├── backend
    ├── src
    │   ├── chain.ts
    │   ├── parser.ts
    │   ├── constants.ts
    │   ├── ingest.ts
    │   └── _index.ts
    ├── README.md
    ├── .gitignore
    ├── babel.config.cjs
    ├── tsup.config.js
    ├── .env.example
    ├── jest.env.cjs
    ├── .prettierrc
    ├── jest.config.cjs
    ├── tsconfig.json
    ├── LICENSE
    ├── .eslintrc.cjs
    └── package.json
├── frontend
    ├── .prettierrc
    ├── .eslintrc.json
    ├── public
    │   ├── favicon.ico
    │   └── images
    │   │   └── github-mark.svg
    ├── vercel.json
    ├── next.config.js
    ├── postcss.config.js
    ├── app
    │   ├── utils
    │   │   ├── constants.tsx
    │   │   └── sendFeedback.tsx
    │   ├── globals.css
    │   ├── page.tsx
    │   ├── components
    │   │   ├── InlineCitation.tsx
    │   │   ├── AutoResizeTextarea.tsx
    │   │   ├── SourceBubble.tsx
    │   │   ├── EmptyState.tsx
    │   │   ├── ChatWindow.tsx
    │   │   └── ChatMessageBubble.tsx
    │   ├── layout.tsx
    │   └── api
    │   │   ├── get_trace
    │   │       └── route.ts
    │   │   ├── feedback
    │   │       └── route.ts
    │   │   └── chat
    │   │       └── stream_log
    │   │           └── route.ts
    ├── .env.example
    ├── .gitignore
    ├── tailwind.config.ts
    ├── tsconfig.json
    └── package.json
├── .vscode
    └── settings.json
├── assets
    └── images
    │   ├── Chat_Your_Data.gif
    │   ├── langsmith_trace.png
    │   ├── langsmith_feedback.png
    │   ├── orbstack_running_chroma.png
    │   └── orbstack_running_chroma_pgsql.png
├── .yarnrc.yml
├── turbo.json
├── package.json
├── LICENSE
├── .github
    └── workflows
    │   ├── update-index.yml
    │   └── ci.yml
├── PRODUCTION.md
├── .gitignore
├── README.md
├── DEPLOYMENT.md
├── LANGSMITH.md
├── RUN_LOCALLY.md
├── CONCEPTS.md
├── MODIFY.md
└── .yarn
    └── plugins
        └── @yarnpkg
            └── plugin-typescript.cjs


/backend/src/chain.ts:
--------------------------------------------------------------------------------
1 | 


--------------------------------------------------------------------------------
/backend/src/parser.ts:
--------------------------------------------------------------------------------
1 | 


--------------------------------------------------------------------------------
/backend/README.md:
--------------------------------------------------------------------------------
1 | # Chat LangChainJS Backend


--------------------------------------------------------------------------------
/frontend/.prettierrc:
--------------------------------------------------------------------------------
1 | {
2 |   "endOfLine": "lf"
3 | }
4 | 


--------------------------------------------------------------------------------
/frontend/.eslintrc.json:
--------------------------------------------------------------------------------
1 | {
2 |   "extends": "next/core-web-vitals"
3 | }
4 | 


--------------------------------------------------------------------------------
/.vscode/settings.json:
--------------------------------------------------------------------------------
1 | {
2 |   "cSpell.words": [
3 |     "productionized"
4 |   ]
5 | }


--------------------------------------------------------------------------------
/backend/src/constants.ts:
--------------------------------------------------------------------------------
1 | export const WEAVIATE_DOCS_INDEX_NAME = "LangChain_agent_docs";
2 | 


--------------------------------------------------------------------------------
/backend/.gitignore:
--------------------------------------------------------------------------------
1 | index.cjs
2 | index.js
3 | index.d.ts
4 | index.d.cts
5 | node_modules
6 | dist
7 | .yarn
8 | 


--------------------------------------------------------------------------------
/frontend/public/favicon.ico:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/frontend/public/favicon.ico


--------------------------------------------------------------------------------
/frontend/vercel.json:
--------------------------------------------------------------------------------
1 | {
2 |   "git": {
3 |     "deploymentEnabled": {
4 |       "main": false
5 |     }
6 |   }
7 | }
8 | 


--------------------------------------------------------------------------------
/assets/images/Chat_Your_Data.gif:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/assets/images/Chat_Your_Data.gif


--------------------------------------------------------------------------------
/assets/images/langsmith_trace.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/assets/images/langsmith_trace.png


--------------------------------------------------------------------------------
/frontend/next.config.js:
--------------------------------------------------------------------------------
1 | /** @type {import('next').NextConfig} */
2 | const nextConfig = {};
3 | 
4 | module.exports = nextConfig;
5 | 


--------------------------------------------------------------------------------
/frontend/postcss.config.js:
--------------------------------------------------------------------------------
1 | module.exports = {
2 |   plugins: {
3 |     tailwindcss: {},
4 |     autoprefixer: {},
5 |   },
6 | };
7 | 


--------------------------------------------------------------------------------
/assets/images/langsmith_feedback.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/assets/images/langsmith_feedback.png


--------------------------------------------------------------------------------
/assets/images/orbstack_running_chroma.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/assets/images/orbstack_running_chroma.png


--------------------------------------------------------------------------------
/backend/babel.config.cjs:
--------------------------------------------------------------------------------
1 | // babel.config.js
2 | module.exports = {
3 |   presets: [["@babel/preset-env", { targets: { node: true } }]],
4 | };
5 | 


--------------------------------------------------------------------------------
/assets/images/orbstack_running_chroma_pgsql.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/langchain-ai/chat-langchainjs/HEAD/assets/images/orbstack_running_chroma_pgsql.png


--------------------------------------------------------------------------------
/.yarnrc.yml:
--------------------------------------------------------------------------------
1 | nodeLinker: node-modules
2 | 
3 | plugins:
4 |   - path: .yarn/plugins/@yarnpkg/plugin-typescript.cjs
5 |     spec: "@yarnpkg/plugin-typescript"
6 | 
7 | yarnPath: .yarn/releases/yarn-3.4.1.cjs


--------------------------------------------------------------------------------
/frontend/app/utils/constants.tsx:
--------------------------------------------------------------------------------
1 | const apiBasePath = process.env.VERCEL_URL
2 |   ? `https://${process.env.VERCEL_URL}`
3 |   : `http://localhost:3000`;
4 | export const apiBaseUrl = `${apiBasePath}/api`;
5 | 


--------------------------------------------------------------------------------
/backend/tsup.config.js:
--------------------------------------------------------------------------------
 1 | import { defineConfig } from "tsup";
 2 | 
 3 | export default defineConfig({
 4 |   entry: ["src/*.ts"],
 5 |   format: ["esm"],
 6 |   dts: true, // Generate declaration file (.d.ts)
 7 |   splitting: false,
 8 |   sourcemap: true,
 9 |   clean: true,
10 | });


--------------------------------------------------------------------------------
/frontend/.env.example:
--------------------------------------------------------------------------------
 1 | # ------------------LangSmith tracing------------------
 2 | LANGCHAIN_TRACING_V2=true
 3 | LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
 4 | LANGCHAIN_API_KEY=
 5 | LANGCHAIN_PROJECT=
 6 | # -----------------------------------------------------
 7 | 
 8 | WEAVIATE_API_KEY=
 9 | WEAVIATE_URL=
10 | WEAVIATE_INDEX_NAME=
11 | 
12 | FIREWORKS_API_KEY=
13 | 
14 | OPENAI_API_KEY=
15 | 


--------------------------------------------------------------------------------
/backend/.env.example:
--------------------------------------------------------------------------------
 1 | # ------------------LangSmith tracing------------------
 2 | LANGCHAIN_TRACING_V2=true
 3 | LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
 4 | LANGCHAIN_API_KEY=
 5 | LANGCHAIN_PROJECT=
 6 | # -----------------------------------------------------
 7 | 
 8 | WEAVIATE_API_KEY=
 9 | WEAVIATE_URL=
10 | WEAVIATE_INDEX_NAME=
11 | FORCE_UPDATE=true
12 | RECORD_MANAGER_DB_URL=
13 | OPENAI_API_KEY=


--------------------------------------------------------------------------------
/frontend/app/globals.css:
--------------------------------------------------------------------------------
 1 | @tailwind base;
 2 | @tailwind components;
 3 | @tailwind utilities;
 4 | 
 5 | body {
 6 |   color: #f8f8f8;
 7 |   background: #131318;
 8 | }
 9 | 
10 | body input,
11 | body textarea {
12 |   color: black;
13 | }
14 | 
15 | a {
16 |   color: #2d7bd4;
17 | }
18 | 
19 | a:hover {
20 |   border-bottom: 1px solid;
21 | }
22 | 
23 | p {
24 |   margin: 8px 0;
25 | }
26 | 
27 | code {
28 |   color: #ffa500;
29 | }
30 | 
31 | li {
32 |   padding: 4px;
33 | }
34 | 


--------------------------------------------------------------------------------
/frontend/app/page.tsx:
--------------------------------------------------------------------------------
 1 | "use client";
 2 | 
 3 | import { v4 as uuidv4 } from "uuid";
 4 | import { ChatWindow } from "../app/components/ChatWindow";
 5 | import { ToastContainer } from "react-toastify";
 6 | 
 7 | import { ChakraProvider } from "@chakra-ui/react";
 8 | 
 9 | export default function Home() {
10 |   return (
11 |     <ChakraProvider>
12 |       <ToastContainer />
13 |       <ChatWindow conversationId={uuidv4()}></ChatWindow>
14 |     </ChakraProvider>
15 |   );
16 | }
17 | 


--------------------------------------------------------------------------------
/backend/jest.env.cjs:
--------------------------------------------------------------------------------
 1 | const { TestEnvironment } = require("jest-environment-node");
 2 | 
 3 | class AdjustedTestEnvironmentToSupportFloat32Array extends TestEnvironment {
 4 |   constructor(config, context) {
 5 |     // Make `instanceof Float32Array` return true in tests
 6 |     // to avoid https://github.com/xenova/transformers.js/issues/57 and https://github.com/jestjs/jest/issues/2549
 7 |     super(config, context);
 8 |     this.global.Float32Array = Float32Array;
 9 |   }
10 | }
11 | 
12 | module.exports = AdjustedTestEnvironmentToSupportFloat32Array;
13 | 


--------------------------------------------------------------------------------
/frontend/.gitignore:
--------------------------------------------------------------------------------
 1 | # See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
 2 | 
 3 | # dependencies
 4 | /node_modules
 5 | /.pnp
 6 | .pnp.js
 7 | 
 8 | # testing
 9 | /coverage
10 | 
11 | # next.js
12 | /.next/
13 | /out/
14 | 
15 | # production
16 | /build
17 | 
18 | # misc
19 | .DS_Store
20 | *.pem
21 | 
22 | # debug
23 | npm-debug.log*
24 | yarn-debug.log*
25 | yarn-error.log*
26 | 
27 | # local env files
28 | .env*.local
29 | 
30 | # vercel
31 | .vercel
32 | 
33 | # typescript
34 | *.tsbuildinfo
35 | next-env.d.ts
36 | 
37 | .yarn/


--------------------------------------------------------------------------------
/backend/.prettierrc:
--------------------------------------------------------------------------------
 1 | {
 2 |   "$schema": "https://json.schemastore.org/prettierrc",
 3 |   "printWidth": 80,
 4 |   "tabWidth": 2,
 5 |   "useTabs": false,
 6 |   "semi": true,
 7 |   "singleQuote": false,
 8 |   "quoteProps": "as-needed",
 9 |   "jsxSingleQuote": false,
10 |   "trailingComma": "es5",
11 |   "bracketSpacing": true,
12 |   "arrowParens": "always",
13 |   "requirePragma": false,
14 |   "insertPragma": false,
15 |   "proseWrap": "preserve",
16 |   "htmlWhitespaceSensitivity": "css",
17 |   "vueIndentScriptAndStyle": false,
18 |   "endOfLine": "lf"
19 | }
20 | 


--------------------------------------------------------------------------------
/frontend/tailwind.config.ts:
--------------------------------------------------------------------------------
 1 | import type { Config } from "tailwindcss";
 2 | 
 3 | const config: Config = {
 4 |   content: [
 5 |     "./pages/**/*.{js,ts,jsx,tsx,mdx}",
 6 |     "./components/**/*.{js,ts,jsx,tsx,mdx}",
 7 |     "./app/**/*.{js,ts,jsx,tsx,mdx}",
 8 |   ],
 9 |   theme: {
10 |     extend: {
11 |       backgroundImage: {
12 |         "gradient-radial": "radial-gradient(var(--tw-gradient-stops))",
13 |         "gradient-conic":
14 |           "conic-gradient(from 180deg at 50% 50%, var(--tw-gradient-stops))",
15 |       },
16 |     },
17 |   },
18 |   plugins: [],
19 | };
20 | export default config;
21 | 


--------------------------------------------------------------------------------
/backend/jest.config.cjs:
--------------------------------------------------------------------------------
 1 | /** @type {import('ts-jest').JestConfigWithTsJest} */
 2 | module.exports = {
 3 |   preset: "ts-jest/presets/default-esm",
 4 |   testEnvironment: "./jest.env.cjs",
 5 |   modulePathIgnorePatterns: ["dist/", "docs/"],
 6 |   moduleNameMapper: {
 7 |     "^(\\.{1,2}/.*)\\.js$": "$1",
 8 |   },
 9 |   transform: {
10 |     '^.+\\.tsx?$': ['@swc/jest'],
11 |   },
12 |   transformIgnorePatterns: [
13 |     "/node_modules/",
14 |     "\\.pnp\\.[^\\/]+$",
15 |     "./scripts/jest-setup-after-env.js",
16 |   ],
17 |   setupFiles: ["dotenv/config"],
18 |   setupFilesAfterEnv: ["./scripts/jest-setup-after-env.js"],
19 |   testTimeout: 20_000,
20 |   passWithNoTests: true
21 | };
22 | 


--------------------------------------------------------------------------------
/frontend/app/components/InlineCitation.tsx:
--------------------------------------------------------------------------------
 1 | import { Source } from "./SourceBubble";
 2 | 
 3 | export function InlineCitation(props: {
 4 |   source: Source;
 5 |   sourceNumber: number;
 6 |   highlighted: boolean;
 7 |   onMouseEnter: () => any;
 8 |   onMouseLeave: () => any;
 9 | }) {
10 |   const { source, sourceNumber, highlighted, onMouseEnter, onMouseLeave } =
11 |     props;
12 |   return (
13 |     <a
14 |       href={source.url}
15 |       target="_blank"
16 |       className={`relative bottom-1.5 text-xs border rounded px-1 ${
17 |         highlighted ? "bg-[rgb(58,58,61)]" : "bg-[rgb(78,78,81)]"
18 |       }`}
19 |       onMouseEnter={onMouseEnter}
20 |       onMouseLeave={onMouseLeave}
21 |     >
22 |       {sourceNumber}
23 |     </a>
24 |   );
25 | }
26 | 


--------------------------------------------------------------------------------
/frontend/tsconfig.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "compilerOptions": {
 3 |     "target": "es5",
 4 |     "lib": ["dom", "dom.iterable", "esnext"],
 5 |     "allowJs": true,
 6 |     "skipLibCheck": true,
 7 |     "strict": true,
 8 |     "forceConsistentCasingInFileNames": true,
 9 |     "noEmit": true,
10 |     "esModuleInterop": true,
11 |     "module": "esnext",
12 |     "moduleResolution": "bundler",
13 |     "resolveJsonModule": true,
14 |     "isolatedModules": true,
15 |     "jsx": "preserve",
16 |     "incremental": true,
17 |     "plugins": [
18 |       {
19 |         "name": "next"
20 |       }
21 |     ],
22 |     "paths": {
23 |       "@/*": ["./*"]
24 |     }
25 |   },
26 |   "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
27 |   "exclude": ["node_modules"]
28 | }
29 | 


--------------------------------------------------------------------------------
/frontend/app/layout.tsx:
--------------------------------------------------------------------------------
 1 | import "./globals.css";
 2 | import type { Metadata } from "next";
 3 | import { Inter } from "next/font/google";
 4 | 
 5 | const inter = Inter({ subsets: ["latin"] });
 6 | 
 7 | export const metadata: Metadata = {
 8 |   title: "Chat LangChain",
 9 |   description: "Chatbot for LangChain",
10 | };
11 | 
12 | export default function RootLayout({
13 |   children,
14 | }: {
15 |   children: React.ReactNode;
16 | }) {
17 |   return (
18 |     <html lang="en" className="h-full">
19 |       <body className={`${inter.className} h-full`}>
20 |         <div
21 |           className="flex flex-col h-full md:p-8"
22 |           style={{ background: "rgb(38, 38, 41)" }}
23 |         >
24 |           {children}
25 |         </div>
26 |       </body>
27 |     </html>
28 |   );
29 | }
30 | 


--------------------------------------------------------------------------------
/turbo.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "$schema": "https://turbo.build/schema.json",
 3 |   "globalDependencies": ["**/.env"],
 4 |   "pipeline": {
 5 |     "build": {
 6 |       "dependsOn": ["^build"],
 7 |       "outputs": ["**/dist/**"],
 8 |       "inputs": ["**/src/**"]
 9 |     },
10 |     "lint": {
11 |       "dependsOn": ["^lint"],
12 |       "inputs": ["**/src/**"]
13 |     },
14 |     "format": {
15 |       "dependsOn": ["^format"],
16 |       "inputs": ["**/src/**"]
17 |     },
18 |     "format:check": {
19 |       "dependsOn": ["^format:check"],
20 |       "inputs": ["**/src/**"]
21 |     },
22 |     "start": {
23 |       "dependsOn": ["^start"],
24 |       "inputs": ["**/src/**"]
25 |     },
26 |     "dev": {
27 |       "dependsOn": ["^dev"],
28 |       "inputs": ["**/src/**"]
29 |     }
30 |   }
31 | }


--------------------------------------------------------------------------------
/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "chat-langchainjs",
 3 |   "private": true,
 4 |   "version": "0.0.0",
 5 |   "author": "LangChainAI",
 6 |   "license": "MIT",
 7 |   "workspaces": [
 8 |     "backend",
 9 |     "frontend"
10 |   ],
11 |   "scripts": {
12 |     "build": "yarn run turbo:command build",
13 |     "turbo:command": "yarn turbo",
14 |     "lint": "yarn run turbo:command lint",
15 |     "lint:fix": "yarn run turbo:command lint:fix",
16 |     "format": "yarn run turbo:command format",
17 |     "format:check": "yarn run turbo:command format:check"
18 |   },
19 |   "devDependencies": {
20 |     "turbo": "latest"
21 |   },
22 |   "packageManager": "yarn@1.22.22+sha512.a6b2f7906b721bba3d67d4aff083df04dad64c399707841b7acf00f6b133b7ac24255f2652fa22ae3534329dc6180534e98d17432037ff6fd140556e2bb3137e"
23 | }
24 | 


--------------------------------------------------------------------------------
/backend/tsconfig.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "compilerOptions": {
 3 |     "outDir": "../dist",
 4 |     "rootDir": "./src",
 5 |     "target": "ES2021",
 6 |     "lib": [
 7 |       "ES2021",
 8 |       "ES2022.Object",
 9 |       "DOM"
10 |     ],
11 |     "module": "NodeNext",
12 |     "moduleResolution": "NodeNext",
13 |     "esModuleInterop": true,
14 |     "declaration": true,
15 |     "noImplicitReturns": true,
16 |     "noFallthroughCasesInSwitch": true,
17 |     "noUnusedLocals": true,
18 |     "noUnusedParameters": true,
19 |     "useDefineForClassFields": true,
20 |     "strictPropertyInitialization": false,
21 |     "allowJs": true,
22 |     "strict": true
23 |   },
24 |   "include": [
25 |     "./src/**/*"
26 |   ],
27 |   "exclude": [
28 |     "**/node_modules/**",
29 |     "./node_modules/**",
30 |     "dist",
31 |   ]
32 | }
33 | 


--------------------------------------------------------------------------------
/frontend/public/images/github-mark.svg:
--------------------------------------------------------------------------------
1 | <svg xmlns="http://www.w3.org/2000/svg" width="98" height="96"><path fill="#fff" fill-rule="evenodd" d="M48.854 0C21.839 0 0 22 0 49.217c0 21.756 13.993 40.172 33.405 46.69 2.427.49 3.316-1.059 3.316-2.362 0-1.141-.08-5.052-.08-9.127-13.59 2.934-16.42-5.867-16.42-5.867-2.184-5.704-5.42-7.17-5.42-7.17-4.448-3.015.324-3.015.324-3.015 4.934.326 7.523 5.052 7.523 5.052 4.367 7.496 11.404 5.378 14.235 4.074.404-3.178 1.699-5.378 3.074-6.6-10.839-1.141-22.243-5.378-22.243-24.283 0-5.378 1.94-9.778 5.014-13.2-.485-1.222-2.184-6.275.486-13.038 0 0 4.125-1.304 13.426 5.052a46.97 46.97 0 0 1 12.214-1.63c4.125 0 8.33.571 12.213 1.63 9.302-6.356 13.427-5.052 13.427-5.052 2.67 6.763.97 11.816.485 13.038 3.155 3.422 5.015 7.822 5.015 13.2 0 18.905-11.404 23.06-22.324 24.283 1.78 1.548 3.316 4.481 3.316 9.126 0 6.6-.08 11.897-.08 13.526 0 1.304.89 2.853 3.316 2.364 19.412-6.52 33.405-24.935 33.405-46.691C97.707 22 75.788 0 48.854 0z" clip-rule="evenodd"/></svg>


--------------------------------------------------------------------------------
/frontend/app/components/AutoResizeTextarea.tsx:
--------------------------------------------------------------------------------
 1 | import { Textarea, TextareaProps } from "@chakra-ui/react";
 2 | import ResizeTextarea from "react-textarea-autosize";
 3 | import React from "react";
 4 | 
 5 | interface ResizeTextareaProps {
 6 |   maxRows?: number;
 7 | }
 8 | 
 9 | const ResizableTextarea: React.FC<ResizeTextareaProps> = ({
10 |   maxRows,
11 |   ...props
12 | }) => {
13 |   return <ResizeTextarea maxRows={maxRows} {...props} />;
14 | };
15 | 
16 | interface AutoResizeTextareaProps extends TextareaProps {
17 |   maxRows?: number;
18 | }
19 | 
20 | export const AutoResizeTextarea = React.forwardRef<
21 |   HTMLTextAreaElement,
22 |   AutoResizeTextareaProps
23 | >((props, ref) => {
24 |   return (
25 |     <Textarea
26 |       minH="unset"
27 |       overflow="auto"
28 |       w="100%"
29 |       resize="none"
30 |       ref={ref as React.RefObject<HTMLTextAreaElement>}
31 |       as={ResizableTextarea}
32 |       {...props}
33 |     />
34 |   );
35 | });
36 | 
37 | AutoResizeTextarea.displayName = "AutoResizeTextarea";
38 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 Harrison Chase
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/backend/LICENSE:
--------------------------------------------------------------------------------
 1 | The MIT License
 2 | 
 3 | Copyright (c) Harrison Chase
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in
13 | all copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
21 | THE SOFTWARE.


--------------------------------------------------------------------------------
/frontend/app/utils/sendFeedback.tsx:
--------------------------------------------------------------------------------
 1 | import { v4 as uuidv4 } from "uuid";
 2 | import { apiBaseUrl } from "./constants";
 3 | 
 4 | type SendFeedbackProps = {
 5 |   key: string;
 6 |   runId: string;
 7 |   score?: number;
 8 |   value?: string;
 9 |   comment?: string;
10 |   feedbackId?: string;
11 |   isExplicit: boolean;
12 | };
13 | 
14 | type FeedbackResponse = {
15 |   feedbackId: string;
16 |   code: number;
17 |   result: string;
18 | };
19 | export const sendFeedback = async ({
20 |   score,
21 |   key,
22 |   runId,
23 |   value,
24 |   comment,
25 |   feedbackId,
26 |   isExplicit = true,
27 | }: SendFeedbackProps) => {
28 |   const feedback_id = feedbackId ?? uuidv4();
29 |   const response = await fetch(apiBaseUrl + "/feedback", {
30 |     method: feedbackId ? "PATCH" : "POST",
31 |     headers: {
32 |       "Content-Type": "application/json",
33 |     },
34 |     body: JSON.stringify({
35 |       score,
36 |       run_id: runId,
37 |       key,
38 |       value,
39 |       feedback_id,
40 |       comment,
41 |       source_info: {
42 |         is_explicit: isExplicit,
43 |       },
44 |     }),
45 |   });
46 |   const data = await response.json();
47 |   return {
48 |     ...data,
49 |     feedbackId: feedback_id,
50 |   } as FeedbackResponse;
51 | };
52 | 


--------------------------------------------------------------------------------
/.github/workflows/update-index.yml:
--------------------------------------------------------------------------------
 1 | name: Update index
 2 | 
 3 | on:
 4 |   workflow_dispatch:
 5 |     inputs:
 6 |       force_update:
 7 |         description: 'Whether to overwrite documents found in the record manager'
 8 |         required: false
 9 |         default: false
10 |         type: boolean
11 |   schedule:
12 |     - cron:  '0 13 * * *'
13 | 
14 | jobs:
15 |   build:
16 |     runs-on: ubuntu-latest
17 |     environment: Indexing
18 |     steps:
19 |       - uses: actions/checkout@v4
20 |       - name: Use Node.js 18.x
21 |         uses: actions/setup-node@v3
22 |         with:
23 |           node-version: 18.x
24 |           cache: "yarn"
25 |       - name: Install dependencies
26 |         run: yarn install --immutable --mode=skip-build
27 |       - name: Build backend
28 |         run: yarn build --filter=backend
29 |       - name: Ingest docs
30 |         run: yarn workspace backend ingest
31 |         env:
32 |           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
33 |           WEAVIATE_URL: ${{ secrets.WEAVIATE_URL }}
34 |           WEAVIATE_API_KEY: ${{ secrets.WEAVIATE_API_KEY }}
35 |           WEAVIATE_INDEX_NAME: ${{ secrets.WEAVIATE_INDEX_NAME }}
36 |           RECORD_MANAGER_DB_URL: ${{ secrets.RECORD_MANAGER_DB_URL }}
37 |           FORCE_UPDATE: ${{ github.event.inputs.force_update }}
38 | 


--------------------------------------------------------------------------------
/frontend/app/api/get_trace/route.ts:
--------------------------------------------------------------------------------
 1 | import { NextRequest, NextResponse } from "next/server";
 2 | 
 3 | import { Client } from "langsmith";
 4 | 
 5 | export const runtime = "edge";
 6 | 
 7 | const client = new Client();
 8 | 
 9 | const pollForRun = async (runId: string, retryCount = 0): Promise<string> => {
10 |   await new Promise((resolve) =>
11 |     setTimeout(resolve, retryCount * retryCount * 100),
12 |   );
13 |   try {
14 |     await client.readRun(runId);
15 |   } catch (e) {
16 |     return pollForRun(runId, retryCount + 1);
17 |   }
18 |   try {
19 |     const sharedLink = await client.readRunSharedLink(runId);
20 |     if (!sharedLink) {
21 |       throw new Error("Run is not shared.");
22 |     }
23 |     return sharedLink;
24 |   } catch (e) {
25 |     return client.shareRun(runId);
26 |   }
27 | };
28 | 
29 | export async function POST(req: NextRequest) {
30 |   try {
31 |     const body = await req.json();
32 |     const { run_id } = body;
33 |     if (run_id === undefined) {
34 |       return NextResponse.json(
35 |         { error: "No run ID provided" },
36 |         { status: 400 },
37 |       );
38 |     }
39 |     const response = await pollForRun(run_id);
40 |     return NextResponse.json(response, { status: 200 });
41 |   } catch (e: any) {
42 |     console.error(e);
43 |     return NextResponse.json({ error: e.message }, { status: 500 });
44 |   }
45 | }
46 | 


--------------------------------------------------------------------------------
/frontend/app/components/SourceBubble.tsx:
--------------------------------------------------------------------------------
 1 | import "react-toastify/dist/ReactToastify.css";
 2 | import { Card, CardBody, Heading } from "@chakra-ui/react";
 3 | import { sendFeedback } from "../utils/sendFeedback";
 4 | 
 5 | export type Source = {
 6 |   url: string;
 7 |   title: string;
 8 | };
 9 | 
10 | export function SourceBubble({
11 |   source,
12 |   highlighted,
13 |   onMouseEnter,
14 |   onMouseLeave,
15 |   runId,
16 | }: {
17 |   source: Source;
18 |   highlighted: boolean;
19 |   onMouseEnter: () => any;
20 |   onMouseLeave: () => any;
21 |   runId?: string;
22 | }) {
23 |   return (
24 |     <a href={source.url} target="_blank">
25 |       <Card
26 |         onClick={async () => {
27 |           window.open(source.url, "_blank");
28 |           if (runId) {
29 |             await sendFeedback({
30 |               key: "user_click",
31 |               runId,
32 |               value: source.url,
33 |               isExplicit: false,
34 |             });
35 |           }
36 |         }}
37 |         backgroundColor={highlighted ? "rgb(58, 58, 61)" : "rgb(78,78,81)"}
38 |         onMouseEnter={onMouseEnter}
39 |         onMouseLeave={onMouseLeave}
40 |         cursor={"pointer"}
41 |         alignSelf={"stretch"}
42 |         height="100%"
43 |         overflow={"hidden"}
44 |       >
45 |         <CardBody>
46 |           <Heading size={"sm"} fontWeight={"normal"} color={"white"}>
47 |             {source.title}
48 |           </Heading>
49 |         </CardBody>
50 |       </Card>
51 |     </a>
52 |   );
53 | }
54 | 


--------------------------------------------------------------------------------
/frontend/app/api/feedback/route.ts:
--------------------------------------------------------------------------------
 1 | import { NextRequest, NextResponse } from "next/server";
 2 | 
 3 | import { Client } from "langsmith";
 4 | 
 5 | export const runtime = "edge";
 6 | 
 7 | const client = new Client();
 8 | 
 9 | export async function POST(req: NextRequest) {
10 |   try {
11 |     const body = await req.json();
12 |     const { run_id, key = "user_score", ...rest } = body;
13 |     if (!run_id) {
14 |       return NextResponse.json(
15 |         { error: "No LangSmith run ID provided" },
16 |         { status: 400 },
17 |       );
18 |     }
19 | 
20 |     await client.createFeedback(run_id, key, rest);
21 | 
22 |     return NextResponse.json(
23 |       { result: "posted feedback successfully" },
24 |       { status: 200 },
25 |     );
26 |   } catch (e: any) {
27 |     console.error(e);
28 |     return NextResponse.json({ error: e.message }, { status: 500 });
29 |   }
30 | }
31 | 
32 | export async function PATCH(req: NextRequest) {
33 |   try {
34 |     const body = await req.json();
35 |     const { feedback_id, score, comment } = body;
36 |     if (feedback_id === undefined) {
37 |       return NextResponse.json(
38 |         { error: "No feedback ID provided" },
39 |         { status: 400 },
40 |       );
41 |     }
42 | 
43 |     await client.updateFeedback(feedback_id, { score, comment });
44 | 
45 |     return NextResponse.json(
46 |       { result: "patched feedback successfully" },
47 |       { status: 200 },
48 |     );
49 |   } catch (e: any) {
50 |     console.error(e);
51 |     return NextResponse.json({ error: e.message }, { status: 500 });
52 |   }
53 | }
54 | 


--------------------------------------------------------------------------------
/frontend/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "chat-langchain",
 3 |   "version": "0.1.0",
 4 |   "private": true,
 5 |   "packageManager": "yarn@1.22.19",
 6 |   "scripts": {
 7 |     "dev": "next dev",
 8 |     "build": "next build",
 9 |     "start": "next start",
10 |     "lint": "next lint",
11 |     "format": "prettier --write .",
12 |     "format:check": "prettier --config .prettierrc --check \"app\""
13 |   },
14 |   "dependencies": {
15 |     "@chakra-ui/icons": "^2.1.0",
16 |     "@chakra-ui/react": "^2.8.1",
17 |     "@emotion/react": "^11.11.1",
18 |     "@emotion/styled": "^11.11.0",
19 |     "@langchain/community": "^0.0.27",
20 |     "@langchain/core": "^0.1.26",
21 |     "@langchain/openai": "^0.0.14",
22 |     "@langchain/weaviate": "^0.0.1",
23 |     "@types/dompurify": "^3.0.5",
24 |     "@types/marked": "^5.0.1",
25 |     "@types/node": "20.4.9",
26 |     "@types/react": "18.2.20",
27 |     "@types/react-dom": "18.2.7",
28 |     "autoprefixer": "10.4.14",
29 |     "dompurify": "^3.0.8",
30 |     "emojisplosion": "^2.6.1",
31 |     "eslint": "8.46.0",
32 |     "eslint-config-next": "13.4.13",
33 |     "framer-motion": "^10.16.4",
34 |     "graphql": "^16.8.1",
35 |     "highlight.js": "^11.8.0",
36 |     "langchain": "^0.1.4",
37 |     "langsmith": "^0.0.41",
38 |     "marked": "^7.0.2",
39 |     "next": "13.5.9",
40 |     "postcss": "8.4.31",
41 |     "react": "18.2.0",
42 |     "react-dom": "18.2.0",
43 |     "react-textarea-autosize": "^8.5.3",
44 |     "react-toastify": "^9.1.3",
45 |     "tailwindcss": "3.3.3",
46 |     "typescript": "5.1.6",
47 |     "weaviate-ts-client": "^2.0.0"
48 |   },
49 |   "devDependencies": {
50 |     "prettier": "^3.0.3",
51 |     "typescript": "5.1.6"
52 |   }
53 | }
54 | 


--------------------------------------------------------------------------------
/backend/.eslintrc.cjs:
--------------------------------------------------------------------------------
 1 | module.exports = {
 2 |   extends: [
 3 |     "airbnb-base",
 4 |     "eslint:recommended",
 5 |     "prettier",
 6 |     "plugin:@typescript-eslint/recommended",
 7 |   ],
 8 |   parserOptions: {
 9 |     ecmaVersion: 12,
10 |     parser: "@typescript-eslint/parser",
11 |     project: "./tsconfig.json",
12 |     sourceType: "module",
13 |   },
14 |   plugins: ["@typescript-eslint", "no-instanceof", "eslint-plugin-jest"],
15 |   ignorePatterns: [
16 |     "src/utils/@cfworker",
17 |     "src/utils/fast-json-patch",
18 |     "src/utils/js-sha1",
19 |     ".eslintrc.cjs",
20 |     "scripts",
21 |     "node_modules",
22 |     "dist",
23 |     "dist-cjs",
24 |     "*.js",
25 |     "*.cjs",
26 |     "*.d.ts",
27 |   ],
28 |   rules: {
29 |     "no-process-env": 2,
30 |     "no-instanceof/no-instanceof": 2,
31 |     "@typescript-eslint/explicit-module-boundary-types": 0,
32 |     "@typescript-eslint/no-empty-function": 0,
33 |     "@typescript-eslint/no-shadow": 0,
34 |     "@typescript-eslint/no-empty-interface": 0,
35 |     "@typescript-eslint/no-use-before-define": ["error", "nofunc"],
36 |     "@typescript-eslint/no-unused-vars": ["warn", { args: "none" }],
37 |     "@typescript-eslint/no-floating-promises": "error",
38 |     "@typescript-eslint/no-misused-promises": "error",
39 |     camelcase: 0,
40 |     "class-methods-use-this": 0,
41 |     "import/extensions": [2, "ignorePackages"],
42 |     "import/no-extraneous-dependencies": [
43 |       "error",
44 |       { devDependencies: ["**/*.test.ts"] },
45 |     ],
46 |     "import/no-unresolved": 0,
47 |     "import/prefer-default-export": 0,
48 |     "keyword-spacing": "error",
49 |     "max-classes-per-file": 0,
50 |     "max-len": 0,
51 |     "no-await-in-loop": 0,
52 |     "no-bitwise": 0,
53 |     "no-console": 0,
54 |     "no-restricted-syntax": 0,
55 |     "no-shadow": 0,
56 |     "no-continue": 0,
57 |     "no-void": 0,
58 |     "no-underscore-dangle": 0,
59 |     "no-use-before-define": 0,
60 |     "no-useless-constructor": 0,
61 |     "no-return-await": 0,
62 |     "consistent-return": 0,
63 |     "no-else-return": 0,
64 |     "func-names": 0,
65 |     "no-lonely-if": 0,
66 |     "prefer-rest-params": 0,
67 |     "new-cap": ["error", { properties: false, capIsNew: false }],
68 |     'jest/no-focused-tests': 'error',
69 |   },
70 | };
71 | 


--------------------------------------------------------------------------------
/.github/workflows/ci.yml:
--------------------------------------------------------------------------------
 1 | # This workflow will do a clean installation of node dependencies, cache/restore them, build the source code and run tests across different versions of node
 2 | # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-nodejs
 3 | 
 4 | name: CI
 5 | 
 6 | on:
 7 |   push:
 8 |     branches: ["main"]
 9 |   pull_request:
10 |   workflow_dispatch:  # Allows triggering the workflow manually in GitHub UI
11 | 
12 | 
13 | # If another push to the same PR or branch happens while this workflow is still running,
14 | # cancel the earlier run in favor of the next run.
15 | #
16 | # There's no point in testing an outdated version of the code. GitHub only allows
17 | # a limited number of job runners to be active at the same time, so it's better to cancel
18 | # pointless jobs early so that more useful jobs can run sooner.
19 | concurrency:
20 |   group: ${{ github.workflow }}-${{ github.ref }}
21 |   cancel-in-progress: true
22 | 
23 | jobs:
24 |   lint:
25 |     name: Check linting
26 |     runs-on: ubuntu-latest
27 |     steps:
28 |       - uses: actions/checkout@v4
29 |       - name: Use Node.js 18.x
30 |         uses: actions/setup-node@v3
31 |         with:
32 |           node-version: 18.x
33 |           cache: "yarn"
34 |       - name: Install dependencies
35 |         run: yarn install --immutable --mode=skip-build
36 |       - name: Check linting
37 |         run: yarn run lint
38 |   
39 |   format:
40 |     name: Check formatting
41 |     runs-on: ubuntu-latest
42 |     steps:
43 |       - uses: actions/checkout@v4
44 |       - name: Use Node.js 18.x
45 |         uses: actions/setup-node@v3
46 |         with:
47 |           node-version: 18.x
48 |           cache: "yarn"
49 |       - name: Install dependencies
50 |         run: yarn install --immutable --mode=skip-build
51 |       - name: Check formatting
52 |         run: yarn run format:check
53 | 
54 |   backend-build:
55 |     name: Build backend
56 |     runs-on: ubuntu-latest
57 |     steps:
58 |       - uses: actions/checkout@v4
59 |       - name: Use Node.js 18.x
60 |         uses: actions/setup-node@v3
61 |         with:
62 |           node-version: 18.x
63 |       - name: Install dependencies
64 |         run: yarn install --immutable
65 |       - name: Build
66 |         run: yarn turbo:command build --filter=backend
67 | 


--------------------------------------------------------------------------------
/PRODUCTION.md:
--------------------------------------------------------------------------------
 1 | # Productionizing
 2 | 
 3 | This doc will outline steps which are recommended to bring this LLM application to production. 
 4 | 
 5 | Bringing LLM applications to production mainly involve preventing/mitigating abuse and guardrails around prompt injection. We'll go over some of your options for these, and suggestions on how to implement them.
 6 | 
 7 | > ### We also have in depth security documentation on the main LangChain documentation site. To visit, click [here](https://js.langchain.com/docs/security).
 8 | 
 9 | ## Abuse
10 | 
11 | Abuse is a big concern when deploying LLMs to production, mainly due to the cost associated with running the model (or in our case, calling an LLM API). The first and simplest step is to require accounts with some sort of "identity verification". Commonly this is done with an email verification, or for slightly more security, a phone number verification. Adding an account creation step will defer some abuse, and confirming email/phone numbers will make it even harder for users to abuse the system.
12 | 
13 | However, accounts are not enough to prevent all abuse. The next step is to add rate limiting to your API. This will prevent users from making too many requests in a short period of time. This can be done in a few ways, but the most common is to use a middleware which checks the number of requests a user has made in the last X minutes, often times linked to their account credentials, or if no account is required, then their IP address. If they've made too many, the system can handle it in a variety of ways, often times putting users into a "timeout" for a period of time, or even banning their account/IP if the abuse is severe.
14 | 
15 | ## Prompt Injection
16 | 
17 | Prompt injection is when a malicious user injects a prompt which tries to make the LLM answer or act in a way you did not originally intend for it to. This sometimes occurs because users want to use the LLM for other reasons than you intended, without having to pay for it themselves. Or the user might be trying to extract information/data they should not have access to, but the LLM does.
18 | 
19 | The first step you should always do is scoping any permissions your LLM has (eg, database permissions) as tightly as possible. For example, if your LLM only has to answer questions about a database, it should most likely only have write access. Scoping permissions is a good practice in general, but especially important when deploying LLMs.
20 | 
21 | For other prompt injection prevention methods, the water is a little more murky. We recommend you research your specific use case, and see what the latest security research says about it as the field is changing at a very rapid pace.


--------------------------------------------------------------------------------
/backend/package.json:
--------------------------------------------------------------------------------
 1 | {
 2 |   "name": "backend",
 3 |   "version": "0.0.0",
 4 |   "description": "Backend for Chat LangChainJS",
 5 |   "type": "module",
 6 |   "engines": {
 7 |     "node": ">=18"
 8 |   },
 9 |   "main": "./index.js",
10 |   "types": "./index.d.ts",
11 |   "repository": {
12 |     "type": "git",
13 |     "url": "git@github.com:langchain-ai/chat-langchainjs.git"
14 |   },
15 |   "homepage": "https://github.com/langchain-ai/chat-langchainjs/tree/main/",
16 |   "scripts": {
17 |     "build": "yarn clean && tsup",
18 |     "ingest:dev": "tsx --experimental-wasm-modules -r dotenv/config ./src/ingest.ts dotenv_config_path=.env.local",
19 |     "ingest": "node ./dist/ingest.js",
20 |     "lint:eslint": "NODE_OPTIONS=--max-old-space-size=4096 eslint --cache --ext .ts,.js src/",
21 |     "lint:dpdm": "dpdm --exit-code circular:1 --no-warning --no-tree src/*.ts src/**/*.ts",
22 |     "lint": "yarn lint:eslint && yarn lint:dpdm",
23 |     "lint:fix": "yarn lint:eslint --fix && yarn lint:dpdm",
24 |     "clean": "rimraf .turbo/ dist/",
25 |     "format": "prettier --config .prettierrc --write \"src\"",
26 |     "format:check": "prettier --config .prettierrc --check \"src\""
27 |   },
28 |   "author": "LangChain",
29 |   "license": "MIT",
30 |   "dependencies": {
31 |     "@langchain/community": "^0.0.27",
32 |     "@langchain/core": "^0.1.27",
33 |     "@langchain/openai": "^0.0.14",
34 |     "@langchain/weaviate": "^0.0.1",
35 |     "cheerio": "^1.0.0-rc.12",
36 |     "jsdom": "^24.0.0",
37 |     "langchain": "^0.1.17",
38 |     "pg": "^8.11.3",
39 |     "weaviate-ts-client": "^2.0.0"
40 |   },
41 |   "devDependencies": {
42 |     "@jest/globals": "^29.5.0",
43 |     "@langchain/scripts": "~0.0",
44 |     "@swc/core": "^1.3.90",
45 |     "@swc/jest": "^0.2.29",
46 |     "@types/pg": "^8",
47 |     "@typescript-eslint/eslint-plugin": "^6.21.0",
48 |     "@typescript-eslint/parser": "^6.21.0",
49 |     "dotenv": "^16.4.1",
50 |     "dpdm": "^3.12.0",
51 |     "eslint": "^8.33.0",
52 |     "eslint-config-airbnb-base": "^15.0.0",
53 |     "eslint-config-prettier": "^8.6.0",
54 |     "eslint-plugin-import": "^2.27.5",
55 |     "eslint-plugin-jest": "^27.6.0",
56 |     "eslint-plugin-no-instanceof": "^1.0.1",
57 |     "eslint-plugin-prettier": "^4.2.1",
58 |     "jest": "^29.5.0",
59 |     "jest-environment-node": "^29.6.4",
60 |     "prettier": "^2.8.3",
61 |     "release-it": "^15.10.1",
62 |     "rimraf": "^5.0.1",
63 |     "tsup": "^8.0.1",
64 |     "tsx": "^3.12.3",
65 |     "typescript": "~5.1.6"
66 |   },
67 |   "publishConfig": {
68 |     "access": "public"
69 |   },
70 |   "keywords": [
71 |     "llm",
72 |     "ai",
73 |     "gpt3",
74 |     "chain",
75 |     "prompt",
76 |     "prompt engineering",
77 |     "chatgpt",
78 |     "machine learning",
79 |     "ml",
80 |     "openai",
81 |     "embeddings",
82 |     "vectorstores"
83 |   ],
84 |   "files": [
85 |     "dist/",
86 |     "index.cjs",
87 |     "index.js",
88 |     "index.d.ts",
89 |     "index.d.cts"
90 |   ]
91 | }
92 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | .env.local
 10 | .env
 11 | 
 12 | .yarn/*
 13 | .yarn/install-state.gz
 14 | !.yarn/patches
 15 | !.yarn/plugins
 16 | !.yarn/releases
 17 | !.yarn/sdks
 18 | !.yarn/versions
 19 | 
 20 | **/.eslintcache
 21 | .eslintcache
 22 | 
 23 | .turbo
 24 | 
 25 | # Distribution / packaging
 26 | .Python
 27 | build/
 28 | develop-eggs/
 29 | dist/
 30 | downloads/
 31 | eggs/
 32 | .eggs/
 33 | lib/
 34 | lib64/
 35 | parts/
 36 | sdist/
 37 | var/
 38 | wheels/
 39 | pip-wheel-metadata/
 40 | share/python-wheels/
 41 | *.egg-info/
 42 | .installed.cfg
 43 | *.egg
 44 | MANIFEST
 45 | 
 46 | # PyInstaller
 47 | #  Usually these files are written by a python script from a template
 48 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 49 | *.manifest
 50 | *.spec
 51 | 
 52 | # Installer logs
 53 | pip-log.txt
 54 | pip-delete-this-directory.txt
 55 | 
 56 | # Unit test / coverage reports
 57 | htmlcov/
 58 | .tox/
 59 | .nox/
 60 | .coverage
 61 | .coverage.*
 62 | .cache
 63 | nosetests.xml
 64 | coverage.xml
 65 | *.cover
 66 | *.py,cover
 67 | .hypothesis/
 68 | .pytest_cache/
 69 | 
 70 | # Translations
 71 | *.mo
 72 | *.pot
 73 | 
 74 | # Django stuff:
 75 | *.log
 76 | local_settings.py
 77 | db.sqlite3
 78 | db.sqlite3-journal
 79 | 
 80 | # Flask stuff:
 81 | instance/
 82 | .webassets-cache
 83 | 
 84 | # Scrapy stuff:
 85 | .scrapy
 86 | 
 87 | # Sphinx documentation
 88 | docs/_build/
 89 | 
 90 | # PyBuilder
 91 | target/
 92 | 
 93 | # Jupyter Notebook
 94 | .ipynb_checkpoints
 95 | 
 96 | # IPython
 97 | profile_default/
 98 | ipython_config.py
 99 | 
100 | # pyenv
101 | .python-version
102 | 
103 | # pipenv
104 | #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
105 | #   However, in case of collaboration, if having platform-specific dependencies or dependencies
106 | #   having no cross-platform support, pipenv may install dependencies that don't work, or not
107 | #   install all needed dependencies.
108 | #Pipfile.lock
109 | 
110 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
111 | __pypackages__/
112 | 
113 | # Celery stuff
114 | celerybeat-schedule
115 | celerybeat.pid
116 | 
117 | # SageMath parsed files
118 | *.sage.py
119 | 
120 | # Environments
121 | .env
122 | .venv
123 | env/
124 | venv/
125 | ENV/
126 | env.bak/
127 | venv.bak/
128 | 
129 | # Spyder project settings
130 | .spyderproject
131 | .spyproject
132 | 
133 | # Rope project settings
134 | .ropeproject
135 | 
136 | # mkdocs documentation
137 | /site
138 | 
139 | # mypy
140 | .mypy_cache/
141 | .dmypy.json
142 | dmypy.json
143 | 
144 | # Pyre type checker
145 | .pyre/
146 | 
147 | # JetBrains
148 | .idea
149 | 
150 | *.db
151 | 
152 | .DS_Store
153 | 
154 | vectorstore.pkl
155 | langchain.readthedocs.io/
156 | .vercel
157 | 
158 | bin/
159 | pyvenv.cfg
160 | node_modules/
161 | .envrc
162 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # 🦜️🔗 Chat LangChain.js
 2 | 
 3 | This repo is an implementation of a locally hosted chatbot specifically focused on question answering over the [LangChain documentation](https://langchain.readthedocs.io/en/latest/).
 4 | Built with [LangChain](https://github.com/langchain-ai/langchainjs/), and [Next.js](https://nextjs.org).
 5 | 
 6 | Deployed version: [chatjs.langchain.com](https://chatjs.langchain.com)
 7 | 
 8 | > Looking for the Python version? Click [here](https://github.com/langchain-ai/chat-langchain)
 9 | 
10 | ## ✅ Local development
11 | 1. Install dependencies via: `yarn install`.
12 | 2. Set the required environment variables listed inside [`backend/.env.example`](backend/.env.example) for the backend, and [`frontend/.env.example`](frontend/.env.example) for the frontend.
13 | 
14 | ### Ingest
15 | 1. Build the backend via `yarn build --filter=backend` (from root).
16 | 2. Run the ingestion script by navigating into `./backend` and running `yarn ingest`.
17 | 
18 | ### Frontend
19 | 1. Navigate into `./frontend` and run `yarn dev` to start the frontend.
20 | 2. Open [localhost:3000](http://localhost:3000) in your browser.
21 | 
22 | ## 📚 Technical description
23 | 
24 | There are two components: ingestion and question-answering.
25 | 
26 | Ingestion has the following steps:
27 | 
28 | 1. Pull html from documentation site as well as the Github Codebase
29 | 2. Load html with LangChain's [RecursiveUrlLoader](https://api.js.langchain.com/classes/langchain_document_loaders_web_recursive_url.RecursiveUrlLoader.html) and [SitemapLoader](https://js.langchain.com/docs/integrations/document_loaders/web_loaders/sitemap)
30 | 3. Split documents with LangChain's [RecursiveCharacterTextSplitter](https://js.langchain.com/docs/modules/data_connection/document_transformers/recursive_text_splitter)
31 | 4. Create a vectorstore of embeddings, using LangChain's [Weaviate vectorstore wrapper](https://js.langchain.com/docs/integrations/vectorstores/weaviate) (with OpenAI's embeddings).
32 | 
33 | Question-Answering has the following steps:
34 | 
35 | 1. Given the chat history and new user input, determine what a standalone question would be using GPT-3.5.
36 | 2. Given that standalone question, look up relevant documents from the vectorstore.
37 | 3. Pass the standalone question and relevant documents to the model to generate and stream the final answer.
38 | 4. Generate a trace URL for the current chat session, as well as the endpoint to collect feedback.
39 | 
40 | ## Documentation
41 | 
42 | Looking to use or modify this Use Case Accelerant for your own needs? We've added a few docs to aid with this:
43 | 
44 | - **[Concepts](./CONCEPTS.md)**: A conceptual overview of the different components of Chat LangChain. Goes over features like ingestion, vector stores, query analysis, etc.
45 | - **[Modify](./MODIFY.md)**: A guide on how to modify Chat LangChain for your own needs. Covers the frontend, backend and everything in between.
46 | - **[Running Locally](./RUN_LOCALLY.md)**: The steps to take to run Chat LangChain 100% locally.
47 | - **[LangSmith](./LANGSMITH.md)**: A guide on adding robustness to your application using LangSmith. Covers observability, evaluations, and feedback.
48 | - **[Production](./PRODUCTION.md)**: Documentation on preparing your application for production usage. Explains different security considerations, and more.
49 | - **[Deployment](./DEPLOYMENT.md)**: How to deploy your application to production. Covers setting up production databases, deploying the frontend, and more.
50 | 


--------------------------------------------------------------------------------
/frontend/app/components/EmptyState.tsx:
--------------------------------------------------------------------------------
  1 | import { MouseEvent } from "react";
  2 | import {
  3 |   Heading,
  4 |   Link,
  5 |   Card,
  6 |   CardHeader,
  7 |   Flex,
  8 |   Spacer,
  9 | } from "@chakra-ui/react";
 10 | 
 11 | export function EmptyState(props: { onChoice: (question: string) => any }) {
 12 |   const handleClick = (e: MouseEvent) => {
 13 |     props.onChoice((e.target as HTMLDivElement).innerText);
 14 |   };
 15 |   return (
 16 |     <div className="rounded flex flex-col items-center max-w-full md:p-8">
 17 |       <Flex marginTop={"25px"} grow={1} maxWidth={"800px"} width={"100%"}>
 18 |         <Card
 19 |           onMouseUp={handleClick}
 20 |           width={"48%"}
 21 |           backgroundColor={"rgb(58, 58, 61)"}
 22 |           _hover={{ backgroundColor: "rgb(78,78,81)" }}
 23 |           cursor={"pointer"}
 24 |           justifyContent={"center"}
 25 |         >
 26 |           <CardHeader justifyContent={"center"}>
 27 |             <Heading
 28 |               fontSize="lg"
 29 |               fontWeight={"medium"}
 30 |               mb={1}
 31 |               color={"gray.200"}
 32 |               textAlign={"center"}
 33 |             >
 34 |               How do I use a RecursiveUrlLoader to load content from a page?
 35 |             </Heading>
 36 |           </CardHeader>
 37 |         </Card>
 38 |         <Spacer />
 39 |         <Card
 40 |           onMouseUp={handleClick}
 41 |           width={"48%"}
 42 |           backgroundColor={"rgb(58, 58, 61)"}
 43 |           _hover={{ backgroundColor: "rgb(78,78,81)" }}
 44 |           cursor={"pointer"}
 45 |           justifyContent={"center"}
 46 |         >
 47 |           <CardHeader justifyContent={"center"}>
 48 |             <Heading
 49 |               fontSize="lg"
 50 |               fontWeight={"medium"}
 51 |               mb={1}
 52 |               color={"gray.200"}
 53 |               textAlign={"center"}
 54 |             >
 55 |               What does RunnablePassthrough.assign() do?
 56 |             </Heading>
 57 |           </CardHeader>
 58 |         </Card>
 59 |       </Flex>
 60 |       <Flex marginTop={"25px"} grow={1} maxWidth={"800px"} width={"100%"}>
 61 |         <Card
 62 |           onMouseUp={handleClick}
 63 |           width={"48%"}
 64 |           backgroundColor={"rgb(58, 58, 61)"}
 65 |           _hover={{ backgroundColor: "rgb(78,78,81)" }}
 66 |           cursor={"pointer"}
 67 |           justifyContent={"center"}
 68 |         >
 69 |           <CardHeader justifyContent={"center"}>
 70 |             <Heading
 71 |               fontSize="lg"
 72 |               fontWeight={"medium"}
 73 |               mb={1}
 74 |               color={"gray.200"}
 75 |               textAlign={"center"}
 76 |             >
 77 |               What are some ways of doing retrieval augmented generation?
 78 |             </Heading>
 79 |           </CardHeader>
 80 |         </Card>
 81 |         <Spacer />
 82 |         <Card
 83 |           onMouseUp={handleClick}
 84 |           width={"48%"}
 85 |           backgroundColor={"rgb(58, 58, 61)"}
 86 |           _hover={{ backgroundColor: "rgb(78,78,81)" }}
 87 |           cursor={"pointer"}
 88 |           justifyContent={"center"}
 89 |         >
 90 |           <CardHeader justifyContent={"center"}>
 91 |             <Heading
 92 |               fontSize="lg"
 93 |               fontWeight={"medium"}
 94 |               mb={1}
 95 |               color={"gray.200"}
 96 |               textAlign={"center"}
 97 |             >
 98 |               How do I run a model locally on my laptop with Ollama?
 99 |             </Heading>
100 |           </CardHeader>
101 |         </Card>
102 |       </Flex>
103 |     </div>
104 |   );
105 | }
106 | 


--------------------------------------------------------------------------------
/DEPLOYMENT.md:
--------------------------------------------------------------------------------
 1 | # Deployment
 2 | 
 3 | We recommend when deploying Chat LangChain JS, you use Vercel for the frontend & edge API, and GitHub action for the recurring ingestion tasks. This setup provides a simple and effective way to deploy and manage your application.
 4 | 
 5 | ## Prerequisites
 6 | 
 7 | First, fork [chat-langchainjs](https://github.com/langchain-ai/chat-langchainjs) to your GitHub account.
 8 | 
 9 | ## Weaviate (Vector Store)
10 | 
11 | We'll use Weaviate for our vector store. You can sign up for an account [here](https://console.weaviate.cloud/).
12 | 
13 | After creating an account click "Create Cluster". Follow the steps to create a new cluster. Once finished wait for the cluster to create, this may take a few minutes.
14 | 
15 | Once your cluster has been created you should see a few sections on the page. The first is the cluster URL. Save this as your `WEAVIATE_URL` environment variable.
16 | 
17 | Next, click "API Keys" and save the API key in the environment variable `WEAVIATE_API_KEY`.
18 | 
19 | The final Weaviate environment variable is "WEAVIATE_INDEX_NAME". This is the name of the index you want to use. You can name it whatever you want, but for this example, we'll use "langchain".
20 | 
21 | After this your vector store will be setup. We can now move onto the record manager.
22 | 
23 | ## Supabase (Record Manager)
24 | 
25 | Visit Supabase to create an account [here](https://supabase.com/dashboard).
26 | 
27 | Once you've created an account, click "New project" on the dashboard page.
28 | Follow the steps, saving the database password after creating it, we'll need this later.
29 | 
30 | Once your project is setup (this also takes a few minutes), navigate to the "Settings" tab, then select "Database" under "Configuration".
31 | 
32 | Here, you should see a "Connection string" section. Copy this string, and insert your database password you saved earlier. This is your `RECORD_MANAGER_DB_URL` environment variable.
33 | 
34 | That's all you need to do for the record manager. The LangChain RecordManager API will handle creating tables for you.
35 | 
36 | ## Vercel (Frontend & Edge API)
37 | 
38 | Create a Vercel account for hosting [here](https://vercel.com/signup).
39 | 
40 | Once you've created your Vercel account, navigate to [your dashboard](https://vercel.com/) and click the button "Add New..." in the top right.
41 | This will open a dropdown. From there select "Project".
42 | 
43 | On the next screen, search for "chat-langchainjs" (if you did not modify the repo name when forking). Once shown, click "Import".
44 | 
45 | Here you should *only* modify the "Environment Variables" section. You should add the following environment variables:
46 | 
47 | > If you have not setup LangSmith, head to the [LangSmith](./LANGSMITH.md) doc for instructions.
48 | 
49 | ```
50 | LANGCHAIN_TRACING_V2=true
51 | LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"
52 | LANGCHAIN_API_KEY=YOUR_API_KEY
53 | LANGCHAIN_PROJECT=YOUR_PROJECT
54 | 
55 | WEAVIATE_API_KEY=YOUR_API_KEY
56 | WEAVIATE_URL=YOUR_WEAVIATE_URL
57 | WEAVIATE_INDEX_NAME=langchain
58 | FORCE_UPDATE=true
59 | RECORD_MANAGER_DB_URL=YOUR_DB_URL
60 | OPENAI_API_KEY=YOUR_OPENAI_KEY
61 | ```
62 | 
63 | Finally, click "Deploy" and your frontend & edge API will be deployed.
64 | 
65 | ## GitHub Action (Recurring Ingestion)
66 | 
67 | Now, in order for your vector store to be updated with new data, you'll need to setup a recurring ingestion task (this will also populate the vector store for the first time).
68 | 
69 | Go to your forked repository, and navigate to the "Settings" tab.
70 | 
71 | Select "Environments" from the left-hand menu, and click "New environment". Enter the name "Indexing" and click "Configure environment".
72 | 
73 | When configuring, click "Add secret" and add the following secrets:
74 | 
75 | ```
76 | OPENAI_API_KEY=
77 | RECORD_MANAGER_DB_URL=
78 | WEAVIATE_API_KEY=
79 | WEAVIATE_INDEX_NAME=langchain
80 | WEAVIATE_URL=
81 | ```
82 | 
83 | These should be the same secrets as were added to Vercel.
84 | 
85 | Next, navigate to the "Actions" tab and confirm you understand your workflows, and enable them.
86 | 
87 | Then, click on the "Update index" workflow, and click "Enable workflow". Finally, click on the "Run workflow" dropdown and click "Run workflow".
88 | 
89 | Once this has finished you can visit your production URL from Vercel, and start using the app!
90 | 


--------------------------------------------------------------------------------
/LANGSMITH.md:
--------------------------------------------------------------------------------
 1 | # LangSmith
 2 | 
 3 | Observability and evaluations are pivotal to any LLM application looking to be productionized, and improve beyond initial deployment.
 4 | For this, we use LangSmith, a tool that encapsulates all the necessary components to monitor and improve your LLM applications.
 5 | In addition to these two development tools, LangSmith also offers a feature for managing feedback from users.
 6 | Getting real user feedback can be invaluable for improving your LLM application, based on facts from actual users, and not just assumptions/theories.
 7 | 
 8 | ## Observability
 9 | 
10 | Observability is simple when using LangChain as your LLM framework. In its simplest form, all you need is to set two environment variables:
11 | 
12 | ```shell
13 | LANGCHAIN_TRACING_V2=true
14 | LANGCHAIN_API_KEY=...
15 | ```
16 | 
17 | Sometimes need to pass a config object through to different functions, like so:
18 | 
19 | ```typescript
20 | new RunnableLambda({ func: (input, config ) => yourRunnable.invoke(input, config) })
21 | ```
22 | 
23 | Doing this keeps all your traces inside the same run in LangSmith, making it easier to debug and track the different paths your application took.
24 | 
25 | LangSmith tracing is already setup in an optimized way for Chat LangChain, and only needs extra configuration if you're extending the application in a way that's not covered by the default tracing. When running queries through Chat LangChain, you can expect to see LangSmith traces like this show up on your project:
26 | 
27 | ![LangSmith Traces](./assets/images/langsmith_trace.png)
28 | 
29 | For more detailed information on LangSmith traces, visit the [LangSmith documentation](https://docs.smith.langchain.com/tracing/).
30 | 
31 | ## Evaluations
32 | 
33 | Evals are a great way to discover issues with your LLM app, areas where it does not perform well, and track regression. LangSmith has a whole suite of tools to aid you with this.
34 | 
35 | For in depth walkthroughs and explanations of LangSmith evaluations, visit the [LangSmith documentation](https://docs.smith.langchain.com/evaluation). This doc will only go over setting up and running evals on Chat LangChain.
36 | 
37 | ### Datasets
38 | 
39 | For Chat LangChain, the team at LangChain has already put together a dataset for evaluating the app. Although this dataset was built for the Python version, many of the questions still apply to the TypeScript version, or require slight modifications.
40 | 
41 | You can find the dataset [here](https://smith.langchain.com/public/452ccafc-18e1-4314-885b-edd735f17b9d/d).
42 | 
43 | The first step is to download the LangSmith node SDK:
44 | 
45 | ```shell
46 | yarn add langsmith
47 | ```
48 | 
49 | Then, you'll want to define some custom criteria to evaluate your dataset on. Some examples are:
50 | 
51 | - **Semantic similarity**: How similar your generated response is to the ground truth (dataset answers).
52 | - **LLM as a judge**: Use an LLM to judge and assign a score to your generated response.
53 | 
54 | Finally, configure your evaluation criteria and use the [`runOnDataset`](https://api.js.langchain.com/functions/langchain_smith.runOnDataset.html) function to evaluate your dataset.
55 | 
56 | Once completed, you'll be able to view the results of your evaluation in the LangSmith dashboard. Using these results, you can improve and tweak your LLM.
57 | 
58 | ## Feedback
59 | 
60 | Gathering feedback from users is a great way to gather human curated data on what works, what doesn't and how you can improve your LLM application. LangSmith makes tracking and gathering feedback as easy as pie.
61 | 
62 | Currently, Chat LangChain supports gathering a simple 👍 or 👎, which is then translated into a binary score, and saved to each run in LangSmith. This feedback is then stored in the --you guessed it-- feedback tab of the LangSmith trace:
63 | 
64 | ![LangSmith Feedback](./assets/images/langsmith_feedback.png)
65 | 
66 | Then, inside LangSmith you can efficiently use this data to visualize and understand your user's feedback, as well as curate datasets by feedback for evaluations.
67 | 
68 | ### Go further
69 | 
70 | In addition to binary scores for feedback, LangSmith also allows for assigning comments to feedback. This can allow for you to gather more detailed and complex feedback from users, further fueling your human curated dataset for improving your LLM application.


--------------------------------------------------------------------------------
/backend/src/ingest.ts:
--------------------------------------------------------------------------------
  1 | /* eslint-disable no-process-env */
  2 | import weaviate, { ApiKey, WeaviateClient } from "weaviate-ts-client";
  3 | import { DocumentInterface } from "@langchain/core/documents";
  4 | import { RecursiveUrlLoader } from "langchain/document_loaders/web/recursive_url";
  5 | import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
  6 | import { OpenAIEmbeddings } from "@langchain/openai";
  7 | import { Embeddings } from "@langchain/core/embeddings";
  8 | import { WeaviateStore } from "@langchain/weaviate";
  9 | import { PostgresRecordManager } from "@langchain/community/indexes/postgres";
 10 | import { SitemapLoader } from "langchain/document_loaders/web/sitemap";
 11 | import { index } from "./_index.js";
 12 | 
 13 | /**
 14 |  * Load all of the LangSmith documentation via the
 15 |  * `RecursiveUrlLoader` and return the documents.
 16 |  * @returns {Promise<Array<DocumentInterface>>}
 17 |  */
 18 | async function loadLangSmithDocs(): Promise<Array<DocumentInterface>> {
 19 |   const loader = new RecursiveUrlLoader("https://docs.smith.langchain.com/", {
 20 |     maxDepth: 8,
 21 |     timeout: 600,
 22 |   });
 23 |   return loader.load();
 24 | }
 25 | 
 26 | /**
 27 |  * Load all of the LangChain.js API references via
 28 |  * the `RecursiveUrlLoader` and return the documents.
 29 |  * @returns {Promise<Array<DocumentInterface>>}
 30 |  */
 31 | async function loadAPIDocs(): Promise<Array<DocumentInterface>> {
 32 |   const loader = new RecursiveUrlLoader(
 33 |     "https://api.js.langchain.com/index.html/",
 34 |     {
 35 |       maxDepth: 8,
 36 |       timeout: 600,
 37 |     }
 38 |   );
 39 |   return loader.load();
 40 | }
 41 | 
 42 | /**
 43 |  * Load all of the LangChain docs via the sitemap.
 44 |  * @returns {Promise<Array<DocumentInterface>>}
 45 |  */
 46 | async function loadLangChainDocs(): Promise<Array<DocumentInterface>> {
 47 |   const loader = new SitemapLoader("https://js.langchain.com/");
 48 |   return loader.load();
 49 | }
 50 | 
 51 | function getEmbeddingsModel(): Embeddings {
 52 |   return new OpenAIEmbeddings();
 53 | }
 54 | 
 55 | async function ingestDocs() {
 56 |   if (
 57 |     !process.env.WEAVIATE_API_KEY ||
 58 |     !process.env.WEAVIATE_URL ||
 59 |     !process.env.WEAVIATE_INDEX_NAME
 60 |   ) {
 61 |     throw new Error(
 62 |       "WEAVIATE_API_KEY, WEAVIATE_URL, and WEAVIATE_INDEX_NAME must be set in the environment"
 63 |     );
 64 |   }
 65 | 
 66 |   const smithDocs = await loadLangSmithDocs();
 67 |   console.debug(`Loaded ${smithDocs.length} docs from LangSmith`);
 68 |   const apiDocs = await loadAPIDocs();
 69 |   console.debug(`Loaded ${apiDocs.length} docs from API`);
 70 |   const langchainDocs = await loadLangChainDocs();
 71 |   console.debug(`Loaded ${langchainDocs.length} docs from documentation`);
 72 | 
 73 |   if (!smithDocs.length || !apiDocs.length || !langchainDocs.length) {
 74 |     process.exit(1);
 75 |   }
 76 | 
 77 |   const textSplitter = new RecursiveCharacterTextSplitter({
 78 |     chunkOverlap: 200,
 79 |     chunkSize: 4000,
 80 |   });
 81 |   const docsTransformed = await textSplitter.splitDocuments([
 82 |     ...smithDocs,
 83 |     ...apiDocs,
 84 |     ...langchainDocs,
 85 |   ]);
 86 | 
 87 |   // We try to return 'source' and 'title' metadata when querying vector store and
 88 |   // Weaviate will error at query time if one of the attributes is missing from a
 89 |   // retrieved document.
 90 | 
 91 |   for (const doc of docsTransformed) {
 92 |     if (!doc.metadata.source) {
 93 |       doc.metadata.source = "";
 94 |     }
 95 |     if (!doc.metadata.title) {
 96 |       doc.metadata.title = "";
 97 |     }
 98 |   }
 99 | 
100 |   // eslint-disable-next-line @typescript-eslint/no-explicit-any
101 |   const weaviateClient = (weaviate as any).client({
102 |     scheme: "https",
103 |     host: process.env.WEAVIATE_URL,
104 |     apiKey: new ApiKey(process.env.WEAVIATE_API_KEY),
105 |   }) as WeaviateClient;
106 | 
107 |   const embeddings = getEmbeddingsModel();
108 |   const vectorStore = new WeaviateStore(embeddings, {
109 |     client: weaviateClient,
110 |     indexName: process.env.WEAVIATE_INDEX_NAME,
111 |     textKey: "text",
112 |   });
113 | 
114 |   const connectionOptions = process.env.RECORD_MANAGER_DB_URL
115 |     ? {
116 |         connectionString: process.env.RECORD_MANAGER_DB_URL,
117 |       }
118 |     : {
119 |         host: process.env.DATABASE_HOST,
120 |         port: Number(process.env.DATABASE_PORT),
121 |         user: process.env.DATABASE_USERNAME,
122 |         password: process.env.DATABASE_PASSWORD,
123 |         database: process.env.DATABASE_NAME,
124 |       };
125 | 
126 |   const recordManager = new PostgresRecordManager(
127 |     `weaviate/${process.env.WEAVIATE_INDEX_NAME}`,
128 |     {
129 |       postgresConnectionOptions: connectionOptions,
130 |     }
131 |   );
132 |   await recordManager.createSchema();
133 | 
134 |   const indexingStats = await index({
135 |     docsSource: docsTransformed,
136 |     recordManager,
137 |     vectorStore,
138 |     cleanup: "full",
139 |     sourceIdKey: "source",
140 |     forceUpdate: process.env.FORCE_UPDATE === "true",
141 |   });
142 | 
143 |   console.log(
144 |     {
145 |       indexingStats,
146 |     },
147 |     "Indexing stats"
148 |   );
149 | 
150 |   try {
151 |     const { data: numVecsData } = await weaviateClient.graphql
152 |       .aggregate()
153 |       .withClassName(process.env.WEAVIATE_INDEX_NAME)
154 |       .withFields("meta { count }")
155 |       .do();
156 |     const numVecs =
157 |       numVecsData.Aggregate[process.env.WEAVIATE_INDEX_NAME][0].meta.count;
158 |     console.log(`LangChain now has this many vectors: ${numVecs}`);
159 |   } catch (e) {
160 |     console.error("Failed to fetch total vectors.");
161 |   }
162 | }
163 | 
164 | ingestDocs().catch((e) => {
165 |   console.error("Failed to ingest docs");
166 |   console.error(e);
167 |   process.exit(1);
168 | });
169 | 


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/RUN_LOCALLY.md:
--------------------------------------------------------------------------------
  1 | # Running locally
  2 | 
  3 | If you wish to run this 100% locally, you'll need to update a few pieces of the code, and download extra software. Because this application was built on top of the LangChain framework, modifying the code to run locally is simple.
  4 | 
  5 | ## Requirements
  6 | 
  7 | To run locally, we'll employ [Ollama](https://ollama.com) for LLM inference and embeddings generation. For the vector store we'll use [Chroma](https://www.trychroma.com/), a free open source vector store. For the record manager, we'll use a simple PostgreSQL database. And finally, to run Chroma and PostgreSQL you'll need to install Docker.
  8 | 
  9 | ## Steps
 10 | 
 11 | ### Docker
 12 | 
 13 | To download and manage Docker containers with a GUI, you can download OrbStack [here](https://orbstack.dev/download). Once setup, we can install Chroma and PostgreSQL.
 14 | 
 15 | ### Chroma
 16 | 
 17 | To download and start a Docker container running Chroma, first clone the official Chroma repository:
 18 | 
 19 | ```shell
 20 | git clone git@github.com:chroma-core/chroma.git
 21 | ```
 22 | 
 23 | Next, navigate into the cloned repository and start the Docker container:
 24 | 
 25 | ```shell
 26 | cd chroma
 27 | docker-compose up -d --build
 28 | ```
 29 | 
 30 | That's it! Now, if you open OrbStack you should see a container named "Chroma" running.
 31 | 
 32 | ![Chroma Container](./assets/images/orbstack_running_chroma.png)
 33 | 
 34 | ### PostgreSQL
 35 | 
 36 | First, pull the PostgreSQL image:
 37 | 
 38 | ```shell
 39 | docker pull postgres
 40 | ```
 41 | 
 42 | Then, run this command to start the image.
 43 | 
 44 | ```shell
 45 | docker run --name postgres -e POSTGRES_PASSWORD=mysecretpassword -d postgres
 46 | ```
 47 | 
 48 | Change "mysecretpassword" to your desired password.
 49 | 
 50 | Once finished you should see a second container running in OrbStack named "postgres"
 51 | 
 52 | ![Chroma and PostgreSQL Container](./assets/images/orbstack_running_chroma_pgsql.png)
 53 | 
 54 | ### Ollama
 55 | 
 56 | To download Ollama, click [here](https://ollama.com/download) and select your operating system to download. Follow along with their onboarding setup.
 57 | 
 58 | Next, download the following models:
 59 | 
 60 | - [**mistral**](https://ollama.com/library/mistral): This model will be used for question rephrasing and answer generation.
 61 | - [**nomic-embed-text**](https://ollama.com/library/nomic-embed-text): We'll use this model for embeddings generation.
 62 | 
 63 | ## Code changes
 64 | 
 65 | ### Ingest script
 66 | 
 67 | To update your ingest script to run using Chroma and your locally running PostgreSQL image, you only need to modify a few lines of code. First, navigate to the [`/backend/src/ingest.ts`](/backend/src/ingest.ts) file.
 68 | 
 69 | Then, find the `ingestDocs` function and update the first if statement to instead check for your PostgreSQL database credentials.
 70 | 
 71 | ```shell
 72 | DATABASE_HOST="127.0.0.1"
 73 | DATABASE_PORT="5432"
 74 | DATABASE_USERNAME="postgres"
 75 | DATABASE_PASSWORD="mysecretpassword"
 76 | DATABASE_NAME="your-db-name" # Replace this with your database name.
 77 | ```
 78 | 
 79 | You'll also need to create a database inside your PostgreSQL container:
 80 | 
 81 | ```shell
 82 | docker exec -it postgres createdb -U postgres your-db-name
 83 | ```
 84 | 
 85 | Next, find the `getEmbeddingsModel` and replace its contents with an [`OllamaEmbeddings`](https://api.js.langchain.com/classes/langchain_community_embeddings_ollama.OllamaEmbeddings.html) instance:
 86 | 
 87 | 
 88 | ```typescript
 89 | import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
 90 | 
 91 | function getEmbeddingsModel(): Embeddings {
 92 |   return new OllamaEmbeddings({
 93 |     model: "nomic-embed-text",
 94 |   });
 95 | }
 96 | ```
 97 | 
 98 | For our databases, we'll want to set one more environment variable to track our collection name (similar to the index name for Weaviate):
 99 | 
100 | ```shell
101 | COLLECTION_NAME="your-collection-name" # Change this to your collection name
102 | ```
103 | 
104 | Directly below where this function is invoked, you can delete the `WeaviateStore` class instantiation and replace it with a `Chroma` class instantiation:
105 | 
106 | ```typescript
107 | import { Chroma } from "@langchain/community/vectorstores/chroma";
108 | 
109 | const vectorStore = new Chroma(embeddings, {
110 |   collectionName: process.env.COLLECTION_NAME
111 | });
112 | ```
113 | 
114 | Then, update the record manager namespace:
115 | 
116 | ```typescript
117 | const recordManager = new PostgresRecordManager(
118 |   `local/${process.env.COLLECTION_NAME}`,
119 |   {
120 |     postgresConnectionOptions: connectionOptions,
121 |   }
122 | );
123 | ```
124 | 
125 | Finally, you can delete the Weaviate specific stats code at the end of the function (this is just for logging info on how many items are stored in the database).
126 | 
127 | ### API Endpoints
128 | 
129 | Next, we need to update the API endpoints to use Ollama for local LLM inference, and Chroma for document retrieval.
130 | 
131 | Navigate to the [`/api/chat/stream_log`](frontend/app/api/chat/stream_log/route.ts) endpoint.
132 | 
133 | First, find the `getRetriever` function and remove the if statement checking for Weaviate environment variables, the Chroma LangChain.js integration does not require any!
134 | 
135 | Then, replace the Weaviate specific code with Chroma and Ollama embeddings:
136 | 
137 | ```typescript
138 | import { Chroma } from "@langchain/community/vectorstores/chroma";
139 | import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
140 | 
141 | const embeddings = new OllamaEmbeddings({
142 |   model: "nomic-embed-text",
143 | });
144 | const vectorstore = await Chroma.fromExistingCollection(
145 |   embeddings,
146 |   {
147 |     collectionName: process.env.COLLECTION_NAME
148 |   },
149 | );
150 | ```
151 | 
152 | Finally, find the `POST` function and replace the `if` statements with a single llm variable instantiation:
153 | 
154 | ```typescript
155 | import { ChatOllama } from "@langchain/community/chat_models/ollama";
156 | 
157 | const llm = new ChatOllama({
158 |   model: "mistral"
159 | });
160 | ```
161 | 
162 | Now you're done! You can run the application 100% locally with just two commands:
163 | 
164 | 1. Ingest docs:
165 | 
166 | ```shell
167 | cd ./backend && yarn build && yarn ingest
168 | ```
169 | 
170 | 2. Start the Next.js application:
171 | 
172 | ```shell
173 | cd ./frontend && yarn build && yarn start
174 | ```


--------------------------------------------------------------------------------
/backend/src/_index.ts:
--------------------------------------------------------------------------------
  1 | import { RecordManagerInterface } from "@langchain/community/indexes/base";
  2 | import { DocumentInterface } from "@langchain/core/documents";
  3 | import { VectorStore } from "@langchain/core/vectorstores";
  4 | import { BaseDocumentLoader } from "langchain/document_loaders/base";
  5 | import {
  6 |   _HashedDocument,
  7 |   _batch,
  8 |   _deduplicateInOrder,
  9 |   _getSourceIdAssigner,
 10 | } from "langchain/indexes";
 11 | 
 12 | const DEFAULTS = {
 13 |   batchSize: 100,
 14 |   cleanupBatchSize: 1000,
 15 |   forceUpdate: false,
 16 | };
 17 | 
 18 | export async function index(options: {
 19 |   docsSource: BaseDocumentLoader | Array<DocumentInterface>;
 20 |   recordManager: RecordManagerInterface;
 21 |   vectorStore: VectorStore;
 22 |   /**
 23 |    * @default 100
 24 |    */
 25 |   batchSize?: number;
 26 |   cleanup?: "incremental" | "full";
 27 |   sourceIdKey: string | ((doc: DocumentInterface) => string);
 28 |   /**
 29 |    * @default {1000}
 30 |    */
 31 |   cleanupBatchSize?: number;
 32 |   /**
 33 |    * @default false
 34 |    */
 35 |   forceUpdate?: boolean;
 36 | }): Promise<{
 37 |   numAdded: number;
 38 |   numSkipped: number;
 39 |   numDeleted: number;
 40 | }> {
 41 |   const {
 42 |     docsSource,
 43 |     recordManager,
 44 |     vectorStore,
 45 |     batchSize,
 46 |     cleanup,
 47 |     sourceIdKey,
 48 |     cleanupBatchSize,
 49 |     forceUpdate,
 50 |   } = { ...DEFAULTS, ...options };
 51 | 
 52 |   if (cleanup === "incremental" && !sourceIdKey) {
 53 |     throw new Error(
 54 |       "Source id key is required when cleanup mode is incremental."
 55 |     );
 56 |   }
 57 | 
 58 |   let docs: Array<DocumentInterface>;
 59 |   if (!Array.isArray(docsSource)) {
 60 |     try {
 61 |       docs = await docsSource.load();
 62 |     } catch (e) {
 63 |       throw new Error(`Error loading documents from source${e}`);
 64 |     }
 65 |   } else {
 66 |     docs = docsSource;
 67 |   }
 68 | 
 69 |   const sourceIdAssigner = _getSourceIdAssigner(sourceIdKey);
 70 | 
 71 |   // Mark when the update started.
 72 |   const indexStartDt = await recordManager.getTime();
 73 |   let numAdded = 0;
 74 |   let numSkipped = 0;
 75 |   let numDeleted = 0;
 76 | 
 77 |   for (const docBatch of _batch(batchSize, docs)) {
 78 |     const hashedDocs = _deduplicateInOrder(
 79 |       docBatch.map((doc) => _HashedDocument.fromDocument(doc))
 80 |     );
 81 | 
 82 |     let sourceIds = hashedDocs.map(sourceIdAssigner);
 83 | 
 84 |     if (cleanup === "incremental") {
 85 |       // If the cleanup mode is incremental, source ids are required.
 86 |       for (let i = 0; i < sourceIds.length; i += 1) {
 87 |         const sourceId = sourceIds[i];
 88 |         const hashedDoc = hashedDocs[i];
 89 |         if (sourceId === null) {
 90 |           throw new Error(
 91 |             `Source ids are required when cleanup mode is incremental.\nDocument that starts with content: ${hashedDoc.pageContent.substring(
 92 |               0,
 93 |               100
 94 |             )} was not assigned as source id.`
 95 |           );
 96 |         }
 97 |       }
 98 |       // source ids cannot be null after for loop above.
 99 |       sourceIds = sourceIds as Array<string>;
100 |     }
101 | 
102 |     const existsBatch = await recordManager.exists(
103 |       hashedDocs.map(({ uid }) => uid)
104 |     );
105 | 
106 |     // Filter out documents that already exist in the record store.
107 |     const uids: Array<string> = [];
108 |     const docsToIndex: Array<DocumentInterface> = [];
109 |     const uidsToRefresh: Array<string> = [];
110 | 
111 |     for (let i = 0; i < hashedDocs.length; i += 1) {
112 |       const hashedDoc = hashedDocs[i];
113 |       const docExists = existsBatch[i];
114 |       if (docExists && !forceUpdate) {
115 |         uidsToRefresh.push(hashedDoc.uid);
116 |         continue;
117 |       }
118 |       uids.push(hashedDoc.uid);
119 |       docsToIndex.push(hashedDoc.toDocument());
120 |     }
121 | 
122 |     // Update refresh timestamp
123 |     if (uidsToRefresh.length) {
124 |       await recordManager.update(uidsToRefresh, { timeAtLeast: indexStartDt });
125 |       numSkipped += uidsToRefresh.length;
126 |     }
127 | 
128 |     // Be pessimistic and assume that all vector store write will fail.
129 |     // First write to vector store
130 |     if (docsToIndex.length) {
131 |       await vectorStore.addDocuments(docsToIndex, { ids: uids });
132 |       numAdded += docsToIndex.length;
133 |     }
134 | 
135 |     // And only then update the record store.
136 |     // Update ALL records, even if they already exist since we want to refresh
137 |     // their timestamp.
138 |     await recordManager.update(
139 |       hashedDocs.map(({ uid }) => uid),
140 |       {
141 |         groupIds: sourceIds,
142 |         timeAtLeast: indexStartDt,
143 |       }
144 |     );
145 | 
146 |     // If source IDs are provided, we can do the deletion incrementally!
147 |     if (cleanup === "incremental") {
148 |       // Get the uids of the documents that were not returned by the loader.
149 | 
150 |       // TS isn't good enough to determine that source ids cannot be null
151 |       // here due to a check that's happening above, so we check again.
152 |       sourceIds.forEach((id) => {
153 |         if (id === null) {
154 |           throw new Error("Source ids cannot be null here.");
155 |         }
156 |       });
157 | 
158 |       const _sourceIds = sourceIds as Array<string>;
159 | 
160 |       const uidsToDelete = await recordManager.listKeys({
161 |         groupIds: _sourceIds,
162 |         before: indexStartDt,
163 |       });
164 | 
165 |       if (uidsToDelete.length) {
166 |         // Then delete from vector store
167 |         await vectorStore.delete({
168 |           ids: uidsToDelete,
169 |         });
170 |         // Finally delete from record store
171 |         await recordManager.deleteKeys(uidsToDelete);
172 |         numDeleted += uidsToDelete.length;
173 |       }
174 |     }
175 |   }
176 | 
177 |   if (cleanup === "full") {
178 |     let uidsToDelete: string[] | undefined = await recordManager.listKeys({
179 |       before: indexStartDt,
180 |       limit: cleanupBatchSize,
181 |     });
182 | 
183 |     while (uidsToDelete.length) {
184 |       // First delete from vector store.
185 |       await vectorStore.delete({
186 |         ids: uidsToDelete,
187 |       });
188 |       // Then delete from record manager.
189 |       await recordManager.deleteKeys(uidsToDelete);
190 |       numDeleted += uidsToDelete.length;
191 | 
192 |       uidsToDelete = await recordManager.listKeys({
193 |         before: indexStartDt,
194 |         limit: cleanupBatchSize,
195 |       });
196 |     }
197 |   }
198 | 
199 |   return {
200 |     numAdded,
201 |     numSkipped,
202 |     numDeleted,
203 |   };
204 | }
205 | 


--------------------------------------------------------------------------------
/CONCEPTS.md:
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 1 | # Concepts
 2 | 
 3 | In this doc we'll go over the different concepts that are implemented in Chat LangChain.
 4 | By the end, you'll have a conceptual understanding of how Chat LangChain works, and it's different architectural components.
 5 | We'll start with the vector store, the basis of the entire system.
 6 | 
 7 | ## Vector Store
 8 | 
 9 | Vector stores, fundamentally, are specialized databases designed to efficiently store and manage vectors, which are high-dimensional arrays of numbers. These vectors are not arbitrary; they are the product of sophisticated text embedding models, such as those provided by [OpenAI's `text-embedding`](https://js.langchain.com/docs/integrations/text_embedding/openai) API.
10 | 
11 | In the context of our application, vector stores play a pivotal role in enhancing the capabilities of our language model. Here's a deeper dive into the process:
12 | 
13 | 1. **Vector Generation**: Whenever new content related to LangChain is introduced or existing content is updated, we use text embedding models to convert this textual information into vectors. Each vector acts as a unique fingerprint of its corresponding text, encapsulating its meaning in a high-dimensional space.
14 | 
15 | 2. **Similarity Searches**: The core utility of storing these vectors comes into play when we need to find information relevant to a user's query. By converting the user's question into a vector using the same embedding model, we can perform a similarity search across our vector store. This search identifies vectors (and thus, documents) whose meanings are closest to the query, based on the distance between vectors in the embedding space.
16 | 
17 | 3. **Context Retrieval and Enhancement**: The documents retrieved through similarity searches are relevant pieces of information that aid the language model in generating relevant answers. By providing this context, we enable the language model to generate responses that are not only accurate but also informed by the most relevant and up-to-date information available in our database.
18 | 
19 | ## Indexing
20 | 
21 | Indexing your documents is a vital part of any production RAG application. In short, indexing allows for your documents to be stored, and searchable to prevent duplicate documents from being stored. This is important for a few reasons:
22 | 
23 | 1. **Duplicate Results**: Say you update your vector store without using an Indexing API. Now you may have two identical documents in your store. Then, when you perform a semantic search, instead of getting K number of different results, you'll get duplicates as the semantic search only returns documents which are semantically close to the query.
24 | 
25 | 2. **Performance**: Indexing your documents allows for faster ingestion. With indexing you don't have to generate embeddings for every document on ingestion, and instead only need to generate embeddings for new documents.
26 | 
27 | In order to help with indexing we use the LangChain indexing API. This API contains all the features required for robust indexing in your application. Indexing is done in two main steps:
28 | 
29 | 1. **Ingestion**: Ingestion is where you pull in all the documents you want to add to your vector store. This could be all of the documents available to you, or just a couple new documents.
30 | 2. **Hashing**: Once the ingestion API is passed your documents, it creates a unique hash for each, containing some metadata like the date it was ingested. This allows for the indexing API to only ingest new documents, and not duplicate documents. These hashes are stored in what we call the "Record Manager".
31 | 3. **Insertion**: Finally, once the documents are hashed, and confirmed to not already exist through the Record Manager, they are inserted into the vector store.
32 | 
33 | The indexing API also uses a Record Manager to store the records of previously indexed documents in between ingestion. This manager stores the hashed values of the documents, and the time they were ingested. This allows for the indexing API to only ingest new documents, and not duplicate documents.
34 | 
35 | ### Record Manager
36 | 
37 | The LangChain Record Manager API provides an interface for managing records in a database that tracks upserted documents before they are ingested into a vector store for LLM usage.
38 | It allows you to efficiently insert, update, delete, and query records.
39 | 
40 | **Key Concepts**
41 | - **Namespace**: Each Record Manager is associated with a namespace. This allows logically separating records for different use cases.
42 | - **Keys**: Each record is uniquely identified by a key within the namespace.
43 | - **Group IDs**: Records can optionally be associated with group IDs to allow filtering and batch operations on related records.
44 | - **Timestamps**: Each record has an updated_at timestamp tracking the last time it was upserted. This enables querying records within time ranges.
45 | 
46 | Using the LangChain Record Manager API allows efficient tracking of which documents need to be added to or updated in the vector store, making the ingestion process more robust and avoiding unnecessary duplication work.
47 | 
48 | ## Query Analysis
49 | 
50 | Finally, we perform query analysis on followup chat conversations. It is important to note that we only do this for followups, and not initial questions. Let's break down the reasoning here:
51 | 
52 | Users are not always the best prompters, and can very easily miss some context or phrase their question poorly. We can be confident that the LLM will not make this mistake.
53 | Additionally, given a chat history (which is always passed in context to a model) you may not need to include certain parts of the question, or the reverse, where you do need to clarify additional information.
54 | 
55 | Doing all this helps make better formed questions for the model, without having to rely on the user to do so.
56 | 
57 | Lastly, we don't perform this on the initial question for two main reasons:
58 | 
59 | 1. **Speed**: Although models are getting faster and faster, they still take longer than we'd like to return a response. This is even more important for the first question, as the chat bot hasn't proved its usefulness to the user yet, and you don't want to lose them due to speed before they've even started.
60 | 2. **Context**: Without a chat history, the model is lacking some important context around the users question.
61 | 
62 | Most users won't format their queries perfectly for LLMs, and that's okay!
63 | To account for this, we have an extra step before final generation which takes the users query and rephrase it to be more suitable for the LLM.
64 | 
65 | The prompt is quite simple:
66 | ```typescript
67 | const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
68 | 
69 | Chat History:
70 | {chat_history}
71 | Follow Up Input: {question}
72 | Standalone Question:`;
73 | ```
74 | 
75 | In doing this, the language model is able to take the users question, and the full chat history which contains other questions, answers and context, and generate a more well formed response. Now using this rephrased question, we can perform a similarity search on the vector store using this question, and often times get back better results as the question is semantically more similar to the previous questions/answers (content the database).


--------------------------------------------------------------------------------
/MODIFY.md:
--------------------------------------------------------------------------------
  1 | # Modifying
  2 | 
  3 | The Chat LangChain repo was built to serve two use cases.
  4 | The first being question answering over the LangChain documentation.
  5 | The second is to offer a production ready chat bot which you can easily customize for your specific use case.
  6 | In this doc we'll go over each step you need to take to customize the repo for your need.
  7 | 
  8 | ## Vector Store
  9 | 
 10 | One of the simplest ways to modify Chat LangChain.js and get a feel for the codebase is to modify the vector store.
 11 | All of the operations in Chat LangChain are largely based around the vector store:
 12 | 
 13 | - ingestion
 14 | - retrieval
 15 | - context
 16 | - etc
 17 | 
 18 | There are two places the vector store is used:
 19 | - **Ingestion**: The vector store is used to store the embeddings of every document used as context. Located in [`./backend/src/ingest.ts`](./backend/src/ingest.ts) you can easily modify the provider to use a different vector store.
 20 | - **Retrieval**: The vector store is used to retrieve documents based on a user's query. Located at [`/frontend/app/api/chat/stream_log/route.ts`](/frontend/app/api/chat/stream_log/route.ts) you can easily modify the provider to use a different vector store.
 21 | 
 22 | ### Steps
 23 | 
 24 | For backend ingestion, locate the `ingestDocs` function.You'll want to modify the first `if` statement to instead check for any required environment variables the new provider you want to use requires. After, scroll down until you find where the `weaviateClient` and `vectorStore` variables are defined:
 25 | 
 26 | ```typescript
 27 | const weaviateClient = (weaviate as any).client({
 28 |   scheme: "https",
 29 |   host: process.env.WEAVIATE_URL,
 30 |   apiKey: new ApiKey(process.env.WEAVIATE_API_KEY),
 31 | }) as WeaviateClient;
 32 | 
 33 | const vectorStore = new WeaviateStore(embeddings, {
 34 |   client: weaviateClient,
 35 |   indexName: process.env.WEAVIATE_INDEX_NAME,
 36 |   textKey: "text",
 37 | });
 38 | ```
 39 | 
 40 | To make transitioning as easy as possible, all you should do is:
 41 | 
 42 | 1. Delete the weaviate client instantiation.
 43 | 2. Replace the vector store instantiation with the new provider's instantiation. Remember to keep the variable name (`vectorStore`) the same. Since all LangChain vector stores are built on top of the same API, no other modifications should be necessary.
 44 | 
 45 | Finally, perform these same steps inside the `stream_log` route, and you're done!
 46 | 
 47 | ## Record Manager
 48 | 
 49 | Continuing with the database, we also employ a record manager for ingesting docs.
 50 | Currently, we use a [`PostgresRecordManager`](https://api.js.langchain.com/classes/langchain_community_indexes_postgres.PostgresRecordManager.html), however you may also swap that out in favor of a [`SQLiteRecordManager`](https://api.js.langchain.com/classes/langchain_community_indexes_sqlite.SQLiteRecordManager.html).
 51 | 
 52 | ```typescript
 53 | const recordManager = new PostgresRecordManager(
 54 |   `weaviate/${process.env.WEAVIATE_INDEX_NAME}`,
 55 |   {
 56 |     postgresConnectionOptions: connectionOptions,
 57 |   }
 58 | );
 59 | await recordManager.createSchema();
 60 | ```
 61 | 
 62 | For more conceptual information on Record Managers with LangChain, see the [concepts](./CONCEPTS.md) doc.
 63 | 
 64 | ## LLM
 65 | 
 66 | The LLM is used inside the `/stream_log` endpoint for generating the final answer, and performing query analysis on followup questions.
 67 | 
 68 | > Want to learn more about query analysis? See our comprehensive set of use case docs [here](https://js.langchain.com/docs/use_cases/query_analysis/).
 69 | 
 70 | Without any modification, we offer a few LLM providers out of the box:
 71 | 
 72 | - `gpt-3.5-turbo` by OpenAI
 73 | - `mixtral-8x7b` by Fireworks
 74 | 
 75 | These are all located at the top of the `stream_log` route's `POST` function. You have a few options for modifying this:
 76 | 
 77 | - Replace all options with a single provider
 78 | - Add more providers
 79 | 
 80 | First, I'll demonstrate how to replace all options with a single provider, as it's the simplest:
 81 | 
 82 | 1. Find the LLM variable declaration at the top of the function, it looks something like this:
 83 | 
 84 | ```typescript
 85 | let llm;
 86 | if (config.configurable.llm === "openai_gpt_3_5_turbo") {
 87 |   llm = new ChatOpenAI({
 88 |     modelName: "gpt-3.5-turbo-1106",
 89 |     temperature: 0,
 90 |   });
 91 | } else if (config.configurable.llm === "fireworks_mixtral") {
 92 |   llm = new ChatFireworks({
 93 |     modelName: "accounts/fireworks/models/mixtral-8x7b-instruct",
 94 |     temperature: 0,
 95 |   });
 96 | } else {
 97 |   throw new Error(
 98 |     "Invalid LLM option passed. Must be 'openai' or 'mixtral'. Received: " +
 99 |       config.llm,
100 |   );
101 | }
102 | ```
103 | 
104 | You should then remove it, and replace with your LLM class of choice, imported from LangChain. Remember to keep the variable name the same so nothing else in the endpoint breaks:
105 | 
106 | ```typescript
107 | const = new ChatYourLLM({
108 |   modelName: "model-name",
109 |   streaming: true,
110 |   temperature: 0,
111 | });
112 | ```
113 | 
114 | Adding alternatives is also quite simple. Just add another class declaration inside the `if` statement. Here's an example:
115 | 
116 | ```typescript
117 | else if (config.configurable.llm === "chat_your_llm") {
118 |   llm = new ChatYourLLM({
119 |     modelName: "model-name",
120 |     streaming: true,
121 |     temperature: 0,
122 |   });
123 | } else {
124 |   throw new Error(
125 |     "Invalid LLM option passed. Must be 'openai' or 'mixtral'. Received: " +
126 |       config.llm,
127 |   )
128 | }
129 | ```
130 | 
131 | That't it!
132 | 
133 | ## Embeddings
134 | 
135 | Chat LangChain uses embeddings inside the ingestion script when storing documents in the vector store.
136 | Without modification, it defaults to use [OpenAI's embeddings model](https://js.langchain.com/docs/integrations/text_embedding/openai).
137 | 
138 | Changing this to the vector store of your choice is simple. First, find the `getEmbeddingsModel` function inside the [`./backend/src/ingest.ts`](./backend/src/ingest.ts) file. It looks something like this:
139 | 
140 | ```typescript
141 | function getEmbeddingsModel(): Embeddings {
142 |   return new OpenAIEmbeddings();
143 | }
144 | ```
145 | 
146 | Then, simply swap out the `OpenAIEmbeddings` class for the model of your choice!
147 | 
148 | Here's an example of what that would look like if you wanted to use Mistral's embeddings model:
149 | 
150 | ```typescript
151 | import { MistralAIEmbeddings } from "@langchain/mistralai";
152 | 
153 | function getEmbeddingsModel(): Embeddings {
154 |   return new MistralAIEmbeddings();
155 | }
156 | ```
157 | 
158 | ## Prompts
159 | 
160 | ### Answer Generation Prompt
161 | 
162 | The prompt used for answer generation is one of the most important parts of this RAG pipeline. Without a good prompt, the LLM will be unable (or severely limited) to generate good answers.
163 | 
164 | The prompt is defined in the `RESPONSE_TEMPLATE` variable.
165 | 
166 | You should modify the parts of this which are LangChain specific to instead fit your needs. If possible, and if your use case does not differ too much from the Chat LangChain use case, you should do your best to keep the same structure as the original prompt. Although there are likely some improvements to be made, we've refined this prompt over many months and lots of user feedback to what we believe to be a well formed prompt.
167 | 
168 | ### Question Rephrasing Prompt
169 | 
170 | Finally, you can (but not necessary required) modify the `REPHRASE_TEMPLATE` variable to contain more domain specific content about, for example, the types of followup questions you expect to receive. Having a good rephrasing prompt will help the LLM to better understand the user's question and generate a better prompt which will have compounding effects downstream.
171 | 
172 | ## Retrieval
173 | 
174 | ### Ingestion Script
175 | 
176 | We'll start by modifying the ingestion script.
177 | 
178 | At a high level, the only LangChain specific part of the ingestion script are the three webpages which is scrapes for documents to add to the vector store. These links are:
179 | - LangSmith Documentation
180 | - LangChain.js API references
181 | - LangChain.js Documentation
182 | 
183 | If all you would like to update is update which website(s) to scrape and ingest, you only need to modify/remove these functions:
184 | 
185 | - `loadLangSmithDocs`
186 | - `loadAPIDocs`
187 | - `loadLangChainDocs`
188 | 
189 | If you want to ingest another way, consult the [document loader](https://js.langchain.com/docs/modules/data_connection/document_loaders/) section of the LangChain docs.
190 | Using any LangChain.js document loader, you'll be able to easily and efficiently fetch & ingest from a large variety of sources. Additionally, the LangChain.js document loader API will always return documents in the same format ([`DocumentInterface`](https://api.js.langchain.com/interfaces/langchain_core_documents.DocumentInterface.html)) so you do not need to modify the format before adding to your indexing API or vector store.
191 | 
192 | ### Retrieval Methods
193 | 
194 | You can however, easily add or remove parts to increase/fit your needs better.
195 | Some ideas of what can be done:
196 | 
197 | - Re-ranking document results
198 | - Parent document retrieval (also would require modifications to the ingestion script)
199 | - Document verification via LLM
200 | 
201 | ## Frontend
202 | 
203 | The frontend doesn't have much LangChain specific code that would need modification.
204 | The main parts are the LangChain UI branding and question suggestions.
205 | 
206 | To modify the main LangChain branding visit the [`ChatWindow.tsx`](frontend/app/components/ChatWindow.tsx) file to modify/remove.
207 | Next, to update the question suggestions, visit the [`EmptyState.tsx`](frontend/app/components/EmptyState.tsx) file.
208 | Finally, update the "View Source" button on the bottom of the page by going back to the [`ChatWindow.tsx`](frontend/app/components/ChatWindow.tsx) file.


--------------------------------------------------------------------------------
/frontend/app/api/chat/stream_log/route.ts:
--------------------------------------------------------------------------------
  1 | import { NextRequest, NextResponse } from "next/server";
  2 | 
  3 | import type { Document } from "@langchain/core/documents";
  4 | 
  5 | import {
  6 |   Runnable,
  7 |   RunnableSequence,
  8 |   RunnableMap,
  9 |   RunnableBranch,
 10 |   RunnableLambda,
 11 | } from "@langchain/core/runnables";
 12 | import { HumanMessage, AIMessage, BaseMessage } from "@langchain/core/messages";
 13 | import { BaseChatModel } from "@langchain/core/language_models/chat_models";
 14 | import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
 15 | import { ChatFireworks } from "@langchain/community/chat_models/fireworks";
 16 | import { StringOutputParser } from "@langchain/core/output_parsers";
 17 | import {
 18 |   PromptTemplate,
 19 |   ChatPromptTemplate,
 20 |   MessagesPlaceholder,
 21 | } from "@langchain/core/prompts";
 22 | 
 23 | import weaviate, { ApiKey } from "weaviate-ts-client";
 24 | import { WeaviateStore } from "@langchain/weaviate";
 25 | 
 26 | export const runtime = "edge";
 27 | 
 28 | const RESPONSE_TEMPLATE = `You are an expert programmer and problem-solver, tasked to answer any question about Langchain.
 29 | Using the provided context, answer the user's question to the best of your ability using the resources provided.
 30 | Generate a comprehensive and informative answer (but no more than 80 words) for a given question based solely on the provided search results (URL and content).
 31 | You must only use information from the provided search results.
 32 | Use an unbiased and journalistic tone.
 33 | Combine search results together into a coherent answer.
 34 | Do not repeat text.
 35 | Cite search results using [\${{number}}] notation.
 36 | Only cite the most relevant results that answer the question accurately.
 37 | Place these citations at the end of the sentence or paragraph that reference them - do not put them all at the end.
 38 | If different results refer to different entities within the same name, write separate answers for each entity.
 39 | If there is nothing in the context relevant to the question at hand, just say "Hmm, I'm not sure." Don't try to make up an answer.
 40 | 
 41 | You should use bullet points in your answer for readability
 42 | Put citations where they apply rather than putting them all at the end.
 43 | 
 44 | Anything between the following \`context\`  html blocks is retrieved from a knowledge bank, not part of the conversation with the user.
 45 | 
 46 | <context>
 47 | {context}
 48 | <context/>
 49 | 
 50 | REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm not sure." Don't try to make up an answer.
 51 | Anything between the preceding 'context' html blocks is retrieved from a knowledge bank, not part of the conversation with the user.`;
 52 | 
 53 | const REPHRASE_TEMPLATE = `Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
 54 | 
 55 | Chat History:
 56 | {chat_history}
 57 | Follow Up Input: {question}
 58 | Standalone Question:`;
 59 | 
 60 | type RetrievalChainInput = {
 61 |   chat_history: string;
 62 |   question: string;
 63 | };
 64 | 
 65 | const getRetriever = async () => {
 66 |   if (
 67 |     !process.env.WEAVIATE_INDEX_NAME ||
 68 |     !process.env.WEAVIATE_API_KEY ||
 69 |     !process.env.WEAVIATE_URL
 70 |   ) {
 71 |     throw new Error(
 72 |       "WEAVIATE_INDEX_NAME, WEAVIATE_API_KEY and WEAVIATE_URL environment variables must be set",
 73 |     );
 74 |   }
 75 | 
 76 |   const client = weaviate.client({
 77 |     scheme: "https",
 78 |     host: process.env.WEAVIATE_URL,
 79 |     apiKey: new ApiKey(process.env.WEAVIATE_API_KEY),
 80 |   });
 81 |   const vectorstore = await WeaviateStore.fromExistingIndex(
 82 |     new OpenAIEmbeddings({}),
 83 |     {
 84 |       client,
 85 |       indexName: process.env.WEAVIATE_INDEX_NAME,
 86 |       textKey: "text",
 87 |       metadataKeys: ["source", "title"],
 88 |     },
 89 |   );
 90 |   return vectorstore.asRetriever({ k: 6 });
 91 | };
 92 | 
 93 | const createRetrieverChain = (llm: BaseChatModel, retriever: Runnable) => {
 94 |   // Small speed/accuracy optimization: no need to rephrase the first question
 95 |   // since there shouldn't be any meta-references to prior chat history
 96 |   const CONDENSE_QUESTION_PROMPT =
 97 |     PromptTemplate.fromTemplate(REPHRASE_TEMPLATE);
 98 |   const condenseQuestionChain = RunnableSequence.from([
 99 |     CONDENSE_QUESTION_PROMPT,
100 |     llm,
101 |     new StringOutputParser(),
102 |   ]).withConfig({
103 |     runName: "CondenseQuestion",
104 |   });
105 |   const hasHistoryCheckFn = RunnableLambda.from(
106 |     (input: RetrievalChainInput) => input.chat_history.length > 0,
107 |   ).withConfig({ runName: "HasChatHistoryCheck" });
108 |   const conversationChain = condenseQuestionChain.pipe(retriever).withConfig({
109 |     runName: "RetrievalChainWithHistory",
110 |   });
111 |   const basicRetrievalChain = RunnableLambda.from(
112 |     (input: RetrievalChainInput) => input.question,
113 |   )
114 |     .withConfig({
115 |       runName: "Itemgetter:question",
116 |     })
117 |     .pipe(retriever)
118 |     .withConfig({ runName: "RetrievalChainWithNoHistory" });
119 | 
120 |   return RunnableBranch.from([
121 |     [hasHistoryCheckFn, conversationChain],
122 |     basicRetrievalChain,
123 |   ]).withConfig({
124 |     runName: "FindDocs",
125 |   });
126 | };
127 | 
128 | const formatDocs = (docs: Document[]) => {
129 |   return docs
130 |     .map((doc, i) => `<doc id='${i}'>${doc.pageContent}</doc>`)
131 |     .join("\n");
132 | };
133 | 
134 | const formatChatHistoryAsString = (history: BaseMessage[]) => {
135 |   return history
136 |     .map((message) => `${message._getType()}: ${message.content}`)
137 |     .join("\n");
138 | };
139 | 
140 | const serializeHistory = (input: any) => {
141 |   const chatHistory = input.chat_history || [];
142 |   const convertedChatHistory = [];
143 |   for (const message of chatHistory) {
144 |     if (message.human !== undefined) {
145 |       convertedChatHistory.push(new HumanMessage({ content: message.human }));
146 |     }
147 |     if (message["ai"] !== undefined) {
148 |       convertedChatHistory.push(new AIMessage({ content: message.ai }));
149 |     }
150 |   }
151 |   return convertedChatHistory;
152 | };
153 | 
154 | const createChain = (llm: BaseChatModel, retriever: Runnable) => {
155 |   const retrieverChain = createRetrieverChain(llm, retriever);
156 |   const context = RunnableMap.from({
157 |     context: RunnableSequence.from([
158 |       ({ question, chat_history }) => ({
159 |         question,
160 |         chat_history: formatChatHistoryAsString(chat_history),
161 |       }),
162 |       retrieverChain,
163 |       RunnableLambda.from(formatDocs).withConfig({
164 |         runName: "FormatDocumentChunks",
165 |       }),
166 |     ]),
167 |     question: RunnableLambda.from(
168 |       (input: RetrievalChainInput) => input.question,
169 |     ).withConfig({
170 |       runName: "Itemgetter:question",
171 |     }),
172 |     chat_history: RunnableLambda.from(
173 |       (input: RetrievalChainInput) => input.chat_history,
174 |     ).withConfig({
175 |       runName: "Itemgetter:chat_history",
176 |     }),
177 |   }).withConfig({ tags: ["RetrieveDocs"] });
178 |   const prompt = ChatPromptTemplate.fromMessages([
179 |     ["system", RESPONSE_TEMPLATE],
180 |     new MessagesPlaceholder("chat_history"),
181 |     ["human", "{question}"],
182 |   ]);
183 | 
184 |   const responseSynthesizerChain = RunnableSequence.from([
185 |     prompt,
186 |     llm,
187 |     new StringOutputParser(),
188 |   ]).withConfig({
189 |     tags: ["GenerateResponse"],
190 |   });
191 |   return RunnableSequence.from([
192 |     {
193 |       question: RunnableLambda.from(
194 |         (input: RetrievalChainInput) => input.question,
195 |       ).withConfig({
196 |         runName: "Itemgetter:question",
197 |       }),
198 |       chat_history: RunnableLambda.from(serializeHistory).withConfig({
199 |         runName: "SerializeHistory",
200 |       }),
201 |     },
202 |     context,
203 |     responseSynthesizerChain,
204 |   ]);
205 | };
206 | 
207 | export async function POST(req: NextRequest) {
208 |   try {
209 |     const body = await req.json();
210 |     const input = body.input;
211 |     const config = body.config;
212 | 
213 |     let llm;
214 |     if (config.configurable.llm === "openai_gpt_3_5_turbo") {
215 |       llm = new ChatOpenAI({
216 |         modelName: "gpt-3.5-turbo-1106",
217 |         temperature: 0,
218 |       });
219 |     } else if (config.configurable.llm === "fireworks_mixtral") {
220 |       llm = new ChatFireworks({
221 |         modelName: "accounts/fireworks/models/mixtral-8x7b-instruct",
222 |         temperature: 0,
223 |       });
224 |     } else {
225 |       throw new Error(
226 |         "Invalid LLM option passed. Must be 'openai' or 'mixtral'. Received: " +
227 |           config.llm,
228 |       );
229 |     }
230 | 
231 |     const retriever = await getRetriever();
232 |     const answerChain = createChain(llm, retriever);
233 | 
234 |     /**
235 |      * Narrows streamed log output down to final output and the FindDocs tagged chain to
236 |      * selectively stream back sources.
237 |      *
238 |      * You can use .stream() to create a ReadableStream with just the final output which
239 |      * you can pass directly to the Response as well:
240 |      * https://js.langchain.com/docs/expression_language/interface#stream
241 |      */
242 |     const stream = answerChain.streamLog(input, config, {
243 |       includeNames: body.includeNames,
244 |     });
245 | 
246 |     // Only return a selection of output to the frontend
247 |     const textEncoder = new TextEncoder();
248 |     const clientStream = new ReadableStream({
249 |       async start(controller) {
250 |         for await (const chunk of stream) {
251 |           controller.enqueue(
252 |             textEncoder.encode(
253 |               "event: data\ndata: " + JSON.stringify(chunk) + "\n\n",
254 |             ),
255 |           );
256 |         }
257 |         controller.enqueue(textEncoder.encode("event: end\n\n"));
258 |         controller.close();
259 |       },
260 |     });
261 | 
262 |     return new Response(clientStream, {
263 |       headers: { "Content-Type": "text/event-stream" },
264 |     });
265 |   } catch (e: any) {
266 |     console.error(e);
267 |     return NextResponse.json({ error: e.message }, { status: 500 });
268 |   }
269 | }
270 | 


--------------------------------------------------------------------------------
/frontend/app/components/ChatWindow.tsx:
--------------------------------------------------------------------------------
  1 | "use client";
  2 | 
  3 | import React, { useEffect, useRef, useState } from "react";
  4 | import { useSearchParams } from "next/navigation";
  5 | import { RemoteRunnable } from "langchain/runnables/remote";
  6 | import { applyPatch } from "@langchain/core/utils/json_patch";
  7 | 
  8 | import { EmptyState } from "../components/EmptyState";
  9 | import { ChatMessageBubble, Message } from "../components/ChatMessageBubble";
 10 | import { AutoResizeTextarea } from "./AutoResizeTextarea";
 11 | import { marked } from "marked";
 12 | import { Renderer } from "marked";
 13 | import hljs from "highlight.js";
 14 | import "highlight.js/styles/gradient-dark.css";
 15 | 
 16 | import "react-toastify/dist/ReactToastify.css";
 17 | import {
 18 |   Heading,
 19 |   Flex,
 20 |   IconButton,
 21 |   InputGroup,
 22 |   InputRightElement,
 23 |   Spinner,
 24 | } from "@chakra-ui/react";
 25 | import { ArrowUpIcon } from "@chakra-ui/icons";
 26 | import { Select, Link } from "@chakra-ui/react";
 27 | import { Source } from "./SourceBubble";
 28 | import { apiBaseUrl } from "../utils/constants";
 29 | 
 30 | const MODEL_TYPES = ["openai_gpt_3_5_turbo", "fireworks_mixtral"];
 31 | 
 32 | const defaultLlmValue =
 33 |   MODEL_TYPES[Math.floor(Math.random() * MODEL_TYPES.length)];
 34 | 
 35 | export function ChatWindow(props: { conversationId: string }) {
 36 |   const conversationId = props.conversationId;
 37 | 
 38 |   const searchParams = useSearchParams();
 39 | 
 40 |   const messageContainerRef = useRef<HTMLDivElement | null>(null);
 41 |   const [messages, setMessages] = useState<Array<Message>>([]);
 42 |   const [input, setInput] = useState("");
 43 |   const [isLoading, setIsLoading] = useState(false);
 44 |   const [llm, setLlm] = useState(
 45 |     searchParams.get("llm") ?? "openai_gpt_3_5_turbo",
 46 |   );
 47 |   const [llmIsLoading, setLlmIsLoading] = useState(true);
 48 |   useEffect(() => {
 49 |     setLlm(searchParams.get("llm") ?? defaultLlmValue);
 50 |     setLlmIsLoading(false);
 51 |   }, []);
 52 | 
 53 |   const [chatHistory, setChatHistory] = useState<
 54 |     { human: string; ai: string }[]
 55 |   >([]);
 56 | 
 57 |   const sendMessage = async (message?: string) => {
 58 |     if (messageContainerRef.current) {
 59 |       messageContainerRef.current.classList.add("grow");
 60 |     }
 61 |     if (isLoading) {
 62 |       return;
 63 |     }
 64 |     const messageValue = message ?? input;
 65 |     if (messageValue === "") return;
 66 |     setInput("");
 67 |     setMessages((prevMessages) => [
 68 |       ...prevMessages,
 69 |       { id: Math.random().toString(), content: messageValue, role: "user" },
 70 |     ]);
 71 |     setIsLoading(true);
 72 | 
 73 |     let accumulatedMessage = "";
 74 |     let runId: string | undefined = undefined;
 75 |     let sources: Source[] | undefined = undefined;
 76 |     let messageIndex: number | null = null;
 77 | 
 78 |     let renderer = new Renderer();
 79 |     renderer.paragraph = (text) => {
 80 |       return text + "\n";
 81 |     };
 82 |     renderer.list = (text) => {
 83 |       return `${text}\n\n`;
 84 |     };
 85 |     renderer.listitem = (text) => {
 86 |       return `\n• ${text}`;
 87 |     };
 88 |     renderer.code = (code, language) => {
 89 |       const validLanguage = hljs.getLanguage(language || "")
 90 |         ? language
 91 |         : "plaintext";
 92 |       const highlightedCode = hljs.highlight(
 93 |         validLanguage || "plaintext",
 94 |         code,
 95 |       ).value;
 96 |       return `<pre class="highlight bg-gray-700" style="padding: 5px; border-radius: 5px; overflow: auto; overflow-wrap: anywhere; white-space: pre-wrap; max-width: 100%; display: block; line-height: 1.2"><code class="${language}" style="color: #d6e2ef; font-size: 12px; ">${highlightedCode}</code></pre>`;
 97 |     };
 98 |     marked.setOptions({ renderer });
 99 | 
100 |     try {
101 |       const sourceStepName = "FindDocs";
102 |       let streamedResponse: Record<string, any> = {};
103 |       const remoteChain = new RemoteRunnable({
104 |         url: apiBaseUrl + "/chat",
105 |         options: {
106 |           timeout: 60000,
107 |         },
108 |       });
109 |       const llmDisplayName = llm ?? "openai_gpt_3_5_turbo";
110 |       const streamLog = remoteChain.streamLog(
111 |         {
112 |           question: messageValue,
113 |           chat_history: chatHistory,
114 |         },
115 |         {
116 |           configurable: {
117 |             llm: llmDisplayName,
118 |           },
119 |           tags: ["model:" + llmDisplayName],
120 |           metadata: {
121 |             conversation_id: conversationId,
122 |             llm: llmDisplayName,
123 |           },
124 |         },
125 |         {
126 |           includeNames: [sourceStepName],
127 |         },
128 |       );
129 |       for await (const chunk of streamLog) {
130 |         streamedResponse = applyPatch(streamedResponse, chunk.ops).newDocument;
131 |         if (
132 |           Array.isArray(
133 |             streamedResponse?.logs?.[sourceStepName]?.final_output?.output,
134 |           )
135 |         ) {
136 |           sources = streamedResponse.logs[
137 |             sourceStepName
138 |           ].final_output.output.map((doc: Record<string, any>) => ({
139 |             url: doc.metadata.source,
140 |             title: doc.metadata.title,
141 |           }));
142 |         }
143 |         if (streamedResponse.id !== undefined) {
144 |           runId = streamedResponse.id;
145 |         }
146 |         if (Array.isArray(streamedResponse?.streamed_output)) {
147 |           accumulatedMessage = streamedResponse.streamed_output.join("");
148 |         }
149 |         const parsedResult = marked.parse(accumulatedMessage);
150 | 
151 |         setMessages((prevMessages) => {
152 |           let newMessages = [...prevMessages];
153 |           if (
154 |             messageIndex === null ||
155 |             newMessages[messageIndex] === undefined
156 |           ) {
157 |             messageIndex = newMessages.length;
158 |             newMessages.push({
159 |               id: Math.random().toString(),
160 |               content: parsedResult.trim(),
161 |               runId,
162 |               sources,
163 |               role: "assistant",
164 |             });
165 |           } else if (newMessages[messageIndex] !== undefined) {
166 |             newMessages[messageIndex].content = parsedResult.trim();
167 |             newMessages[messageIndex].runId = runId;
168 |             newMessages[messageIndex].sources = sources;
169 |           }
170 |           return newMessages;
171 |         });
172 |       }
173 |       setChatHistory((prevChatHistory) => [
174 |         ...prevChatHistory,
175 |         { human: messageValue, ai: accumulatedMessage },
176 |       ]);
177 |       setIsLoading(false);
178 |     } catch (e) {
179 |       setMessages((prevMessages) => prevMessages.slice(0, -1));
180 |       setIsLoading(false);
181 |       setInput(messageValue);
182 |       throw e;
183 |     }
184 |   };
185 | 
186 |   const sendInitialQuestion = async (question: string) => {
187 |     await sendMessage(question);
188 |   };
189 | 
190 |   const insertUrlParam = (key: string, value?: string) => {
191 |     if (window.history.pushState) {
192 |       const searchParams = new URLSearchParams(window.location.search);
193 |       searchParams.set(key, value ?? "");
194 |       const newurl =
195 |         window.location.protocol +
196 |         "//" +
197 |         window.location.host +
198 |         window.location.pathname +
199 |         "?" +
200 |         searchParams.toString();
201 |       window.history.pushState({ path: newurl }, "", newurl);
202 |     }
203 |   };
204 | 
205 |   return (
206 |     <div className="flex flex-col items-center p-8 rounded grow max-h-full">
207 |       <Flex
208 |         direction={"column"}
209 |         alignItems={"center"}
210 |         marginTop={messages.length > 0 ? "" : "64px"}
211 |       >
212 |         <Heading
213 |           fontSize={messages.length > 0 ? "2xl" : "3xl"}
214 |           fontWeight={"medium"}
215 |           mb={1}
216 |           color={"white"}
217 |         >
218 |           Chat LangChain 🦜🔗
219 |         </Heading>
220 |         {messages.length > 0 ? (
221 |           <Heading fontSize="md" fontWeight={"normal"} mb={1} color={"white"}>
222 |             We appreciate feedback!
223 |           </Heading>
224 |         ) : (
225 |           <Heading
226 |             fontSize="xl"
227 |             fontWeight={"normal"}
228 |             color={"white"}
229 |             marginTop={"10px"}
230 |             textAlign={"center"}
231 |           >
232 |             Ask me anything about LangChain&apos;s{" "}
233 |             <Link href="https://js.langchain.com/" color={"blue.200"}>
234 |               TypeScript documentation!
235 |             </Link>
236 |           </Heading>
237 |         )}
238 |         <div className="text-white flex flex-wrap items-center mt-4">
239 |           <div className="flex items-center mb-2">
240 |             <span className="shrink-0 mr-2">Powered by</span>
241 |             {llmIsLoading ? (
242 |               <Spinner className="my-2"></Spinner>
243 |             ) : (
244 |               <Select
245 |                 value={llm}
246 |                 onChange={(e) => {
247 |                   insertUrlParam("llm", e.target.value);
248 |                   setLlm(e.target.value);
249 |                 }}
250 |                 width={"240px"}
251 |               >
252 |                 <option value="openai_gpt_3_5_turbo">GPT-3.5-Turbo</option>
253 |                 <option value="fireworks_mixtral">
254 |                   Mixtral (via Fireworks.ai)
255 |                 </option>
256 |               </Select>
257 |             )}
258 |           </div>
259 |         </div>
260 |       </Flex>
261 |       <div
262 |         className="flex flex-col-reverse w-full mb-2 overflow-auto"
263 |         ref={messageContainerRef}
264 |       >
265 |         {messages.length > 0 ? (
266 |           [...messages]
267 |             .reverse()
268 |             .map((m, index) => (
269 |               <ChatMessageBubble
270 |                 key={m.id}
271 |                 message={m}
272 |                 aiEmoji="🦜"
273 |                 isMostRecent={index === 0}
274 |                 messageCompleted={!isLoading}
275 |               />
276 |             ))
277 |         ) : (
278 |           <EmptyState onChoice={sendInitialQuestion} />
279 |         )}
280 |       </div>
281 |       <InputGroup size="md" alignItems={"center"}>
282 |         <AutoResizeTextarea
283 |           value={input}
284 |           maxRows={5}
285 |           marginRight={"56px"}
286 |           placeholder="What does RunnablePassthrough.assign() do?"
287 |           textColor={"white"}
288 |           borderColor={"rgb(58, 58, 61)"}
289 |           onChange={(e) => setInput(e.target.value)}
290 |           onKeyDown={(e) => {
291 |             if (e.key === "Enter" && !e.shiftKey) {
292 |               e.preventDefault();
293 |               sendMessage();
294 |             } else if (e.key === "Enter" && e.shiftKey) {
295 |               e.preventDefault();
296 |               setInput(input + "\n");
297 |             }
298 |           }}
299 |         />
300 |         <InputRightElement h="full">
301 |           <IconButton
302 |             colorScheme="blue"
303 |             rounded={"full"}
304 |             aria-label="Send"
305 |             icon={isLoading ? <Spinner /> : <ArrowUpIcon />}
306 |             type="submit"
307 |             onClick={(e) => {
308 |               e.preventDefault();
309 |               sendMessage();
310 |             }}
311 |           />
312 |         </InputRightElement>
313 |       </InputGroup>
314 | 
315 |       {messages.length === 0 ? (
316 |         <footer className="flex justify-center absolute bottom-8">
317 |           <a
318 |             href="https://github.com/langchain-ai/chat-langchainjs"
319 |             target="_blank"
320 |             className="text-white flex items-center"
321 |           >
322 |             <img src="/images/github-mark.svg" className="h-4 mr-1" />
323 |             <span>View Source</span>
324 |           </a>
325 |         </footer>
326 |       ) : (
327 |         ""
328 |       )}
329 |     </div>
330 |   );
331 | }
332 | 


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/frontend/app/components/ChatMessageBubble.tsx:
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  1 | import { toast } from "react-toastify";
  2 | import "react-toastify/dist/ReactToastify.css";
  3 | import { emojisplosion } from "emojisplosion";
  4 | import { useState, useRef } from "react";
  5 | import * as DOMPurify from "dompurify";
  6 | import { SourceBubble, Source } from "./SourceBubble";
  7 | import {
  8 |   VStack,
  9 |   Flex,
 10 |   Heading,
 11 |   HStack,
 12 |   Box,
 13 |   Button,
 14 |   Divider,
 15 |   Spacer,
 16 | } from "@chakra-ui/react";
 17 | import { sendFeedback } from "../utils/sendFeedback";
 18 | import { apiBaseUrl } from "../utils/constants";
 19 | import { InlineCitation } from "./InlineCitation";
 20 | 
 21 | export type Message = {
 22 |   id: string;
 23 |   createdAt?: Date;
 24 |   content: string;
 25 |   role: "system" | "user" | "assistant" | "function";
 26 |   runId?: string;
 27 |   sources?: Source[];
 28 |   name?: string;
 29 |   function_call?: { name: string };
 30 | };
 31 | export type Feedback = {
 32 |   feedback_id: string;
 33 |   run_id: string;
 34 |   key: string;
 35 |   score: number;
 36 |   comment?: string;
 37 | };
 38 | 
 39 | const filterSources = (sources: Source[]) => {
 40 |   const filtered: Source[] = [];
 41 |   const urlMap = new Map<string, number>();
 42 |   const indexMap = new Map<number, number>();
 43 |   sources.forEach((source, i) => {
 44 |     const { url } = source;
 45 |     const index = urlMap.get(url);
 46 |     if (index === undefined) {
 47 |       urlMap.set(url, i);
 48 |       indexMap.set(i, filtered.length);
 49 |       filtered.push(source);
 50 |     } else {
 51 |       const resolvedIndex = indexMap.get(index);
 52 |       if (resolvedIndex !== undefined) {
 53 |         indexMap.set(i, resolvedIndex);
 54 |       }
 55 |     }
 56 |   });
 57 |   return { filtered, indexMap };
 58 | };
 59 | 
 60 | const createAnswerElements = (
 61 |   content: string,
 62 |   filteredSources: Source[],
 63 |   sourceIndexMap: Map<number, number>,
 64 |   highlighedSourceLinkStates: boolean[],
 65 |   setHighlightedSourceLinkStates: React.Dispatch<
 66 |     React.SetStateAction<boolean[]>
 67 |   >,
 68 | ) => {
 69 |   const matches = Array.from(
 70 |     content.matchAll(/\[\^?\$?{?(\d+)}?\^?\]|\[\$\{(\d+)\}\]/g),
 71 |   );
 72 |   const elements: JSX.Element[] = [];
 73 |   let prevIndex = 0;
 74 | 
 75 |   matches.forEach((match) => {
 76 |     const sourceNum = parseInt(match[1], 10);
 77 |     const resolvedNum = sourceIndexMap.get(sourceNum) ?? 10;
 78 |     if (match.index !== null && resolvedNum < filteredSources.length) {
 79 |       elements.push(
 80 |         <span
 81 |           key={`content:${prevIndex}`}
 82 |           dangerouslySetInnerHTML={{
 83 |             __html: DOMPurify.sanitize(content.slice(prevIndex, match.index)),
 84 |           }}
 85 |         ></span>,
 86 |       );
 87 |       elements.push(
 88 |         <InlineCitation
 89 |           key={`citation:${prevIndex}`}
 90 |           source={filteredSources[resolvedNum]}
 91 |           sourceNumber={resolvedNum}
 92 |           highlighted={highlighedSourceLinkStates[resolvedNum]}
 93 |           onMouseEnter={() =>
 94 |             setHighlightedSourceLinkStates(
 95 |               filteredSources.map((_, i) => i === resolvedNum),
 96 |             )
 97 |           }
 98 |           onMouseLeave={() =>
 99 |             setHighlightedSourceLinkStates(filteredSources.map(() => false))
100 |           }
101 |         />,
102 |       );
103 |       prevIndex = (match?.index ?? 0) + match[0].length;
104 |     }
105 |   });
106 |   elements.push(
107 |     <span
108 |       key={`content:${prevIndex}`}
109 |       dangerouslySetInnerHTML={{
110 |         __html: DOMPurify.sanitize(content.slice(prevIndex)),
111 |       }}
112 |     ></span>,
113 |   );
114 |   return elements;
115 | };
116 | 
117 | export function ChatMessageBubble(props: {
118 |   message: Message;
119 |   aiEmoji?: string;
120 |   isMostRecent: boolean;
121 |   messageCompleted: boolean;
122 | }) {
123 |   const { role, content, runId } = props.message;
124 |   const isUser = role === "user";
125 |   const [isLoading, setIsLoading] = useState(false);
126 |   const [traceIsLoading, setTraceIsLoading] = useState(false);
127 |   const [feedback, setFeedback] = useState<Feedback | null>(null);
128 |   const [comment, setComment] = useState("");
129 |   const [feedbackColor, setFeedbackColor] = useState("");
130 |   const upButtonRef = useRef(null);
131 |   const downButtonRef = useRef(null);
132 | 
133 |   const cumulativeOffset = function (element: HTMLElement | null) {
134 |     var top = 0,
135 |       left = 0;
136 |     do {
137 |       top += element?.offsetTop || 0;
138 |       left += element?.offsetLeft || 0;
139 |       element = (element?.offsetParent as HTMLElement) || null;
140 |     } while (element);
141 | 
142 |     return {
143 |       top: top,
144 |       left: left,
145 |     };
146 |   };
147 | 
148 |   const sendUserFeedback = async (score: number, key: string) => {
149 |     let run_id = runId;
150 |     if (run_id === undefined) {
151 |       return;
152 |     }
153 |     if (isLoading) {
154 |       return;
155 |     }
156 |     setIsLoading(true);
157 |     try {
158 |       const data = await sendFeedback({
159 |         score,
160 |         runId: run_id,
161 |         key,
162 |         feedbackId: feedback?.feedback_id,
163 |         comment,
164 |         isExplicit: true,
165 |       });
166 |       if (data.code === 200) {
167 |         setFeedback({ run_id, score, key, feedback_id: data.feedbackId });
168 |         score == 1 ? animateButton("upButton") : animateButton("downButton");
169 |         if (comment) {
170 |           setComment("");
171 |         }
172 |       }
173 |     } catch (e: any) {
174 |       console.error("Error:", e);
175 |       toast.error(e.message);
176 |     }
177 |     setIsLoading(false);
178 |   };
179 |   const viewTrace = async () => {
180 |     try {
181 |       setTraceIsLoading(true);
182 |       const response = await fetch(apiBaseUrl + "/get_trace", {
183 |         method: "POST",
184 |         headers: {
185 |           "Content-Type": "application/json",
186 |         },
187 |         body: JSON.stringify({
188 |           run_id: runId,
189 |         }),
190 |       });
191 | 
192 |       const data = await response.json();
193 | 
194 |       if (data.code === 400) {
195 |         toast.error("Unable to view trace");
196 |         throw new Error("Unable to view trace");
197 |       } else {
198 |         const url = data.replace(/['"]+/g, "");
199 |         window.open(url, "_blank");
200 |         setTraceIsLoading(false);
201 |       }
202 |     } catch (e: any) {
203 |       console.error("Error:", e);
204 |       setTraceIsLoading(false);
205 |       toast.error(e.message);
206 |     }
207 |   };
208 | 
209 |   const sources = props.message.sources ?? [];
210 |   const { filtered: filteredSources, indexMap: sourceIndexMap } =
211 |     filterSources(sources);
212 | 
213 |   // Use an array of highlighted states as a state since React
214 |   // complains when creating states in a loop
215 |   const [highlighedSourceLinkStates, setHighlightedSourceLinkStates] = useState(
216 |     filteredSources.map(() => false),
217 |   );
218 |   const answerElements =
219 |     role === "assistant"
220 |       ? createAnswerElements(
221 |           content,
222 |           filteredSources,
223 |           sourceIndexMap,
224 |           highlighedSourceLinkStates,
225 |           setHighlightedSourceLinkStates,
226 |         )
227 |       : [];
228 | 
229 |   const animateButton = (buttonId: string) => {
230 |     let button: HTMLButtonElement | null;
231 |     if (buttonId === "upButton") {
232 |       button = upButtonRef.current;
233 |     } else if (buttonId === "downButton") {
234 |       button = downButtonRef.current;
235 |     } else {
236 |       return;
237 |     }
238 |     if (!button) return;
239 |     let resolvedButton = button as HTMLButtonElement;
240 |     resolvedButton.classList.add("animate-ping");
241 |     setTimeout(() => {
242 |       resolvedButton.classList.remove("animate-ping");
243 |     }, 500);
244 | 
245 |     emojisplosion({
246 |       emojiCount: 10,
247 |       uniqueness: 1,
248 |       position() {
249 |         const offset = cumulativeOffset(button);
250 | 
251 |         return {
252 |           x: offset.left + resolvedButton.clientWidth / 2,
253 |           y: offset.top + resolvedButton.clientHeight / 2,
254 |         };
255 |       },
256 |       emojis: buttonId === "upButton" ? ["👍"] : ["👎"],
257 |     });
258 |   };
259 | 
260 |   return (
261 |     <VStack align="start" spacing={5} pb={5}>
262 |       {!isUser && filteredSources.length > 0 && (
263 |         <>
264 |           <Flex direction={"column"} width={"100%"}>
265 |             <VStack spacing={"5px"} align={"start"} width={"100%"}>
266 |               <Heading
267 |                 fontSize="lg"
268 |                 fontWeight={"medium"}
269 |                 mb={1}
270 |                 color={"blue.300"}
271 |                 paddingBottom={"10px"}
272 |               >
273 |                 Sources
274 |               </Heading>
275 |               <HStack spacing={"10px"} maxWidth={"100%"} overflow={"auto"}>
276 |                 {filteredSources.map((source, index) => (
277 |                   <Box key={index} alignSelf={"stretch"} width={40}>
278 |                     <SourceBubble
279 |                       source={source}
280 |                       highlighted={highlighedSourceLinkStates[index]}
281 |                       onMouseEnter={() =>
282 |                         setHighlightedSourceLinkStates(
283 |                           filteredSources.map((_, i) => i === index),
284 |                         )
285 |                       }
286 |                       onMouseLeave={() =>
287 |                         setHighlightedSourceLinkStates(
288 |                           filteredSources.map(() => false),
289 |                         )
290 |                       }
291 |                       runId={runId}
292 |                     />
293 |                   </Box>
294 |                 ))}
295 |               </HStack>
296 |             </VStack>
297 |           </Flex>
298 | 
299 |           <Heading size="lg" fontWeight="medium" color="blue.300">
300 |             Answer
301 |           </Heading>
302 |         </>
303 |       )}
304 | 
305 |       {isUser ? (
306 |         <Heading size="lg" fontWeight="medium" color="white">
307 |           {content}
308 |         </Heading>
309 |       ) : (
310 |         <Box className="whitespace-pre-wrap" color="white">
311 |           {answerElements}
312 |         </Box>
313 |       )}
314 | 
315 |       {props.message.role !== "user" &&
316 |         props.isMostRecent &&
317 |         props.messageCompleted && (
318 |           <HStack spacing={2}>
319 |             <Button
320 |               ref={upButtonRef}
321 |               size="sm"
322 |               variant="outline"
323 |               colorScheme={feedback === null ? "green" : "gray"}
324 |               onClick={() => {
325 |                 if (feedback === null && props.message.runId) {
326 |                   sendUserFeedback(1, "user_score");
327 |                   animateButton("upButton");
328 |                   setFeedbackColor("border-4 border-green-300");
329 |                 } else {
330 |                   toast.error("You have already provided your feedback.");
331 |                 }
332 |               }}
333 |             >
334 |               👍
335 |             </Button>
336 |             <Button
337 |               ref={downButtonRef}
338 |               size="sm"
339 |               variant="outline"
340 |               colorScheme={feedback === null ? "red" : "gray"}
341 |               onClick={() => {
342 |                 if (feedback === null && props.message.runId) {
343 |                   sendUserFeedback(0, "user_score");
344 |                   animateButton("downButton");
345 |                   setFeedbackColor("border-4 border-red-300");
346 |                 } else {
347 |                   toast.error("You have already provided your feedback.");
348 |                 }
349 |               }}
350 |             >
351 |               👎
352 |             </Button>
353 |             <Spacer />
354 |             <Button
355 |               size="sm"
356 |               variant="outline"
357 |               colorScheme={runId === null ? "blue" : "gray"}
358 |               onClick={async (e) => {
359 |                 e.preventDefault();
360 |                 await viewTrace();
361 |               }}
362 |               isLoading={traceIsLoading}
363 |               loadingText="🔄"
364 |               color="white"
365 |             >
366 |               🦜🛠️ View trace
367 |             </Button>
368 |           </HStack>
369 |         )}
370 | 
371 |       {!isUser && <Divider mt={4} mb={4} />}
372 |     </VStack>
373 |   );
374 | }
375 | 


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0)),Ss={AddObject:"addObject",Analytics:"analytics",Browser:"browse",DeleteIndex:"deleteIndex",DeleteObject:"deleteObject",EditSettings:"editSettings",ListIndexes:"listIndexes",Logs:"logs",Recommendation:"recommendation",Search:"search",SeeUnretrievableAttributes:"seeUnretrievableAttributes",Settings:"settings",Usage:"usage"},k={AddObject:"addObject",UpdateObject:"updateObject",PartialUpdateObject:"partialUpdateObject",PartialUpdateObjectNoCreate:"partialUpdateObjectNoCreate",DeleteObject:"deleteObject"},ee={Settings:"settings",Synonyms:"synonyms",Rules:"rules"},Ds={None:"none",StopIfEnoughMatches:"stopIfEnoughMatches"},Rs={Synonym:"synonym",OneWaySynonym:"oneWaySynonym",AltCorrection1:"altCorrection1",AltCorrection2:"altCorrection2",Placeholder:"placeholder"};i.ApiKeyACLEnum=Ss;i.BatchActionEnum=k;i.ScopeEnum=ee;i.StrategyEnum=Ds;i.SynonymEnum=Rs;i.addApiKey=Rr;i.assignUserID=vr;i.assignUserIDs=xr;i.batch=pt;i.browseObjects=Yr;i.browseRules=Zr;i.browseSynonyms=es;i.chunkedBatch=te;i.clearObjects=ts;i.clearRules=rs;i.clearSynonyms=ss;i.copyIndex=Z;i.copyRules=qr;i.copySettings=Er;i.copySynonyms=Tr;i.createBrowsablePromise=Y;i.createMissingObjectIDError=ct;i.createObjectNotFoundError=ut;i.createSearchClient=Dr;i.createValidUntilNotFoundError=lt;i.deleteApiKey=Mr;i.deleteBy=ns;i.deleteIndex=as;i.deleteObject=os;i.deleteObjects=yt;i.deleteRule=is;i.deleteSynonym=cs;i.exists=us;i.findObject=ls;i.generateSecuredApiKey=wr;i.getApiKey=$;i.getLogs=kr;i.getObject=ds;i.getObjectPosition=ps;i.getObjects=ms;i.getRule=hs;i.getSecuredApiKeyRemainingValidity=Cr;i.getSettings=gt;i.getSynonym=ys;i.getTask=bt;i.getTopUserIDs=Ur;i.getUserID=Nr;i.hasPendingMappings=Wr;i.initIndex=L;i.listApiKeys=Hr;i.listClusters=_r;i.listIndices=Fr;i.listUserIDs=Br;i.moveIndex=Kr;i.multipleBatch=zr;i.multipleGetObjects=Gr;i.multipleQueries=$r;i.multipleSearchForFacetValues=Lr;i.partialUpdateObject=gs;i.partialUpdateObjects=Pt;i.removeUserID=Vr;i.replaceAllObjects=fs;i.replaceAllRules=bs;i.replaceAllSynonyms=Ps;i.restoreApiKey=Qr;i.saveObject=js;i.saveObjects=he;i.saveRule=Os;i.saveRules=ye;i.saveSynonym=Is;i.saveSynonyms=ge;i.search=ft;i.searchForFacetValues=dt;i.searchRules=mt;i.searchSynonyms=ht;i.searchUserIDs=Jr;i.setSettings=As;i.updateApiKey=Xr;i.waitTask=D});var It=I((on,Ot)=>{Ot.exports=jt()});var At=I(re=>{"use strict";Object.defineProperty(re,"__esModule",{value:!0});function vs(){return{debug(e,t){return Promise.resolve()},info(e,t){return Promise.resolve()},error(e,t){return Promise.resolve()}}}var xs={Debug:1,Info:2,Error:3};re.LogLevelEnum=xs;re.createNullLogger=vs});var Dt=I((un,St)=>{St.exports=At()});var xt=I(fe=>{"use strict";Object.defineProperty(fe,"__esModule",{value:!0});var Rt=require("http"),vt=require("https"),qs=require("url");function Es(){let e={keepAlive:!0},t=new Rt.Agent(e),r=new vt.Agent(e);return{send(s){return new Promise(n=>{let a=qs.parse(s.url),o=a.query===null?a.pathname:`${a.pathname}?${a.query}`,d=u({agent:a.protocol==="https:"?r:t,hostname:a.hostname,path:o,method:s.method,headers:s.headers},a.port!==void 0?{port:a.port||""}:{}),y=(a.protocol==="https:"?vt:Rt).request(d,h=>{let S="";h.on("data",O=>S+=O),h.on("end",()=>{clearTimeout(f),clearTimeout(p),n({status:h.statusCode||0,content:S,isTimedOut:!1})})}),b=(h,S)=>setTimeout(()=>{y.abort(),n({status:0,content:S,isTimedOut:!0})},h*1e3),f=b(s.connectTimeout,"Connection timeout"),p;y.on("error",h=>{clearTimeout(f),clearTimeout(p),n({status:0,content:h.message,isTimedOut:!1})}),y.once("response",()=>{clearTimeout(f),p=b(s.responseTimeout,"Socket timeout")}),s.data!==void 0&&y.write(s.data),y.end()})},destroy(){return t.destroy(),r.destroy(),Promise.resolve()}}}fe.createNodeHttpRequester=Es});var Et=I((dn,qt)=>{qt.exports=xt()});var kt=I((pn,Tt)=>{"use strict";var Mt=Ee(),Ts=we(),W=st(),be=F(),Pe=it(),c=It(),Ms=Dt(),ws=Et(),ks=K();function wt(e,t,r){let s={appId:e,apiKey:t,timeouts:{connect:2,read:5,write:30},requester:ws.createNodeHttpRequester(),logger:Ms.createNullLogger(),responsesCache:Mt.createNullCache(),requestsCache:Mt.createNullCache(),hostsCache:Ts.createInMemoryCache(),userAgent:ks.createUserAgent(be.version).add({segment:"Node.js",version:process.versions.node})};return c.createSearchClient(g(u(u({},s),r),{methods:{search:c.multipleQueries,searchForFacetValues:c.multipleSearchForFacetValues,multipleBatch:c.multipleBatch,multipleGetObjects:c.multipleGetObjects,multipleQueries:c.multipleQueries,copyIndex:c.copyIndex,copySettings:c.copySettings,copyRules:c.copyRules,copySynonyms:c.copySynonyms,moveIndex:c.moveIndex,listIndices:c.listIndices,getLogs:c.getLogs,listClusters:c.listClusters,multipleSearchForFacetValues:c.multipleSearchForFacetValues,getApiKey:c.getApiKey,addApiKey:c.addApiKey,listApiKeys:c.listApiKeys,updateApiKey:c.updateApiKey,deleteApiKey:c.deleteApiKey,restoreApiKey:c.restoreApiKey,assignUserID:c.assignUserID,assignUserIDs:c.assignUserIDs,getUserID:c.getUserID,searchUserIDs:c.searchUserIDs,listUserIDs:c.listUserIDs,getTopUserIDs:c.getTopUserIDs,removeUserID:c.removeUserID,hasPendingMappings:c.hasPendingMappings,generateSecuredApiKey:c.generateSecuredApiKey,getSecuredApiKeyRemainingValidity:c.getSecuredApiKeyRemainingValidity,destroy:be.destroy,initIndex:n=>a=>c.initIndex(n)(a,{methods:{batch:c.batch,delete:c.deleteIndex,getObject:c.getObject,getObjects:c.getObjects,saveObject:c.saveObject,saveObjects:c.saveObjects,search:c.search,searchForFacetValues:c.searchForFacetValues,waitTask:c.waitTask,setSettings:c.setSettings,getSettings:c.getSettings,partialUpdateObject:c.partialUpdateObject,partialUpdateObjects:c.partialUpdateObjects,deleteObject:c.deleteObject,deleteObjects:c.deleteObjects,deleteBy:c.deleteBy,clearObjects:c.clearObjects,browseObjects:c.browseObjects,getObjectPosition:c.getObjectPosition,findObject:c.findObject,exists:c.exists,saveSynonym:c.saveSynonym,saveSynonyms:c.saveSynonyms,getSynonym:c.getSynonym,searchSynonyms:c.searchSynonyms,browseSynonyms:c.browseSynonyms,deleteSynonym:c.deleteSynonym,clearSynonyms:c.clearSynonyms,replaceAllObjects:c.replaceAllObjects,replaceAllSynonyms:c.replaceAllSynonyms,searchRules:c.searchRules,getRule:c.getRule,deleteRule:c.deleteRule,saveRule:c.saveRule,saveRules:c.saveRules,replaceAllRules:c.replaceAllRules,browseRules:c.browseRules,clearRules:c.clearRules}}),initAnalytics:()=>n=>W.createAnalyticsClient(g(u(u({},s),n),{methods:{addABTest:W.addABTest,getABTest:W.getABTest,getABTests:W.getABTests,stopABTest:W.stopABTest,deleteABTest:W.deleteABTest}})),initRecommendation:()=>n=>Pe.createRecommendationClient(g(u(u({},s),n),{methods:{getPersonalizationStrategy:Pe.getPersonalizationStrategy,setPersonalizationStrategy:Pe.setPersonalizationStrategy}}))}}))}wt.version=be.version;Tt.exports=wt});var Ut=I((mn,je)=>{var Ct=kt();je.exports=Ct;je.exports.default=Ct});var Ws={};Vt(Ws,{default:()=>Ks});var Oe=C(require("@yarnpkg/core")),E=C(require("@yarnpkg/core")),Ie=C(require("@yarnpkg/plugin-essentials")),Ht=C(require("semver"));var se=C(require("@yarnpkg/core")),Nt=C(Ut()),Cs="e8e1bd300d860104bb8c58453ffa1eb4",Us="OFCNCOG2CU",Wt=async(e,t)=>{var a;let r=se.structUtils.stringifyIdent(e),n=Ns(t).initIndex("npm-search");try{return((a=(await n.getObject(r,{attributesToRetrieve:["types"]})).types)==null?void 0:a.ts)==="definitely-typed"}catch(o){return!1}},Ns=e=>(0,Nt.default)(Us,Cs,{requester:{async send(r){try{let s=await se.httpUtils.request(r.url,r.data||null,{configuration:e,headers:r.headers});return{content:s.body,isTimedOut:!1,status:s.statusCode}}catch(s){return{content:s.response.body,isTimedOut:!1,status:s.response.statusCode}}}}});var _t=e=>e.scope?`${e.scope}__${e.name}`:`${e.name}`,Hs=async(e,t,r,s)=>{if(r.scope==="types")return;let{project:n}=e,{configuration:a}=n,o=a.makeResolver(),d={project:n,resolver:o,report:new E.ThrowReport};if(!await Wt(r,a))return;let b=_t(r),f=E.structUtils.parseRange(r.range).selector;if(!E.semverUtils.validRange(f)){let P=await o.getCandidates(r,new Map,d);f=E.structUtils.parseRange(P[0].reference).selector}let p=Ht.default.coerce(f);if(p===null)return;let h=`${Ie.suggestUtils.Modifier.CARET}${p.major}`,S=E.structUtils.makeDescriptor(E.structUtils.makeIdent("types",b),h),O=E.miscUtils.mapAndFind(n.workspaces,P=>{var T,V;let x=(T=P.manifest.dependencies.get(r.identHash))==null?void 0:T.descriptorHash,v=(V=P.manifest.devDependencies.get(r.identHash))==null?void 0:V.descriptorHash;if(x!==r.descriptorHash&&v!==r.descriptorHash)return E.miscUtils.mapAndFind.skip;let j=[];for(let Ae of Oe.Manifest.allDependencies){let Se=P.manifest[Ae].get(S.identHash);typeof Se!="undefined"&&j.push([Ae,Se])}return j.length===0?E.miscUtils.mapAndFind.skip:j});if(typeof O!="undefined")for(let[P,x]of O)e.manifest[P].set(x.identHash,x);else{try{if((await o.getCandidates(S,new Map,d)).length===0)return}catch{return}e.manifest[Ie.suggestUtils.Target.DEVELOPMENT].set(S.identHash,S)}},_s=async(e,t,r)=>{if(r.scope==="types")return;let s=_t(r),n=E.structUtils.makeIdent("types",s);for(let a of Oe.Manifest.allDependencies)typeof e.manifest[a].get(n.identHash)!="undefined"&&e.manifest[a].delete(n.identHash)},Fs=(e,t)=>{t.publishConfig&&t.publishConfig.typings&&(t.typings=t.publishConfig.typings),t.publishConfig&&t.publishConfig.types&&(t.types=t.publishConfig.types)},Bs={hooks:{afterWorkspaceDependencyAddition:Hs,afterWorkspaceDependencyRemoval:_s,beforeWorkspacePacking:Fs}},Ks=Bs;return Ws;})();
 7 | return plugin;
 8 | }
 9 | };
10 | 


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