Release 0.1.12 (#136)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

.changeset/config.json

@@ -1,7 +1,7 @@
 {
   "$schema": "https://unpkg.com/@changesets/config@3.0.0/schema.json",
   "changelog": "@changesets/cli/changelog",
-  "commit": true,
+  "commit": false,
   "fixed": [],
   "linked": [],
   "access": "public",

.changeset/cool-ears-train.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Use `gpt-4-turbo` model as default. Upgrade Python llama-index to 0.10.28

.changeset/curvy-eels-boil.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Remove asking for AI models and use defaults instead (OpenAIs GPT-4 Vision Preview and Embeddings v3). Use `--ask-models` CLI parameter to select models.

.changeset/dirty-cars-leave.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Add observability for Python

.changeset/four-tools-buy.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Use poetry run generate to generate embeddings for FastAPI

.changeset/khaki-phones-flash.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Use Settings object for LlamaIndex configuration

.changeset/lovely-olives-carry.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Add Qdrant support

.github/workflows/e2e.yml

@@ -26,7 +26,7 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Set up python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -35,12 +35,13 @@ jobs:
         with:
           version: ${{ env.POETRY_VERSION }}
 
+      - uses: pnpm/action-setup@v3
+
       - name: Setup Node.js ${{ matrix.node-version }}
         uses: actions/setup-node@v4
         with:
           node-version: ${{ matrix.node-version }}
-
-      - uses: pnpm/action-setup@v3
+          cache: "pnpm"
 
       - name: Install dependencies
         run: pnpm install

.github/workflows/lint_on_push_or_pull.yml

@@ -14,12 +14,13 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
+      - uses: pnpm/action-setup@v3
+
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
           node-version-file: ".nvmrc"
-
-      - uses: pnpm/action-setup@v3
+          cache: "pnpm"
 
       - name: Install dependencies
         run: pnpm install

.github/workflows/publish_github.yml

@@ -0,0 +1,36 @@
+name: Publish to GitHub Releases
+
+on:
+  push:
+    tags:
+      - "v*"
+
+jobs:
+  build-and-publish:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v3
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+
+      - name: Install dependencies
+        run: pnpm install
+
+      - name: Build tarball
+        run: |
+          pnpm pack
+
+      - name: Create release
+        uses: ncipollo/release-action@v1
+        with:
+          artifacts: "create-llama-*.tgz"
+          name: Release ${{ github.ref }}
+          bodyFile: "CHANGELOG.md"
+          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release.yml

@@ -15,17 +15,41 @@ jobs:
       - name: Checkout Repo
         uses: actions/checkout@v4
 
+      - uses: pnpm/action-setup@v3
+
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
           node-version-file: ".nvmrc"
-
-      - uses: pnpm/action-setup@v3
+          cache: "pnpm"
 
       - name: Install dependencies
         run: pnpm install
 
-      - name: Create Release Pull Request
+      - name: Add auth token to .npmrc file
+        run: |
+          cat << EOF >> ".npmrc"
+            //registry.npmjs.org/:_authToken=$NPM_TOKEN
+          EOF
+        env:
+          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
+
+      - name: Get changeset status
+        id: get-changeset-status
+        run: |
+          pnpm changeset status --output .changeset/status.json
+          new_version=$(jq -r '.releases[0].newVersion' < .changeset/status.json)
+          rm -v .changeset/status.json
+          echo "new-version=${new_version}" >> "$GITHUB_OUTPUT"
+
+      - name: Create Release Pull Request or Publish to npm
+        id: changesets
         uses: changesets/action@v1
+        with:
+          commit: Release ${{ steps.get-changeset-status.outputs.new-version }}
+          title: Release ${{ steps.get-changeset-status.outputs.new-version }}
+          # build package and call changeset publish
+          publish: pnpm release
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}

CHANGELOG.md

@@ -1,5 +1,108 @@
 # create-llama
 
+## 0.1.12
+
+### Patch Changes
+
+- aa69014: Fix NextJS for TS 5.2
+
+## 0.1.11
+
+### Patch Changes
+
+- 48b96ff: Add DuckDuckGo search tool
+- 9c9decb: Reuse function tool instances and improve e2b interpreter tool for Python
+- 02ed277: Add Groq as a model provider
+- 0748f2e: Remove hard-coded Gemini supported models
+
+## 0.1.10
+
+### Patch Changes
+
+- 9112d08: Add OpenAPI tool for Typescript
+- 8f03f8d: Add OLLAMA_REQUEST_TIMEOUT variable to config Ollama timeout (Python)
+- 8f03f8d: Apply nest_asyncio for llama parse
+
+## 0.1.9
+
+### Patch Changes
+
+- a42fa53: Add CSV upload
+- 563b51d: Fix Vercel streaming (python) to stream data events instantly
+- d60b3c5: Add E2B code interpreter tool for FastAPI
+- 956538e: Add OpenAPI action tool for FastAPI
+
+## 0.1.8
+
+### Patch Changes
+
+- cd50a33: Add interpreter tool for TS using e2b.dev
+
+## 0.1.7
+
+### Patch Changes
+
+- 260d37a: Add system prompt env variable for TS
+- bbd5b8d: Fix postgres connection leaking issue
+- bb53425: Support HTTP proxies by setting the GLOBAL_AGENT_HTTP_PROXY env variable
+- 69c2e16: Fix streaming for Express
+- 7873bfb: Update Ollama provider to run with the base URL from the environment variable
+
+## 0.1.6
+
+### Patch Changes
+
+- 56537a1: Display PDF files in source nodes
+
+## 0.1.5
+
+### Patch Changes
+
+- 84db798: feat: support display latex in chat markdown
+
+## 0.1.4
+
+### Patch Changes
+
+- 0bc8e75: Use ingestion pipeline for dedicated vector stores (Python only)
+- cb1001d: Add ChromaDB vector store
+
+## 0.1.3
+
+### Patch Changes
+
+- 416073d: Directly import vector stores to work with NextJS
+
+## 0.1.2
+
+### Patch Changes
+
+- 056e376: Add support for displaying tool outputs (including weather widget as example)
+
+## 0.1.1
+
+### Patch Changes
+
+- 7bd3ed5: Support Anthropic and Gemini as model providers
+- 7bd3ed5: Support new agents from LITS 0.3
+- cfb5257: Display events (e.g. retrieving nodes) per chat message
+
+## 0.1.0
+
+### Minor Changes
+
+- f1c3e8d: Add Llama3 and Phi3 support using Ollama
+
+### Patch Changes
+
+- a0dec80: Use `gpt-4-turbo` model as default. Upgrade Python llama-index to 0.10.28
+- 753229d: Remove asking for AI models and use defaults instead (OpenAIs GPT-4 Vision Preview and Embeddings v3). Use `--ask-models` CLI parameter to select models.
+- 1d78202: Add observability for Python
+- 6acccd2: Use poetry run generate to generate embeddings for FastAPI
+- 9efcffe: Use Settings object for LlamaIndex configuration
+- 418bf9b: refactor: use tsx instead of ts-node
+- 1be69a5: Add Qdrant support
+
 ## 0.0.32
 
 ### Patch Changes

CONTRIBUTING.md

@@ -7,7 +7,7 @@ Install NodeJS. Preferably v18 using nvm or n.
 Inside the `create-llama` directory:
 
 ```
-npm i -g pnpm ts-node
+npm i -g pnpm
 pnpm install
 ```
 

create-app.ts

@@ -30,10 +30,8 @@ export async function createApp({
   appPath,
   packageManager,
   frontend,
-  openAiKey,
+  modelConfig,
   llamaCloudKey,
-  model,
-  embeddingModel,
   communityProjectConfig,
   llamapack,
   vectorDb,
@@ -77,10 +75,8 @@ export async function createApp({
     ui,
     packageManager,
     isOnline,
-    openAiKey,
+    modelConfig,
     llamaCloudKey,
-    model,
-    embeddingModel,
     communityProjectConfig,
     llamapack,
     vectorDb,

helpers/copy.ts

@@ -52,6 +52,7 @@ export const copy = async (
 export const assetRelocator = (name: string) => {
   switch (name) {
     case "gitignore":
+    case "npmrc":
     case "eslintrc.json": {
       return `.${name}`;
     }

helpers/env-variables.ts

@@ -1,12 +1,14 @@
 import fs from "fs/promises";
 import path from "path";
+import { TOOL_SYSTEM_PROMPT_ENV_VAR, Tool } from "./tools";
 import {
+  ModelConfig,
   TemplateDataSource,
   TemplateFramework,
   TemplateVectorDB,
 } from "./types";
 
-type EnvVar = {
+export type EnvVar = {
   name?: string;
   description?: string;
   value?: string;
@@ -28,14 +30,20 @@ const renderEnvVar = (envVars: EnvVar[]): string => {
   );
 };
 
-const getVectorDBEnvs = (vectorDb: TemplateVectorDB) => {
+const getVectorDBEnvs = (
+  vectorDb?: TemplateVectorDB,
+  framework?: TemplateFramework,
+): EnvVar[] => {
+  if (!vectorDb || !framework) {
+    return [];
+  }
   switch (vectorDb) {
     case "mongo":
       return [
         {
-          name: "MONGO_URI",
+          name: "MONGODB_URI",
           description:
-            "For generating a connection URI, see https://docs.timescale.com/use-timescale/latest/services/create-a-service\nThe MongoDB connection URI.",
+            "For generating a connection URI, see https://www.mongodb.com/docs/manual/reference/connection-string/ \nThe MongoDB connection URI.",
         },
         {
           name: "MONGODB_DATABASE",
@@ -125,61 +133,137 @@ const getVectorDBEnvs = (vectorDb: TemplateVectorDB) => {
             "Optional API key for authenticating requests to Qdrant.",
         },
       ];
+    case "chroma":
+      const envs = [
+        {
+          name: "CHROMA_COLLECTION",
+          description: "The name of the collection in your Chroma database",
+        },
+        {
+          name: "CHROMA_HOST",
+          description: "The API endpoint for your Chroma database",
+        },
+        {
+          name: "CHROMA_PORT",
+          description: "The port for your Chroma database",
+        },
+      ];
+      // TS Version doesn't support config local storage path
+      if (framework === "fastapi") {
+        envs.push({
+          name: "CHROMA_PATH",
+          description: `The local path to the Chroma database. 
+Specify this if you are using a local Chroma database. 
+Otherwise, use CHROMA_HOST and CHROMA_PORT config above`,
+        });
+      }
+      return envs;
     default:
       return [];
   }
 };
 
-export const createBackendEnvFile = async (
-  root: string,
-  opts: {
-    openAiKey?: string;
-    llamaCloudKey?: string;
-    vectorDb?: TemplateVectorDB;
-    model?: string;
-    embeddingModel?: string;
-    framework?: TemplateFramework;
-    dataSources?: TemplateDataSource[];
-    port?: number;
-  },
-) => {
-  // Init env values
-  const envFileName = ".env";
-  const defaultEnvs = [
+const getModelEnvs = (modelConfig: ModelConfig): EnvVar[] => {
+  return [
+    {
+      name: "MODEL_PROVIDER",
+      description: "The provider for the AI models to use.",
+      value: modelConfig.provider,
+    },
     {
-      render: true,
       name: "MODEL",
       description: "The name of LLM model to use.",
-      value: opts.model,
-    },
-    {
-      render: true,
-      name: "OPENAI_API_KEY",
-      description: "The OpenAI API key to use.",
-      value: opts.openAiKey,
-    },
-    {
-      name: "LLAMA_CLOUD_API_KEY",
-      description: `The Llama Cloud API key.`,
-      value: opts.llamaCloudKey,
+      value: modelConfig.model,
     },
     {
       name: "EMBEDDING_MODEL",
       description: "Name of the embedding model to use.",
-      value: opts.embeddingModel,
+      value: modelConfig.embeddingModel,
     },
     {
       name: "EMBEDDING_DIM",
       description: "Dimension of the embedding model to use.",
-      value: 1536,
+      value: modelConfig.dimensions.toString(),
     },
-    // Add vector database environment variables
-    ...(opts.vectorDb ? getVectorDBEnvs(opts.vectorDb) : []),
+    {
+      name: "CONVERSATION_STARTERS",
+      description: "The questions to help users get started (multi-line).",
+    },
+    ...(modelConfig.provider === "openai"
+      ? [
+          {
+            name: "OPENAI_API_KEY",
+            description: "The OpenAI API key to use.",
+            value: modelConfig.apiKey,
+          },
+          {
+            name: "LLM_TEMPERATURE",
+            description: "Temperature for sampling from the model.",
+          },
+          {
+            name: "LLM_MAX_TOKENS",
+            description: "Maximum number of tokens to generate.",
+          },
+        ]
+      : []),
+    ...(modelConfig.provider === "anthropic"
+      ? [
+          {
+            name: "ANTHROPIC_API_KEY",
+            description: "The Anthropic API key to use.",
+            value: modelConfig.apiKey,
+          },
+        ]
+      : []),
+    ...(modelConfig.provider === "groq"
+      ? [
+          {
+            name: "GROQ_API_KEY",
+            description: "The Groq API key to use.",
+            value: modelConfig.apiKey,
+          },
+        ]
+      : []),
+    ...(modelConfig.provider === "gemini"
+      ? [
+          {
+            name: "GOOGLE_API_KEY",
+            description: "The Google API key to use.",
+            value: modelConfig.apiKey,
+          },
+        ]
+      : []),
+    ...(modelConfig.provider === "ollama"
+      ? [
+          {
+            name: "OLLAMA_BASE_URL",
+            description:
+              "The base URL for the Ollama API. Eg: http://127.0.0.1:11434",
+          },
+        ]
+      : []),
   ];
-  let envVars: EnvVar[] = [];
-  if (opts.framework === "fastapi") {
-    envVars = [
-      ...defaultEnvs,
+};
+
+const getFrameworkEnvs = (
+  framework: TemplateFramework,
+  port?: number,
+): EnvVar[] => {
+  const sPort = port?.toString() || "8000";
+  const result: EnvVar[] = [
+    {
+      name: "FILESERVER_URL_PREFIX",
+      description:
+        "FILESERVER_URL_PREFIX is the URL prefix of the server storing the images generated by the interpreter.",
+      value:
+        framework === "nextjs"
+          ? // FIXME: if we are using nextjs, port should be 3000
+            "http://localhost:3000/api/files"
+          : `http://localhost:${sPort}/api/files`,
+    },
+  ];
+  if (framework === "fastapi") {
+    result.push(
       ...[
         {
           name: "APP_HOST",
@@ -189,51 +273,104 @@ export const createBackendEnvFile = async (
         {
           name: "APP_PORT",
           description: "The port to start the backend app.",
-          value: opts.port?.toString() || "8000",
-        },
-        {
-          name: "LLM_TEMPERATURE",
-          description: "Temperature for sampling from the model.",
-        },
-        {
-          name: "LLM_MAX_TOKENS",
-          description: "Maximum number of tokens to generate.",
-        },
-        {
-          name: "TOP_K",
-          description:
-            "The number of similar embeddings to return when retrieving documents.",
-          value: "3",
-        },
-        {
-          name: "SYSTEM_PROMPT",
-          description: `Custom system prompt.
-Example:
-SYSTEM_PROMPT="
-We have provided context information below.
----------------------
-{context_str}
----------------------
-Given this information, please answer the question: {query_str}
-"`,
+          value: sPort,
         },
       ],
-    ];
-  } else {
-    envVars = [
-      ...defaultEnvs,
-      ...[
-        opts.framework === "nextjs"
-          ? {
-              name: "NEXT_PUBLIC_MODEL",
-              description:
-                "The LLM model to use (hardcode to front-end artifact).",
-              value: opts.model,
-            }
-          : {},
-      ],
-    ];
+    );
   }
+  return result;
+};
+
+const getEngineEnvs = (): EnvVar[] => {
+  return [
+    {
+      name: "TOP_K",
+      description:
+        "The number of similar embeddings to return when retrieving documents.",
+      value: "3",
+    },
+    {
+      name: "STREAM_TIMEOUT",
+      description:
+        "The time in milliseconds to wait for the stream to return a response.",
+      value: "60000",
+    },
+  ];
+};
+
+const getToolEnvs = (tools?: Tool[]): EnvVar[] => {
+  if (!tools?.length) return [];
+  const toolEnvs: EnvVar[] = [];
+  tools.forEach((tool) => {
+    if (tool.envVars?.length) {
+      toolEnvs.push(
+        // Don't include the system prompt env var here
+        // It should be handled separately by merging with the default system prompt
+        ...tool.envVars.filter(
+          (env) => env.name !== TOOL_SYSTEM_PROMPT_ENV_VAR,
+        ),
+      );
+    }
+  });
+  return toolEnvs;
+};
+
+const getSystemPromptEnv = (tools?: Tool[]): EnvVar => {
+  const defaultSystemPrompt =
+    "You are a helpful assistant who helps users with their questions.";
+
+  // build tool system prompt by merging all tool system prompts
+  let toolSystemPrompt = "";
+  tools?.forEach((tool) => {
+    const toolSystemPromptEnv = tool.envVars?.find(
+      (env) => env.name === TOOL_SYSTEM_PROMPT_ENV_VAR,
+    );
+    if (toolSystemPromptEnv) {
+      toolSystemPrompt += toolSystemPromptEnv.value + "\n";
+    }
+  });
+
+  const systemPrompt = toolSystemPrompt
+    ? `\"${toolSystemPrompt}\"`
+    : defaultSystemPrompt;
+
+  return {
+    name: "SYSTEM_PROMPT",
+    description: "The system prompt for the AI model.",
+    value: systemPrompt,
+  };
+};
+
+export const createBackendEnvFile = async (
+  root: string,
+  opts: {
+    llamaCloudKey?: string;
+    vectorDb?: TemplateVectorDB;
+    modelConfig: ModelConfig;
+    framework: TemplateFramework;
+    dataSources?: TemplateDataSource[];
+    port?: number;
+    tools?: Tool[];
+  },
+) => {
+  // Init env values
+  const envFileName = ".env";
+  const envVars: EnvVar[] = [
+    {
+      name: "LLAMA_CLOUD_API_KEY",
+      description: `The Llama Cloud API key.`,
+      value: opts.llamaCloudKey,
+    },
+    // Add model environment variables
+    ...getModelEnvs(opts.modelConfig),
+    // Add engine environment variables
+    ...getEngineEnvs(),
+    // Add vector database environment variables
+    ...getVectorDBEnvs(opts.vectorDb, opts.framework),
+    ...getFrameworkEnvs(opts.framework, opts.port),
+    ...getToolEnvs(opts.tools),
+    getSystemPromptEnv(opts.tools),
+  ];
   // Render and write env file
   const content = renderEnvVar(envVars);
   await fs.writeFile(path.join(root, envFileName), content);
@@ -244,20 +381,9 @@ export const createFrontendEnvFile = async (
   root: string,
   opts: {
     customApiPath?: string;
-    model?: string;
   },
 ) => {
   const defaultFrontendEnvs = [
-    {
-      name: "MODEL",
-      description: "The OpenAI model to use.",
-      value: opts.model,
-    },
-    {
-      name: "NEXT_PUBLIC_MODEL",
-      description: "The OpenAI model to use (hardcode to front-end artifact).",
-      value: opts.model,
-    },
     {
       name: "NEXT_PUBLIC_CHAT_API",
       description: "The backend API for chat endpoint.",

helpers/index.ts

@@ -15,6 +15,7 @@ import { ConfigFileType, writeToolsConfig } from "./tools";
 import {
   FileSourceConfig,
   InstallTemplateArgs,
+  ModelConfig,
   TemplateDataSource,
   TemplateFramework,
   TemplateVectorDB,
@@ -24,8 +25,8 @@ import { installTSTemplate } from "./typescript";
 // eslint-disable-next-line max-params
 async function generateContextData(
   framework: TemplateFramework,
+  modelConfig: ModelConfig,
   packageManager?: PackageManager,
-  openAiKey?: string,
   vectorDb?: TemplateVectorDB,
   llamaCloudKey?: string,
   useLlamaParse?: boolean,
@@ -36,12 +37,12 @@ async function generateContextData(
         ? "poetry run generate"
         : `${packageManager} run generate`,
     )}`;
-    const openAiKeyConfigured = openAiKey || process.env["OPENAI_API_KEY"];
+    const modelConfigured = modelConfig.isConfigured();
     const llamaCloudKeyConfigured = useLlamaParse
       ? llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
       : true;
     const hasVectorDb = vectorDb && vectorDb !== "none";
-    if (openAiKeyConfigured && llamaCloudKeyConfigured && !hasVectorDb) {
+    if (modelConfigured && llamaCloudKeyConfigured && !hasVectorDb) {
       // If all the required environment variables are set, run the generate script
       if (framework === "fastapi") {
         if (isHavingPoetryLockFile()) {
@@ -63,7 +64,7 @@ async function generateContextData(
 
     // generate the message of what to do to run the generate script manually
     const settings = [];
-    if (!openAiKeyConfigured) settings.push("your OpenAI key");
+    if (!modelConfigured) settings.push("your model provider API key");
     if (!llamaCloudKeyConfigured) settings.push("your Llama Cloud key");
     if (hasVectorDb) settings.push("your Vector DB environment variables");
     const settingsMessage =
@@ -141,14 +142,13 @@ export const installTemplate = async (
 
     // Copy the environment file to the target directory.
     await createBackendEnvFile(props.root, {
-      openAiKey: props.openAiKey,
+      modelConfig: props.modelConfig,
       llamaCloudKey: props.llamaCloudKey,
       vectorDb: props.vectorDb,
-      model: props.model,
-      embeddingModel: props.embeddingModel,
       framework: props.framework,
       dataSources: props.dataSources,
       port: props.externalPort,
+      tools: props.tools,
     });
 
     if (props.dataSources.length > 0) {
@@ -163,18 +163,22 @@ export const installTemplate = async (
       ) {
         await generateContextData(
           props.framework,
+          props.modelConfig,
           props.packageManager,
-          props.openAiKey,
           props.vectorDb,
           props.llamaCloudKey,
           props.useLlamaParse,
         );
       }
     }
+
+    // Create tool-output directory
+    if (props.tools && props.tools.length > 0) {
+      await fsExtra.mkdir(path.join(props.root, "tool-output"));
+    }
   } else {
     // this is a frontend for a full-stack app, create .env file with model information
     await createFrontendEnvFile(props.root, {
-      model: props.model,
       customApiPath: props.customApiPath,
     });
   }

helpers/providers/anthropic.ts

@@ -0,0 +1,106 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+const MODELS = [
+  "claude-3-opus",
+  "claude-3-sonnet",
+  "claude-3-haiku",
+  "claude-2.1",
+  "claude-instant-1.2",
+];
+const DEFAULT_MODEL = MODELS[0];
+
+// TODO: get embedding vector dimensions from the anthropic sdk (currently not supported)
+// Use huggingface embedding models for now
+enum HuggingFaceEmbeddingModelType {
+  XENOVA_ALL_MINILM_L6_V2 = "all-MiniLM-L6-v2",
+  XENOVA_ALL_MPNET_BASE_V2 = "all-mpnet-base-v2",
+}
+type ModelData = {
+  dimensions: number;
+};
+const EMBEDDING_MODELS: Record<HuggingFaceEmbeddingModelType, ModelData> = {
+  [HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2]: {
+    dimensions: 384,
+  },
+  [HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2]: {
+    dimensions: 768,
+  },
+};
+const DEFAULT_EMBEDDING_MODEL = Object.keys(EMBEDDING_MODELS)[0];
+const DEFAULT_DIMENSIONS = Object.values(EMBEDDING_MODELS)[0].dimensions;
+
+type AnthropicQuestionsParams = {
+  apiKey?: string;
+  askModels: boolean;
+};
+
+export async function askAnthropicQuestions({
+  askModels,
+  apiKey,
+}: AnthropicQuestionsParams): Promise<ModelConfigParams> {
+  const config: ModelConfigParams = {
+    apiKey,
+    model: DEFAULT_MODEL,
+    embeddingModel: DEFAULT_EMBEDDING_MODEL,
+    dimensions: DEFAULT_DIMENSIONS,
+    isConfigured(): boolean {
+      if (config.apiKey) {
+        return true;
+      }
+      if (process.env["ANTHROPIC_API_KEY"]) {
+        return true;
+      }
+      return false;
+    },
+  };
+
+  if (!config.apiKey) {
+    const { key } = await prompts(
+      {
+        type: "text",
+        name: "key",
+        message:
+          "Please provide your Anthropic API key (or leave blank to use ANTHROPIC_API_KEY env variable):",
+      },
+      questionHandlers,
+    );
+    config.apiKey = key || process.env.ANTHROPIC_API_KEY;
+  }
+
+  // use default model values in CI or if user should not be asked
+  const useDefaults = ciInfo.isCI || !askModels;
+  if (!useDefaults) {
+    const { model } = await prompts(
+      {
+        type: "select",
+        name: "model",
+        message: "Which LLM model would you like to use?",
+        choices: MODELS.map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.model = model;
+
+    const { embeddingModel } = await prompts(
+      {
+        type: "select",
+        name: "embeddingModel",
+        message: "Which embedding model would you like to use?",
+        choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.embeddingModel = embeddingModel;
+    config.dimensions =
+      EMBEDDING_MODELS[
+        embeddingModel as HuggingFaceEmbeddingModelType
+      ].dimensions;
+  }
+
+  return config;
+}

helpers/providers/gemini.ts

@@ -0,0 +1,87 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+const MODELS = ["gemini-1.5-pro-latest", "gemini-pro", "gemini-pro-vision"];
+type ModelData = {
+  dimensions: number;
+};
+const EMBEDDING_MODELS: Record<string, ModelData> = {
+  "embedding-001": { dimensions: 768 },
+  "text-embedding-004": { dimensions: 768 },
+};
+
+const DEFAULT_MODEL = MODELS[0];
+const DEFAULT_EMBEDDING_MODEL = Object.keys(EMBEDDING_MODELS)[0];
+const DEFAULT_DIMENSIONS = Object.values(EMBEDDING_MODELS)[0].dimensions;
+
+type GeminiQuestionsParams = {
+  apiKey?: string;
+  askModels: boolean;
+};
+
+export async function askGeminiQuestions({
+  askModels,
+  apiKey,
+}: GeminiQuestionsParams): Promise<ModelConfigParams> {
+  const config: ModelConfigParams = {
+    apiKey,
+    model: DEFAULT_MODEL,
+    embeddingModel: DEFAULT_EMBEDDING_MODEL,
+    dimensions: DEFAULT_DIMENSIONS,
+    isConfigured(): boolean {
+      if (config.apiKey) {
+        return true;
+      }
+      if (process.env["GOOGLE_API_KEY"]) {
+        return true;
+      }
+      return false;
+    },
+  };
+
+  if (!config.apiKey) {
+    const { key } = await prompts(
+      {
+        type: "text",
+        name: "key",
+        message:
+          "Please provide your Google API key (or leave blank to use GOOGLE_API_KEY env variable):",
+      },
+      questionHandlers,
+    );
+    config.apiKey = key || process.env.GOOGLE_API_KEY;
+  }
+
+  // use default model values in CI or if user should not be asked
+  const useDefaults = ciInfo.isCI || !askModels;
+  if (!useDefaults) {
+    const { model } = await prompts(
+      {
+        type: "select",
+        name: "model",
+        message: "Which LLM model would you like to use?",
+        choices: MODELS.map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.model = model;
+
+    const { embeddingModel } = await prompts(
+      {
+        type: "select",
+        name: "embeddingModel",
+        message: "Which embedding model would you like to use?",
+        choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.embeddingModel = embeddingModel;
+    config.dimensions = EMBEDDING_MODELS[embeddingModel].dimensions;
+  }
+
+  return config;
+}

helpers/providers/groq.ts

@@ -0,0 +1,99 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+const MODELS = ["llama3-8b", "llama3-70b", "mixtral-8x7b"];
+const DEFAULT_MODEL = MODELS[0];
+
+// Use huggingface embedding models for now as Groq doesn't support embedding models
+enum HuggingFaceEmbeddingModelType {
+  XENOVA_ALL_MINILM_L6_V2 = "all-MiniLM-L6-v2",
+  XENOVA_ALL_MPNET_BASE_V2 = "all-mpnet-base-v2",
+}
+type ModelData = {
+  dimensions: number;
+};
+const EMBEDDING_MODELS: Record<HuggingFaceEmbeddingModelType, ModelData> = {
+  [HuggingFaceEmbeddingModelType.XENOVA_ALL_MINILM_L6_V2]: {
+    dimensions: 384,
+  },
+  [HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2]: {
+    dimensions: 768,
+  },
+};
+const DEFAULT_EMBEDDING_MODEL = Object.keys(EMBEDDING_MODELS)[0];
+const DEFAULT_DIMENSIONS = Object.values(EMBEDDING_MODELS)[0].dimensions;
+
+type GroqQuestionsParams = {
+  apiKey?: string;
+  askModels: boolean;
+};
+
+export async function askGroqQuestions({
+  askModels,
+  apiKey,
+}: GroqQuestionsParams): Promise<ModelConfigParams> {
+  const config: ModelConfigParams = {
+    apiKey,
+    model: DEFAULT_MODEL,
+    embeddingModel: DEFAULT_EMBEDDING_MODEL,
+    dimensions: DEFAULT_DIMENSIONS,
+    isConfigured(): boolean {
+      if (config.apiKey) {
+        return true;
+      }
+      if (process.env["GROQ_API_KEY"]) {
+        return true;
+      }
+      return false;
+    },
+  };
+
+  if (!config.apiKey) {
+    const { key } = await prompts(
+      {
+        type: "text",
+        name: "key",
+        message:
+          "Please provide your Groq API key (or leave blank to use GROQ_API_KEY env variable):",
+      },
+      questionHandlers,
+    );
+    config.apiKey = key || process.env.GROQ_API_KEY;
+  }
+
+  // use default model values in CI or if user should not be asked
+  const useDefaults = ciInfo.isCI || !askModels;
+  if (!useDefaults) {
+    const { model } = await prompts(
+      {
+        type: "select",
+        name: "model",
+        message: "Which LLM model would you like to use?",
+        choices: MODELS.map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.model = model;
+
+    const { embeddingModel } = await prompts(
+      {
+        type: "select",
+        name: "embeddingModel",
+        message: "Which embedding model would you like to use?",
+        choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.embeddingModel = embeddingModel;
+    config.dimensions =
+      EMBEDDING_MODELS[
+        embeddingModel as HuggingFaceEmbeddingModelType
+      ].dimensions;
+  }
+
+  return config;
+}

helpers/providers/index.ts

@@ -0,0 +1,72 @@
+import ciInfo from "ci-info";
+import prompts from "prompts";
+import { questionHandlers } from "../../questions";
+import { ModelConfig, ModelProvider } from "../types";
+import { askAnthropicQuestions } from "./anthropic";
+import { askGeminiQuestions } from "./gemini";
+import { askGroqQuestions } from "./groq";
+import { askOllamaQuestions } from "./ollama";
+import { askOpenAIQuestions } from "./openai";
+
+const DEFAULT_MODEL_PROVIDER = "openai";
+
+export type ModelConfigQuestionsParams = {
+  openAiKey?: string;
+  askModels: boolean;
+};
+
+export type ModelConfigParams = Omit<ModelConfig, "provider">;
+
+export async function askModelConfig({
+  askModels,
+  openAiKey,
+}: ModelConfigQuestionsParams): Promise<ModelConfig> {
+  let modelProvider: ModelProvider = DEFAULT_MODEL_PROVIDER;
+  if (askModels && !ciInfo.isCI) {
+    const { provider } = await prompts(
+      {
+        type: "select",
+        name: "provider",
+        message: "Which model provider would you like to use",
+        choices: [
+          {
+            title: "OpenAI",
+            value: "openai",
+          },
+          { title: "Groq", value: "groq" },
+          { title: "Ollama", value: "ollama" },
+          { title: "Anthropic", value: "anthropic" },
+          { title: "Gemini", value: "gemini" },
+        ],
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    modelProvider = provider;
+  }
+
+  let modelConfig: ModelConfigParams;
+  switch (modelProvider) {
+    case "ollama":
+      modelConfig = await askOllamaQuestions({ askModels });
+      break;
+    case "groq":
+      modelConfig = await askGroqQuestions({ askModels });
+      break;
+    case "anthropic":
+      modelConfig = await askAnthropicQuestions({ askModels });
+      break;
+    case "gemini":
+      modelConfig = await askGeminiQuestions({ askModels });
+      break;
+    default:
+      modelConfig = await askOpenAIQuestions({
+        openAiKey,
+        askModels,
+      });
+  }
+  return {
+    ...modelConfig,
+    provider: modelProvider,
+  };
+}

helpers/providers/ollama.ts

@@ -0,0 +1,95 @@
+import ciInfo from "ci-info";
+import ollama, { type ModelResponse } from "ollama";
+import { red } from "picocolors";
+import prompts from "prompts";
+import { ModelConfigParams } from ".";
+import { questionHandlers, toChoice } from "../../questions";
+
+type ModelData = {
+  dimensions: number;
+};
+const MODELS = ["llama3:8b", "wizardlm2:7b", "gemma:7b", "phi3"];
+const DEFAULT_MODEL = MODELS[0];
+// TODO: get embedding vector dimensions from the ollama sdk (currently not supported)
+const EMBEDDING_MODELS: Record<string, ModelData> = {
+  "nomic-embed-text": { dimensions: 768 },
+  "mxbai-embed-large": { dimensions: 1024 },
+  "all-minilm": { dimensions: 384 },
+};
+const DEFAULT_EMBEDDING_MODEL: string = Object.keys(EMBEDDING_MODELS)[0];
+
+type OllamaQuestionsParams = {
+  askModels: boolean;
+};
+
+export async function askOllamaQuestions({
+  askModels,
+}: OllamaQuestionsParams): Promise<ModelConfigParams> {
+  const config: ModelConfigParams = {
+    model: DEFAULT_MODEL,
+    embeddingModel: DEFAULT_EMBEDDING_MODEL,
+    dimensions: EMBEDDING_MODELS[DEFAULT_EMBEDDING_MODEL].dimensions,
+    isConfigured(): boolean {
+      return true;
+    },
+  };
+
+  // use default model values in CI or if user should not be asked
+  const useDefaults = ciInfo.isCI || !askModels;
+  if (!useDefaults) {
+    const { model } = await prompts(
+      {
+        type: "select",
+        name: "model",
+        message: "Which LLM model would you like to use?",
+        choices: MODELS.map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    await ensureModel(model);
+    config.model = model;
+
+    const { embeddingModel } = await prompts(
+      {
+        type: "select",
+        name: "embeddingModel",
+        message: "Which embedding model would you like to use?",
+        choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    await ensureModel(embeddingModel);
+    config.embeddingModel = embeddingModel;
+    config.dimensions = EMBEDDING_MODELS[embeddingModel].dimensions;
+  }
+
+  return config;
+}
+
+async function ensureModel(modelName: string) {
+  try {
+    if (modelName.split(":").length === 1) {
+      // model doesn't have a version suffix, use latest
+      modelName = modelName + ":latest";
+    }
+    const { models } = await ollama.list();
+    const found =
+      models.find((model: ModelResponse) => model.name === modelName) !==
+      undefined;
+    if (!found) {
+      console.log(
+        red(
+          `Model ${modelName} was not pulled yet. Call 'ollama pull ${modelName}' and try again.`,
+        ),
+      );
+      process.exit(1);
+    }
+  } catch (error) {
+    console.log(
+      red("Listing Ollama models failed. Is 'ollama' running? " + error),
+    );
+    process.exit(1);
+  }
+}

helpers/providers/openai.ts

@@ -0,0 +1,144 @@
+import ciInfo from "ci-info";
+import got from "got";
+import ora from "ora";
+import { red } from "picocolors";
+import prompts from "prompts";
+import { ModelConfigParams, ModelConfigQuestionsParams } from ".";
+import { questionHandlers } from "../../questions";
+
+const OPENAI_API_URL = "https://api.openai.com/v1";
+
+const DEFAULT_MODEL = "gpt-3.5-turbo";
+const DEFAULT_EMBEDDING_MODEL = "text-embedding-3-large";
+
+export async function askOpenAIQuestions({
+  openAiKey,
+  askModels,
+}: ModelConfigQuestionsParams): Promise<ModelConfigParams> {
+  const config: ModelConfigParams = {
+    apiKey: openAiKey,
+    model: DEFAULT_MODEL,
+    embeddingModel: DEFAULT_EMBEDDING_MODEL,
+    dimensions: getDimensions(DEFAULT_EMBEDDING_MODEL),
+    isConfigured(): boolean {
+      if (config.apiKey) {
+        return true;
+      }
+      if (process.env["OPENAI_API_KEY"]) {
+        return true;
+      }
+      return false;
+    },
+  };
+
+  if (!config.apiKey) {
+    const { key } = await prompts(
+      {
+        type: "text",
+        name: "key",
+        message: askModels
+          ? "Please provide your OpenAI API key (or leave blank to use OPENAI_API_KEY env variable):"
+          : "Please provide your OpenAI API key (leave blank to skip):",
+        validate: (value: string) => {
+          if (askModels && !value) {
+            if (process.env.OPENAI_API_KEY) {
+              return true;
+            }
+            return "OPENAI_API_KEY env variable is not set - key is required";
+          }
+          return true;
+        },
+      },
+      questionHandlers,
+    );
+    config.apiKey = key || process.env.OPENAI_API_KEY;
+  }
+
+  // use default model values in CI or if user should not be asked
+  const useDefaults = ciInfo.isCI || !askModels;
+  if (!useDefaults) {
+    const { model } = await prompts(
+      {
+        type: "select",
+        name: "model",
+        message: "Which LLM model would you like to use?",
+        choices: await getAvailableModelChoices(false, config.apiKey),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.model = model;
+
+    const { embeddingModel } = await prompts(
+      {
+        type: "select",
+        name: "embeddingModel",
+        message: "Which embedding model would you like to use?",
+        choices: await getAvailableModelChoices(true, config.apiKey),
+        initial: 0,
+      },
+      questionHandlers,
+    );
+    config.embeddingModel = embeddingModel;
+    config.dimensions = getDimensions(embeddingModel);
+  }
+
+  return config;
+}
+
+async function getAvailableModelChoices(
+  selectEmbedding: boolean,
+  apiKey?: string,
+) {
+  if (!apiKey) {
+    throw new Error("need OpenAI key to retrieve model choices");
+  }
+  const isLLMModel = (modelId: string) => {
+    return modelId.startsWith("gpt");
+  };
+
+  const isEmbeddingModel = (modelId: string) => {
+    return modelId.includes("embedding");
+  };
+
+  const spinner = ora("Fetching available models").start();
+  try {
+    const response = await got(`${OPENAI_API_URL}/models`, {
+      headers: {
+        Authorization: "Bearer " + apiKey,
+      },
+      timeout: 5000,
+      responseType: "json",
+    });
+    const data: any = await response.body;
+    spinner.stop();
+    return data.data
+      .filter((model: any) =>
+        selectEmbedding ? isEmbeddingModel(model.id) : isLLMModel(model.id),
+      )
+      .map((el: any) => {
+        return {
+          title: el.id,
+          value: el.id,
+        };
+      });
+  } catch (error) {
+    spinner.stop();
+    if ((error as any).response?.statusCode === 401) {
+      console.log(
+        red(
+          "Invalid OpenAI API key provided! Please provide a valid key and try again!",
+        ),
+      );
+    } else {
+      console.log(red("Request failed: " + error));
+    }
+    process.exit(1);
+  }
+}
+
+function getDimensions(modelName: string) {
+  // at 2024-04-24 all OpenAI embedding models support 1536 dimensions except
+  // "text-embedding-3-large", see https://openai.com/blog/new-embedding-models-and-api-updates
+  return modelName === "text-embedding-3-large" ? 1024 : 1536;
+}

helpers/proxy.ts

@@ -0,0 +1,8 @@
+/* Function to conditionally load the global-agent/bootstrap module */
+export async function initializeGlobalAgent() {
+  if (process.env.GLOBAL_AGENT_HTTP_PROXY) {
+    /* Dynamically import global-agent/bootstrap */
+    await import("global-agent/bootstrap");
+    console.log("Proxy enabled via global-agent.");
+  }
+}

helpers/python.ts

@@ -10,6 +10,7 @@ import { isPoetryAvailable, tryPoetryInstall } from "./poetry";
 import { Tool } from "./tools";
 import {
   InstallTemplateArgs,
+  ModelConfig,
   TemplateDataSource,
   TemplateVectorDB,
 } from "./types";
@@ -21,8 +22,9 @@ interface Dependency {
 }
 
 const getAdditionalDependencies = (
+  modelConfig: ModelConfig,
   vectorDb?: TemplateVectorDB,
-  dataSource?: TemplateDataSource,
+  dataSources?: TemplateDataSource[],
   tools?: Tool[],
 ) => {
   const dependencies: Dependency[] = [];
@@ -41,6 +43,7 @@ const getAdditionalDependencies = (
         name: "llama-index-vector-stores-postgres",
         version: "^0.1.1",
       });
+      break;
     }
     case "pinecone": {
       dependencies.push({
@@ -67,47 +70,105 @@ const getAdditionalDependencies = (
       });
       break;
     }
+    case "qdrant": {
+      dependencies.push({
+        name: "llama-index-vector-stores-qdrant",
+        version: "^0.2.8",
+      });
+      break;
+    }
+    case "chroma": {
+      dependencies.push({
+        name: "llama-index-vector-stores-chroma",
+        version: "^0.1.8",
+      });
+      break;
+    }
   }
 
   // Add data source dependencies
-  const dataSourceType = dataSource?.type;
-  switch (dataSourceType) {
-    case "file":
-      dependencies.push({
-        name: "docx2txt",
-        version: "^0.8",
-      });
-      break;
-    case "web":
-      dependencies.push({
-        name: "llama-index-readers-web",
-        version: "^0.1.6",
-      });
-      break;
-    case "db":
-      dependencies.push({
-        name: "llama-index-readers-database",
-        version: "^0.1.3",
-      });
-      dependencies.push({
-        name: "pymysql",
-        version: "^1.1.0",
-        extras: ["rsa"],
-      });
-      dependencies.push({
-        name: "psycopg2",
-        version: "^2.9.9",
-      });
-      break;
+  if (dataSources) {
+    for (const ds of dataSources) {
+      const dsType = ds?.type;
+      switch (dsType) {
+        case "file":
+          dependencies.push({
+            name: "docx2txt",
+            version: "^0.8",
+          });
+          break;
+        case "web":
+          dependencies.push({
+            name: "llama-index-readers-web",
+            version: "^0.1.6",
+          });
+          break;
+        case "db":
+          dependencies.push({
+            name: "llama-index-readers-database",
+            version: "^0.1.3",
+          });
+          dependencies.push({
+            name: "pymysql",
+            version: "^1.1.0",
+            extras: ["rsa"],
+          });
+          dependencies.push({
+            name: "psycopg2",
+            version: "^2.9.9",
+          });
+          break;
+      }
+    }
   }
 
   // Add tools dependencies
+  console.log("Adding tools dependencies");
   tools?.forEach((tool) => {
     tool.dependencies?.forEach((dep) => {
       dependencies.push(dep);
     });
   });
 
+  switch (modelConfig.provider) {
+    case "ollama":
+      dependencies.push({
+        name: "llama-index-llms-ollama",
+        version: "0.1.2",
+      });
+      dependencies.push({
+        name: "llama-index-embeddings-ollama",
+        version: "0.1.2",
+      });
+      break;
+    case "openai":
+      dependencies.push({
+        name: "llama-index-agent-openai",
+        version: "0.2.6",
+      });
+      break;
+    case "anthropic":
+      dependencies.push({
+        name: "llama-index-llms-anthropic",
+        version: "0.1.10",
+      });
+      dependencies.push({
+        name: "llama-index-embeddings-huggingface",
+        version: "0.2.0",
+      });
+      break;
+    case "gemini":
+      dependencies.push({
+        name: "llama-index-llms-gemini",
+        version: "0.1.10",
+      });
+      dependencies.push({
+        name: "llama-index-embeddings-gemini",
+        version: "0.1.6",
+      });
+      break;
+  }
+
   return dependencies;
 };
 
@@ -205,8 +266,8 @@ export const installPythonTemplate = async ({
   dataSources,
   tools,
   postInstallAction,
-  useLlamaParse,
   observability,
+  modelConfig,
 }: Pick<
   InstallTemplateArgs,
   | "root"
@@ -215,9 +276,9 @@ export const installPythonTemplate = async ({
   | "vectorDb"
   | "dataSources"
   | "tools"
-  | "useLlamaParse"
   | "postInstallAction"
   | "observability"
+  | "modelConfig"
 >) => {
   console.log("\nInitializing Python project with template:", template, "\n");
   const templatePath = path.join(templatesDir, "types", template, framework);
@@ -257,9 +318,14 @@ export const installPythonTemplate = async ({
     cwd: path.join(compPath, "engines", "python", engine),
   });
 
-  const addOnDependencies = dataSources
-    .map((ds) => getAdditionalDependencies(vectorDb, ds, tools))
-    .flat();
+  console.log("Adding additional dependencies");
+
+  const addOnDependencies = getAdditionalDependencies(
+    modelConfig,
+    vectorDb,
+    dataSources,
+    tools,
+  );
 
   if (observability === "opentelemetry") {
     addOnDependencies.push({

helpers/tools.ts

@@ -2,15 +2,25 @@ import fs from "fs/promises";
 import path from "path";
 import { red } from "picocolors";
 import yaml from "yaml";
+import { EnvVar } from "./env-variables";
 import { makeDir } from "./make-dir";
 import { TemplateFramework } from "./types";
 
+export const TOOL_SYSTEM_PROMPT_ENV_VAR = "TOOL_SYSTEM_PROMPT";
+
+export enum ToolType {
+  LLAMAHUB = "llamahub",
+  LOCAL = "local",
+}
+
 export type Tool = {
   display: string;
   name: string;
   config?: Record<string, any>;
   dependencies?: ToolDependencies[];
   supportedFrameworks?: Array<TemplateFramework>;
+  type: ToolType;
+  envVars?: EnvVar[];
 };
 
 export type ToolDependencies = {
@@ -20,7 +30,7 @@ export type ToolDependencies = {
 
 export const supportedTools: Tool[] = [
   {
-    display: "Google Search (configuration required after installation)",
+    display: "Google Search",
     name: "google.GoogleSearchToolSpec",
     config: {
       engine:
@@ -35,6 +45,37 @@ export const supportedTools: Tool[] = [
       },
     ],
     supportedFrameworks: ["fastapi"],
+    type: ToolType.LLAMAHUB,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for google search tool.",
+        value: `You are a Google search agent. You help users to get information from Google search.`,
+      },
+    ],
+  },
+  {
+    // For python app, we will use a local DuckDuckGo search tool (instead of DuckDuckGo search tool in LlamaHub)
+    // to get the same results as the TS app.
+    display: "DuckDuckGo Search",
+    name: "duckduckgo",
+    dependencies: [
+      {
+        name: "duckduckgo-search",
+        version: "6.1.7",
+      },
+    ],
+    supportedFrameworks: ["fastapi", "nextjs", "express"],
+    type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for DuckDuckGo search tool.",
+        value: `You are a DuckDuckGo search agent. 
+You can use the duckduckgo search tool to get information from the web to answer user questions.
+For better results, you can specify the region parameter to get results from a specific region but it's optional.`,
+      },
+    ],
   },
   {
     display: "Wikipedia",
@@ -46,6 +87,88 @@ export const supportedTools: Tool[] = [
       },
     ],
     supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LLAMAHUB,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for wiki tool.",
+        value: `You are a Wikipedia agent. You help users to get information from Wikipedia.`,
+      },
+    ],
+  },
+  {
+    display: "Weather",
+    name: "weather",
+    dependencies: [],
+    supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for weather tool.",
+        value: `You are a weather forecast agent. You help users to get the weather forecast for a given location.`,
+      },
+    ],
+  },
+  {
+    display: "Code Interpreter",
+    name: "interpreter",
+    dependencies: [
+      {
+        name: "e2b_code_interpreter",
+        version: "0.0.7",
+      },
+    ],
+    supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: "E2B_API_KEY",
+        description:
+          "E2B_API_KEY key is required to run code interpreter tool. Get it here: https://e2b.dev/docs/getting-started/api-key",
+      },
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for code interpreter tool.",
+        value: `-You are a Python interpreter that can run any python code in a secure environment.
+- The python code runs in a Jupyter notebook. Every time you call the 'interpreter' tool, the python code is executed in a separate cell. 
+- You are given tasks to complete and you run python code to solve them.
+- It's okay to make multiple calls to interpreter tool. If you get an error or the result is not what you expected, you can call the tool again. Don't give up too soon!
+- Plot visualizations using matplotlib or any other visualization library directly in the notebook.
+- You can install any pip package (if it exists) by running a cell with pip install.`,
+      },
+    ],
+  },
+  {
+    display: "OpenAPI action",
+    name: "openapi_action.OpenAPIActionToolSpec",
+    dependencies: [
+      {
+        name: "llama-index-tools-openapi",
+        version: "0.1.3",
+      },
+      {
+        name: "jsonschema",
+        version: "^4.22.0",
+      },
+      {
+        name: "llama-index-tools-requests",
+        version: "0.1.3",
+      },
+    ],
+    config: {
+      openapi_uri: "The URL or file path of the OpenAPI schema",
+    },
+    supportedFrameworks: ["fastapi", "express", "nextjs"],
+    type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for openapi action tool.",
+        value:
+          "You are an OpenAPI action agent. You help users to make requests to the provided OpenAPI schema.",
+      },
+    ],
   },
 ];
 
@@ -72,9 +195,15 @@ export const getTools = (toolsName: string[]): Tool[] => {
   return tools;
 };
 
+export const toolRequiresConfig = (tool: Tool): boolean => {
+  const hasConfig = Object.keys(tool.config || {}).length > 0;
+  const hasEmptyEnvVar = tool.envVars?.some((envVar) => !envVar.value) ?? false;
+  return hasConfig || hasEmptyEnvVar;
+};
+
 export const toolsRequireConfig = (tools?: Tool[]): boolean => {
   if (tools) {
-    return tools?.some((tool) => Object.keys(tool.config || {}).length > 0);
+    return tools?.some(toolRequiresConfig);
   }
   return false;
 };
@@ -89,10 +218,19 @@ export const writeToolsConfig = async (
   tools: Tool[] = [],
   type: ConfigFileType = ConfigFileType.YAML,
 ) => {
-  if (tools.length === 0) return; // no tools selected, no config need
-  const configContent: Record<string, any> = {};
+  const configContent: {
+    [key in ToolType]: Record<string, any>;
+  } = {
+    local: {},
+    llamahub: {},
+  };
   tools.forEach((tool) => {
-    configContent[tool.name] = tool.config ?? {};
+    if (tool.type === ToolType.LLAMAHUB) {
+      configContent.llamahub[tool.name] = tool.config ?? {};
+    }
+    if (tool.type === ToolType.LOCAL) {
+      configContent.local[tool.name] = tool.config ?? {};
+    }
   });
   const configPath = path.join(root, "config");
   await makeDir(configPath);

helpers/types.ts

@@ -1,6 +1,20 @@
 import { PackageManager } from "../helpers/get-pkg-manager";
 import { Tool } from "./tools";
 
+export type ModelProvider =
+  | "openai"
+  | "groq"
+  | "ollama"
+  | "anthropic"
+  | "gemini";
+export type ModelConfig = {
+  provider: ModelProvider;
+  apiKey?: string;
+  model: string;
+  embeddingModel: string;
+  dimensions: number;
+  isConfigured(): boolean;
+};
 export type TemplateType = "streaming" | "community" | "llamapack";
 export type TemplateFramework = "nextjs" | "express" | "fastapi";
 export type TemplateUI = "html" | "shadcn";
@@ -11,7 +25,8 @@ export type TemplateVectorDB =
   | "pinecone"
   | "milvus"
   | "astra"
-  | "qdrant";
+  | "qdrant"
+  | "chroma";
 export type TemplatePostInstallAction =
   | "none"
   | "VSCode"
@@ -59,11 +74,9 @@ export interface InstallTemplateArgs {
   ui: TemplateUI;
   dataSources: TemplateDataSource[];
   customApiPath?: string;
-  openAiKey?: string;
+  modelConfig: ModelConfig;
   llamaCloudKey?: string;
   useLlamaParse?: boolean;
-  model: string;
-  embeddingModel: string;
   communityProjectConfig?: CommunityProjectConfig;
   llamapack?: string;
   vectorDb?: TemplateVectorDB;

helpers/typescript.ts

@@ -105,7 +105,7 @@ export const installTSTemplate = async ({
   const enginePath = path.join(root, relativeEngineDestPath, "engine");
 
   // copy vector db component
-  console.log("\nUsing vector DB:", vectorDb, "\n");
+  console.log("\nUsing vector DB:", vectorDb ?? "none", "\n");
   await copy("**", enginePath, {
     parents: true,
     cwd: path.join(compPath, "vectordbs", "typescript", vectorDb ?? "none"),
@@ -205,7 +205,7 @@ async function updatePackageJson({
     // add generate script if using context engine
     packageJson.scripts = {
       ...packageJson.scripts,
-      generate: `ts-node ${path.join(
+      generate: `tsx ${path.join(
         relativeEngineDestPath,
         "engine",
         "generate.ts",

index.ts

@@ -12,12 +12,16 @@ import { createApp } from "./create-app";
 import { getDataSources } from "./helpers/datasources";
 import { getPkgManager } from "./helpers/get-pkg-manager";
 import { isFolderEmpty } from "./helpers/is-folder-empty";
+import { initializeGlobalAgent } from "./helpers/proxy";
 import { runApp } from "./helpers/run-app";
 import { getTools } from "./helpers/tools";
 import { validateNpmName } from "./helpers/validate-pkg";
 import packageJson from "./package.json";
 import { QuestionArgs, askQuestions, onPromptState } from "./questions";
 
+// Run the initialization function
+initializeGlobalAgent();
+
 let projectPath: string = "";
 
 const handleSigTerm = () => process.exit(0);
@@ -107,20 +111,6 @@ const program = new Commander.Command(packageJson.name)
     `
 
   Whether to generate a frontend for your backend.
-`,
-  )
-  .option(
-    "--model <model>",
-    `
-
-  Select OpenAI model to use. E.g. gpt-3.5-turbo.
-`,
-  )
-  .option(
-    "--embedding-model <embeddingModel>",
-    `
-
-  Select OpenAI embedding model to use. E.g. text-embedding-ada-002.
 `,
   )
   .option(
@@ -201,9 +191,7 @@ if (process.argv.includes("--tools")) {
 if (process.argv.includes("--no-llama-parse")) {
   program.useLlamaParse = false;
 }
-if (process.argv.includes("--ask-models")) {
-  program.askModels = true;
-}
+program.askModels = process.argv.includes("--ask-models");
 if (process.argv.includes("--no-files")) {
   program.dataSources = [];
 } else {
@@ -290,7 +278,11 @@ async function run(): Promise<void> {
   }
 
   const preferences = (conf.get("preferences") || {}) as QuestionArgs;
-  await askQuestions(program as unknown as QuestionArgs, preferences);
+  await askQuestions(
+    program as unknown as QuestionArgs,
+    preferences,
+    program.openAiKey,
+  );
 
   await createApp({
     template: program.template,
@@ -299,10 +291,8 @@ async function run(): Promise<void> {
     appPath: resolvedProjectPath,
     packageManager,
     frontend: program.frontend,
-    openAiKey: program.openAiKey,
+    modelConfig: program.modelConfig,
     llamaCloudKey: program.llamaCloudKey,
-    model: program.model,
-    embeddingModel: program.embeddingModel,
     communityProjectConfig: program.communityProjectConfig,
     llamapack: program.llamapack,
     vectorDb: program.vectorDb,

package.json

@@ -1,12 +1,12 @@
 {
   "name": "create-llama",
-  "version": "0.0.32",
+  "version": "0.1.12",
+  "description": "Create LlamaIndex-powered apps with one command",
   "keywords": [
     "rag",
     "llamaindex",
     "next.js"
   ],
-  "description": "Create LlamaIndex-powered apps with one command",
   "repository": {
     "type": "git",
     "url": "https://github.com/run-llama/LlamaIndexTS",
@@ -20,32 +20,30 @@
     "dist"
   ],
   "scripts": {
-    "clean": "rimraf --glob ./dist ./templates/**/__pycache__ ./templates/**/node_modules ./templates/**/poetry.lock",
-    "format": "prettier --ignore-unknown --cache --check .",
-    "format:write": "prettier --ignore-unknown --write .",
-    "dev": "ncc build ./index.ts -w -o dist/",
     "build": "bash ./scripts/build.sh",
     "build:ncc": "pnpm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
-    "lint": "eslint . --ignore-pattern dist --ignore-pattern e2e/cache",
+    "clean": "rimraf --glob ./dist ./templates/**/__pycache__ ./templates/**/node_modules ./templates/**/poetry.lock",
+    "dev": "ncc build ./index.ts -w -o dist/",
     "e2e": "playwright test",
+    "format": "prettier --ignore-unknown --cache --check .",
+    "format:write": "prettier --ignore-unknown --write .",
+    "lint": "eslint . --ignore-pattern dist --ignore-pattern e2e/cache",
+    "new-snapshot": "pnpm run build && changeset version --snapshot",
+    "new-version": "pnpm run build && changeset version",
+    "pack-install": "bash ./scripts/pack.sh",
     "prepare": "husky",
     "release": "pnpm run build && changeset publish",
-    "new-version": "pnpm run build && changeset version",
-    "release-snapshot": "pnpm run build && changeset publish --tag snapshot",
-    "new-snapshot": "pnpm run build && changeset version --snapshot",
-    "pack-install": "bash ./scripts/pack.sh"
+    "release-snapshot": "pnpm run build && changeset publish --tag snapshot"
   },
-  "devDependencies": {
-    "@playwright/test": "^1.41.1",
+  "dependencies": {
     "@types/async-retry": "1.4.2",
     "@types/ci-info": "2.0.0",
     "@types/cross-spawn": "6.0.0",
+    "@types/fs-extra": "11.0.4",
     "@types/node": "^20.11.7",
     "@types/prompts": "2.0.1",
     "@types/tar": "6.1.5",
     "@types/validate-npm-package-name": "3.0.0",
-    "@types/fs-extra": "11.0.4",
-    "@vercel/ncc": "0.38.1",
     "async-retry": "1.3.1",
     "async-sema": "3.0.1",
     "ci-info": "github:watson/ci-info#f43f6a1cefff47fb361c88cf4b943fdbcaafe540",
@@ -53,29 +51,35 @@
     "conf": "10.2.0",
     "cross-spawn": "7.0.3",
     "fast-glob": "3.3.1",
+    "fs-extra": "11.2.0",
+    "global-agent": "^3.0.0",
     "got": "10.7.0",
+    "ollama": "^0.5.0",
+    "ora": "^8.0.1",
     "picocolors": "1.0.0",
     "prompts": "2.1.0",
-    "rimraf": "^5.0.5",
     "smol-toml": "^1.1.4",
     "tar": "6.1.15",
     "terminal-link": "^3.0.0",
     "update-check": "1.5.4",
     "validate-npm-package-name": "3.0.0",
-    "wait-port": "^1.1.0",
+    "yaml": "2.4.1"
+  },
+  "devDependencies": {
     "@changesets/cli": "^2.27.1",
+    "@playwright/test": "^1.41.1",
+    "@vercel/ncc": "0.38.1",
     "eslint": "^8.56.0",
+    "eslint-config-prettier": "^8.10.0",
     "husky": "^9.0.10",
     "prettier": "^3.2.5",
     "prettier-plugin-organize-imports": "^3.2.4",
+    "rimraf": "^5.0.5",
     "typescript": "^5.3.3",
-    "eslint-config-prettier": "^8.10.0",
-    "ora": "^8.0.1",
-    "fs-extra": "11.2.0",
-    "yaml": "2.4.1"
+    "wait-port": "^1.1.0"
   },
+  "packageManager": "pnpm@9.0.5",
   "engines": {
     "node": ">=16.14.0"
-  },
-  "packageManager": "pnpm@8.15.1"
+  }
 }

questions.ts

@@ -1,8 +1,6 @@
 import { execSync } from "child_process";
 import ciInfo from "ci-info";
 import fs from "fs";
-import got from "got";
-import ora from "ora";
 import path from "path";
 import { blue, green, red } from "picocolors";
 import prompts from "prompts";
@@ -16,10 +14,13 @@ import { COMMUNITY_OWNER, COMMUNITY_REPO } from "./helpers/constant";
 import { EXAMPLE_FILE } from "./helpers/datasources";
 import { templatesDir } from "./helpers/dir";
 import { getAvailableLlamapackOptions } from "./helpers/llama-pack";
+import { askModelConfig } from "./helpers/providers";
 import { getProjectOptions } from "./helpers/repo";
-import { supportedTools, toolsRequireConfig } from "./helpers/tools";
-
-const OPENAI_API_URL = "https://api.openai.com/v1";
+import {
+  supportedTools,
+  toolRequiresConfig,
+  toolsRequireConfig,
+} from "./helpers/tools";
 
 export type QuestionArgs = Omit<
   InstallAppArgs,
@@ -67,16 +68,13 @@ if ($dialogResult -eq [System.Windows.Forms.DialogResult]::OK)
 }
 `;
 
-const defaults: QuestionArgs = {
+const defaults: Omit<QuestionArgs, "modelConfig"> = {
   template: "streaming",
   framework: "nextjs",
   ui: "shadcn",
   frontend: false,
-  openAiKey: "",
   llamaCloudKey: "",
   useLlamaParse: false,
-  model: "gpt-4-turbo",
-  embeddingModel: "text-embedding-3-large",
   communityProjectConfig: undefined,
   llamapack: "",
   postInstallAction: "dependencies",
@@ -84,7 +82,7 @@ const defaults: QuestionArgs = {
   tools: [],
 };
 
-const handlers = {
+export const questionHandlers = {
   onCancel: () => {
     console.error("Exiting.");
     process.exit(1);
@@ -103,6 +101,7 @@ const getVectorDbChoices = (framework: TemplateFramework) => {
     { title: "Milvus", value: "milvus" },
     { title: "Astra", value: "astra" },
     { title: "Qdrant", value: "qdrant" },
+    { title: "ChromaDB", value: "chroma" },
   ];
 
   const vectordbLang = framework === "fastapi" ? "python" : "typescript";
@@ -232,63 +231,15 @@ export const onPromptState = (state: any) => {
   }
 };
 
-const getAvailableModelChoices = async (
-  selectEmbedding: boolean,
-  apiKey?: string,
-) => {
-  const isLLMModel = (modelId: string) => {
-    return modelId.startsWith("gpt");
-  };
-
-  const isEmbeddingModel = (modelId: string) => {
-    return modelId.includes("embedding");
-  };
-
-  if (apiKey) {
-    const spinner = ora("Fetching available models").start();
-    try {
-      const response = await got(`${OPENAI_API_URL}/models`, {
-        headers: {
-          Authorization: "Bearer " + apiKey,
-        },
-        timeout: 5000,
-        responseType: "json",
-      });
-      const data: any = await response.body;
-      spinner.stop();
-      return data.data
-        .filter((model: any) =>
-          selectEmbedding ? isEmbeddingModel(model.id) : isLLMModel(model.id),
-        )
-        .map((el: any) => {
-          return {
-            title: el.id,
-            value: el.id,
-          };
-        });
-    } catch (error) {
-      spinner.stop();
-      if ((error as any).response?.statusCode === 401) {
-        console.log(
-          red(
-            "Invalid OpenAI API key provided! Please provide a valid key and try again!",
-          ),
-        );
-      } else {
-        console.log(red("Request failed: " + error));
-      }
-      process.exit(1);
-    }
-  }
-};
-
 export const askQuestions = async (
   program: QuestionArgs,
   preferences: QuestionArgs,
+  openAiKey?: string,
 ) => {
-  const getPrefOrDefault = <K extends keyof QuestionArgs>(
+  const getPrefOrDefault = <K extends keyof Omit<QuestionArgs, "modelConfig">>(
     field: K,
-  ): QuestionArgs[K] => preferences[field] ?? defaults[field];
+  ): Omit<QuestionArgs, "modelConfig">[K] =>
+    preferences[field] ?? defaults[field];
 
   // Ask for next action after installation
   async function askPostInstallAction() {
@@ -311,8 +262,8 @@ export const askQuestions = async (
           },
         ];
 
-        const openAiKeyConfigured =
-          program.openAiKey || process.env["OPENAI_API_KEY"];
+        const modelConfigured =
+          !program.llamapack && program.modelConfig.isConfigured();
         // If using LlamaParse, require LlamaCloud API key
         const llamaCloudKeyConfigured = program.useLlamaParse
           ? program.llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
@@ -321,10 +272,9 @@ export const askQuestions = async (
         // Can run the app if all tools do not require configuration
         if (
           !hasVectorDb &&
-          openAiKeyConfigured &&
+          modelConfigured &&
           llamaCloudKeyConfigured &&
-          !toolsRequireConfig(program.tools) &&
-          !program.llamapack
+          !toolsRequireConfig(program.tools)
         ) {
           actionChoices.push({
             title:
@@ -341,7 +291,7 @@ export const askQuestions = async (
             choices: actionChoices,
             initial: 1,
           },
-          handlers,
+          questionHandlers,
         );
 
         program.postInstallAction = action;
@@ -374,7 +324,7 @@ export const askQuestions = async (
           ],
           initial: 0,
         },
-        handlers,
+        questionHandlers,
       );
       program.template = template;
       preferences.template = template;
@@ -397,7 +347,7 @@ export const askQuestions = async (
         })),
         initial: 0,
       },
-      handlers,
+      questionHandlers,
     );
     const projectConfig = JSON.parse(communityProjectConfig);
     program.communityProjectConfig = projectConfig;
@@ -418,7 +368,7 @@ export const askQuestions = async (
         })),
         initial: 0,
       },
-      handlers,
+      questionHandlers,
     );
     program.llamapack = llamapack;
     preferences.llamapack = llamapack;
@@ -444,7 +394,7 @@ export const askQuestions = async (
           choices,
           initial: 0,
         },
-        handlers,
+        questionHandlers,
       );
       program.framework = framework;
       preferences.framework = framework;
@@ -453,7 +403,6 @@ export const askQuestions = async (
 
   if (program.framework === "express" || program.framework === "fastapi") {
     // if a backend-only framework is selected, ask whether we should create a frontend
-    // (only for streaming backends)
     if (program.frontend === undefined) {
       if (ciInfo.isCI) {
         program.frontend = getPrefOrDefault("frontend");
@@ -504,7 +453,7 @@ export const askQuestions = async (
           ],
           initial: 0,
         },
-        handlers,
+        questionHandlers,
       );
 
       program.observability = observability;
@@ -512,67 +461,13 @@ export const askQuestions = async (
     }
   }
 
-  if (!program.openAiKey) {
-    const { key } = await prompts(
-      {
-        type: "text",
-        name: "key",
-        message: program.askModels
-          ? "Please provide your OpenAI API key (or leave blank to reuse OPENAI_API_KEY env variable):"
-          : "Please provide your OpenAI API key (leave blank to skip):",
-        validate: (value: string) => {
-          if (program.askModels && !value) {
-            if (process.env.OPENAI_API_KEY) {
-              return true;
-            }
-            return "OpenAI API key is required";
-          }
-          return true;
-        },
-      },
-      handlers,
-    );
-
-    program.openAiKey = key || process.env.OPENAI_API_KEY;
-    preferences.openAiKey = key || process.env.OPENAI_API_KEY;
-  }
-
-  if (!program.model) {
-    if (ciInfo.isCI || !program.askModels) {
-      program.model = defaults.model;
-    } else {
-      const { model } = await prompts(
-        {
-          type: "select",
-          name: "model",
-          message: "Which LLM model would you like to use?",
-          choices: await getAvailableModelChoices(false, program.openAiKey),
-          initial: 0,
-        },
-        handlers,
-      );
-      program.model = model;
-      preferences.model = model;
-    }
-  }
-
-  if (!program.embeddingModel) {
-    if (ciInfo.isCI || !program.askModels) {
-      program.embeddingModel = defaults.embeddingModel;
-    } else {
-      const { embeddingModel } = await prompts(
-        {
-          type: "select",
-          name: "embeddingModel",
-          message: "Which embedding model would you like to use?",
-          choices: await getAvailableModelChoices(true, program.openAiKey),
-          initial: 0,
-        },
-        handlers,
-      );
-      program.embeddingModel = embeddingModel;
-      preferences.embeddingModel = embeddingModel;
-    }
+  if (!program.modelConfig) {
+    const modelConfig = await askModelConfig({
+      openAiKey,
+      askModels: program.askModels ?? false,
+    });
+    program.modelConfig = modelConfig;
+    preferences.modelConfig = modelConfig;
   }
 
   if (!program.dataSources) {
@@ -596,7 +491,7 @@ export const askQuestions = async (
             ),
             initial: firstQuestion ? 1 : 0,
           },
-          handlers,
+          questionHandlers,
         );
 
         if (selectedSource === "no" || selectedSource === "none") {
@@ -642,7 +537,7 @@ export const askQuestions = async (
                   return true;
                 },
               },
-              handlers,
+              questionHandlers,
             );
 
             program.dataSources.push({
@@ -687,7 +582,7 @@ export const askQuestions = async (
             ];
             program.dataSources.push({
               type: "db",
-              config: await prompts(dbPrompts, handlers),
+              config: await prompts(dbPrompts, questionHandlers),
             });
           }
         }
@@ -714,7 +609,7 @@ export const askQuestions = async (
           active: "yes",
           inactive: "no",
         },
-        handlers,
+        questionHandlers,
       );
       program.useLlamaParse = useLlamaParse;
 
@@ -727,7 +622,7 @@ export const askQuestions = async (
             message:
               "Please provide your LlamaIndex Cloud API key (leave blank to skip):",
           },
-          handlers,
+          questionHandlers,
         );
         program.llamaCloudKey = llamaCloudKey;
       }
@@ -746,7 +641,7 @@ export const askQuestions = async (
           choices: getVectorDbChoices(program.framework),
           initial: 0,
         },
-        handlers,
+        questionHandlers,
       );
       program.vectorDb = vectorDb;
       preferences.vectorDb = vectorDb;
@@ -761,7 +656,7 @@ export const askQuestions = async (
         t.supportedFrameworks?.includes(program.framework),
       );
       const toolChoices = options.map((tool) => ({
-        title: tool.display,
+        title: `${tool.display}${toolRequiresConfig(tool) ? " (needs configuration)" : ""}`,
         value: tool.name,
       }));
       const { toolsName } = await prompts({
@@ -781,3 +676,7 @@ export const askQuestions = async (
 
   await askPostInstallAction();
 };
+
+export const toChoice = (value: string) => {
+  return { title: value, value };
+};

templates/components/engines/python/agent/tools.py

@@ -1,35 +0,0 @@
-import os
-import yaml
-import importlib
-
-from llama_index.core.tools.tool_spec.base import BaseToolSpec
-from llama_index.core.tools.function_tool import FunctionTool
-
-
-class ToolFactory:
-
-    @staticmethod
-    def create_tool(tool_name: str, **kwargs) -> list[FunctionTool]:
-        try:
-            tool_package, tool_cls_name = tool_name.split(".")
-            module_name = f"llama_index.tools.{tool_package}"
-            module = importlib.import_module(module_name)
-            tool_class = getattr(module, tool_cls_name)
-            tool_spec: BaseToolSpec = tool_class(**kwargs)
-            return tool_spec.to_tool_list()
-        except (ImportError, AttributeError) as e:
-            raise ValueError(f"Unsupported tool: {tool_name}") from e
-        except TypeError as e:
-            raise ValueError(
-                f"Could not create tool: {tool_name}. With config: {kwargs}"
-            ) from e
-
-    @staticmethod
-    def from_env() -> list[FunctionTool]:
-        tools = []
-        if os.path.exists("config/tools.yaml"):
-            with open("config/tools.yaml", "r") as f:
-                tool_configs = yaml.safe_load(f)
-                for name, config in tool_configs.items():
-                    tools += ToolFactory.create_tool(name, **config)
-        return tools

templates/components/engines/python/agent/tools/__init__.py

@@ -0,0 +1,56 @@
+import os
+import yaml
+import json
+import importlib
+from cachetools import cached, LRUCache
+from llama_index.core.tools.tool_spec.base import BaseToolSpec
+from llama_index.core.tools.function_tool import FunctionTool
+
+
+class ToolType:
+    LLAMAHUB = "llamahub"
+    LOCAL = "local"
+
+
+class ToolFactory:
+
+    TOOL_SOURCE_PACKAGE_MAP = {
+        ToolType.LLAMAHUB: "llama_index.tools",
+        ToolType.LOCAL: "app.engine.tools",
+    }
+
+    def load_tools(tool_type: str, tool_name: str, config: dict) -> list[FunctionTool]:
+        source_package = ToolFactory.TOOL_SOURCE_PACKAGE_MAP[tool_type]
+        try:
+            if "ToolSpec" in tool_name:
+                tool_package, tool_cls_name = tool_name.split(".")
+                module_name = f"{source_package}.{tool_package}"
+                module = importlib.import_module(module_name)
+                tool_class = getattr(module, tool_cls_name)
+                tool_spec: BaseToolSpec = tool_class(**config)
+                return tool_spec.to_tool_list()
+            else:
+                module = importlib.import_module(f"{source_package}.{tool_name}")
+                tools = module.get_tools()
+                if not all(isinstance(tool, FunctionTool) for tool in tools):
+                    raise ValueError(
+                        f"The module {module} does not contain valid tools"
+                    )
+                return tools
+        except ImportError as e:
+            raise ValueError(f"Failed to import tool {tool_name}: {e}")
+        except AttributeError as e:
+            raise ValueError(f"Failed to load tool {tool_name}: {e}")
+
+    @staticmethod
+    def from_env() -> list[FunctionTool]:
+        tools = []
+        if os.path.exists("config/tools.yaml"):
+            with open("config/tools.yaml", "r") as f:
+                tool_configs = yaml.safe_load(f)
+                for tool_type, config_entries in tool_configs.items():
+                    for tool_name, config in config_entries.items():
+                        tools.extend(
+                            ToolFactory.load_tools(tool_type, tool_name, config)
+                        )
+        return tools

templates/components/engines/python/agent/tools/duckduckgo.py

@@ -0,0 +1,36 @@
+from llama_index.core.tools.function_tool import FunctionTool
+
+
+def duckduckgo_search(
+    query: str,
+    region: str = "wt-wt",
+    max_results: int = 10,
+):
+    """
+    Use this function to search for any query in DuckDuckGo.
+    Args:
+        query (str): The query to search in DuckDuckGo.
+        region Optional(str): The region to be used for the search in [country-language] convention, ex us-en, uk-en, ru-ru, etc...
+        max_results Optional(int): The maximum number of results to be returned. Default is 10.
+    """
+    try:
+        from duckduckgo_search import DDGS
+    except ImportError:
+        raise ImportError(
+            "duckduckgo_search package is required to use this function."
+            "Please install it by running: `poetry add duckduckgo_search` or `pip install duckduckgo_search`"
+        )
+
+    params = {
+        "keywords": query,
+        "region": region,
+        "max_results": max_results,
+    }
+    results = []
+    with DDGS() as ddg:
+        results = list(ddg.text(**params))
+    return results
+
+
+def get_tools():
+    return [FunctionTool.from_defaults(duckduckgo_search)]

templates/components/engines/python/agent/tools/interpreter.py

@@ -0,0 +1,142 @@
+import os
+import logging
+import base64
+import uuid
+from pydantic import BaseModel
+from typing import List, Tuple, Dict, Optional
+from llama_index.core.tools import FunctionTool
+from e2b_code_interpreter import CodeInterpreter
+from e2b_code_interpreter.models import Logs
+
+
+logger = logging.getLogger(__name__)
+
+
+class InterpreterExtraResult(BaseModel):
+    type: str
+    content: Optional[str] = None
+    filename: Optional[str] = None
+    url: Optional[str] = None
+
+
+class E2BToolOutput(BaseModel):
+    is_error: bool
+    logs: Logs
+    results: List[InterpreterExtraResult] = []
+
+
+class E2BCodeInterpreter:
+
+    output_dir = "tool-output"
+
+    def __init__(self):
+        api_key = os.getenv("E2B_API_KEY")
+        filesever_url_prefix = os.getenv("FILESERVER_URL_PREFIX")
+        if not api_key:
+            raise ValueError(
+                "E2B_API_KEY key is required to run code interpreter. Get it here: https://e2b.dev/docs/getting-started/api-key"
+            )
+        if not filesever_url_prefix:
+            raise ValueError(
+                "FILESERVER_URL_PREFIX is required to display file output from sandbox"
+            )
+
+        self.filesever_url_prefix = filesever_url_prefix
+        self.interpreter = CodeInterpreter(api_key=api_key)
+
+    def __del__(self):
+        self.interpreter.close()
+
+    def get_output_path(self, filename: str) -> str:
+        # if output directory doesn't exist, create it
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir, exist_ok=True)
+        return os.path.join(self.output_dir, filename)
+
+    def save_to_disk(self, base64_data: str, ext: str) -> Dict:
+        filename = f"{uuid.uuid4()}.{ext}"  # generate a unique filename
+        buffer = base64.b64decode(base64_data)
+        output_path = self.get_output_path(filename)
+
+        try:
+            with open(output_path, "wb") as file:
+                file.write(buffer)
+        except IOError as e:
+            logger.error(f"Failed to write to file {output_path}: {str(e)}")
+            raise e
+
+        logger.info(f"Saved file to {output_path}")
+
+        return {
+            "outputPath": output_path,
+            "filename": filename,
+        }
+
+    def get_file_url(self, filename: str) -> str:
+        return f"{self.filesever_url_prefix}/{self.output_dir}/{filename}"
+
+    def parse_result(self, result) -> List[InterpreterExtraResult]:
+        """
+        The result could include multiple formats (e.g. png, svg, etc.) but encoded in base64
+        We save each result to disk and return saved file metadata (extension, filename, url)
+        """
+        if not result:
+            return []
+
+        output = []
+
+        try:
+            formats = result.formats()
+            results = [result[format] for format in formats]
+
+            for ext, data in zip(formats, results):
+                match ext:
+                    case "png" | "svg" | "jpeg" | "pdf":
+                        result = self.save_to_disk(data, ext)
+                        filename = result["filename"]
+                        output.append(
+                            InterpreterExtraResult(
+                                type=ext,
+                                filename=filename,
+                                url=self.get_file_url(filename),
+                            )
+                        )
+                    case _:
+                        output.append(
+                            InterpreterExtraResult(
+                                type=ext,
+                                content=data,
+                            )
+                        )
+        except Exception as error:
+            logger.exception(error, exc_info=True)
+            logger.error("Error when parsing output from E2b interpreter tool", error)
+
+        return output
+
+    def interpret(self, code: str) -> E2BToolOutput:
+        """
+        Execute python code in a Jupyter notebook cell, the toll will return result, stdout, stderr, display_data, and error.
+
+        Parameters:
+            code (str): The python code to be executed in a single cell.
+        """
+        logger.info(
+            f"\n{'='*50}\n> Running following AI-generated code:\n{code}\n{'='*50}"
+        )
+        exec = self.interpreter.notebook.exec_cell(code)
+
+        if exec.error:
+            logger.error("Error when executing code", exec.error)
+            output = E2BToolOutput(is_error=True, logs=exec.logs, results=[])
+        else:
+            if len(exec.results) == 0:
+                output = E2BToolOutput(is_error=False, logs=exec.logs, results=[])
+            else:
+                results = self.parse_result(exec.results[0])
+                output = E2BToolOutput(is_error=False, logs=exec.logs, results=results)
+        return output
+
+
+def get_tools():
+    return [FunctionTool.from_defaults(E2BCodeInterpreter().interpret)]

templates/components/engines/python/agent/tools/openapi_action.py

@@ -0,0 +1,78 @@
+from typing import Dict, List, Tuple
+from llama_index.tools.openapi import OpenAPIToolSpec
+from llama_index.tools.requests import RequestsToolSpec
+
+
+class OpenAPIActionToolSpec(OpenAPIToolSpec, RequestsToolSpec):
+    """
+    A combination of OpenAPI and Requests tool specs that can parse OpenAPI specs and make requests.
+
+    openapi_uri: str: The file path or URL to the OpenAPI spec.
+    domain_headers: dict: Whitelist domains and the headers to use.
+    """
+
+    spec_functions = OpenAPIToolSpec.spec_functions + RequestsToolSpec.spec_functions
+    # Cached parsed specs by URI
+    _specs: Dict[str, Tuple[Dict, List[str]]] = {}
+
+    def __init__(self, openapi_uri: str, domain_headers: dict = None, **kwargs):
+        if domain_headers is None:
+            domain_headers = {}
+        if openapi_uri not in self._specs:
+            openapi_spec, servers = self._load_openapi_spec(openapi_uri)
+            self._specs[openapi_uri] = (openapi_spec, servers)
+        else:
+            openapi_spec, servers = self._specs[openapi_uri]
+
+        # Add the servers to the domain headers if they are not already present
+        for server in servers:
+            if server not in domain_headers:
+                domain_headers[server] = {}
+
+        OpenAPIToolSpec.__init__(self, spec=openapi_spec)
+        RequestsToolSpec.__init__(self, domain_headers)
+
+    @staticmethod
+    def _load_openapi_spec(uri: str) -> Tuple[Dict, List[str]]:
+        """
+        Load an OpenAPI spec from a URI.
+
+        Args:
+            uri (str): A file path or URL to the OpenAPI spec.
+
+        Returns:
+            List[Document]: A list of Document objects.
+        """
+        import yaml
+        from urllib.parse import urlparse
+
+        if uri.startswith("http"):
+            import requests
+
+            response = requests.get(uri)
+            if response.status_code != 200:
+                raise ValueError(
+                    "Could not initialize OpenAPIActionToolSpec: "
+                    f"Failed to load OpenAPI spec from {uri}, status code: {response.status_code}"
+                )
+            spec = yaml.safe_load(response.text)
+        elif uri.startswith("file"):
+            filepath = urlparse(uri).path
+            with open(filepath, "r") as file:
+                spec = yaml.safe_load(file)
+        else:
+            raise ValueError(
+                "Could not initialize OpenAPIActionToolSpec: Invalid OpenAPI URI provided. "
+                "Only HTTP and file path are supported."
+            )
+        # Add the servers to the whitelist
+        try:
+            servers = [
+                urlparse(server["url"]).netloc for server in spec.get("servers", [])
+            ]
+        except KeyError as e:
+            raise ValueError(
+                "Could not initialize OpenAPIActionToolSpec: Invalid OpenAPI spec provided. "
+                "Could not get `servers` from the spec."
+            ) from e
+        return spec, servers

templates/components/engines/python/agent/tools/weather.py

@@ -0,0 +1,73 @@
+"""Open Meteo weather map tool spec."""
+
+import logging
+import requests
+import pytz
+from llama_index.core.tools import FunctionTool
+
+logger = logging.getLogger(__name__)
+
+
+class OpenMeteoWeather:
+    geo_api = "https://geocoding-api.open-meteo.com/v1"
+    weather_api = "https://api.open-meteo.com/v1"
+
+    @classmethod
+    def _get_geo_location(cls, location: str) -> dict:
+        """Get geo location from location name."""
+        params = {"name": location, "count": 10, "language": "en", "format": "json"}
+        response = requests.get(f"{cls.geo_api}/search", params=params)
+        if response.status_code != 200:
+            raise Exception(f"Failed to fetch geo location: {response.status_code}")
+        else:
+            data = response.json()
+            result = data["results"][0]
+            geo_location = {
+                "id": result["id"],
+                "name": result["name"],
+                "latitude": result["latitude"],
+                "longitude": result["longitude"],
+            }
+            return geo_location
+
+    @classmethod
+    def get_weather_information(cls, location: str) -> dict:
+        """Use this function to get the weather of any given location.
+        Note that the weather code should follow WMO Weather interpretation codes (WW):
+        0: Clear sky
+        1, 2, 3: Mainly clear, partly cloudy, and overcast
+        45, 48: Fog and depositing rime fog
+        51, 53, 55: Drizzle: Light, moderate, and dense intensity
+        56, 57: Freezing Drizzle: Light and dense intensity
+        61, 63, 65: Rain: Slight, moderate and heavy intensity
+        66, 67: Freezing Rain: Light and heavy intensity
+        71, 73, 75: Snow fall: Slight, moderate, and heavy intensity
+        77: Snow grains
+        80, 81, 82: Rain showers: Slight, moderate, and violent
+        85, 86: Snow showers slight and heavy
+        95: Thunderstorm: Slight or moderate
+        96, 99: Thunderstorm with slight and heavy hail
+        """
+        logger.info(
+            f"Calling open-meteo api to get weather information of location: {location}"
+        )
+        geo_location = cls._get_geo_location(location)
+        timezone = pytz.timezone("UTC").zone
+        params = {
+            "latitude": geo_location["latitude"],
+            "longitude": geo_location["longitude"],
+            "current": "temperature_2m,weather_code",
+            "hourly": "temperature_2m,weather_code",
+            "daily": "weather_code",
+            "timezone": timezone,
+        }
+        response = requests.get(f"{cls.weather_api}/forecast", params=params)
+        if response.status_code != 200:
+            raise Exception(
+                f"Failed to fetch weather information: {response.status_code}"
+            )
+        return response.json()
+
+
+def get_tools():
+    return [FunctionTool.from_defaults(OpenMeteoWeather.get_weather_information)]

templates/components/engines/python/chat/__init__.py

@@ -1,5 +1,6 @@
 import os
 from app.engine.index import get_index
+from fastapi import HTTPException
 
 
 def get_chat_engine():
@@ -8,8 +9,11 @@ def get_chat_engine():
 
     index = get_index()
     if index is None:
-        raise Exception(
-            "StorageContext is empty - call 'poetry run generate' to generate the storage first"
+        raise HTTPException(
+            status_code=500,
+            detail=str(
+                "StorageContext is empty - call 'poetry run generate' to generate the storage first"
+            ),
         )
 
     return index.as_chat_engine(

templates/components/engines/typescript/agent/chat.ts

@@ -1,17 +1,12 @@
-import {
-  BaseTool,
-  OpenAIAgent,
-  QueryEngineTool,
-  Settings,
-  ToolFactory,
-} from "llamaindex";
+import { BaseToolWithCall, OpenAIAgent, QueryEngineTool } from "llamaindex";
 import fs from "node:fs/promises";
 import path from "node:path";
 import { getDataSource } from "./index";
 import { STORAGE_CACHE_DIR } from "./shared";
+import { createTools } from "./tools";
 
 export async function createChatEngine() {
-  let tools: BaseTool[] = [];
+  const tools: BaseToolWithCall[] = [];
 
   // Add a query engine tool if we have a data source
   // Delete this code if you don't have a data source
@@ -28,17 +23,20 @@ export async function createChatEngine() {
     );
   }
 
+  const configFile = path.join("config", "tools.json");
+  let toolConfig: any;
   try {
     // add tools from config file if it exists
-    const config = JSON.parse(
-      await fs.readFile(path.join("config", "tools.json"), "utf8"),
-    );
-    tools = tools.concat(await ToolFactory.createTools(config));
-  } catch {}
+    toolConfig = JSON.parse(await fs.readFile(configFile, "utf8"));
+  } catch (e) {
+    console.info(`Could not read ${configFile} file. Using no tools.`);
+  }
+  if (toolConfig) {
+    tools.push(...(await createTools(toolConfig)));
+  }
 
   return new OpenAIAgent({
     tools,
-    llm: Settings.llm,
-    verbose: true,
+    systemPrompt: process.env.SYSTEM_PROMPT,
   });
 }

templates/components/engines/typescript/agent/tools/duckduckgo.ts

@@ -0,0 +1,61 @@
+import { JSONSchemaType } from "ajv";
+import { search } from "duck-duck-scrape";
+import { BaseTool, ToolMetadata } from "llamaindex";
+
+export type DuckDuckGoParameter = {
+  query: string;
+  region?: string;
+};
+
+export type DuckDuckGoToolParams = {
+  metadata?: ToolMetadata<JSONSchemaType<DuckDuckGoParameter>>;
+};
+
+const DEFAULT_META_DATA: ToolMetadata<JSONSchemaType<DuckDuckGoParameter>> = {
+  name: "duckduckgo",
+  description: "Use this function to search for any query in DuckDuckGo.",
+  parameters: {
+    type: "object",
+    properties: {
+      query: {
+        type: "string",
+        description: "The query to search in DuckDuckGo.",
+      },
+      region: {
+        type: "string",
+        description:
+          "Optional, The region to be used for the search in [country-language] convention, ex us-en, uk-en, ru-ru, etc...",
+        nullable: true,
+      },
+    },
+    required: ["query"],
+  },
+};
+
+type DuckDuckGoSearchResult = {
+  title: string;
+  description: string;
+  url: string;
+};
+
+export class DuckDuckGoSearchTool implements BaseTool<DuckDuckGoParameter> {
+  metadata: ToolMetadata<JSONSchemaType<DuckDuckGoParameter>>;
+
+  constructor(params: DuckDuckGoToolParams) {
+    this.metadata = params.metadata ?? DEFAULT_META_DATA;
+  }
+
+  async call(input: DuckDuckGoParameter) {
+    const { query, region } = input;
+    const options = region ? { region } : {};
+    const searchResults = await search(query, options);
+
+    return searchResults.results.map((result) => {
+      return {
+        title: result.title,
+        description: result.description,
+        url: result.url,
+      } as DuckDuckGoSearchResult;
+    });
+  }
+}

templates/components/engines/typescript/agent/tools/index.ts

@@ -0,0 +1,57 @@
+import { BaseToolWithCall } from "llamaindex";
+import { ToolsFactory } from "llamaindex/tools/ToolsFactory";
+import { DuckDuckGoSearchTool, DuckDuckGoToolParams } from "./duckduckgo";
+import { InterpreterTool, InterpreterToolParams } from "./interpreter";
+import { OpenAPIActionTool } from "./openapi-action";
+import { WeatherTool, WeatherToolParams } from "./weather";
+
+type ToolCreator = (config: unknown) => Promise<BaseToolWithCall[]>;
+
+export async function createTools(toolConfig: {
+  local: Record<string, unknown>;
+  llamahub: any;
+}): Promise<BaseToolWithCall[]> {
+  // add local tools from the 'tools' folder (if configured)
+  const tools = await createLocalTools(toolConfig.local);
+  // add tools from LlamaIndexTS (if configured)
+  tools.push(...(await ToolsFactory.createTools(toolConfig.llamahub)));
+  return tools;
+}
+
+const toolFactory: Record<string, ToolCreator> = {
+  weather: async (config: unknown) => {
+    return [new WeatherTool(config as WeatherToolParams)];
+  },
+  interpreter: async (config: unknown) => {
+    return [new InterpreterTool(config as InterpreterToolParams)];
+  },
+  "openapi_action.OpenAPIActionToolSpec": async (config: unknown) => {
+    const { openapi_uri, domain_headers } = config as {
+      openapi_uri: string;
+      domain_headers: Record<string, Record<string, string>>;
+    };
+    const openAPIActionTool = new OpenAPIActionTool(
+      openapi_uri,
+      domain_headers,
+    );
+    return await openAPIActionTool.toToolFunctions();
+  },
+  duckduckgo: async (config: unknown) => {
+    return [new DuckDuckGoSearchTool(config as DuckDuckGoToolParams)];
+  },
+};
+
+async function createLocalTools(
+  localConfig: Record<string, unknown>,
+): Promise<BaseToolWithCall[]> {
+  const tools: BaseToolWithCall[] = [];
+
+  for (const [key, toolConfig] of Object.entries(localConfig)) {
+    if (key in toolFactory) {
+      const newTools = await toolFactory[key](toolConfig);
+      tools.push(...newTools);
+    }
+  }
+
+  return tools;
+}

templates/components/engines/typescript/agent/tools/interpreter.ts

@@ -0,0 +1,189 @@
+import { CodeInterpreter, Logs, Result } from "@e2b/code-interpreter";
+import type { JSONSchemaType } from "ajv";
+import fs from "fs";
+import { BaseTool, ToolMetadata } from "llamaindex";
+import crypto from "node:crypto";
+import path from "node:path";
+
+export type InterpreterParameter = {
+  code: string;
+};
+
+export type InterpreterToolParams = {
+  metadata?: ToolMetadata<JSONSchemaType<InterpreterParameter>>;
+  apiKey?: string;
+  fileServerURLPrefix?: string;
+};
+
+export type InterpreterToolOutput = {
+  isError: boolean;
+  logs: Logs;
+  extraResult: InterpreterExtraResult[];
+};
+
+type InterpreterExtraType =
+  | "html"
+  | "markdown"
+  | "svg"
+  | "png"
+  | "jpeg"
+  | "pdf"
+  | "latex"
+  | "json"
+  | "javascript";
+
+export type InterpreterExtraResult = {
+  type: InterpreterExtraType;
+  content?: string;
+  filename?: string;
+  url?: string;
+};
+
+const DEFAULT_META_DATA: ToolMetadata<JSONSchemaType<InterpreterParameter>> = {
+  name: "interpreter",
+  description:
+    "Execute python code in a Jupyter notebook cell and return any result, stdout, stderr, display_data, and error.",
+  parameters: {
+    type: "object",
+    properties: {
+      code: {
+        type: "string",
+        description: "The python code to execute in a single cell.",
+      },
+    },
+    required: ["code"],
+  },
+};
+
+export class InterpreterTool implements BaseTool<InterpreterParameter> {
+  private readonly outputDir = "tool-output";
+  private apiKey?: string;
+  private fileServerURLPrefix?: string;
+  metadata: ToolMetadata<JSONSchemaType<InterpreterParameter>>;
+  codeInterpreter?: CodeInterpreter;
+
+  constructor(params?: InterpreterToolParams) {
+    this.metadata = params?.metadata || DEFAULT_META_DATA;
+    this.apiKey = params?.apiKey || process.env.E2B_API_KEY;
+    this.fileServerURLPrefix =
+      params?.fileServerURLPrefix || process.env.FILESERVER_URL_PREFIX;
+
+    if (!this.apiKey) {
+      throw new Error(
+        "E2B_API_KEY key is required to run code interpreter. Get it here: https://e2b.dev/docs/getting-started/api-key",
+      );
+    }
+    if (!this.fileServerURLPrefix) {
+      throw new Error(
+        "FILESERVER_URL_PREFIX is required to display file output from sandbox",
+      );
+    }
+  }
+
+  public async initInterpreter() {
+    if (!this.codeInterpreter) {
+      this.codeInterpreter = await CodeInterpreter.create({
+        apiKey: this.apiKey,
+      });
+    }
+    return this.codeInterpreter;
+  }
+
+  public async codeInterpret(code: string): Promise<InterpreterToolOutput> {
+    console.log(
+      `\n${"=".repeat(50)}\n> Running following AI-generated code:\n${code}\n${"=".repeat(50)}`,
+    );
+    const interpreter = await this.initInterpreter();
+    const exec = await interpreter.notebook.execCell(code);
+    if (exec.error) console.error("[Code Interpreter error]", exec.error);
+    const extraResult = await this.getExtraResult(exec.results[0]);
+    const result: InterpreterToolOutput = {
+      isError: !!exec.error,
+      logs: exec.logs,
+      extraResult,
+    };
+    return result;
+  }
+
+  async call(input: InterpreterParameter): Promise<InterpreterToolOutput> {
+    const result = await this.codeInterpret(input.code);
+    return result;
+  }
+
+  async close() {
+    await this.codeInterpreter?.close();
+  }
+
+  private async getExtraResult(
+    res?: Result,
+  ): Promise<InterpreterExtraResult[]> {
+    if (!res) return [];
+    const output: InterpreterExtraResult[] = [];
+
+    try {
+      const formats = res.formats(); // formats available for the result. Eg: ['png', ...]
+      const results = formats.map((f) => res[f as keyof Result]); // get base64 data for each format
+
+      // save base64 data to file and return the url
+      for (let i = 0; i < formats.length; i++) {
+        const ext = formats[i];
+        const data = results[i];
+        switch (ext) {
+          case "png":
+          case "jpeg":
+          case "svg":
+          case "pdf":
+            const { filename } = this.saveToDisk(data, ext);
+            output.push({
+              type: ext as InterpreterExtraType,
+              filename,
+              url: this.getFileUrl(filename),
+            });
+            break;
+          default:
+            output.push({
+              type: ext as InterpreterExtraType,
+              content: data,
+            });
+            break;
+        }
+      }
+    } catch (error) {
+      console.error("Error when parsing e2b response", error);
+    }
+
+    return output;
+  }
+
+  // Consider saving to cloud storage instead but it may cost more for you
+  // See: https://e2b.dev/docs/sandbox/api/filesystem#write-to-file
+  private saveToDisk(
+    base64Data: string,
+    ext: string,
+  ): {
+    outputPath: string;
+    filename: string;
+  } {
+    const filename = `${crypto.randomUUID()}.${ext}`; // generate a unique filename
+    const buffer = Buffer.from(base64Data, "base64");
+    const outputPath = this.getOutputPath(filename);
+    fs.writeFileSync(outputPath, buffer);
+    console.log(`Saved file to ${outputPath}`);
+    return {
+      outputPath,
+      filename,
+    };
+  }
+
+  private getOutputPath(filename: string): string {
+    // if outputDir doesn't exist, create it
+    if (!fs.existsSync(this.outputDir)) {
+      fs.mkdirSync(this.outputDir, { recursive: true });
+    }
+    return path.join(this.outputDir, filename);
+  }
+
+  private getFileUrl(filename: string): string {
+    return `${this.fileServerURLPrefix}/${this.outputDir}/${filename}`;
+  }
+}

templates/components/engines/typescript/agent/tools/openapi-action.ts

@@ -0,0 +1,164 @@
+import SwaggerParser from "@apidevtools/swagger-parser";
+import { JSONSchemaType } from "ajv";
+import got from "got";
+import { FunctionTool, JSONValue, ToolMetadata } from "llamaindex";
+
+interface DomainHeaders {
+  [key: string]: { [header: string]: string };
+}
+
+type Input = {
+  url: string;
+  params: object;
+};
+
+type APIInfo = {
+  description: string;
+  title: string;
+};
+
+export class OpenAPIActionTool {
+  // cache the loaded specs by URL
+  private static specs: Record<string, any> = {};
+
+  private readonly INVALID_URL_PROMPT =
+    "This url did not include a hostname or scheme. Please determine the complete URL and try again.";
+
+  private createLoadSpecMetaData = (info: APIInfo) => {
+    return {
+      name: "load_openapi_spec",
+      description: `Use this to retrieve the OpenAPI spec for the API named ${info.title} with the following description: ${info.description}. Call it before making any requests to the API.`,
+    };
+  };
+
+  private readonly createMethodCallMetaData = (
+    method: "POST" | "PATCH" | "GET",
+    info: APIInfo,
+  ) => {
+    return {
+      name: `${method.toLowerCase()}_request`,
+      description: `Use this to call the ${method} method on the API named ${info.title}`,
+      parameters: {
+        type: "object",
+        properties: {
+          url: {
+            type: "string",
+            description: `The url to make the ${method} request against`,
+          },
+          params: {
+            type: "object",
+            description:
+              method === "GET"
+                ? "the URL parameters to provide with the get request"
+                : `the key-value pairs to provide with the ${method} request`,
+          },
+        },
+        required: ["url"],
+      },
+    } as ToolMetadata<JSONSchemaType<Input>>;
+  };
+
+  constructor(
+    public openapi_uri: string,
+    public domainHeaders: DomainHeaders = {},
+  ) {}
+
+  async loadOpenapiSpec(url: string): Promise<any> {
+    const api = await SwaggerParser.validate(url);
+    return {
+      servers: "servers" in api ? api.servers : "",
+      info: { description: api.info.description, title: api.info.title },
+      endpoints: api.paths,
+    };
+  }
+
+  async getRequest(input: Input): Promise<JSONValue> {
+    if (!this.validUrl(input.url)) {
+      return this.INVALID_URL_PROMPT;
+    }
+    try {
+      const data = await got
+        .get(input.url, {
+          headers: this.getHeadersForUrl(input.url),
+          searchParams: input.params as URLSearchParams,
+        })
+        .json();
+      return data as JSONValue;
+    } catch (error) {
+      return error as JSONValue;
+    }
+  }
+
+  async postRequest(input: Input): Promise<JSONValue> {
+    if (!this.validUrl(input.url)) {
+      return this.INVALID_URL_PROMPT;
+    }
+    try {
+      const res = await got.post(input.url, {
+        headers: this.getHeadersForUrl(input.url),
+        json: input.params,
+      });
+      return res.body as JSONValue;
+    } catch (error) {
+      return error as JSONValue;
+    }
+  }
+
+  async patchRequest(input: Input): Promise<JSONValue> {
+    if (!this.validUrl(input.url)) {
+      return this.INVALID_URL_PROMPT;
+    }
+    try {
+      const res = await got.patch(input.url, {
+        headers: this.getHeadersForUrl(input.url),
+        json: input.params,
+      });
+      return res.body as JSONValue;
+    } catch (error) {
+      return error as JSONValue;
+    }
+  }
+
+  public async toToolFunctions() {
+    if (!OpenAPIActionTool.specs[this.openapi_uri]) {
+      console.log(`Loading spec for URL: ${this.openapi_uri}`);
+      const spec = await this.loadOpenapiSpec(this.openapi_uri);
+      OpenAPIActionTool.specs[this.openapi_uri] = spec;
+    }
+    const spec = OpenAPIActionTool.specs[this.openapi_uri];
+    // TODO: read endpoints with parameters from spec and create one tool for each endpoint
+    // For now, we just create a tool for each HTTP method which does not work well for passing parameters
+    return [
+      FunctionTool.from(() => {
+        return spec;
+      }, this.createLoadSpecMetaData(spec.info)),
+      FunctionTool.from(
+        this.getRequest.bind(this),
+        this.createMethodCallMetaData("GET", spec.info),
+      ),
+      FunctionTool.from(
+        this.postRequest.bind(this),
+        this.createMethodCallMetaData("POST", spec.info),
+      ),
+      FunctionTool.from(
+        this.patchRequest.bind(this),
+        this.createMethodCallMetaData("PATCH", spec.info),
+      ),
+    ];
+  }
+
+  private validUrl(url: string): boolean {
+    const parsed = new URL(url);
+    return !!parsed.protocol && !!parsed.hostname;
+  }
+
+  private getDomain(url: string): string {
+    const parsed = new URL(url);
+    return parsed.hostname;
+  }
+
+  private getHeadersForUrl(url: string): { [header: string]: string } {
+    const domain = this.getDomain(url);
+    return this.domainHeaders[domain] || {};
+  }
+}

templates/components/engines/typescript/agent/tools/weather.ts

@@ -0,0 +1,81 @@
+import type { JSONSchemaType } from "ajv";
+import { BaseTool, ToolMetadata } from "llamaindex";
+
+interface GeoLocation {
+  id: string;
+  name: string;
+  latitude: number;
+  longitude: number;
+}
+
+export type WeatherParameter = {
+  location: string;
+};
+
+export type WeatherToolParams = {
+  metadata?: ToolMetadata<JSONSchemaType<WeatherParameter>>;
+};
+
+const DEFAULT_META_DATA: ToolMetadata<JSONSchemaType<WeatherParameter>> = {
+  name: "get_weather_information",
+  description: `
+    Use this function to get the weather of any given location.
+    Note that the weather code should follow WMO Weather interpretation codes (WW):
+    0: Clear sky
+    1, 2, 3: Mainly clear, partly cloudy, and overcast
+    45, 48: Fog and depositing rime fog
+    51, 53, 55: Drizzle: Light, moderate, and dense intensity
+    56, 57: Freezing Drizzle: Light and dense intensity
+    61, 63, 65: Rain: Slight, moderate and heavy intensity
+    66, 67: Freezing Rain: Light and heavy intensity
+    71, 73, 75: Snow fall: Slight, moderate, and heavy intensity
+    77: Snow grains
+    80, 81, 82: Rain showers: Slight, moderate, and violent
+    85, 86: Snow showers slight and heavy
+    95: Thunderstorm: Slight or moderate
+    96, 99: Thunderstorm with slight and heavy hail
+  `,
+  parameters: {
+    type: "object",
+    properties: {
+      location: {
+        type: "string",
+        description: "The location to get the weather information",
+      },
+    },
+    required: ["location"],
+  },
+};
+
+export class WeatherTool implements BaseTool<WeatherParameter> {
+  metadata: ToolMetadata<JSONSchemaType<WeatherParameter>>;
+
+  private getGeoLocation = async (location: string): Promise<GeoLocation> => {
+    const apiUrl = `https://geocoding-api.open-meteo.com/v1/search?name=${location}&count=10&language=en&format=json`;
+    const response = await fetch(apiUrl);
+    const data = await response.json();
+    const { id, name, latitude, longitude } = data.results[0];
+    return { id, name, latitude, longitude };
+  };
+
+  private getWeatherByLocation = async (location: string) => {
+    console.log(
+      "Calling open-meteo api to get weather information of location:",
+      location,
+    );
+    const { latitude, longitude } = await this.getGeoLocation(location);
+    const timezone = Intl.DateTimeFormat().resolvedOptions().timeZone;
+    const apiUrl = `https://api.open-meteo.com/v1/forecast?latitude=${latitude}&longitude=${longitude}&current=temperature_2m,weather_code&hourly=temperature_2m,weather_code&daily=weather_code&timezone=${timezone}`;
+    const response = await fetch(apiUrl);
+    const data = await response.json();
+    return data;
+  };
+
+  constructor(params?: WeatherToolParams) {
+    this.metadata = params?.metadata || DEFAULT_META_DATA;
+  }
+
+  async call(input: WeatherParameter) {
+    return await this.getWeatherByLocation(input.location);
+  }
+}

templates/components/engines/typescript/chat/chat.ts

@@ -9,10 +9,13 @@ export async function createChatEngine() {
     );
   }
   const retriever = index.asRetriever();
-  retriever.similarityTopK = 3;
+  retriever.similarityTopK = process.env.TOP_K
+    ? parseInt(process.env.TOP_K)
+    : 3;
 
   return new ContextChatEngine({
     chatModel: Settings.llm,
     retriever,
+    systemPrompt: process.env.SYSTEM_PROMPT,
   });
 }

templates/components/loaders/python/file.py

@@ -1,7 +1,10 @@
 import os
+import logging
 from llama_parse import LlamaParse
 from pydantic import BaseModel, validator
 
+logger = logging.getLogger(__name__)
+
 
 class FileLoaderConfig(BaseModel):
     data_dir: str = "data"
@@ -20,18 +23,44 @@ def llama_parse_parser():
             "LLAMA_CLOUD_API_KEY environment variable is not set. "
             "Please set it in .env file or in your shell environment then run again!"
         )
-    parser = LlamaParse(result_type="markdown", verbose=True, language="en")
+    parser = LlamaParse(
+        result_type="markdown",
+        verbose=True,
+        language="en",
+        ignore_errors=False,
+    )
     return parser
 
 
 def get_file_documents(config: FileLoaderConfig):
     from llama_index.core.readers import SimpleDirectoryReader
 
-    reader = SimpleDirectoryReader(
-        config.data_dir,
-        recursive=True,
-    )
-    if config.use_llama_parse:
-        parser = llama_parse_parser()
-        reader.file_extractor = {".pdf": parser}
-    return reader.load_data()
+    try:
+        reader = SimpleDirectoryReader(
+            config.data_dir, recursive=True, filename_as_id=True, raise_on_error=True
+        )
+        if config.use_llama_parse:
+            # LlamaParse is async first,
+            # so we need to use nest_asyncio to run it in sync mode
+            import nest_asyncio
+
+            nest_asyncio.apply()
+
+            parser = llama_parse_parser()
+            reader.file_extractor = {".pdf": parser}
+        return reader.load_data()
+    except Exception as e:
+        import sys, traceback
+
+        # Catch the error if the data dir is empty
+        # and return as empty document list
+        _, _, exc_traceback = sys.exc_info()
+        function_name = traceback.extract_tb(exc_traceback)[-1].name
+        if function_name == "_add_files":
+            logger.warning(
+                f"Failed to load file documents, error message: {e} . Return as empty document list."
+            )
+            return []
+        else:
+            # Raise the error if it is not the case of empty data dir
+            raise e

templates/components/ui/html/chat/chat-input.tsx

@@ -1,5 +1,7 @@
 "use client";
 
+import { Message } from "./chat-messages";
+
 export interface ChatInputProps {
   /** The current value of the input */
   input?: string;
@@ -12,7 +14,8 @@ export interface ChatInputProps {
   /** Form submission handler to automatically reset input and append a user message  */
   handleSubmit: (e: React.FormEvent<HTMLFormElement>) => void;
   isLoading: boolean;
-  multiModal?: boolean;
+  messages: Message[];
+  setInput?: (input: string) => void;
 }
 
 export default function ChatInput(props: ChatInputProps) {

templates/components/ui/html/chat/chat-messages.tsx

@@ -19,8 +19,12 @@ export default function ChatMessages({
   isLoading?: boolean;
   stop?: () => void;
   reload?: () => void;
+  append?: (
+    message: Message | Omit<Message, "id">,
+  ) => Promise<string | null | undefined>;
 }) {
   const scrollableChatContainerRef = useRef<HTMLDivElement>(null);
+  const lastMessage = messages[messages.length - 1];
 
   const scrollToBottom = () => {
     if (scrollableChatContainerRef.current) {
@@ -31,14 +35,14 @@ export default function ChatMessages({
 
   useEffect(() => {
     scrollToBottom();
-  }, [messages.length]);
+  }, [messages.length, lastMessage]);
 
   return (
-    <div className="w-full max-w-5xl p-4 bg-white rounded-xl shadow-xl">
-      <div
-        className="flex flex-col gap-5 divide-y h-[50vh] overflow-auto"
-        ref={scrollableChatContainerRef}
-      >
+    <div
+      className="flex-1 w-full max-w-5xl p-4 bg-white rounded-xl shadow-xl overflow-auto"
+      ref={scrollableChatContainerRef}
+    >
+      <div className="flex flex-col gap-5 divide-y">
         {messages.map((m: Message) => (
           <ChatItem key={m.id} {...m} />
         ))}

templates/components/ui/html/chat/hooks/use-config.ts

@@ -0,0 +1,30 @@
+"use client";
+
+import { useEffect, useMemo, useState } from "react";
+
+export interface ChatConfig {
+  chatAPI?: string;
+  starterQuestions?: string[];
+}
+
+export function useClientConfig() {
+  const API_ROUTE = "/api/chat/config";
+  const chatAPI = process.env.NEXT_PUBLIC_CHAT_API;
+  const [config, setConfig] = useState<ChatConfig>({
+    chatAPI,
+  });
+
+  const configAPI = useMemo(() => {
+    const backendOrigin = chatAPI ? new URL(chatAPI).origin : "";
+    return `${backendOrigin}${API_ROUTE}`;
+  }, [chatAPI]);
+
+  useEffect(() => {
+    fetch(configAPI)
+      .then((response) => response.json())
+      .then((data) => setConfig({ ...data, chatAPI }))
+      .catch((error) => console.error("Error fetching config", error));
+  }, [chatAPI, configAPI]);
+
+  return config;
+}

templates/components/vectordbs/python/astra/generate.py

@@ -1,37 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.astra_db import AstraDBVectorStore
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    documents = get_documents()
-    store = AstraDBVectorStore(
-        token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
-        api_endpoint=os.environ["ASTRA_DB_ENDPOINT"],
-        collection_name=os.environ["ASTRA_DB_COLLECTION"],
-        embedding_dimension=int(os.environ["EMBEDDING_DIM"]),
-    )
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(f"Successfully created embeddings in the AstraDB")
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/astra/index.py

@@ -1,21 +0,0 @@
-import logging
-import os
-
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.astra_db import AstraDBVectorStore
-
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to index from AstraDB...")
-    store = AstraDBVectorStore(
-        token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
-        api_endpoint=os.environ["ASTRA_DB_ENDPOINT"],
-        collection_name=os.environ["ASTRA_DB_COLLECTION"],
-        embedding_dimension=int(os.environ["EMBEDDING_DIM"]),
-    )
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to index from AstraDB.")
-    return index

templates/components/vectordbs/python/astra/vectordb.py

@@ -0,0 +1,20 @@
+import os
+from llama_index.vector_stores.astra_db import AstraDBVectorStore
+
+
+def get_vector_store():
+    endpoint = os.getenv("ASTRA_DB_ENDPOINT")
+    token = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
+    collection = os.getenv("ASTRA_DB_COLLECTION")
+    if not endpoint or not token or not collection:
+        raise ValueError(
+            "Please config ASTRA_DB_ENDPOINT, ASTRA_DB_APPLICATION_TOKEN and ASTRA_DB_COLLECTION"
+            " to your environment variables or config them in the .env file"
+        )
+    store = AstraDBVectorStore(
+        token=token,
+        api_endpoint=endpoint,
+        collection_name=collection,
+        embedding_dimension=int(os.getenv("EMBEDDING_DIM")),
+    )
+    return store

templates/components/vectordbs/python/chroma/vectordb.py

@@ -0,0 +1,24 @@
+import os
+from llama_index.vector_stores.chroma import ChromaVectorStore
+
+
+def get_vector_store():
+    collection_name = os.getenv("CHROMA_COLLECTION", "default")
+    chroma_path = os.getenv("CHROMA_PATH")
+    # if CHROMA_PATH is set, use a local ChromaVectorStore from the path
+    # otherwise, use a remote ChromaVectorStore (ChromaDB Cloud is not supported yet)
+    if chroma_path:
+        store = ChromaVectorStore.from_params(
+            persist_dir=chroma_path, collection_name=collection_name
+        )
+    else:
+        if not os.getenv("CHROMA_HOST") or not os.getenv("CHROMA_PORT"):
+            raise ValueError(
+                "Please provide either CHROMA_PATH or CHROMA_HOST and CHROMA_PORT"
+            )
+        store = ChromaVectorStore.from_params(
+            host=os.getenv("CHROMA_HOST"),
+            port=int(os.getenv("CHROMA_PORT")),
+            collection_name=collection_name,
+        )
+    return store

templates/components/vectordbs/python/milvus/generate.py

@@ -1,39 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.milvus import MilvusVectorStore
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    # load the documents and create the index
-    documents = get_documents()
-    store = MilvusVectorStore(
-        uri=os.environ["MILVUS_ADDRESS"],
-        user=os.getenv("MILVUS_USERNAME"),
-        password=os.getenv("MILVUS_PASSWORD"),
-        collection_name=os.getenv("MILVUS_COLLECTION"),
-        dim=int(os.getenv("EMBEDDING_DIM")),
-    )
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(f"Successfully created embeddings in the Milvus")
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/milvus/index.py

@@ -1,22 +0,0 @@
-import logging
-import os
-
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.milvus import MilvusVectorStore
-
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to index from Milvus...")
-    store = MilvusVectorStore(
-        uri=os.getenv("MILVUS_ADDRESS"),
-        user=os.getenv("MILVUS_USERNAME"),
-        password=os.getenv("MILVUS_PASSWORD"),
-        collection_name=os.getenv("MILVUS_COLLECTION"),
-        dim=int(os.getenv("EMBEDDING_DIM")),
-    )
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to index from Milvus.")
-    return index

templates/components/vectordbs/python/milvus/vectordb.py

@@ -0,0 +1,20 @@
+import os
+from llama_index.vector_stores.milvus import MilvusVectorStore
+
+
+def get_vector_store():
+    address = os.getenv("MILVUS_ADDRESS")
+    collection = os.getenv("MILVUS_COLLECTION")
+    if not address or not collection:
+        raise ValueError(
+            "Please set MILVUS_ADDRESS and MILVUS_COLLECTION to your environment variables"
+            " or config them in the .env file"
+        )
+    store = MilvusVectorStore(
+        uri=address,
+        user=os.getenv("MILVUS_USERNAME"),
+        password=os.getenv("MILVUS_PASSWORD"),
+        collection_name=collection,
+        dim=int(os.getenv("EMBEDDING_DIM")),
+    )
+    return store

templates/components/vectordbs/python/mongo/generate.py

@@ -1,43 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    # load the documents and create the index
-    documents = get_documents()
-    store = MongoDBAtlasVectorSearch(
-        db_name=os.environ["MONGODB_DATABASE"],
-        collection_name=os.environ["MONGODB_VECTORS"],
-        index_name=os.environ["MONGODB_VECTOR_INDEX"],
-    )
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(
-        f"Successfully created embeddings in the MongoDB collection {os.environ['MONGODB_VECTORS']}"
-    )
-    logger.info(
-        """IMPORTANT: You can't query your index yet because you need to create a vector search index in MongoDB's UI now.
-See https://github.com/run-llama/mongodb-demo/tree/main?tab=readme-ov-file#create-a-vector-search-index"""
-    )
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/mongo/index.py

@@ -1,20 +0,0 @@
-import logging
-import os
-
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
-
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to index from MongoDB...")
-    store = MongoDBAtlasVectorSearch(
-        db_name=os.environ["MONGODB_DATABASE"],
-        collection_name=os.environ["MONGODB_VECTORS"],
-        index_name=os.environ["MONGODB_VECTOR_INDEX"],
-    )
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to index from MongoDB.")
-    return index

templates/components/vectordbs/python/mongo/vectordb.py

@@ -0,0 +1,20 @@
+import os
+from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
+
+
+def get_vector_store():
+    db_uri = os.getenv("MONGODB_URI")
+    db_name = os.getenv("MONGODB_DATABASE")
+    collection_name = os.getenv("MONGODB_VECTORS")
+    index_name = os.getenv("MONGODB_VECTOR_INDEX")
+    if not db_uri or not db_name or not collection_name or not index_name:
+        raise ValueError(
+            "Please set MONGODB_URI, MONGODB_DATABASE, MONGODB_VECTORS, and MONGODB_VECTOR_INDEX"
+            " to your environment variables or config them in .env file"
+        )
+    store = MongoDBAtlasVectorSearch(
+        db_name=db_name,
+        collection_name=collection_name,
+        index_name=index_name,
+    )
+    return store

templates/components/vectordbs/python/none/constants.py

@@ -1 +0,0 @@
-STORAGE_DIR = "storage"  # directory to cache the generated index

templates/components/vectordbs/python/none/generate.py

@@ -2,11 +2,11 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
+import os
 import logging
 from llama_index.core.indices import (
     VectorStoreIndex,
 )
-from app.engine.constants import STORAGE_DIR
 from app.engine.loaders import get_documents
 from app.settings import init_settings
 
@@ -18,14 +18,15 @@ logger = logging.getLogger()
 def generate_datasource():
     init_settings()
     logger.info("Creating new index")
+    storage_dir = os.environ.get("STORAGE_DIR", "storage")
     # load the documents and create the index
     documents = get_documents()
     index = VectorStoreIndex.from_documents(
         documents,
     )
     # store it for later
-    index.storage_context.persist(STORAGE_DIR)
-    logger.info(f"Finished creating new index. Stored in {STORAGE_DIR}")
+    index.storage_context.persist(storage_dir)
+    logger.info(f"Finished creating new index. Stored in {storage_dir}")
 
 
 if __name__ == "__main__":

templates/components/vectordbs/python/none/index.py

@@ -1,20 +1,30 @@
-import logging
 import os
+import logging
+from datetime import timedelta
 
-from app.engine.constants import STORAGE_DIR
+from cachetools import cached, TTLCache
 from llama_index.core.storage import StorageContext
 from llama_index.core.indices import load_index_from_storage
 
 logger = logging.getLogger("uvicorn")
 
 
+@cached(
+    TTLCache(maxsize=10, ttl=timedelta(minutes=5).total_seconds()),
+    key=lambda *args, **kwargs: "global_storage_context",
+)
+def get_storage_context(persist_dir: str) -> StorageContext:
+    return StorageContext.from_defaults(persist_dir=persist_dir)
+
+
 def get_index():
+    storage_dir = os.getenv("STORAGE_DIR", "storage")
     # check if storage already exists
-    if not os.path.exists(STORAGE_DIR):
+    if not os.path.exists(storage_dir):
         return None
     # load the existing index
-    logger.info(f"Loading index from {STORAGE_DIR}...")
-    storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
+    logger.info(f"Loading index from {storage_dir}...")
+    storage_context = get_storage_context(storage_dir)
     index = load_index_from_storage(storage_context)
-    logger.info(f"Finished loading index from {STORAGE_DIR}")
+    logger.info(f"Finished loading index from {storage_dir}")
     return index

templates/components/vectordbs/python/pg/constants.py

@@ -1,2 +0,0 @@
-PGVECTOR_SCHEMA = "public"
-PGVECTOR_TABLE = "llamaindex_embedding"

templates/components/vectordbs/python/pg/generate.py

@@ -1,35 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import logging
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.core.storage import StorageContext
-
-from app.engine.loaders import get_documents
-from app.settings import init_settings
-from app.engine.utils import init_pg_vector_store_from_env
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    # load the documents and create the index
-    documents = get_documents()
-    store = init_pg_vector_store_from_env()
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(
-        f"Successfully created embeddings in the PG vector store, schema={store.schema_name} table={store.table_name}"
-    )
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/pg/index.py

@@ -1,13 +0,0 @@
-import logging
-from llama_index.core.indices.vector_store import VectorStoreIndex
-from app.engine.utils import init_pg_vector_store_from_env
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to index from PGVector...")
-    store = init_pg_vector_store_from_env()
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to index from PGVector.")
-    return index

templates/components/vectordbs/python/pg/utils.py

@@ -1,27 +0,0 @@
-import os
-from llama_index.vector_stores.postgres import PGVectorStore
-from urllib.parse import urlparse
-from app.engine.constants import PGVECTOR_SCHEMA, PGVECTOR_TABLE
-
-
-def init_pg_vector_store_from_env():
-    original_conn_string = os.environ.get("PG_CONNECTION_STRING")
-    if original_conn_string is None or original_conn_string == "":
-        raise ValueError("PG_CONNECTION_STRING environment variable is not set.")
-
-    # The PGVectorStore requires both two connection strings, one for psycopg2 and one for asyncpg
-    # Update the configured scheme with the psycopg2 and asyncpg schemes
-    original_scheme = urlparse(original_conn_string).scheme + "://"
-    conn_string = original_conn_string.replace(
-        original_scheme, "postgresql+psycopg2://"
-    )
-    async_conn_string = original_conn_string.replace(
-        original_scheme, "postgresql+asyncpg://"
-    )
-
-    return PGVectorStore(
-        connection_string=conn_string,
-        async_connection_string=async_conn_string,
-        schema_name=PGVECTOR_SCHEMA,
-        table_name=PGVECTOR_TABLE,
-    )

templates/components/vectordbs/python/pg/vectordb.py

@@ -0,0 +1,37 @@
+import os
+from llama_index.vector_stores.postgres import PGVectorStore
+from urllib.parse import urlparse
+
+PGVECTOR_SCHEMA = "public"
+PGVECTOR_TABLE = "llamaindex_embedding"
+
+vector_store: PGVectorStore = None
+
+
+def get_vector_store():
+    global vector_store
+
+    if vector_store is None:
+        original_conn_string = os.environ.get("PG_CONNECTION_STRING")
+        if original_conn_string is None or original_conn_string == "":
+            raise ValueError("PG_CONNECTION_STRING environment variable is not set.")
+
+        # The PGVectorStore requires both two connection strings, one for psycopg2 and one for asyncpg
+        # Update the configured scheme with the psycopg2 and asyncpg schemes
+        original_scheme = urlparse(original_conn_string).scheme + "://"
+        conn_string = original_conn_string.replace(
+            original_scheme, "postgresql+psycopg2://"
+        )
+        async_conn_string = original_conn_string.replace(
+            original_scheme, "postgresql+asyncpg://"
+        )
+
+        vector_store = PGVectorStore(
+            connection_string=conn_string,
+            async_connection_string=async_conn_string,
+            schema_name=PGVECTOR_SCHEMA,
+            table_name=PGVECTOR_TABLE,
+            embed_dim=int(os.environ.get("EMBEDDING_DIM", 1024)),
+        )
+
+    return vector_store

templates/components/vectordbs/python/pinecone/generate.py

@@ -1,39 +0,0 @@
-from dotenv import load_dotenv
-
-load_dotenv()
-
-import os
-import logging
-from llama_index.core.storage import StorageContext
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.pinecone import PineconeVectorStore
-from app.settings import init_settings
-from app.engine.loaders import get_documents
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index")
-    # load the documents and create the index
-    documents = get_documents()
-    store = PineconeVectorStore(
-        api_key=os.environ["PINECONE_API_KEY"],
-        index_name=os.environ["PINECONE_INDEX_NAME"],
-        environment=os.environ["PINECONE_ENVIRONMENT"],
-    )
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(
-        f"Successfully created embeddings and save to your Pinecone index {os.environ['PINECONE_INDEX_NAME']}"
-    )
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/pinecone/index.py

@@ -1,20 +0,0 @@
-import logging
-import os
-
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.pinecone import PineconeVectorStore
-
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to index from Pinecone...")
-    store = PineconeVectorStore(
-        api_key=os.environ["PINECONE_API_KEY"],
-        index_name=os.environ["PINECONE_INDEX_NAME"],
-        environment=os.environ["PINECONE_ENVIRONMENT"],
-    )
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to index from Pinecone.")
-    return index

templates/components/vectordbs/python/pinecone/vectordb.py

@@ -0,0 +1,19 @@
+import os
+from llama_index.vector_stores.pinecone import PineconeVectorStore
+
+
+def get_vector_store():
+    api_key = os.getenv("PINECONE_API_KEY")
+    index_name = os.getenv("PINECONE_INDEX_NAME")
+    environment = os.getenv("PINECONE_ENVIRONMENT")
+    if not api_key or not index_name or not environment:
+        raise ValueError(
+            "Please set PINECONE_API_KEY, PINECONE_INDEX_NAME, and PINECONE_ENVIRONMENT"
+            " to your environment variables or config them in the .env file"
+        )
+    store = PineconeVectorStore(
+        api_key=api_key,
+        index_name=index_name,
+        environment=environment,
+    )
+    return store

templates/components/vectordbs/python/qdrant/generate.py

@@ -1,37 +0,0 @@
-import logging
-import os
-from app.engine.loaders import get_documents
-from app.settings import init_settings
-from dotenv import load_dotenv
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.core.storage import StorageContext
-from llama_index.vector_stores.qdrant import QdrantVectorStore
-load_dotenv()
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger()
-
-
-def generate_datasource():
-    init_settings()
-    logger.info("Creating new index with Qdrant")
-    # load the documents and create the index
-    documents = get_documents()
-    store = QdrantVectorStore(
-        collection_name=os.getenv("QDRANT_COLLECTION"),
-        url=os.getenv("QDRANT_URL"),
-        api_key=os.getenv("QDRANT_API_KEY"),
-    )
-    storage_context = StorageContext.from_defaults(vector_store=store)
-    VectorStoreIndex.from_documents(
-        documents,
-        storage_context=storage_context,
-        show_progress=True,  # this will show you a progress bar as the embeddings are created
-    )
-    logger.info(
-        f"Successfully uploaded documents to the {os.getenv('QDRANT_COLLECTION')} collection."
-    )
-
-
-if __name__ == "__main__":
-    generate_datasource()

templates/components/vectordbs/python/qdrant/index.py

@@ -1,20 +0,0 @@
-import logging
-import os
-
-from llama_index.core.indices import VectorStoreIndex
-from llama_index.vector_stores.qdrant import QdrantVectorStore
-
-
-logger = logging.getLogger("uvicorn")
-
-
-def get_index():
-    logger.info("Connecting to Qdrant collection..")
-    store = QdrantVectorStore(
-        collection_name=os.getenv("QDRANT_COLLECTION"),
-        url=os.getenv("QDRANT_URL"),
-        api_key=os.getenv("QDRANT_API_KEY"),
-    )
-    index = VectorStoreIndex.from_vector_store(store)
-    logger.info("Finished connecting to Qdrant collection.")
-    return index

templates/components/vectordbs/python/qdrant/vectordb.py

@@ -0,0 +1,19 @@
+import os
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+
+
+def get_vector_store():
+    collection_name = os.getenv("QDRANT_COLLECTION")
+    url = os.getenv("QDRANT_URL")
+    api_key = os.getenv("QDRANT_API_KEY")
+    if not collection_name or not url:
+        raise ValueError(
+            "Please set QDRANT_COLLECTION, QDRANT_URL"
+            " to your environment variables or config them in the .env file"
+        )
+    store = QdrantVectorStore(
+        collection_name=collection_name,
+        url=url,
+        api_key=api_key,
+    )
+    return store

templates/components/vectordbs/typescript/astra/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  AstraDBVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { AstraDBVectorStore } from "llamaindex/storage/vectorStore/AstraDBVectorStore";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
 import { checkRequiredEnvVars } from "./shared";

templates/components/vectordbs/typescript/astra/index.ts

@@ -1,5 +1,6 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
-import { AstraDBVectorStore, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { AstraDBVectorStore } from "llamaindex/storage/vectorStore/AstraDBVectorStore";
 import { checkRequiredEnvVars } from "./shared";
 
 export async function getDataSource() {

templates/components/vectordbs/typescript/chroma/generate.ts

@@ -0,0 +1,37 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { ChromaVectorStore } from "llamaindex/storage/vectorStore/ChromaVectorStore";
+import { getDocuments } from "./loader";
+import { initSettings } from "./settings";
+import { checkRequiredEnvVars } from "./shared";
+
+dotenv.config();
+
+async function loadAndIndex() {
+  // load objects from storage and convert them into LlamaIndex Document objects
+  const documents = await getDocuments();
+
+  // create vector store
+  const chromaUri = `http://${process.env.CHROMA_HOST}:${process.env.CHROMA_PORT}`;
+
+  const vectorStore = new ChromaVectorStore({
+    collectionName: process.env.CHROMA_COLLECTION,
+    chromaClientParams: { path: chromaUri },
+  });
+
+  // create index from all the Documentss and store them in Pinecone
+  console.log("Start creating embeddings...");
+  const storageContext = await storageContextFromDefaults({ vectorStore });
+  await VectorStoreIndex.fromDocuments(documents, { storageContext });
+  console.log(
+    "Successfully created embeddings and save to your ChromaDB index.",
+  );
+}
+
+(async () => {
+  checkRequiredEnvVars();
+  initSettings();
+  await loadAndIndex();
+  console.log("Finished generating storage.");
+})();

templates/components/vectordbs/typescript/chroma/index.ts

@@ -0,0 +1,16 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import { VectorStoreIndex } from "llamaindex";
+import { ChromaVectorStore } from "llamaindex/storage/vectorStore/ChromaVectorStore";
+import { checkRequiredEnvVars } from "./shared";
+
+export async function getDataSource() {
+  checkRequiredEnvVars();
+  const chromaUri = `http://${process.env.CHROMA_HOST}:${process.env.CHROMA_PORT}`;
+
+  const store = new ChromaVectorStore({
+    collectionName: process.env.CHROMA_COLLECTION,
+    chromaClientParams: { path: chromaUri },
+  });
+
+  return await VectorStoreIndex.fromVectorStore(store);
+}

templates/components/vectordbs/typescript/chroma/shared.ts

@@ -0,0 +1,18 @@
+const REQUIRED_ENV_VARS = ["CHROMA_COLLECTION", "CHROMA_HOST", "CHROMA_PORT"];
+
+export function checkRequiredEnvVars() {
+  const missingEnvVars = REQUIRED_ENV_VARS.filter((envVar) => {
+    return !process.env[envVar];
+  });
+
+  if (missingEnvVars.length > 0) {
+    console.log(
+      `The following environment variables are required but missing: ${missingEnvVars.join(
+        ", ",
+      )}`,
+    );
+    throw new Error(
+      `Missing environment variables: ${missingEnvVars.join(", ")}`,
+    );
+  }
+}

templates/components/vectordbs/typescript/milvus/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  MilvusVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { MilvusVectorStore } from "llamaindex/storage/vectorStore/MilvusVectorStore";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
 import { checkRequiredEnvVars, getMilvusClient } from "./shared";

templates/components/vectordbs/typescript/milvus/index.ts

@@ -1,4 +1,5 @@
-import { MilvusVectorStore, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { MilvusVectorStore } from "llamaindex/storage/vectorStore/MilvusVectorStore";
 import { checkRequiredEnvVars, getMilvusClient } from "./shared";
 
 export async function getDataSource() {

templates/components/vectordbs/typescript/mongo/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  MongoDBAtlasVectorSearch,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { MongoDBAtlasVectorSearch } from "llamaindex/storage/vectorStore/MongoDBAtlasVectorSearch";
 import { MongoClient } from "mongodb";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
@@ -12,7 +9,7 @@ import { checkRequiredEnvVars } from "./shared";
 
 dotenv.config();
 
-const mongoUri = process.env.MONGO_URI!;
+const mongoUri = process.env.MONGODB_URI!;
 const databaseName = process.env.MONGODB_DATABASE!;
 const vectorCollectionName = process.env.MONGODB_VECTORS!;
 const indexName = process.env.MONGODB_VECTOR_INDEX;

templates/components/vectordbs/typescript/mongo/index.ts

@@ -1,5 +1,6 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
-import { MongoDBAtlasVectorSearch, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { MongoDBAtlasVectorSearch } from "llamaindex/storage/vectorStore/MongoDBAtlasVectorSearch";
 import { MongoClient } from "mongodb";
 import { checkRequiredEnvVars } from "./shared";
 

templates/components/vectordbs/typescript/mongo/shared.ts

@@ -1,5 +1,5 @@
 const REQUIRED_ENV_VARS = [
-  "MONGO_URI",
+  "MONGODB_URI",
   "MONGODB_DATABASE",
   "MONGODB_VECTORS",
   "MONGODB_VECTOR_INDEX",

templates/components/vectordbs/typescript/none/generate.ts

@@ -1,4 +1,5 @@
-import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
 
 import * as dotenv from "dotenv";
 

templates/components/vectordbs/typescript/none/index.ts

@@ -1,8 +1,5 @@
-import {
-  SimpleDocumentStore,
-  storageContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { SimpleDocumentStore, VectorStoreIndex } from "llamaindex";
+import { storageContextFromDefaults } from "llamaindex/storage/StorageContext";
 import { STORAGE_CACHE_DIR } from "./shared";
 
 export async function getDataSource() {

templates/components/vectordbs/typescript/pg/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  PGVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { PGVectorStore } from "llamaindex/storage/vectorStore/PGVectorStore";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
 import {

templates/components/vectordbs/typescript/pg/index.ts

@@ -1,5 +1,6 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
-import { PGVectorStore, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { PGVectorStore } from "llamaindex/storage/vectorStore/PGVectorStore";
 import {
   PGVECTOR_SCHEMA,
   PGVECTOR_TABLE,

templates/components/vectordbs/typescript/pg/shared.ts

@@ -2,7 +2,7 @@ export const PGVECTOR_COLLECTION = "data";
 export const PGVECTOR_SCHEMA = "public";
 export const PGVECTOR_TABLE = "llamaindex_embedding";
 
-const REQUIRED_ENV_VARS = ["PG_CONNECTION_STRING", "OPENAI_API_KEY"];
+const REQUIRED_ENV_VARS = ["PG_CONNECTION_STRING"];
 
 export function checkRequiredEnvVars() {
   const missingEnvVars = REQUIRED_ENV_VARS.filter((envVar) => {

templates/components/vectordbs/typescript/pinecone/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  PineconeVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { PineconeVectorStore } from "llamaindex/storage/vectorStore/PineconeVectorStore";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
 import { checkRequiredEnvVars } from "./shared";

templates/components/vectordbs/typescript/pinecone/index.ts

@@ -1,5 +1,6 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
-import { PineconeVectorStore, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { PineconeVectorStore } from "llamaindex/storage/vectorStore/PineconeVectorStore";
 import { checkRequiredEnvVars } from "./shared";
 
 export async function getDataSource() {

templates/components/vectordbs/typescript/qdrant/generate.ts

@@ -1,10 +1,7 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  QdrantVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { QdrantVectorStore } from "llamaindex/storage/vectorStore/QdrantVectorStore";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
 import { checkRequiredEnvVars, getQdrantClient } from "./shared";
@@ -18,7 +15,10 @@ async function loadAndIndex() {
   const documents = await getDocuments();
 
   // Connect to Qdrant
-  const vectorStore = new QdrantVectorStore(collectionName, getQdrantClient());
+  const vectorStore = new QdrantVectorStore({
+    collectionName,
+    client: getQdrantClient(),
+  });
 
   const storageContext = await storageContextFromDefaults({ vectorStore });
   await VectorStoreIndex.fromDocuments(documents, {

templates/components/vectordbs/typescript/qdrant/index.ts

@@ -1,5 +1,6 @@
 import * as dotenv from "dotenv";
-import { QdrantVectorStore, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { QdrantVectorStore } from "llamaindex/storage/vectorStore/QdrantVectorStore";
 import { checkRequiredEnvVars, getQdrantClient } from "./shared";
 
 dotenv.config();
@@ -7,7 +8,10 @@ dotenv.config();
 export async function getDataSource() {
   checkRequiredEnvVars();
   const collectionName = process.env.QDRANT_COLLECTION;
-  const store = new QdrantVectorStore(collectionName, getQdrantClient());
+  const store = new QdrantVectorStore({
+    collectionName,
+    client: getQdrantClient(),
+  });
 
   return await VectorStoreIndex.fromVectorStore(store);
 }

templates/types/streaming/express/gitignore

@@ -1,3 +1,5 @@
 # local env files
 .env
-node_modules/
+node_modules/
+
+tool-output/