Basic LLM Chat UI

Here is a very simple webcomponent that can have a conversation with a local ollama instance, or which can be easily configured to use any of the other models that langchain supports. (If you want to use something that has an API key you'll end up exposing the API key to the world, so maybe it's not a great idea to have the browser talk directly.)

Chat Component

Here's the base component. It assumes that there's one conversation perpage, and it keeps the query and response as attributes. When a new one is created, it takes passes the previous completions to the backing model to give it some memory.

generate-response.js:

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  import { Ollama } from "@langchain/community/llms/ollama";
  import { ChatOpenAI } from "@langchain/openai";

  import {
      ChatPromptTemplate,
  } from "@langchain/core/prompts";
  import { StringOutputParser } from "@langchain/core/output_parsers"
  import { marked } from 'marked';

  class GenerateResponse extends HTMLElement {
      connectedCallback() {
          this.state = {
              llm: this.getAttribute( 'llm' ) || 'ollama',
              api_key: this.getAttribute( 'api_key' ),
              status: "",
              model: this.getAttribute( "model" ) || "mistral",
              response: "",
              query: this.getAttribute( "query" )
          }

          this.doQuery();
          this.render();
      }

      async doQuery() {
          const model = this.state.model;
          this.state.status = `generating response from ${model}`
          this.render();

          let chatModel = new Ollama( { model } );
          if( this.state.llm == 'openAI' ) {
              chatModel = new ChatOpenAI( {apiKey: this.state.api_key} );
          }

          const messages = [
              [
                  "system",
                  "You are a helpful AI assistant"
              ]
          ];

          // Get the history
          const exchange = document.querySelectorAll( "generate-response" )
          for( let m of exchange ) {
              if( m.state.query ) {
                  messages.push( [ "user", m.state.query ] )
              }

              if( m.state.response != "" ) {
                  messages.push( [ "ai", m.state.response ] )
              }
          }

          const prompt = ChatPromptTemplate.fromMessages(messages);
          const outputParser = new StringOutputParser();
          const llmChain = prompt.pipe(chatModel).pipe(outputParser);
          const answer = await llmChain.stream({
              input: this.state.query
          });

          for await (const chunk of answer) {
              this.state.response = this.state.response + chunk;
              this.render()
          }

          this.state.status = "";
          this.render();
      }
      
      render() {
          let h = ""

          h += `<h2 font-header text-2xl>${this.state.query}</h2>`
          
          if( this.state.response == "" ) {
              h += `<sl-progress-bar indeterminate py-2></sl-progress-bar>`
          }

          if( this.state.status != "" ) {
              h += `<p>${this.state.status}</p>`
          }

          if( this.state.response != "" ) {
              h += `<div>`
              h += marked.parse( this.state.response )
              h += `</div>`
          }

          this.innerHTML = h;
      }
  }

  customElements.define("generate-response", GenerateResponse );

LLM Selector

Right we can chose between 2 different LLM providers, but it's easy to add more. I'm playing with ollama and OpenAI's version. This simply makes a tab group for both.

llm-selector.js:

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  class LLMSelector extends HTMLElement {
      get llm() {
          return this.state.llm;
      }

      get model() {
          const model = this.querySelector( `#${this.state.llm}` );
          return model.model;
      }

      get api_key() {
          const model = this.querySelector( `#${this.state.llm}` );
          return model.api_key;
      }
      
      connectedCallback() {
          this.state = {llm: 'ollama'}
          
          this.innerHTML = `
  <sl-tab-group>
    <sl-tab slot="nav" panel="ollama">Ollama</sl-tab>
    <sl-tab slot="nav" panel="openAI">OpenAI</sl-tab>

    <sl-tab-panel name="ollama"><ollama-models id="ollama"></ollama-models></sl-tab-panel>
    <sl-tab-panel name="openAI"><openai-models id="openAI"></openai-models></sl-tab-panel>
  </sl-tab-group>`

          this.querySelector( "sl-tab-group" ).addEventListener( "sl-tab-show", (e) => {
              this.state.llm = e.detail.name;
          })
      }

  }

  customElements.define("llm-selector", LLMSelector );

Ollama Model Selection

For ollama, we pull down a list of installed LLMs and let the user decide. mistral is default.

ollama-models.js:

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  class OllamaModels extends HTMLElement {
      connectedCallback() {
          this.state = {
              status: "",
              loading: true,
              tags: [],
              model: "mistral",
              api_key: "",
          }

          this.listTags();
          this.render();
      }

      get model() {
          return this.state.model;
      }

      get api_key() {
          return this.state.api_key;
      }

      async listTags() {
          const response = await fetch( "http://localhost:11434/api/tags" );
          const tags = await response.json();
          console.log( tags );
          this.state.tags = tags;
          this.state.loading = false;
          this.render();
      }

      render() {
          if( !this.state.loading ) {
              let h = `<sl-select hoist id="model" label="Model" help-text="Please select which model to run against.">`
              for( let model of this.state.tags.models ) {
                  h += `<sl-option value="${model.name}">${model.name}</sl-option>`
                  }
              h += `</sl-select>`

              this.innerHTML = h;

              this.querySelector( "sl-select" ).addEventListener( 'sl-change', (e) => {
                  this.state.model = e.target.value;
              } );
          }
      }
  }

  customElements.define("ollama-models", OllamaModels );

OpenAI Component

Mainly we need to get the api key, which we will store in local storage for future ease of use. Once you enter it, it stays in your browser.

openai-models.js:

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  class OpenaiModels extends HTMLElement {

      connectedCallback() {
          this.state = {
              api: localStorage.getItem( "openai_api_key" ),
              models: ['gpt-4-turbo', 'gpt-4', 'gpt-3.5-turbo', 'gpt-3.5'],
              model: 'gpt-4-turbo'
          }

          this.render();
      }

      get model() {
          return this.state.model;
      }
      
      get api_key() {
          return this.state.api;
      }
      
      render() {
          let h = `<sl-input id='api' label="API KEY" value="${this.state.api}"></sl-input>`;

          h = h + `<sl-select hoist id="model" label="Model" help-text="Please select which model to run against.">`
          for( let model of this.state.models ) {
              h += `<sl-option value="${model}">${model}</sl-option>`
          }
          h += `</sl-select>`

          this.innerHTML = h;

          this.querySelector( 'sl-input' ).addEventListener( "sl-input", (e) => {
              this.state.api = e.target.value;
              localStorage.setItem( "openai_api_key", this.state.api );
              console.log( this.state.api );
          } );

          this.querySelector( "sl-select" ).addEventListener( 'sl-change', (e) => {
              this.state.model = e.target.value;
          } );

      }
  }

  customElements.define( "openai-models", OpenaiModels );

HTML & JavaScript

And the framework that all this hangs off of:

main.js:

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  import '@unocss/reset/tailwind.css';
  import '@shoelace-style/shoelace/dist/themes/light.css';
  import '@shoelace-style/shoelace';
  import './generate-response.js';
  import './llm-selector.js';
  import './ollama-models.js';
  import './openai-models.js';
  import './main.css';

  // For icons
  import { setBasePath } from '@shoelace-style/shoelace/dist/utilities/base-path.js';
  setBasePath('./node_modules/@shoelace-style/shoelace/dist');


  // Wiring up stuff
  promptinput.addEventListener( "keypress", (e) => {
      if( e.keyCode == 13 ) {
          const response = document.createElement( "generate-response" );
          response.setAttribute( 'llm', selector.llm );
          response.setAttribute( 'api_key', selector.api_key );
          response.setAttribute( 'query', promptinput.value );
          response.setAttribute( 'model', selector.model );
          chat.appendChild( response );

          console.log(chat)
          
          promptinput.value = "";
      }
  })

And then wire it all together:

index.html:

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    <html>
    <head>
      <title>Chat bot</title>
      <script src="main.js" type="module"></script>
      <meta name="viewport" content="width=device-width, initial-scale=1" />
    </head>
    <body font-sans>
      <div max-w-prose mx-auto prose>
        <h1 text-4xl font-bold>Chat Bot</h1>

        <llm-selector id="selector"></llm-selector>

        <div id="chat">
        </div>

        <sl-input label="How can I help?" id="promptinput">
          <sl-icon name="chat" slot="suffix"></sl-icon>
        </sl-input>
      </div>
    </body>
  </html>

Boilerplate

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  npm i vite unocss @shoelace-style/shoelace vite-plugin-static-copy langchain marked

package.json:

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  {
      "scripts": {
          "dev": "unocss \"**/*.html\" -o main.css --watch & vite",
          "build": "unocss \"**/*.html\" -o main.css && vite build"
      },
      "type": "module",
      "dependencies": {
          "@shoelace-style/shoelace": "^2.15.0",
          "langchain": "^0.1.35",
          "unocss": "^0.59.4",
          "marked": "^12.0.2",
          "vite": "^5.2.10",
          "vite-plugin-static-copy": "^1.0.3"
      }
  }

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  // uno.config.ts
  import {
      defineConfig,
      presetAttributify,
      presetTypography,
      presetUno
  } from 'unocss'

  import presetWebFonts from '@unocss/preset-web-fonts';

  const fonts = presetWebFonts({
      provider: 'google', // default provider
      fonts: {
          sans: [ { name: 'Quicksand', weights: [ '300', '700'] } ] // Quicksand
      }
  })

  export default defineConfig({
    presets: [
        presetAttributify(), // required when using attributify mode
        presetUno(), // required
        presetTypography(),
        fonts,
    ],
  })

Run with

1
  npm run dev

Basic LLM Chat UI

the base of a few experiments

Contents

Chat Component

LLM Selector

Ollama Model Selection

OpenAI Component

HTML & JavaScript

Boilerplate

References

Oh javascript

I need a trigger warning