Release 0.2.7 (#293)

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

CHANGELOG.md

@@ -1,5 +1,25 @@
 # create-llama
 
+## 0.2.7
+
+### Patch Changes
+
+- 505b8e9: bump: use latest ai package version
+- cf3ec97: Dynamically select model for Groq
+- 8c1087f: feat: enhance style for markdown
+
+## 0.2.6
+
+### Patch Changes
+
+- adc40cf: fix: vercel ai update crash sending annotations
+
+## 0.2.5
+
+### Patch Changes
+
+- 38a8be8: fix: filter in mongo vector store
+
 ## 0.2.4
 
 ### Patch Changes

helpers/providers/groq.ts

@@ -3,8 +3,55 @@ 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];
+import got from "got";
+import ora from "ora";
+import { red } from "picocolors";
+
+const GROQ_API_URL = "https://api.groq.com/openai/v1";
+
+async function getAvailableModelChoicesGroq(apiKey: string) {
+  if (!apiKey) {
+    throw new Error("Need Groq API key to retrieve model choices");
+  }
+
+  const spinner = ora("Fetching available models from Groq").start();
+  try {
+    const response = await got(`${GROQ_API_URL}/models`, {
+      headers: {
+        Authorization: `Bearer ${apiKey}`,
+      },
+      timeout: 5000,
+      responseType: "json",
+    });
+    const data: any = await response.body;
+    spinner.stop();
+
+    // Filter out the Whisper models
+    return data.data
+      .filter((model: any) => !model.id.toLowerCase().includes("whisper"))
+      .map((el: any) => {
+        return {
+          title: el.id,
+          value: el.id,
+        };
+      });
+  } catch (error: unknown) {
+    spinner.stop();
+    console.log(error);
+    if ((error as any).response?.statusCode === 401) {
+      console.log(
+        red(
+          "Invalid Groq API key provided! Please provide a valid key and try again!",
+        ),
+      );
+    } else {
+      console.log(red("Request failed: " + error));
+    }
+    process.exit(1);
+  }
+}
+
+const DEFAULT_MODEL = "llama3-70b-8192";
 
 // Use huggingface embedding models for now as Groq doesn't support embedding models
 enum HuggingFaceEmbeddingModelType {
@@ -66,12 +113,14 @@ export async function askGroqQuestions({
   // use default model values in CI or if user should not be asked
   const useDefaults = ciInfo.isCI || !askModels;
   if (!useDefaults) {
+    const modelChoices = await getAvailableModelChoicesGroq(config.apiKey!);
+
     const { model } = await prompts(
       {
         type: "select",
         name: "model",
         message: "Which LLM model would you like to use?",
-        choices: MODELS.map(toChoice),
+        choices: modelChoices,
         initial: 0,
       },
       questionHandlers,

package.json

@@ -1,6 +1,6 @@
 {
   "name": "create-llama",
-  "version": "0.2.4",
+  "version": "0.2.7",
   "description": "Create LlamaIndex-powered apps with one command",
   "keywords": [
     "rag",

templates/components/services/python/file.py

@@ -3,7 +3,7 @@ import mimetypes
 import os
 from io import BytesIO
 from pathlib import Path
-from typing import Any, List, Tuple
+from typing import List, Optional, Tuple
 
 from app.engine.index import IndexConfig, get_index
 from llama_index.core import VectorStoreIndex
@@ -72,7 +72,12 @@ class PrivateFileService:
         return documents
 
     @staticmethod
-    def process_file(file_name: str, base64_content: str, params: Any) -> List[str]:
+    def process_file(
+        file_name: str, base64_content: str, params: Optional[dict] = None
+    ) -> List[str]:
+        if params is None:
+            params = {}
+
         file_data, extension = PrivateFileService.preprocess_base64_file(base64_content)
 
         # Add the nodes to the index and persist it

templates/components/settings/python/settings.py

@@ -126,13 +126,7 @@ def init_fastembed():
 def init_groq():
     from llama_index.llms.groq import Groq
 
-    model_map: Dict[str, str] = {
-        "llama3-8b": "llama3-8b-8192",
-        "llama3-70b": "llama3-70b-8192",
-        "mixtral-8x7b": "mixtral-8x7b-32768",
-    }
-
-    Settings.llm = Groq(model=model_map[os.getenv("MODEL")])
+    Settings.llm = Groq(model=os.getenv("MODEL"))
     # Groq does not provide embeddings, so we use FastEmbed instead
     init_fastembed()
 

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

@@ -1,14 +1,11 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  MongoDBAtlasVectorSearch,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
+import { MongoDBAtlasVectorSearch } from "llamaindex/storage/vectorStore/MongoDBAtlasVectorStore";
 import { MongoClient } from "mongodb";
 import { getDocuments } from "./loader";
 import { initSettings } from "./settings";
-import { checkRequiredEnvVars } from "./shared";
+import { checkRequiredEnvVars, POPULATED_METADATA_FIELDS } from "./shared";
 
 dotenv.config();
 
@@ -30,6 +27,12 @@ async function loadAndIndex() {
     dbName: databaseName,
     collectionName: vectorCollectionName, // this is where your embeddings will be stored
     indexName: indexName, // this is the name of the index you will need to create
+    indexedMetadataFields: POPULATED_METADATA_FIELDS,
+    embeddingDefinition: {
+      dimensions: process.env.EMBEDDING_DIM
+        ? parseInt(process.env.EMBEDDING_DIM)
+        : 1536,
+    },
   });
 
   // now create an index from all the Documents and store them in Atlas

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

@@ -1,16 +1,23 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
-import { MongoDBAtlasVectorSearch, VectorStoreIndex } from "llamaindex";
+import { VectorStoreIndex } from "llamaindex";
+import { MongoDBAtlasVectorSearch } from "llamaindex/storage/vectorStore/MongoDBAtlasVectorStore";
 import { MongoClient } from "mongodb";
-import { checkRequiredEnvVars } from "./shared";
+import { checkRequiredEnvVars, POPULATED_METADATA_FIELDS } from "./shared";
 
 export async function getDataSource(params?: any) {
   checkRequiredEnvVars();
-  const client = new MongoClient(process.env.MONGO_URI!);
+  const client = new MongoClient(process.env.MONGODB_URI!);
   const store = new MongoDBAtlasVectorSearch({
     mongodbClient: client,
     dbName: process.env.MONGODB_DATABASE!,
     collectionName: process.env.MONGODB_VECTORS!,
     indexName: process.env.MONGODB_VECTOR_INDEX,
+    indexedMetadataFields: POPULATED_METADATA_FIELDS,
+    embeddingDefinition: {
+      dimensions: process.env.EMBEDDING_DIM
+        ? parseInt(process.env.EMBEDDING_DIM)
+        : 1536,
+    },
   });
 
   return await VectorStoreIndex.fromVectorStore(store);

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

@@ -5,6 +5,8 @@ const REQUIRED_ENV_VARS = [
   "MONGODB_VECTOR_INDEX",
 ];
 
+export const POPULATED_METADATA_FIELDS = ["private", "doc_id"]; // for filtering in MongoDB VectorSearchIndex
+
 export function checkRequiredEnvVars() {
   const missingEnvVars = REQUIRED_ENV_VARS.filter((envVar) => {
     return !process.env[envVar];

templates/types/multiagent/fastapi/pyproject.toml

@@ -12,7 +12,8 @@ generate = "app.engine.generate:generate_datasource"
 [tool.poetry.dependencies]
 python = "^3.11"
 llama-index-agent-openai = ">=0.3.0,<0.4.0"
-llama-index = "^0.11.4"
+llama-index = "0.11.9"
+llama-index-core = "0.11.9"
 fastapi = "^0.112.2"
 python-dotenv = "^1.0.0"
 uvicorn = { extras = ["standard"], version = "^0.23.2" }

templates/types/streaming/express/package.json

@@ -15,12 +15,12 @@
     "dev": "concurrently \"tsup index.ts --format esm --dts --watch\" \"nodemon --watch dist/index.js\""
   },
   "dependencies": {
-    "ai": "^3.0.21",
+    "ai": "3.3.38",
     "cors": "^2.8.5",
     "dotenv": "^16.3.1",
     "duck-duck-scrape": "^2.2.5",
     "express": "^4.18.2",
-    "llamaindex": "0.5.20",
+    "llamaindex": "0.6.2",
     "pdf2json": "3.0.5",
     "ajv": "^8.12.0",
     "@e2b/code-interpreter": "^0.0.5",

templates/types/streaming/express/src/controllers/engine/settings.ts

@@ -138,14 +138,8 @@ function initGroq() {
     "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
   };
 
-  const modelMap: Record<string, string> = {
-    "llama3-8b": "llama3-8b-8192",
-    "llama3-70b": "llama3-70b-8192",
-    "mixtral-8x7b": "mixtral-8x7b-32768",
-  };
-
   Settings.llm = new Groq({
-    model: modelMap[process.env.MODEL!],
+    model: process.env.MODEL!,
   });
 
   Settings.embedModel = new HuggingFaceEmbedding({

templates/types/streaming/nextjs/app/api/chat/engine/settings.ts

@@ -138,14 +138,8 @@ function initGroq() {
     "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
   };
 
-  const modelMap: Record<string, string> = {
-    "llama3-8b": "llama3-8b-8192",
-    "llama3-70b": "llama3-70b-8192",
-    "mixtral-8x7b": "mixtral-8x7b-32768",
-  };
-
   Settings.llm = new Groq({
-    model: modelMap[process.env.MODEL!],
+    model: process.env.MODEL!,
   });
 
   Settings.embedModel = new HuggingFaceEmbedding({

templates/types/streaming/nextjs/app/components/chat-section.tsx

@@ -29,6 +29,7 @@ export default function ChatSection() {
       const message = JSON.parse(error.message);
       alert(message.detail);
     },
+    sendExtraMessageFields: true,
   });
 
   return (

templates/types/streaming/nextjs/app/components/ui/chat/chat-message/markdown.tsx

@@ -133,7 +133,11 @@ export default function Markdown({
               return <></>;
             }
           }
-          return <a href={href}>{children}</a>;
+          return (
+            <a href={href} target="_blank">
+              {children}
+            </a>
+          );
         },
       }}
     >

templates/types/streaming/nextjs/app/markdown.css

@@ -2,11 +2,15 @@
 .custom-markdown ul {
   list-style-type: disc;
   margin-left: 20px;
+  margin-top: 20px;
+  margin-bottom: 20px;
 }
 
 .custom-markdown ol {
   list-style-type: decimal;
   margin-left: 20px;
+  margin-top: 20px;
+  margin-bottom: 20px;
 }
 
 .custom-markdown li {
@@ -21,3 +25,55 @@
 .custom-markdown ol ol {
   margin-left: 20px;
 }
+
+.custom-markdown img {
+  border-radius: 8px;
+  box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+  margin: 10px 0;
+}
+
+.custom-markdown a {
+  text-decoration: underline;
+  color: #007bff;
+}
+
+.custom-markdown h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+  font-weight: bold;
+  margin-bottom: 20px;
+  margin-top: 20px;
+}
+
+.custom-markdown h6 {
+  font-size: 16px;
+}
+
+.custom-markdown h5 {
+  font-size: 18px;
+}
+
+.custom-markdown h4 {
+  font-size: 20px;
+}
+
+.custom-markdown h3 {
+  font-size: 22px;
+}
+
+.custom-markdown h2 {
+  font-size: 24px;
+}
+
+.custom-markdown h1 {
+  font-size: 26px;
+}
+
+.custom-markdown hr {
+  border: 0;
+  border-top: 1px solid #e1e4e8;
+  margin: 20px 0;
+}

templates/types/streaming/nextjs/package.json

@@ -17,7 +17,7 @@
     "@radix-ui/react-hover-card": "^1.0.7",
     "@radix-ui/react-select": "^2.1.1",
     "@radix-ui/react-slot": "^1.0.2",
-    "ai": "^3.0.21",
+    "ai": "3.3.38",
     "ajv": "^8.12.0",
     "class-variance-authority": "^0.7.0",
     "clsx": "^2.1.1",
@@ -25,7 +25,7 @@
     "duck-duck-scrape": "^2.2.5",
     "formdata-node": "^6.0.3",
     "got": "^14.4.1",
-    "llamaindex": "0.5.20",
+    "llamaindex": "0.6.2",
     "lucide-react": "^0.294.0",
     "next": "^14.2.4",
     "react": "^18.2.0",