>:
    <span class="hljs-keyword">return</span> response(messages)`,wrap:!1}}),V=new q({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> typing <span class="hljs-keyword">import</span> <span class="hljs-type">List</span>, <span class="hljs-type">Dict</span>
<span class="hljs-keyword">from</span> transformers.agents.llm_engine <span class="hljs-keyword">import</span> MessageRole, get_clean_message_list
<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> InferenceClient

llama_role_conversions = {
    MessageRole.TOOL_RESPONSE: MessageRole.USER,
}


<span class="hljs-keyword">class</span> <span class="hljs-title class_">HfEngine</span>:
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, model: <span class="hljs-built_in">str</span> = <span class="hljs-string">&quot;meta-llama/Meta-Llama-3-8B-Instruct&quot;</span></span>):
        self.model = model
        self.client = InferenceClient(model=self.model, timeout=<span class="hljs-number">120</span>)

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, messages: <span class="hljs-type">List</span>[<span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, <span class="hljs-built_in">str</span>]], stop_sequences=[]</span>) -&gt; <span class="hljs-built_in">str</span>:
        <span class="hljs-comment"># Get clean message list</span>
        messages = get_clean_message_list(messages, role_conversions=llama_role_conversions)

        <span class="hljs-comment"># Get LLM output</span>
        response = self.client.chat_completion(messages, stop=stop_sequences, max_tokens=<span class="hljs-number">1500</span>)
        response = response.choices[<span class="hljs-number">0</span>].message.content

        <span class="hljs-comment"># Remove stop sequences from LLM output</span>
        <span class="hljs-keyword">for</span> stop_seq <span class="hljs-keyword">in</span> stop_sequences:
            <span class="hljs-keyword">if</span> response[-<span class="hljs-built_in">len</span>(stop_seq) :] == stop_seq:
                response = response[: -<span class="hljs-built_in">len</span>(stop_seq)]
        <span class="hljs-keyword">return</span> response`,wrap:!1}}),z=new P({props:{title:"Adapting the recipe for any LLM",local:"adapting-the-recipe-for-any-llm",headingTag:"h2"}}),Q=new P({props:{title:"OpenAI",local:"openai",headingTag:"h3"}}),S=new q({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> os
<span class="hljs-keyword">from</span> openai <span class="hljs-keyword">import</span> OpenAI

openai_role_conversions = {
    MessageRole.TOOL_RESPONSE: MessageRole.USER,
}


<span class="hljs-keyword">class</span> <span class="hljs-title class_">OpenAIEngine</span>:
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, model_name=<span class="hljs-string">&quot;gpt-4o&quot;</span></span>):
        self.model_name = model_name
        self.client = OpenAI(
            api_key=os.getenv(<span class="hljs-string">&quot;OPENAI_API_KEY&quot;</span>),
        )

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, messages, stop_sequences=[]</span>):
        messages = get_clean_message_list(messages, role_conversions=openai_role_conversions)

        response = self.client.chat.completions.create(
            model=self.model_name,
            messages=messages,
            stop=stop_sequences,
            temperature=<span class="hljs-number">0.5</span>,
        )
        <span class="hljs-keyword">return</span> response.choices[<span class="hljs-number">0</span>].message.content`,wrap:!1}}),$=new P({props:{title:"Anthropic",local:"anthropic",headingTag:"h3"}}),R=new q({props:{code:" ",highlighted:`<span class="hljs-keyword">from</span> anthropic <span class="hljs-keyword">import</span> Anthropic, AnthropicBedrock


<span class="hljs-comment"># Cf this page for using Anthropic from Bedrock: https://docs.anthropic.com/en/api/claude-on-amazon-bedrock</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">AnthropicEngine</span>:
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, model_name=<span class="hljs-string">&quot;claude-3-5-sonnet-20240620&quot;</span>, use_bedrock=<span class="hljs-literal">False</span></span>):
        self.model_name = model_name
        <span class="hljs-keyword">if</span> use_bedrock:
            self.model_name = <span class="hljs-string">&quot;anthropic.claude-3-5-sonnet-20240620-v1:0&quot;</span>
            self.client = AnthropicBedrock(
                aws_access_key=os.getenv(<span class="hljs-string">&quot;AWS_BEDROCK_ID&quot;</span>),
                aws_secret_key=os.getenv(<span class="hljs-string">&quot;AWS_BEDROCK_KEY&quot;</span>),
                aws_region=<span class="hljs-string">&quot;us-east-1&quot;</span>,
            )
        <span class="hljs-keyword">else</span>:
            self.client = Anthropic(
                api_key=os.getenv(<span class="hljs-string">&quot;ANTHROPIC_API_KEY&quot;</span>),
            )

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, messages, stop_sequences=[]</span>):
        messages = get_clean_message_list(messages, role_conversions=openai_role_conversions)
        index_system_message, system_prompt = <span class="hljs-literal">None</span>, <span class="hljs-literal">None</span>
        <span class="hljs-keyword">for</span> index, message <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(messages):
            <span class="hljs-keyword">if</span> message[<span class="hljs-string">&quot;role&quot;</span>] == MessageRole.SYSTEM:
                index_system_message = index
                system_prompt = message[<span class="hljs-string">&quot;content&quot;</span>]
        <span class="hljs-keyword">if</span> system_prompt <span class="hljs-keyword">is</span> <span class="hljs-literal">None</span>:
            <span class="hljs-keyword">raise</span> Exception(<span class="hljs-string">&quot;No system prompt found!&quot;</span>)

        filtered_messages = [message <span class="hljs-keyword">for</span> i, message <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(messages) <span class="hljs-keyword">if</span> i != index_system_message]
        <span class="hljs-keyword">if</span> <span class="hljs-built_in">len</span>(filtered_messages) == <span class="hljs-number">0</span>:
            <span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Error, no user message:&quot;</span>, messages)
            <span class="hljs-keyword">assert</span> <span class="hljs-literal">False</span>

        response = self.client.messages.create(
            model=self.model_name,
            system=system_prompt,
            messages=filtered_messages,
            stop_sequences=stop_sequences,
            temperature=<span class="hljs-number">0.5</span>,
            max_tokens=<span class="hljs-number">2000</span>,
        )
        full_response_text = <span class="hljs-string">&quot;&quot;</span>
        <span class="hljs-keyword">for</span> content_block <span class="hljs-keyword">in</span> response.content:
            <span class="hljs-keyword">if</span> content_block.<span class="hljs-built_in">type</span> == <span class="hljs-string">&quot;text&quot;</span>:
                full_response_text += content_block.text
        <span class="hljs-keyword">return</span> full_response_text`,wrap:!1}}),Y=new P({props:{title:"Next steps",local:"next-steps",headingTag:"h3"}}),F=new 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