chore: bump LITS 0.9 (#505)

---------
Co-authored-by: leehuwuj <[email protected]>

.changeset/grumpy-snails-listen.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+chore: bump LITS 0.9

helpers/typescript.ts

@@ -6,7 +6,7 @@ import { assetRelocator, copy } from "../helpers/copy";
 import { callPackageManager } from "../helpers/install";
 import { templatesDir } from "./dir";
 import { PackageManager } from "./get-pkg-manager";
-import { InstallTemplateArgs } from "./types";
+import { InstallTemplateArgs, ModelProvider, TemplateVectorDB } from "./types";
 
 /**
  * Install a LlamaIndex internal template to a given `root` directory.
@@ -27,6 +27,7 @@ export const installTSTemplate = async ({
   dataSources,
   useLlamaParse,
   useCase,
+  modelConfig,
 }: InstallTemplateArgs & { backend: boolean }) => {
   console.log(bold(`Using ${packageManager}.`));
 
@@ -181,6 +182,12 @@ export const installTSTemplate = async ({
     cwd: path.join(compPath, "loaders", "typescript", loaderFolder),
   });
 
+  // copy provider settings
+  await copy("**", enginePath, {
+    parents: true,
+    cwd: path.join(compPath, "providers", "typescript", modelConfig.provider),
+  });
+
   // Select and copy engine code based on data sources and tools
   let engine;
   tools = tools ?? [];
@@ -239,6 +246,8 @@ export const installTSTemplate = async ({
     ui,
     observability,
     vectorDb,
+    backend,
+    modelConfig,
   });
 
   if (
@@ -249,6 +258,68 @@ export const installTSTemplate = async ({
   }
 };
 
+const providerDependencies: {
+  [key in ModelProvider]?: Record<string, string>;
+} = {
+  openai: {
+    "@llamaindex/openai": "^0.1.52",
+  },
+  gemini: {
+    "@llamaindex/google": "^0.0.7",
+  },
+  ollama: {
+    "@llamaindex/ollama": "^0.0.40",
+  },
+  mistral: {
+    "@llamaindex/mistral": "^0.0.5",
+  },
+  "azure-openai": {
+    "@llamaindex/openai": "^0.1.52",
+  },
+  groq: {
+    "@llamaindex/groq": "^0.0.51",
+    "@llamaindex/huggingface": "^0.0.36", // groq uses huggingface as default embedding model
+  },
+  anthropic: {
+    "@llamaindex/anthropic": "^0.1.0",
+    "@llamaindex/huggingface": "^0.0.36", // anthropic uses huggingface as default embedding model
+  },
+};
+
+const vectorDbDependencies: Record<TemplateVectorDB, Record<string, string>> = {
+  astra: {
+    "@llamaindex/astra": "^0.0.5",
+  },
+  chroma: {
+    "@llamaindex/chroma": "^0.0.5",
+  },
+  llamacloud: {},
+  milvus: {
+    "@zilliz/milvus2-sdk-node": "^2.4.6",
+    "@llamaindex/milvus": "^0.1.0",
+  },
+  mongo: {
+    mongodb: "6.7.0",
+    "@llamaindex/mongodb": "^0.0.5",
+  },
+  none: {},
+  pg: {
+    pg: "^8.12.0",
+    pgvector: "^0.2.0",
+    "@llamaindex/postgres": "^0.0.33",
+  },
+  pinecone: {
+    "@llamaindex/pinecone": "^0.0.5",
+  },
+  qdrant: {
+    "@qdrant/js-client-rest": "^1.11.0",
+    "@llamaindex/qdrant": "^0.1.0",
+  },
+  weaviate: {
+    "@llamaindex/weaviate": "^0.0.5",
+  },
+};
+
 async function updatePackageJson({
   root,
   appName,
@@ -258,6 +329,8 @@ async function updatePackageJson({
   ui,
   observability,
   vectorDb,
+  backend,
+  modelConfig,
 }: Pick<
   InstallTemplateArgs,
   | "root"
@@ -267,8 +340,10 @@ async function updatePackageJson({
   | "ui"
   | "observability"
   | "vectorDb"
+  | "modelConfig"
 > & {
   relativeEngineDestPath: string;
+  backend: boolean;
 }): Promise<any> {
   const packageJsonFile = path.join(root, "package.json");
   const packageJson: any = JSON.parse(
@@ -308,32 +383,25 @@ async function updatePackageJson({
     };
   }
 
-  if (vectorDb === "pg") {
+  if (backend) {
     packageJson.dependencies = {
       ...packageJson.dependencies,
-      pg: "^8.12.0",
-      pgvector: "^0.2.0",
+      "@llamaindex/readers": "^2.0.0",
     };
-  }
 
-  if (vectorDb === "qdrant") {
-    packageJson.dependencies = {
-      ...packageJson.dependencies,
-      "@qdrant/js-client-rest": "^1.11.0",
-    };
-  }
-  if (vectorDb === "mongo") {
-    packageJson.dependencies = {
-      ...packageJson.dependencies,
-      mongodb: "^6.7.0",
-    };
-  }
+    if (vectorDb && vectorDb in vectorDbDependencies) {
+      packageJson.dependencies = {
+        ...packageJson.dependencies,
+        ...vectorDbDependencies[vectorDb],
+      };
+    }
 
-  if (vectorDb === "milvus") {
-    packageJson.dependencies = {
-      ...packageJson.dependencies,
-      "@zilliz/milvus2-sdk-node": "^2.4.6",
-    };
+    if (modelConfig.provider && modelConfig.provider in providerDependencies) {
+      packageJson.dependencies = {
+        ...packageJson.dependencies,
+        ...providerDependencies[modelConfig.provider],
+      };
+    }
   }
 
   if (observability === "traceloop") {

templates/components/loaders/typescript/file/loader.ts

@@ -1,7 +1,7 @@
 import {
   FILE_EXT_TO_READER,
   SimpleDirectoryReader,
-} from "llamaindex/readers/index";
+} from "@llamaindex/readers/directory";
 
 export const DATA_DIR = "./data";
 

templates/components/loaders/typescript/llama_parse/loader.ts

@@ -1,8 +1,8 @@
-import { LlamaParseReader } from "llamaindex";
 import {
   FILE_EXT_TO_READER,
   SimpleDirectoryReader,
-} from "llamaindex/readers/index";
+} from "@llamaindex/readers/directory";
+import { LlamaParseReader } from "llamaindex";
 
 export const DATA_DIR = "./data";
 

templates/components/providers/typescript/anthropic/provider.ts

@@ -0,0 +1,19 @@
+import {
+  ALL_AVAILABLE_ANTHROPIC_MODELS,
+  Anthropic,
+} from "@llamaindex/anthropic";
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  const embedModelMap: Record<string, string> = {
+    "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
+    "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
+  };
+  Settings.llm = new Anthropic({
+    model: process.env.MODEL as keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS,
+  });
+  Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
+  });
+}

templates/components/providers/typescript/azure-openai/provider.ts

@@ -0,0 +1,49 @@
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  // Map Azure OpenAI model names to OpenAI model names (only for TS)
+  const AZURE_OPENAI_MODEL_MAP: Record<string, string> = {
+    "gpt-35-turbo": "gpt-3.5-turbo",
+    "gpt-35-turbo-16k": "gpt-3.5-turbo-16k",
+    "gpt-4o": "gpt-4o",
+    "gpt-4": "gpt-4",
+    "gpt-4-32k": "gpt-4-32k",
+    "gpt-4-turbo": "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09": "gpt-4-turbo",
+    "gpt-4-vision-preview": "gpt-4-vision-preview",
+    "gpt-4-1106-preview": "gpt-4-1106-preview",
+    "gpt-4o-2024-05-13": "gpt-4o-2024-05-13",
+  };
+
+  const azureConfig = {
+    apiKey: process.env.AZURE_OPENAI_KEY,
+    endpoint: process.env.AZURE_OPENAI_ENDPOINT,
+    apiVersion:
+      process.env.AZURE_OPENAI_API_VERSION || process.env.OPENAI_API_VERSION,
+  };
+
+  Settings.llm = new OpenAI({
+    model:
+      AZURE_OPENAI_MODEL_MAP[process.env.MODEL ?? "gpt-35-turbo"] ??
+      "gpt-3.5-turbo",
+    maxTokens: process.env.LLM_MAX_TOKENS
+      ? Number(process.env.LLM_MAX_TOKENS)
+      : undefined,
+    azure: {
+      ...azureConfig,
+      deployment: process.env.AZURE_OPENAI_LLM_DEPLOYMENT,
+    },
+  });
+
+  Settings.embedModel = new OpenAIEmbedding({
+    model: process.env.EMBEDDING_MODEL,
+    dimensions: process.env.EMBEDDING_DIM
+      ? parseInt(process.env.EMBEDDING_DIM)
+      : undefined,
+    azure: {
+      ...azureConfig,
+      deployment: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
+    },
+  });
+}

templates/components/providers/typescript/gemini/provider.ts

@@ -0,0 +1,16 @@
+import {
+  Gemini,
+  GEMINI_EMBEDDING_MODEL,
+  GEMINI_MODEL,
+  GeminiEmbedding,
+} from "@llamaindex/google";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  Settings.llm = new Gemini({
+    model: process.env.MODEL as GEMINI_MODEL,
+  });
+  Settings.embedModel = new GeminiEmbedding({
+    model: process.env.EMBEDDING_MODEL as GEMINI_EMBEDDING_MODEL,
+  });
+}

templates/components/providers/typescript/groq/provider.ts

@@ -0,0 +1,18 @@
+import { Groq } from "@llamaindex/groq";
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  const embedModelMap: Record<string, string> = {
+    "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
+    "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
+  };
+
+  Settings.llm = new Groq({
+    model: process.env.MODEL!,
+  });
+
+  Settings.embedModel = new HuggingFaceEmbedding({
+    modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
+  });
+}

templates/components/providers/typescript/mistral/provider.ts

@@ -0,0 +1,16 @@
+import {
+  ALL_AVAILABLE_MISTRAL_MODELS,
+  MistralAI,
+  MistralAIEmbedding,
+  MistralAIEmbeddingModelType,
+} from "@llamaindex/mistral";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  Settings.llm = new MistralAI({
+    model: process.env.MODEL as keyof typeof ALL_AVAILABLE_MISTRAL_MODELS,
+  });
+  Settings.embedModel = new MistralAIEmbedding({
+    model: process.env.EMBEDDING_MODEL as MistralAIEmbeddingModelType,
+  });
+}

templates/components/providers/typescript/ollama/provider.ts

@@ -0,0 +1,16 @@
+import { Ollama, OllamaEmbedding } from "@llamaindex/ollama";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  const config = {
+    host: process.env.OLLAMA_BASE_URL ?? "http://127.0.0.1:11434",
+  };
+  Settings.llm = new Ollama({
+    model: process.env.MODEL ?? "",
+    config,
+  });
+  Settings.embedModel = new OllamaEmbedding({
+    model: process.env.EMBEDDING_MODEL ?? "",
+    config,
+  });
+}

templates/components/providers/typescript/openai/provider.ts

@@ -0,0 +1,17 @@
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
+
+export function setupProvider() {
+  Settings.llm = new OpenAI({
+    model: process.env.MODEL ?? "gpt-4o-mini",
+    maxTokens: process.env.LLM_MAX_TOKENS
+      ? Number(process.env.LLM_MAX_TOKENS)
+      : undefined,
+  });
+  Settings.embedModel = new OpenAIEmbedding({
+    model: process.env.EMBEDDING_MODEL,
+    dimensions: process.env.EMBEDDING_DIM
+      ? parseInt(process.env.EMBEDDING_DIM)
+      : undefined,
+  });
+}