fix: code review and bugs

questions.ts

@@ -189,95 +189,84 @@ export const askQuestions = async (
     }
   }
 
-  if (
-    program.framework === "express" ||
-    program.framework === "nextjs" ||
-    program.framework === "fastapi"
-  ) {
-    if (!program.model) {
-      if (ciInfo.isCI) {
-        program.model = getPrefOrDefault("model");
-      } else {
-        const { model } = await prompts(
-          {
-            type: "select",
-            name: "model",
-            message: "Which model would you like to use?",
-            choices: [
-              { title: "gpt-3.5-turbo", value: "gpt-3.5-turbo" },
-              { title: "gpt-4", value: "gpt-4" },
-              { title: "gpt-4-1106-preview", value: "gpt-4-1106-preview" },
-              {
-                title: "gpt-4-vision-preview",
-                value: "gpt-4-vision-preview",
-              },
-            ],
-            initial: 0,
-          },
-          handlers,
-        );
-        program.model = model;
-        preferences.model = model;
-      }
+  if (!program.model) {
+    if (ciInfo.isCI) {
+      program.model = getPrefOrDefault("model");
+    } else {
+      const { model } = await prompts(
+        {
+          type: "select",
+          name: "model",
+          message: "Which model would you like to use?",
+          choices: [
+            { title: "gpt-3.5-turbo", value: "gpt-3.5-turbo" },
+            { title: "gpt-4", value: "gpt-4" },
+            { title: "gpt-4-1106-preview", value: "gpt-4-1106-preview" },
+            {
+              title: "gpt-4-vision-preview",
+              value: "gpt-4-vision-preview",
+            },
+          ],
+          initial: 0,
+        },
+        handlers,
+      );
+      program.model = model;
+      preferences.model = model;
+    }
+  }
+
+  if (!program.engine) {
+    if (ciInfo.isCI) {
+      program.engine = getPrefOrDefault("engine");
+    } else {
+      const { engine } = await prompts(
+        {
+          type: "select",
+          name: "engine",
+          message: "Which data source would you like to use?",
+          choices: [
+            {
+              title: "No data, just a simple chat",
+              value: "simple",
+            },
+            { title: "Use an example PDF", value: "context" },
+          ],
+          initial: 1,
+        },
+        handlers,
+      );
+      program.engine = engine;
+      preferences.engine = engine;
     }
   }
 
   if (
-    program.framework === "express" ||
-    program.framework === "nextjs" ||
-    program.framework === "fastapi"
+    program.engine !== "simple" &&
+    !program.vectorDb &&
+    program.framework !== "fastapi"
   ) {
-    if (!program.engine) {
-      if (ciInfo.isCI) {
-        program.engine = getPrefOrDefault("engine");
-      } else {
-        const { engine } = await prompts(
-          {
-            type: "select",
-            name: "engine",
-            message: "Which data source would you like to use?",
-            choices: [
-              {
-                title: "No data, just a simple chat",
-                value: "simple",
-              },
-              { title: "Use an example PDF", value: "context" },
-            ],
-            initial: 1,
-          },
-          handlers,
-        );
-        program.engine = engine;
-        preferences.engine = engine;
-      }
-    }
-    if (
-      program.engine !== "simple" &&
-      !program.vectorDb &&
-      program.framework !== "fastapi"
-    ) {
-      if (ciInfo.isCI) {
-        program.vectorDb = getPrefOrDefault("vectorDb");
-      } else {
-        const { vectorDb } = await prompts(
-          {
-            type: "select",
-            name: "vectorDb",
-            message: "Would you like to use a vector database?",
-            choices: [
-              {
-                title: "No, just store the data in the file system",
-                value: "none",
-              },
-              { title: "MongoDB", value: "mongo" },
-            ],
-            initial: 0,
-          },
-          handlers,
-        );
-        program.vectorDb = vectorDb;
-        preferences.vectorDb = vectorDb;
-      }
+    if (ciInfo.isCI) {
+      program.vectorDb = getPrefOrDefault("vectorDb");
+    } else {
+      const { vectorDb } = await prompts(
+        {
+          type: "select",
+          name: "vectorDb",
+          message: "Would you like to use a vector database?",
+          choices: [
+            {
+              title: "No, just store the data in the file system",
+              value: "none",
+            },
+            { title: "MongoDB", value: "mongo" },
+          ],
+          initial: 0,
+        },
+        handlers,
+      );
+      program.vectorDb = vectorDb;
+      preferences.vectorDb = vectorDb;
     }
   }
 

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

-from llama_index import ServiceContext
+import os
 
-from app.context import create_base_context
+from llama_index import ServiceContext
+from llama_index.llms import OpenAI
 from app.engine.constants import CHUNK_SIZE, CHUNK_OVERLAP
 
 
+def create_base_context():
+    model = os.getenv("MODEL", "gpt-3.5-turbo")
+    return ServiceContext.from_defaults(
+        llm=OpenAI(model=model),
+    )
+
+
 def create_service_context():
     base = create_base_context()
     return ServiceContext.from_defaults(

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

@@ -14,7 +14,7 @@ def get_chat_engine():
     # check if storage already exists
     if not os.path.exists(STORAGE_DIR):
         raise Exception(
-            "StorageContext is empty - call 'npm run generate' to generate the storage first"
+            "StorageContext is empty - call 'python app/engine/generate.py' to generate the storage first"
         )
     logger = logging.getLogger("uvicorn")
     # load the existing index

templates/types/simple/express/src/controllers/chat.controller.ts

@@ -33,7 +33,7 @@ export const chat = async (req: Request, res: Response) => {
     }
 
     const llm = new OpenAI({
-      model: process.env.MODEL,
+      model: process.env.MODEL || "gpt-3.5-turbo",
     });
 
     const lastMessageContent = getLastMessageContent(

templates/types/simple/fastapi/app/api/routers/chat.py

@@ -2,7 +2,8 @@ from typing import List
 
 from fastapi import APIRouter, Depends, HTTPException, status
 from llama_index import VectorStoreIndex
-from llama_index.llms.base import MessageRole, ChatMessage
+from llama_index.llms.base import ChatMessage
+from llama_index.llms.types import MessageRole
 from pydantic import BaseModel
 from app.engine.index import get_chat_engine
 

templates/types/simple/fastapi/app/engine/index.py

 import os
-import logging
 
-from llama_index import (
-    SimpleDirectoryReader,
-    StorageContext,
-    VectorStoreIndex,
-    load_index_from_storage,
-    ServiceContext,
-)
+from llama_index import ServiceContext
+from llama_index.chat_engine import SimpleChatEngine
 from llama_index.llms import OpenAI
 
-STORAGE_DIR = "./storage"  # directory to cache the generated index
-DATA_DIR = "./data"  # directory containing the documents to index
 
 def create_base_context():
     model = os.getenv("MODEL", "gpt-3.5-turbo")
@@ -19,26 +11,6 @@ def create_base_context():
         llm=OpenAI(model=model),
     )
 
-def get_index():
-    service_context = create_base_context()
-    logger = logging.getLogger("uvicorn")
-    # check if storage already exists
-    if not os.path.exists(STORAGE_DIR):
-        logger.info("Creating new index")
-        # load the documents and create the index
-        documents = SimpleDirectoryReader(DATA_DIR).load_data()
-        index = VectorStoreIndex.from_documents(documents,service_context=service_context)
-        # store it for later
-        index.storage_context.persist(STORAGE_DIR)
-        logger.info(f"Finished creating new index. Stored in {STORAGE_DIR}")
-    else:
-        # load the existing index
-        logger.info(f"Loading index from {STORAGE_DIR}...")
-        storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
-        index = load_index_from_storage(storage_context,service_context=service_context)
-        logger.info(f"Finished loading index from {STORAGE_DIR}")
-    return index
 
 def get_chat_engine():
-    index = get_index()
-    return index.as_chat_engine()
+    return SimpleChatEngine.from_defaults(service_context=create_base_context())

templates/types/streaming/express/src/controllers/chat.controller.ts

@@ -35,7 +35,7 @@ export const chat = async (req: Request, res: Response) => {
     }
 
     const llm = new OpenAI({
-      model: process.env.MODEL,
+      model: process.env.MODEL || "gpt-3.5-turbo",
     });
 
     const chatEngine = await createChatEngine(llm);

templates/types/streaming/fastapi/README-template.md

@@ -17,10 +17,10 @@ Example `.env` file:
 OPENAI_API_KEY=<openai_api_key>
 ```
 
-Second, generate the embeddings of the documents in the `./data` directory:
+Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
 
 ```
-python app/engine/generate.py 
+python app/engine/generate.py
 ```
 
 Third, run the development server:

templates/types/streaming/fastapi/app/engine/index.py

 import os
-import logging
 
-from llama_index import (
-    SimpleDirectoryReader,
-    StorageContext,
-    VectorStoreIndex,
-    load_index_from_storage,
-    ServiceContext,
-)
+from llama_index import ServiceContext
+from llama_index.chat_engine import SimpleChatEngine
 from llama_index.llms import OpenAI
 
-STORAGE_DIR = "./storage"  # directory to cache the generated index
-DATA_DIR = "./data"  # directory containing the documents to index
 
 def create_base_context():
     model = os.getenv("MODEL", "gpt-3.5-turbo")
@@ -19,26 +11,6 @@ def create_base_context():
         llm=OpenAI(model=model),
     )
 
-def get_index():
-    service_context = create_base_context()
-    logger = logging.getLogger("uvicorn")
-    # check if storage already exists
-    if not os.path.exists(STORAGE_DIR):
-        logger.info("Creating new index")
-        # load the documents and create the index
-        documents = SimpleDirectoryReader(DATA_DIR).load_data()
-        index = VectorStoreIndex.from_documents(documents,service_context=service_context)
-        # store it for later
-        index.storage_context.persist(STORAGE_DIR)
-        logger.info(f"Finished creating new index. Stored in {STORAGE_DIR}")
-    else:
-        # load the existing index
-        logger.info(f"Loading index from {STORAGE_DIR}...")
-        storage_context = StorageContext.from_defaults(persist_dir=STORAGE_DIR)
-        index = load_index_from_storage(storage_context,service_context=service_context)
-        logger.info(f"Finished loading index from {STORAGE_DIR}")
-    return index
 
 def get_chat_engine():
-    index = get_index()
-    return index.as_chat_engine()
+    return SimpleChatEngine.from_defaults(service_context=create_base_context())

templates/types/streaming/nextjs/app/api/chat/route.ts

@@ -42,7 +42,7 @@ export async function POST(request: NextRequest) {
     }
 
     const llm = new OpenAI({
-      model: process.env.MODEL,
+      model: process.env.MODEL || "gpt-3.5-turbo",
       maxTokens: 512,
     });