RELEASING: Releasing 1 package(s)

Releases:
  create-llama@0.0.28

[skip ci]

.changeset/few-chicken-exercise.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Add "Start in VSCode" option to postInstallAction

.changeset/plenty-onions-relate.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Add devcontainers to generated code

.gitignore

@@ -45,6 +45,7 @@ playwright-report/
 blob-report/
 playwright/.cache/
 .tsbuildinfo
+packages/create-llama/e2e/cache
 
 # intellij
 **/.idea

.prettierignore

@@ -4,3 +4,4 @@ pnpm-lock.yaml
 lib/
 dist/
 .docusaurus/
+packages/create-llama/e2e/cache/

apps/docs/docs/modules/ingestion_pipeline/transformations.md

@@ -1,6 +1,6 @@
 # Transformations
 
-A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
+A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformation class has both a `transform` definition responsible for transforming the nodes.
 
 Currently, the following components are Transformation objects:
 

packages/create-llama/CHANGELOG.md

@@ -1,5 +1,13 @@
 # create-llama
 
+## 0.0.28
+
+### Patch Changes
+
+- 89a49f4: Add more config variables to .env file
+- fdf48dd: Add "Start in VSCode" option to postInstallAction
+- fdf48dd: Add devcontainers to generated code
+
 ## 0.0.27
 
 ### Patch Changes

packages/create-llama/create-app.ts

@@ -113,7 +113,7 @@ export async function createApp({
       path.join(root, "README.md"),
     );
   } else {
-    await installTemplate({ ...args, backend: true, forBackend: framework });
+    await installTemplate({ ...args, backend: true });
   }
 
   process.chdir(root);

packages/create-llama/e2e/utils.ts

@@ -175,7 +175,7 @@ export async function runCreateLlama(
 }
 
 export async function createTestDir() {
-  const cwd = path.join(__dirname, ".cache", crypto.randomUUID());
+  const cwd = path.join(__dirname, "cache", crypto.randomUUID());
   await mkdir(cwd, { recursive: true });
   return cwd;
 }

packages/create-llama/helpers/env-variables.ts

@@ -0,0 +1,241 @@
+import fs from "fs/promises";
+import path from "path";
+import {
+  FileSourceConfig,
+  TemplateDataSource,
+  TemplateFramework,
+  TemplateVectorDB,
+} from "./types";
+
+type EnvVar = {
+  name?: string;
+  description?: string;
+  value?: string;
+};
+
+const renderEnvVar = (envVars: EnvVar[]): string => {
+  return envVars.reduce(
+    (prev, env) =>
+      prev +
+      (env.description
+        ? `# ${env.description.replaceAll("\n", "\n# ")}\n`
+        : "") +
+      (env.name
+        ? env.value
+          ? `${env.name}=${env.value}\n\n`
+          : `# ${env.name}=\n\n`
+        : ""),
+    "",
+  );
+};
+
+const getVectorDBEnvs = (vectorDb: TemplateVectorDB) => {
+  switch (vectorDb) {
+    case "mongo":
+      return [
+        {
+          name: "MONGO_URI",
+          description:
+            "For generating a connection URI, see https://docs.timescale.com/use-timescale/latest/services/create-a-service\nThe MongoDB connection URI.",
+        },
+        {
+          name: "MONGODB_DATABASE",
+        },
+        {
+          name: "MONGODB_VECTORS",
+        },
+        {
+          name: "MONGODB_VECTOR_INDEX",
+        },
+      ];
+    case "pg":
+      return [
+        {
+          name: "PG_CONNECTION_STRING",
+          description:
+            "For generating a connection URI, see https://docs.timescale.com/use-timescale/latest/services/create-a-service\nThe PostgreSQL connection string.",
+        },
+      ];
+
+    case "pinecone":
+      return [
+        {
+          name: "PINECONE_API_KEY",
+          description:
+            "Configuration for Pinecone vector store\nThe Pinecone API key.",
+        },
+        {
+          name: "PINECONE_ENVIRONMENT",
+        },
+        {
+          name: "PINECONE_INDEX_NAME",
+        },
+      ];
+    default:
+      return [];
+  }
+};
+
+const getDataSourceEnvs = (dataSource: TemplateDataSource) => {
+  switch (dataSource.type) {
+    case "web":
+      return [
+        {
+          name: "BASE_URL",
+          description: "The base URL to start web scraping.",
+        },
+        {
+          name: "URL_PREFIX",
+          description: "The prefix of the URL to start web scraping.",
+        },
+        {
+          name: "MAX_DEPTH",
+          description: "The maximum depth to scrape.",
+        },
+      ];
+    default:
+      return [];
+  }
+};
+
+export const createBackendEnvFile = async (
+  root: string,
+  opts: {
+    openAiKey?: string;
+    llamaCloudKey?: string;
+    vectorDb?: TemplateVectorDB;
+    model?: string;
+    embeddingModel?: string;
+    framework?: TemplateFramework;
+    dataSource?: TemplateDataSource;
+    port?: number;
+  },
+) => {
+  // Init env values
+  const envFileName = ".env";
+  const defaultEnvs = [
+    {
+      render: true,
+      name: "MODEL",
+      description: "The name of LLM model to use.",
+      value: opts.model || "gpt-3.5-turbo",
+    },
+    {
+      render: true,
+      name: "OPENAI_API_KEY",
+      description: "The OpenAI API key to use.",
+      value: opts.openAiKey,
+    },
+    // Add vector database environment variables
+    ...(opts.vectorDb ? getVectorDBEnvs(opts.vectorDb) : []),
+    // Add data source environment variables
+    ...(opts.dataSource ? getDataSourceEnvs(opts.dataSource) : []),
+  ];
+  let envVars: EnvVar[] = [];
+  if (opts.framework === "fastapi") {
+    envVars = [
+      ...defaultEnvs,
+      ...[
+        {
+          name: "APP_HOST",
+          description: "The address to start the backend app.",
+          value: "0.0.0.0",
+        },
+        {
+          name: "APP_PORT",
+          description: "The port to start the backend app.",
+          value: opts.port?.toString() || "8000",
+        },
+        {
+          name: "EMBEDDING_MODEL",
+          description: "Name of the embedding model to use.",
+          value: opts.embeddingModel,
+        },
+        {
+          name: "EMBEDDING_DIM",
+          description: "Dimension of the embedding model to use.",
+        },
+        {
+          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}
+"`,
+        },
+        (opts?.dataSource?.config as FileSourceConfig).useLlamaParse
+          ? {
+              name: "LLAMA_CLOUD_API_KEY",
+              description: `The Llama Cloud API key.`,
+              value: opts.llamaCloudKey,
+            }
+          : {},
+      ],
+    ];
+  } else {
+    envVars = [
+      ...defaultEnvs,
+      ...[
+        opts.framework === "nextjs"
+          ? {
+              name: "NEXT_PUBLIC_MODEL",
+              description:
+                "The LLM model to use (hardcode to front-end artifact).",
+            }
+          : {},
+      ],
+    ];
+  }
+  // Render and write env file
+  const content = renderEnvVar(envVars);
+  await fs.writeFile(path.join(root, envFileName), content);
+  console.log(`Created '${envFileName}' file. Please check the settings.`);
+};
+
+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.",
+      value: opts.customApiPath
+        ? opts.customApiPath
+        : "http://localhost:8000/api/chat",
+    },
+  ];
+  const content = renderEnvVar(defaultFrontendEnvs);
+  await fs.writeFile(path.join(root, ".env"), content);
+};

packages/create-llama/helpers/index.ts

@@ -7,6 +7,7 @@ import { cyan } from "picocolors";
 
 import { COMMUNITY_OWNER, COMMUNITY_REPO } from "./constant";
 import { templatesDir } from "./dir";
+import { createBackendEnvFile, createFrontendEnvFile } from "./env-variables";
 import { PackageManager } from "./get-pkg-manager";
 import { installLlamapackProject } from "./llama-pack";
 import { isHavingPoetryLockFile, tryPoetryRun } from "./poetry";
@@ -18,89 +19,9 @@ import {
   TemplateDataSource,
   TemplateFramework,
   TemplateVectorDB,
-  WebSourceConfig,
 } from "./types";
 import { installTSTemplate } from "./typescript";
 
-const createEnvLocalFile = async (
-  root: string,
-  opts?: {
-    openAiKey?: string;
-    llamaCloudKey?: string;
-    vectorDb?: TemplateVectorDB;
-    model?: string;
-    embeddingModel?: string;
-    framework?: TemplateFramework;
-    dataSource?: TemplateDataSource;
-  },
-) => {
-  const envFileName = ".env";
-  let content = "";
-
-  const model = opts?.model || "gpt-3.5-turbo";
-  content += `MODEL=${model}\n`;
-  if (opts?.framework === "nextjs") {
-    content += `NEXT_PUBLIC_MODEL=${model}\n`;
-  }
-  console.log("\nUsing OpenAI model: ", model, "\n");
-
-  if (opts?.openAiKey) {
-    content += `OPENAI_API_KEY=${opts?.openAiKey}\n`;
-  }
-
-  if (opts?.embeddingModel) {
-    content += `EMBEDDING_MODEL=${opts?.embeddingModel}\n`;
-  }
-
-  if ((opts?.dataSource?.config as FileSourceConfig).useLlamaParse) {
-    if (opts?.llamaCloudKey) {
-      content += `LLAMA_CLOUD_API_KEY=${opts?.llamaCloudKey}\n`;
-    } else {
-      content += `# Please obtain the Llama Cloud API key from https://cloud.llamaindex.ai/api-key 
-# and set it to the LLAMA_CLOUD_API_KEY variable below.
-# LLAMA_CLOUD_API_KEY=`;
-    }
-  }
-
-  switch (opts?.vectorDb) {
-    case "mongo": {
-      content += `# For generating a connection URI, see https://www.mongodb.com/docs/guides/atlas/connection-string\n`;
-      content += `MONGO_URI=\n`;
-      content += `MONGODB_DATABASE=\n`;
-      content += `MONGODB_VECTORS=\n`;
-      content += `MONGODB_VECTOR_INDEX=\n`;
-      break;
-    }
-    case "pg": {
-      content += `# For generating a connection URI, see https://docs.timescale.com/use-timescale/latest/services/create-a-service\n`;
-      content += `PG_CONNECTION_STRING=\n`;
-      break;
-    }
-    case "pinecone": {
-      content += `PINECONE_API_KEY=\n`;
-      content += `PINECONE_ENVIRONMENT=\n`;
-      content += `PINECONE_INDEX_NAME=\n`;
-      break;
-    }
-  }
-
-  switch (opts?.dataSource?.type) {
-    case "web": {
-      const webConfig = opts?.dataSource.config as WebSourceConfig;
-      content += `# web loader config\n`;
-      content += `BASE_URL=${webConfig.baseUrl}\n`;
-      content += `URL_PREFIX=${webConfig.baseUrl}\n`;
-      content += `MAX_DEPTH=${webConfig.depth}\n`;
-      break;
-    }
-  }
-
-  if (content) {
-    await fs.writeFile(path.join(root, envFileName), content);
-    console.log(`Created '${envFileName}' file. Please check the settings.`);
-  }
-};
-
 // eslint-disable-next-line max-params
 async function generateContextData(
   framework: TemplateFramework,
@@ -232,7 +153,7 @@ export const installTemplate = async (
     // This is a backend, so we need to copy the test data and create the env file.
 
     // Copy the environment file to the target directory.
-    await createEnvLocalFile(props.root, {
+    await createBackendEnvFile(props.root, {
       openAiKey: props.openAiKey,
       llamaCloudKey: props.llamaCloudKey,
       vectorDb: props.vectorDb,
@@ -240,6 +161,7 @@ export const installTemplate = async (
       embeddingModel: props.embeddingModel,
       framework: props.framework,
       dataSource: props.dataSource,
+      port: props.externalPort,
     });
 
     if (props.engine === "context") {
@@ -260,8 +182,10 @@ export const installTemplate = async (
     }
   } else {
     // this is a frontend for a full-stack app, create .env file with model information
-    const content = `MODEL=${props.model}\nNEXT_PUBLIC_MODEL=${props.model}\n`;
-    await fs.writeFile(path.join(props.root, ".env"), content);
+    createFrontendEnvFile(props.root, {
+      model: props.model,
+      customApiPath: props.customApiPath,
+    });
   }
 };
 

packages/create-llama/helpers/types.ts

@@ -41,7 +41,6 @@ export interface InstallTemplateArgs {
   customApiPath?: string;
   openAiKey?: string;
   llamaCloudKey?: string;
-  forBackend?: string;
   model: string;
   embeddingModel: string;
   communityProjectPath?: string;

packages/create-llama/helpers/typescript.ts

@@ -61,10 +61,10 @@ export const installTSTemplate = async ({
   ui,
   eslint,
   customApiPath,
-  forBackend,
   vectorDb,
   postInstallAction,
-}: InstallTemplateArgs) => {
+  backend,
+}: InstallTemplateArgs & { backend: boolean }) => {
   console.log(bold(`Using ${packageManager}.`));
 
   /**
@@ -82,23 +82,20 @@ export const installTSTemplate = async ({
   });
 
   /**
-   * If the backend is next.js, rename next.config.app.js to next.config.js
-   * If not, rename next.config.static.js to next.config.js
+   * If next.js is not used as a backend, update next.config.js to use static site generation.
    */
-  if (framework == "nextjs" && forBackend === "nextjs") {
-    const nextConfigAppPath = path.join(root, "next.config.app.js");
-    const nextConfigPath = path.join(root, "next.config.js");
-    await fs.rename(nextConfigAppPath, nextConfigPath);
-    // delete next.config.static.js
-    const nextConfigStaticPath = path.join(root, "next.config.static.js");
-    await fs.rm(nextConfigStaticPath);
-  } else if (framework == "nextjs" && typeof forBackend === "undefined") {
-    const nextConfigStaticPath = path.join(root, "next.config.static.js");
-    const nextConfigPath = path.join(root, "next.config.js");
-    await fs.rename(nextConfigStaticPath, nextConfigPath);
-    // delete next.config.app.js
-    const nextConfigAppPath = path.join(root, "next.config.app.js");
-    await fs.rm(nextConfigAppPath);
+  if (framework === "nextjs" && !backend) {
+    // update next.config.json for static site generation
+    const nextConfigJsonFile = path.join(root, "next.config.json");
+    const nextConfigJson: any = JSON.parse(
+      await fs.readFile(nextConfigJsonFile, "utf8"),
+    );
+    nextConfigJson.output = "export";
+    nextConfigJson.images = { unoptimized: true };
+    await fs.writeFile(
+      nextConfigJsonFile,
+      JSON.stringify(nextConfigJson, null, 2) + os.EOL,
+    );
   }
 
   /**
@@ -174,10 +171,6 @@ export const installTSTemplate = async ({
     const apiPath = path.join(root, "app", "api");
     await fs.rm(apiPath, { recursive: true });
     // modify the dev script to use the custom api path
-    packageJson.scripts = {
-      ...packageJson.scripts,
-      dev: `cross-env NEXT_PUBLIC_CHAT_API=${customApiPath} next dev`,
-    };
   }
 
   if (engine === "context" && relativeEngineDestPath) {

packages/create-llama/package.json

@@ -1,6 +1,6 @@
 {
   "name": "create-llama",
-  "version": "0.0.27",
+  "version": "0.0.28",
   "keywords": [
     "rag",
     "llamaindex",
@@ -23,7 +23,7 @@
     "clean": "rimraf --glob ./dist ./templates/**/__pycache__ ./templates/**/node_modules ./templates/**/poetry.lock",
     "dev": "ncc build ./index.ts -w -o dist/",
     "build": "npm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
-    "lint": "eslint . --ignore-pattern dist",
+    "lint": "eslint . --ignore-pattern dist --ignore-pattern e2e/cache",
     "e2e": "playwright test",
     "prepublishOnly": "cd ../../ && pnpm run build:release"
   },

packages/create-llama/templates/components/engines/python/agent/__init__.py

@@ -1,3 +1,4 @@
+import os
 from llama_index.core.settings import Settings
 from llama_index.core.agent import AgentRunner
 from llama_index.core.tools.query_engine import QueryEngineTool
@@ -6,11 +7,13 @@ from app.engine.index import get_index
 
 
 def get_chat_engine():
+    system_prompt = os.getenv("SYSTEM_PROMPT")
+    top_k = os.getenv("TOP_K", "3")
     tools = []
 
     # Add query tool
     index = get_index()
-    query_engine = index.as_query_engine(similarity_top_k=3)
+    query_engine = index.as_query_engine(similarity_top_k=int(top_k))
     query_engine_tool = QueryEngineTool.from_defaults(query_engine=query_engine)
     tools.append(query_engine_tool)
 
@@ -20,5 +23,6 @@ def get_chat_engine():
     return AgentRunner.from_llm(
         llm=Settings.llm,
         tools=tools,
+        system_prompt=system_prompt,
         verbose=True,
     )

packages/create-llama/templates/components/engines/python/chat/__init__.py

@@ -1,7 +1,13 @@
+import os
 from app.engine.index import get_index
 
 
 def get_chat_engine():
+    system_prompt = os.getenv("SYSTEM_PROMPT")
+    top_k = os.getenv("TOP_K", 3)
+
     return get_index().as_chat_engine(
-        similarity_top_k=3, chat_mode="condense_plus_context"
+        similarity_top_k=int(top_k),
+        system_prompt=system_prompt,
+        chat_mode="condense_plus_context",
     )

packages/create-llama/templates/components/loaders/python/llama_parse/loader.py

@@ -1,3 +1,4 @@
+import os
 from llama_parse import LlamaParse
 from llama_index.core import SimpleDirectoryReader
 
@@ -5,6 +6,11 @@ DATA_DIR = "data"  # directory containing the documents
 
 
 def get_documents():
+    if os.getenv("LLAMA_CLOUD_API_KEY") is None:
+        raise ValueError(
+            "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")
 
     reader = SimpleDirectoryReader(DATA_DIR, file_extractor={".pdf": parser})

packages/create-llama/templates/types/simple/fastapi/app/settings.py

@@ -1,14 +1,41 @@
 import os
+from typing import Dict
+from llama_index.core.settings import Settings
 from llama_index.llms.openai import OpenAI
 from llama_index.embeddings.openai import OpenAIEmbedding
-from llama_index.core.settings import Settings
+
+
+def llm_config_from_env() -> Dict:
+    from llama_index.core.constants import DEFAULT_TEMPERATURE
+
+    model = os.getenv("MODEL")
+    temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
+    max_tokens = os.getenv("LLM_MAX_TOKENS")
+
+    config = {
+        "model": model,
+        "temperature": float(temperature),
+        "max_tokens": int(max_tokens) if max_tokens is not None else None,
+    }
+    return config
+
+
+def embedding_config_from_env() -> Dict:
+    model = os.getenv("EMBEDDING_MODEL")
+    dimension = os.getenv("EMBEDDING_DIM")
+
+    config = {
+        "model": model,
+        "dimension": int(dimension) if dimension is not None else None,
+    }
+    return config
 
 
 def init_settings():
-    llm_model = os.getenv("MODEL", "gpt-3.5-turbo")
-    embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002")
+    llm_configs = llm_config_from_env()
+    embedding_configs = embedding_config_from_env()
 
-    Settings.llm = OpenAI(model=llm_model)
-    Settings.embed_model = OpenAIEmbedding(model=embedding_model)
-    Settings.chunk_size = 1024
-    Settings.chunk_overlap = 20
+    Settings.llm = OpenAI(**llm_configs)
+    Settings.embed_model = OpenAIEmbedding(**embedding_configs)
+    Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
+    Settings.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "20"))

packages/create-llama/templates/types/simple/fastapi/main.py

@@ -32,4 +32,7 @@ app.include_router(chat_router, prefix="/api/chat")
 
 
 if __name__ == "__main__":
-    uvicorn.run(app="main:app", host="0.0.0.0", reload=True)
+    app_host = os.getenv("APP_HOST", "0.0.0.0")
+    app_port = int(os.getenv("APP_PORT", "8000"))
+
+    uvicorn.run(app="main:app", host=app_host, port=app_port, reload=True)

packages/create-llama/templates/types/streaming/fastapi/app/settings.py

@@ -1,14 +1,41 @@
 import os
+from typing import Dict
+from llama_index.core.settings import Settings
 from llama_index.llms.openai import OpenAI
 from llama_index.embeddings.openai import OpenAIEmbedding
-from llama_index.core.settings import Settings
+
+
+def llm_config_from_env() -> Dict:
+    from llama_index.core.constants import DEFAULT_TEMPERATURE
+
+    model = os.getenv("MODEL")
+    temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
+    max_tokens = os.getenv("LLM_MAX_TOKENS")
+
+    config = {
+        "model": model,
+        "temperature": float(temperature),
+        "max_tokens": int(max_tokens) if max_tokens is not None else None,
+    }
+    return config
+
+
+def embedding_config_from_env() -> Dict:
+    model = os.getenv("EMBEDDING_MODEL")
+    dimension = os.getenv("EMBEDDING_DIM")
+
+    config = {
+        "model": model,
+        "dimension": int(dimension) if dimension is not None else None,
+    }
+    return config
 
 
 def init_settings():
-    llm_model = os.getenv("MODEL", "gpt-3.5-turbo")
-    embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002")
+    llm_configs = llm_config_from_env()
+    embedding_configs = embedding_config_from_env()
 
-    Settings.llm = OpenAI(model=llm_model)
-    Settings.embed_model = OpenAIEmbedding(model=embedding_model)
-    Settings.chunk_size = 1024
-    Settings.chunk_overlap = 20
+    Settings.llm = OpenAI(**llm_configs)
+    Settings.embed_model = OpenAIEmbedding(**embedding_configs)
+    Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
+    Settings.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "20"))

packages/create-llama/templates/types/streaming/fastapi/main.py

@@ -33,4 +33,7 @@ app.include_router(chat_router, prefix="/api/chat")
 
 
 if __name__ == "__main__":
-    uvicorn.run(app="main:app", host="0.0.0.0", reload=True)
+    app_host = os.getenv("APP_HOST", "0.0.0.0")
+    app_port = int(os.getenv("APP_PORT", "8000"))
+
+    uvicorn.run(app="main:app", host=app_host, port=app_port, reload=True)

packages/create-llama/templates/types/streaming/nextjs/next.config.app.js

@@ -1,19 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  webpack: (config) => {
-    // See https://webpack.js.org/configuration/resolve/#resolvealias
-    config.resolve.alias = {
-      ...config.resolve.alias,
-      sharp$: false,
-      "onnxruntime-node$": false,
-    };
-    return config;
-  },
-  experimental: {
-    outputFileTracingIncludes: {
-      "/*": ["./cache/**/*"],
-    },
-  },
-};
-
-module.exports = nextConfig;

packages/create-llama/templates/types/streaming/nextjs/next.config.json

@@ -0,0 +1,7 @@
+{
+  "experimental": {
+    "outputFileTracingIncludes": {
+      "/*": ["./cache/**/*"]
+    }
+  }
+}

packages/create-llama/templates/types/streaming/nextjs/next.config.mjs

@@ -0,0 +1,8 @@
+/** @type {import('next').NextConfig} */
+import fs from "fs";
+import webpack from "./webpack.config.mjs";
+
+const nextConfig = JSON.parse(fs.readFileSync("./next.config.json", "utf-8"));
+nextConfig.webpack = webpack;
+
+export default nextConfig;

packages/create-llama/templates/types/streaming/nextjs/next.config.static.js

@@ -1,21 +0,0 @@
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  output: "export",
-  images: { unoptimized: true },
-  webpack: (config) => {
-    // See https://webpack.js.org/configuration/resolve/#resolvealias
-    config.resolve.alias = {
-      ...config.resolve.alias,
-      sharp$: false,
-      "onnxruntime-node$": false,
-    };
-    return config;
-  },
-  experimental: {
-    outputFileTracingIncludes: {
-      "/*": ["./cache/**/*"],
-    },
-  },
-};
-
-module.exports = nextConfig;

packages/create-llama/templates/types/streaming/nextjs/webpack.config.mjs

@@ -0,0 +1,10 @@
+// webpack config must be a function in NextJS that is used to patch the default webpack config provided by NextJS, see https://nextjs.org/docs/pages/api-reference/next-config-js/webpack
+export default function webpack(config) {
+  // See https://webpack.js.org/configuration/resolve/#resolvealias
+  config.resolve.alias = {
+    ...config.resolve.alias,
+    sharp$: false,
+    "onnxruntime-node$": false,
+  };
+  return config;
+}