openenv/pr_800/en/_app/immutable/nodes/9.cb3b59bf.js

import{s as Ls,n as Ds,o as Os}from"../chunks/scheduler.2b22cead.js";import{S as Ps,i as Ks,e as T,s as n,c as i,h as te,a as c,d as e,b as a,f as qs,g as o,j as y,k as Hs,l as se,m as l,n as p,t as M,o as r,p as m}from"../chunks/index.1a0e8013.js";import{C as ee,H as J,E as le}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.361fc810.js";import{C as h}from"../chunks/CodeBlock.0beca4be.js";function ne($s){let w,ut,bt,It,j,gt,U,Ct,b,Bs="A chat-based environment for LLMs with built-in tokenization and message history management. This environment is designed to work directly with language models and provides a minimal, flexible foundation for conversation-based RL training.",ft,d,$t,u,vs="ChatEnvironment is a lightweight environment that:",Bt,I,Zs="<li>Manages conversation history in Huggingface chat format</li> <li>Handles tokenization internally using any compatible tokenizer</li> <li>Stores both messages and tokens for efficient model interaction</li> <li>Provides a clean interface for building chat-based RL agents</li>",vt,g,ks="ChatEnvironment can be used in <strong>two ways</strong>:",Zt,C,Ws="<li><strong>Direct usage</strong>: Import and use ChatEnvironment directly in your Python code (best for local development)</li> <li><strong>HTTP client</strong>: Use ChatEnv client to connect to a ChatEnvironment server (best for distributed/containerized deployments)</li>",kt,f,Wt,$,Et,B,Gt,v,Xt,Z,Nt,k,zt,W,Es="Before using the HTTP client, build the Docker image:",Yt,E,Vt,G,Qt,X,At,N,Rt,z,Gs="Actions contain only tokens (PyTorch tensors) that interface directly with models:",_t,Y,Ft,V,St,Q,Xs="Observations contain both the message history and flattened tokens:",xt,A,qt,R,Ht,_,Ns="Internal state tracking message and token history:",Lt,F,Dt,S,Ot,x,Pt,q,zs="Resets the environment to initial state with optional system prompt.",Kt,H,ts,L,Ys="Takes an action (tokens), decodes to text, adds to history, returns updated observation.",ss,D,es,O,Vs="Convenience method to convert a message dict to a tokenized ChatAction.",ls,P,ns,K,as,tt,is,st,os,et,Qs="You can add transforms to compute rewards or modify observations:",ps,lt,Ms,nt,rs,at,As="If you’re generating tokens from a model, you can create actions directly:",ms,it,Js,ot,Ts,pt,Rs="ChatEnvironment is intentionally minimal and flexible:",cs,Mt,_s="<li><strong>No HTTP overhead</strong>: Works directly with Python objects and tensors</li> <li><strong>Tokenizer ownership</strong>: Environment handles tokenization consistently</li> <li><strong>Dual representation</strong>: Maintains both human-readable messages and model-ready tokens</li> <li><strong>Transform support</strong>: Extensible reward computation and observation modification</li> <li><strong>Type-safe</strong>: Uses typed Messages compatible with Huggingface format</li>",ys,rt,hs,mt,Fs="ChatEnvironment pairs naturally with language models:",ws,Jt,js,Tt,Us,ct,bs,yt,ds,ht,Ss="<li>Python 3.10+</li> <li>PyTorch</li> <li>A tokenizer with <code>apply_chat_template</code> method (e.g., Huggingface transformers)</li>",us,wt,Is,jt,xs="<li>ChatEnvironment does <strong>not</strong> generate responses - it only manages conversation state</li> <li>You need to provide tokens from your model or other source</li> <li>The environment is thread-safe for single-threaded use only</li> <li>For multi-turn conversations, alternate between user and assistant messages</li>",gs,Ut,Cs,dt,fs;return j=new ee({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),U=new J({props:{title:"Chat Environment",local:"chat-environment",headingTag:"h1"}}),d=new J({props:{title:"Overview",local:"overview",headingTag:"h2"}}),f=new J({props:{title:"Quick Start",local:"quick-start",headingTag:"h2"}}),$=new J({props:{title:"Option 1: Direct Usage (Local)",local:"option-1-direct-usage-local",headingTag:"h3"}}),B=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-keyword">from</span> envs.chat_env <span class="hljs-keyword">import</span> ChatAction, ChatObservation
<span class="hljs-keyword">from</span> envs.chat_env.server <span class="hljs-keyword">import</span> ChatEnvironment
<span class="hljs-keyword">from</span> openenv.core.env_server <span class="hljs-keyword">import</span> Message

<span class="hljs-comment"># Initialize with a tokenizer and optional system prompt</span>
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
env = ChatEnvironment(
    tokenizer=tokenizer,
    system_prompt=<span class="hljs-string">&quot;You are a helpful assistant.&quot;</span>,
    system_role=<span class="hljs-string">&quot;system&quot;</span>
)

<span class="hljs-comment"># Reset the environment</span>
obs = env.reset()
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Messages: <span class="hljs-subst">{obs.messages}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Tokens shape: <span class="hljs-subst">{obs.tokens.shape}</span>&quot;</span>)

<span class="hljs-comment"># Create an action from a message</span>
user_message: Message = {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;user&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;Hello!&quot;</span>}
action = env.message_to_action(user_message)

<span class="hljs-comment"># Step the environment</span>
obs = env.step(action)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Updated messages: <span class="hljs-subst">{obs.messages}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Updated tokens shape: <span class="hljs-subst">{obs.tokens.shape}</span>&quot;</span>)`,lang:"python",wrap:!1}}),v=new J({props:{title:"Option 2: HTTP Client (Distributed)",local:"option-2-http-client-distributed",headingTag:"h3"}}),Z=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-keyword">from</span> envs.chat_env <span class="hljs-keyword">import</span> ChatEnv, ChatAction
<span class="hljs-keyword">import</span> torch

<span class="hljs-comment"># Create environment from Docker image</span>
client = ChatEnv.from_docker_image(<span class="hljs-string">&quot;chat-env:latest&quot;</span>)

<span class="hljs-comment"># Or connect to existing server</span>
<span class="hljs-comment"># client = ChatEnv(base_url=&quot;http://localhost:8000&quot;)</span>

<span class="hljs-comment"># Reset</span>
result = client.reset()
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Initial messages: <span class="hljs-subst">{result.observation.messages}</span>&quot;</span>)

<span class="hljs-comment"># Send an action with tokens</span>
tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
message = {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;user&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;Hello!&quot;</span>}
action = client.message_to_action(message, tokenizer)

result = client.step(action)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Messages: <span class="hljs-subst">{result.observation.messages}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Reward: <span class="hljs-subst">{result.reward}</span>&quot;</span>)

<span class="hljs-comment"># Cleanup</span>
client.close()`,lang:"python",wrap:!1}}),k=new J({props:{title:"Building the Docker Image",local:"building-the-docker-image",headingTag:"h3"}}),E=new h({props:{code:"JTIzJTIwRnJvbSUyMHByb2plY3QlMjByb290JTBBZG9ja2VyJTIwYnVpbGQlMjAtdCUyMGNoYXQtZW52JTNBbGF0ZXN0JTIwLWYlMjBlbnZzJTJGY2hhdF9lbnYlMkZzZXJ2ZXIlMkZEb2NrZXJmaWxlJTIwLiUwQSUwQSUyMyUyME9wdGlvbmFsbHklMjBzcGVjaWZ5JTIwYSUyMGRpZmZlcmVudCUyMHRva2VuaXplciUwQWRvY2tlciUyMGJ1aWxkJTIwLXQlMjBjaGF0LWVudiUzQWxhdGVzdCUyMCU1QyUwQSUyMCUyMC0tYnVpbGQtYXJnJTIwVE9LRU5JWkVSX05BTUUlM0RtZXRhLWxsYW1hJTJGTGxhbWEtMi03Yi1jaGF0LWhmJTIwJTVDJTBBJTIwJTIwLWYlMjBlbnZzJTJGY2hhdF9lbnYlMkZzZXJ2ZXIlMkZEb2NrZXJmaWxlJTIwLg==",highlighted:`<span class="hljs-comment"># From project root</span>
docker build -t chat-env:latest -f envs/chat_env/server/Dockerfile .

<span class="hljs-comment"># Optionally specify a different tokenizer</span>
docker build -t chat-env:latest \\
  --build-arg TOKENIZER_NAME=meta-llama/Llama-2-7b-chat-hf \\
  -f envs/chat_env/server/Dockerfile .`,lang:"bash",wrap:!1}}),G=new J({props:{title:"Architecture",local:"architecture",headingTag:"h2"}}),X=new J({props:{title:"Data Models",local:"data-models",headingTag:"h3"}}),N=new J({props:{title:"ChatAction",local:"chataction",headingTag:"h4"}}),Y=new h({props:{code:"JTQwZGF0YWNsYXNzJTBBY2xhc3MlMjBDaGF0QWN0aW9uKEFjdGlvbiklM0ElMEElMjAlMjAlMjAlMjB0b2tlbnMlM0ElMjB0b3JjaC5UZW5zb3IlMjAlMjAlMjMlMjBSZXF1aXJlZCUyQyUyMGNhbm5vdCUyMGJlJTIwZW1wdHk=",highlighted:`<span class="hljs-meta">@dataclass</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">ChatAction</span>(<span class="hljs-title class_ inherited__">Action</span>):
    tokens: torch.Tensor  <span class="hljs-comment"># Required, cannot be empty</span>`,lang:"python",wrap:!1}}),V=new J({props:{title:"ChatObservation",local:"chatobservation",headingTag:"h4"}}),A=new h({props:{code:"JTQwZGF0YWNsYXNzJTBBY2xhc3MlMjBDaGF0T2JzZXJ2YXRpb24oT2JzZXJ2YXRpb24pJTNBJTBBJTIwJTIwJTIwJTIwbWVzc2FnZXMlM0ElMjBsaXN0JTVCTWVzc2FnZSU1RCUyMCUyMCUyMyUyMExpc3QlMjBvZiUyMCU3QiUyMnJvbGUlMjIlM0ElMjBzdHIlMkMlMjAlMjJjb250ZW50JTIyJTNBJTIwc3RyJTdEJTBBJTIwJTIwJTIwJTIwdG9rZW5zJTNBJTIwdG9yY2guVGVuc29yJTIwJTIwJTIwJTIwJTIwJTIzJTIwRmxhdHRlbmVkJTIwdGVuc29yJTIwb2YlMjBhbGwlMjBjb252ZXJzYXRpb24lMjB0b2tlbnMlMEElMjAlMjAlMjAlMjAlMjMlMjBJbmhlcml0ZWQlM0ElMjBkb25lJTJDJTIwcmV3YXJkJTJDJTIwbWV0YWRhdGE=",highlighted:`<span class="hljs-meta">@dataclass</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">ChatObservation</span>(<span class="hljs-title class_ inherited__">Observation</span>):
    messages: <span class="hljs-built_in">list</span>[Message]  <span class="hljs-comment"># List of {&quot;role&quot;: str, &quot;content&quot;: str}</span>
    tokens: torch.Tensor     <span class="hljs-comment"># Flattened tensor of all conversation tokens</span>
    <span class="hljs-comment"># Inherited: done, reward, metadata</span>`,lang:"python",wrap:!1}}),R=new J({props:{title:"ChatState",local:"chatstate",headingTag:"h4"}}),F=new h({props:{code:"JTQwZGF0YWNsYXNzJTBBY2xhc3MlMjBDaGF0U3RhdGUoU3RhdGUpJTNBJTBBJTIwJTIwJTIwJTIwaGlzdG9yeV9tZXNzYWdlcyUzQSUyMGxpc3QlNUJNZXNzYWdlJTVEJTBBJTIwJTIwJTIwJTIwaGlzdG9yeV90b2tlbnMlM0ElMjBsaXN0JTVCdG9yY2guVGVuc29yJTVEJTBBJTIwJTIwJTIwJTIwJTIzJTIwSW5oZXJpdGVkJTNBJTIwZXBpc29kZV9pZCUyQyUyMHN0ZXBfY291bnQ=",highlighted:`<span class="hljs-meta">@dataclass</span>
<span class="hljs-keyword">class</span> <span class="hljs-title class_">ChatState</span>(<span class="hljs-title class_ inherited__">State</span>):
    history_messages: <span class="hljs-built_in">list</span>[Message]
    history_tokens: <span class="hljs-built_in">list</span>[torch.Tensor]
    <span class="hljs-comment"># Inherited: episode_id, step_count</span>`,lang:"python",wrap:!1}}),S=new J({props:{title:"Key Methods",local:"key-methods",headingTag:"h3"}}),x=new J({props:{title:"reset() -> ChatObservation",local:"reset--gt-chatobservation",headingTag:"h4"}}),H=new J({props:{title:"step(action: ChatAction) -> ChatObservation",local:"stepaction-chataction--gt-chatobservation",headingTag:"h4"}}),D=new J({props:{title:"message_to_action(message: Message) -> ChatAction",local:"messagetoactionmessage-message--gt-chataction",headingTag:"h4"}}),P=new J({props:{title:"Usage Patterns",local:"usage-patterns",headingTag:"h2"}}),K=new J({props:{title:"Basic Conversation",local:"basic-conversation",headingTag:"h3"}}),tt=new h({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMEFmcm9tJTIwZW52cy5jaGF0X2Vudi5zZXJ2ZXIlMjBpbXBvcnQlMjBDaGF0RW52aXJvbm1lbnQlMEFmcm9tJTIwb3BlbmVudi5jb3JlLmVudl9zZXJ2ZXIlMjBpbXBvcnQlMjBNZXNzYWdlJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZ3B0MiUyMiklMEFlbnYlMjAlM0QlMjBDaGF0RW52aXJvbm1lbnQodG9rZW5pemVyJTNEdG9rZW5pemVyKSUwQSUwQSUyMyUyMFJlc2V0JTBBb2JzJTIwJTNEJTIwZW52LnJlc2V0KCklMEElMEElMjMlMjBVc2VyJTIwdHVybiUwQXVzZXJfbXNnJTNBJTIwTWVzc2FnZSUyMCUzRCUyMCU3QiUyMnJvbGUlMjIlM0ElMjAlMjJ1c2VyJTIyJTJDJTIwJTIyY29udGVudCUyMiUzQSUyMCUyMldoYXQlMjBpcyUyMDIlMkIyJTNGJTIyJTdEJTBBYWN0aW9uJTIwJTNEJTIwZW52Lm1lc3NhZ2VfdG9fYWN0aW9uKHVzZXJfbXNnKSUwQW9icyUyMCUzRCUyMGVudi5zdGVwKGFjdGlvbiklMEElMEElMjMlMjBBc3Npc3RhbnQlMjB0dXJuJTBBYXNzaXN0YW50X21zZyUzQSUyME1lc3NhZ2UlMjAlM0QlMjAlN0IlMjJyb2xlJTIyJTNBJTIwJTIyYXNzaXN0YW50JTIyJTJDJTIwJTIyY29udGVudCUyMiUzQSUyMCUyMjIlMkIyJTIwZXF1YWxzJTIwNC4lMjIlN0QlMEFhY3Rpb24lMjAlM0QlMjBlbnYubWVzc2FnZV90b19hY3Rpb24oYXNzaXN0YW50X21zZyklMEFvYnMlMjAlM0QlMjBlbnYuc3RlcChhY3Rpb24pJTBBJTBBJTIzJTIwQWNjZXNzJTIwY29udmVyc2F0aW9uJTIwaGlzdG9yeSUwQXByaW50KGYlMjJGdWxsJTIwY29udmVyc2F0aW9uJTNBJTIwJTdCb2JzLm1lc3NhZ2VzJTdEJTIyKSUwQXByaW50KGYlMjJBbGwlMjB0b2tlbnMlM0ElMjAlN0JvYnMudG9rZW5zJTdEJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer
<span class="hljs-keyword">from</span> envs.chat_env.server <span class="hljs-keyword">import</span> ChatEnvironment
<span class="hljs-keyword">from</span> openenv.core.env_server <span class="hljs-keyword">import</span> Message

tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;gpt2&quot;</span>)
env = ChatEnvironment(tokenizer=tokenizer)

<span class="hljs-comment"># Reset</span>
obs = env.reset()

<span class="hljs-comment"># User turn</span>
user_msg: Message = {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;user&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;What is 2+2?&quot;</span>}
action = env.message_to_action(user_msg)
obs = env.step(action)

<span class="hljs-comment"># Assistant turn</span>
assistant_msg: Message = {<span class="hljs-string">&quot;role&quot;</span>: <span class="hljs-string">&quot;assistant&quot;</span>, <span class="hljs-string">&quot;content&quot;</span>: <span class="hljs-string">&quot;2+2 equals 4.&quot;</span>}
action = env.message_to_action(assistant_msg)
obs = env.step(action)

<span class="hljs-comment"># Access conversation history</span>
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Full conversation: <span class="hljs-subst">{obs.messages}</span>&quot;</span>)
<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;All tokens: <span class="hljs-subst">{obs.tokens}</span>&quot;</span>)`,lang:"python",wrap:!1}}),st=new J({props:{title:"With Transforms",local:"with-transforms",headingTag:"h3"}}),lt=new h({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> openenv.core.env_server <span class="hljs-keyword">import</span> Transform, Observation

<span class="hljs-keyword">class</span> <span class="hljs-title class_">LengthRewardTransform</span>(<span class="hljs-title class_ inherited__">Transform</span>):
    <span class="hljs-string">&quot;&quot;&quot;Reward based on response length.&quot;&quot;&quot;</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__call__</span>(<span class="hljs-params">self, observation: Observation</span>) -&gt; Observation:
        <span class="hljs-keyword">if</span> <span class="hljs-built_in">hasattr</span>(observation, <span class="hljs-string">&#x27;messages&#x27;</span>) <span class="hljs-keyword">and</span> observation.messages:
            last_message = observation.messages[-<span class="hljs-number">1</span>]
            observation.reward = <span class="hljs-built_in">len</span>(last_message[<span class="hljs-string">&#x27;content&#x27;</span>]) * <span class="hljs-number">0.1</span>
        <span class="hljs-keyword">return</span> observation

env = ChatEnvironment(
    tokenizer=tokenizer,
    transform=LengthRewardTransform()
)`,lang:"python",wrap:!1}}),nt=new J({props:{title:"Direct Token Usage",local:"direct-token-usage",headingTag:"h3"}}),it=new h({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZW52cy5jaGF0X2VudiUyMGltcG9ydCUyMENoYXRBY3Rpb24lMEElMEElMjMlMjBBc3N1bWUlMjB5b3UlMjBoYXZlJTIwdG9rZW5zJTIwZnJvbSUyMHlvdXIlMjBtb2RlbCUwQWdlbmVyYXRlZF90b2tlbnMlMjAlM0QlMjB0b3JjaC50ZW5zb3IoJTVCJTVCMSUyQyUyMDIlMkMlMjAzJTJDJTIwNCUyQyUyMDUlNUQlNUQpJTBBJTBBJTIzJTIwQ3JlYXRlJTIwYWN0aW9uJTIwZGlyZWN0bHklMEFhY3Rpb24lMjAlM0QlMjBDaGF0QWN0aW9uKHRva2VucyUzRGdlbmVyYXRlZF90b2tlbnMpJTBBJTBBJTIzJTIwU3RlcCUyMGVudmlyb25tZW50JTBBb2JzJTIwJTNEJTIwZW52LnN0ZXAoYWN0aW9uKQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> envs.chat_env <span class="hljs-keyword">import</span> ChatAction

<span class="hljs-comment"># Assume you have tokens from your model</span>
generated_tokens = torch.tensor([[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">5</span>]])

<span class="hljs-comment"># Create action directly</span>
action = ChatAction(tokens=generated_tokens)

<span class="hljs-comment"># Step environment</span>
obs = env.step(action)`,lang:"python",wrap:!1}}),ot=new J({props:{title:"Design Philosophy",local:"design-philosophy",headingTag:"h2"}}),rt=new J({props:{title:"Integration with Models",local:"integration-with-models",headingTag:"h2"}}),Jt=new h({props:{code:"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",highlighted:`<span class="hljs-comment"># Pseudo-code for RL training loop</span>
model = YourLanguageModel()
env = ChatEnvironment(tokenizer=model.tokenizer)

<span class="hljs-keyword">for</span> episode <span class="hljs-keyword">in</span> <span class="hljs-built_in">range</span>(num_episodes):
    obs = env.reset()

    <span class="hljs-keyword">while</span> <span class="hljs-keyword">not</span> obs.done:
        <span class="hljs-comment"># Model generates response tokens</span>
        action_tokens = model.generate(obs.tokens)
        action = ChatAction(tokens=action_tokens)

        <span class="hljs-comment"># Step environment</span>
        obs = env.step(action)

        <span class="hljs-comment"># Use obs.reward for RL updates</span>
        model.update(obs.reward)`,lang:"python",wrap:!1}}),Tt=new J({props:{title:"Project Structure",local:"project-structure",headingTag:"h2"}}),ct=new h({props:{code:"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",highlighted:`chat_env/
├── __init__.py              <span class="hljs-meta"># Module exports (ChatEnv, ChatAction, etc.)</span>
├── README.md                <span class="hljs-meta"># This file</span>
├── <span class="hljs-keyword">client</span>.py                <span class="hljs-meta"># ChatEnv HTTP client</span>
├── models.py                <span class="hljs-meta"># ChatAction, ChatObservation, ChatState</span>
└── <span class="hljs-keyword">server</span>/
    ├── __init__.py          <span class="hljs-meta"># Server module exports</span>
    ├── chat_environment.py  <span class="hljs-meta"># Core ChatEnvironment implementation</span>
    ├── app.py               <span class="hljs-meta"># FastAPI server application</span>
    ├── test_chat_env.py     <span class="hljs-meta"># Unit tests</span>
    └── Dockerfile           <span class="hljs-meta"># Container image for HTTP server</span>`,lang:"",wrap:!1}}),yt=new J({props:{title:"Requirements",local:"requirements",headingTag:"h2"}}),wt=new J({props:{title:"Notes",local:"notes",headingTag:"h2"}}),Ut=new 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