Initial commit from Create Llama

Author: manekinekko

.devcontainer/devcontainer.json

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+{
+  "image": "mcr.microsoft.com/vscode/devcontainers/typescript-node:dev-20-bullseye",
+  "features": {
+    "ghcr.io/devcontainers-contrib/features/turborepo-npm:1": {},
+    "ghcr.io/devcontainers-contrib/features/typescript:2": {},
+    "ghcr.io/devcontainers/features/python:1": {
+      "version": "3.11",
+      "toolsToInstall": [
+        "flake8",
+        "black",
+        "mypy",
+        "poetry"
+      ]
+    }
+  },
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md"
+      ]
+    },
+    "vscode": {
+      "extensions": [
+        "ms-vscode.typescript-language-features",
+        "esbenp.prettier-vscode",
+        "ms-python.python",
+        "ms-python.black-formatter",
+        "ms-python.vscode-flake8",
+        "ms-python.vscode-pylance"
+      ],
+      "settings": {
+        "python.formatting.provider": "black",
+        "python.languageServer": "Pylance",
+        "python.analysis.typeCheckingMode": "basic"
+      }
+    }
+  },
+  "containerEnv": {
+    "POETRY_VIRTUALENVS_CREATE": "false"
+  },
+  "forwardPorts": [
+    3000,
+    8000
+  ],
+  "postCreateCommand": "npm install"
+}

.env

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+# The Llama Cloud API key.
+# LLAMA_CLOUD_API_KEY=
+
+# The provider for the AI models to use.
+MODEL_PROVIDER=openai
+
+# The name of LLM model to use.
+MODEL=gpt-3.5-turbo
+
+# Name of the embedding model to use.
+EMBEDDING_MODEL=text-embedding-3-large
+
+# Dimension of the embedding model to use.
+EMBEDDING_DIM=1024
+
+# The OpenAI API key to use.
+# OPENAI_API_KEY=
+
+# Temperature for sampling from the model.
+# LLM_TEMPERATURE=
+
+# Maximum number of tokens to generate.
+# LLM_MAX_TOKENS=
+
+# The number of similar embeddings to return when retrieving documents.
+TOP_K=3
+
+# FILESERVER_URL_PREFIX is the URL prefix of the server storing the images generated by the interpreter.
+FILESERVER_URL_PREFIX=http://localhost:3000/api/files
+
+# The system prompt for the AI model.
+SYSTEM_PROMPT=You are a helpful assistant who helps users with their questions.
+

.eslintrc.json

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+{
+  "extends": ["next/core-web-vitals", "prettier"],
+  "rules": {
+    "max-params": ["error", 4],
+    "prefer-const": "error"
+  }
+}

.gitignore

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+# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
+
+# dependencies
+/node_modules
+/.pnp
+.pnp.js
+
+# testing
+/coverage
+
+# next.js
+/.next/
+/out/
+
+# production
+/build
+
+# misc
+.DS_Store
+*.pem
+
+# debug
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# local env files
+.env*.local
+
+# vercel
+.vercel
+
+# typescript
+*.tsbuildinfo
+next-env.d.ts
+
+tool-output/

Dockerfile

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+FROM node:20-alpine as build
+
+WORKDIR /app
+
+# Install dependencies
+COPY package.json package-lock.* ./
+RUN npm install
+
+# Build the application
+COPY . .
+RUN npm run build
+
+# ====================================
+FROM build as release
+
+CMD ["npm", "run", "start"]

README.md

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+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Next.js](https://nextjs.org/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, install the dependencies:
+
+```
+npm install
+```
+
+Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
+
+```
+npm run generate
+```
+
+Third, run the development server:
+
+```
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.
+
+This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
+
+## Using Docker
+
+1. Build an image for the Next.js app:
+
+```
+docker build -t <your_app_image_name> .
+```
+
+2. Generate embeddings:
+
+Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
+
+```
+docker run \
+  --rm \
+  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
+  -v $(pwd)/config:/app/config \
+  -v $(pwd)/data:/app/data \
+  -v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
+  <your_app_image_name> \
+  npm run generate
+```
+
+3. Start the app:
+
+```
+docker run \
+  --rm \
+  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
+  -v $(pwd)/config:/app/config \
+  -v $(pwd)/cache:/app/cache \ # Use your file system to store gea vector database
+  -p 3000:3000 \
+  <your_app_image_name>
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
+- [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features).
+
+You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!

app/api/chat/engine/chat.ts

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+import { ContextChatEngine, Settings } from "llamaindex";
+import { getDataSource } from "./index";
+
+export async function createChatEngine() {
+  const index = await getDataSource();
+  if (!index) {
+    throw new Error(
+      `StorageContext is empty - call 'npm run generate' to generate the storage first`,
+    );
+  }
+  const retriever = index.asRetriever();
+  retriever.similarityTopK = process.env.TOP_K
+    ? parseInt(process.env.TOP_K)
+    : 3;
+
+  return new ContextChatEngine({
+    chatModel: Settings.llm,
+    retriever,
+    systemPrompt: process.env.SYSTEM_PROMPT,
+  });
+}

app/api/chat/engine/generate.ts

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+import { VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
+
+import * as dotenv from "dotenv";
+
+import { getDocuments } from "./loader";
+import { initSettings } from "./settings";
+import { STORAGE_CACHE_DIR } from "./shared";
+
+// Load environment variables from local .env file
+dotenv.config();
+
+async function getRuntime(func: any) {
+  const start = Date.now();
+  await func();
+  const end = Date.now();
+  return end - start;
+}
+
+async function generateDatasource() {
+  console.log(`Generating storage context...`);
+  // Split documents, create embeddings and store them in the storage context
+  const ms = await getRuntime(async () => {
+    const storageContext = await storageContextFromDefaults({
+      persistDir: STORAGE_CACHE_DIR,
+    });
+    const documents = await getDocuments();
+    await VectorStoreIndex.fromDocuments(documents, {
+      storageContext,
+    });
+  });
+  console.log(`Storage context successfully generated in ${ms / 1000}s.`);
+}
+
+(async () => {
+  initSettings();
+  await generateDatasource();
+  console.log("Finished generating storage.");
+})();

app/api/chat/engine/index.ts

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+import { SimpleDocumentStore, VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
+import { STORAGE_CACHE_DIR } from "./shared";
+
+export async function getDataSource() {
+  const storageContext = await storageContextFromDefaults({
+    persistDir: `${STORAGE_CACHE_DIR}`,
+  });
+
+  const numberOfDocs = Object.keys(
+    (storageContext.docStore as SimpleDocumentStore).toDict(),
+  ).length;
+  if (numberOfDocs === 0) {
+    return null;
+  }
+  return await VectorStoreIndex.init({
+    storageContext,
+  });
+}

app/api/chat/engine/loader.ts

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+import { SimpleDirectoryReader } from "llamaindex";
+
+export const DATA_DIR = "./data";
+
+export async function getDocuments() {
+  return await new SimpleDirectoryReader().loadData({
+    directoryPath: DATA_DIR,
+  });
+}