diff --git a/apps/docs/docs/modules/agent/multi_document_agent.mdx b/apps/docs/docs/modules/agent/multi_document_agent.mdx
index cbe9e3c2111bb2efd36f2595a01cd387c22e5483..7c4188f9483a660debd728964b358392786ff953 100644
--- a/apps/docs/docs/modules/agent/multi_document_agent.mdx
+++ b/apps/docs/docs/modules/agent/multi_document_agent.mdx
@@ -33,7 +33,7 @@ import {
   SimpleToolNodeMapping,
   SummaryIndex,
   VectorStoreIndex,
-  serviceContextFromDefaults,
+  Settings,
   storageContextFromDefaults,
 } from "llamaindex";
 ```
@@ -147,12 +147,10 @@ for (const title of wikiTitles) {
 We will be using gpt-4 for this example and we will use the `StorageContext` to store the documents in-memory.
 
 ```ts
-const llm = new OpenAI({
+Settings.llm = new OpenAI({
   model: "gpt-4",
 });
 
-const ctx = serviceContextFromDefaults({ llm });
-
 const storageContext = await storageContextFromDefaults({
   persistDir: "./storage",
 });
@@ -189,14 +187,12 @@ for (const title of wikiTitles) {
 
   // create the vector index for specific search
   const vectorIndex = await VectorStoreIndex.init({
-    serviceContext: serviceContext,
     storageContext: storageContext,
     nodes,
   });
 
   // create the summary index for broader search
   const summaryIndex = await SummaryIndex.init({
-    serviceContext: serviceContext,
     nodes,
   });
 
@@ -278,7 +274,6 @@ const objectIndex = await ObjectIndex.fromObjects(
   toolMapping,
   VectorStoreIndex,
   {
-    serviceContext,
     storageContext,
   },
 );
diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/huggingface.md b/apps/docs/docs/modules/embeddings/available_embeddings/huggingface.md
index c758842779a9d2ab256e96abd4e3b62f788cfb15..41c0a0b41ec65b5b469880d6552c7e2cf23f6047 100644
--- a/apps/docs/docs/modules/embeddings/available_embeddings/huggingface.md
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/huggingface.md
@@ -3,17 +3,14 @@
 To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `llamaindex`.
 
 ```ts
-import { HuggingFaceEmbedding, serviceContextFromDefaults } from "llamaindex";
+import { HuggingFaceEmbedding, Settings } from "llamaindex";
 
-const huggingFaceEmbeds = new HuggingFaceEmbedding();
-
-const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
+// Update Embed Model
+Settings.embedModel = new HuggingFaceEmbedding();
 
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 const queryEngine = index.asQueryEngine();
 
@@ -29,8 +26,8 @@ If you're not using a quantized model, set the `quantized` parameter to `false`.
 
 For example, to use the not quantized `BAAI/bge-small-en-v1.5` model, you can use the following code:
 
-```
-const embedModel = new HuggingFaceEmbedding({
+```ts
+Settings.embedModel = new HuggingFaceEmbedding({
   modelType: "BAAI/bge-small-en-v1.5",
   quantized: false,
 });
diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/mistral.md b/apps/docs/docs/modules/embeddings/available_embeddings/mistral.md
index ee2da3a3329865e3555329f46305b74b80d40223..a747482ed99423ac74bdf9574aa0cf30f329d20d 100644
--- a/apps/docs/docs/modules/embeddings/available_embeddings/mistral.md
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/mistral.md
@@ -3,21 +3,16 @@
 To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `llamaindex`.
 
 ```ts
-import { MistralAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+import { MistralAIEmbedding, Settings } from "llamaindex";
 
-const mistralEmbedModel = new MistralAIEmbedding({
+// Update Embed Model
+Settings.embedModel = new MistralAIEmbedding({
   apiKey: "<YOUR_API_KEY>",
 });
 
-const serviceContext = serviceContextFromDefaults({
-  embedModel: mistralEmbedModel,
-});
-
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 const queryEngine = index.asQueryEngine();
 
diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/ollama.md b/apps/docs/docs/modules/embeddings/available_embeddings/ollama.md
index 7ccd8eeb498048376004b31eb35783e00ff3e502..e7a699ee841374463e20a1d7010ba11dfe9fd5ab 100644
--- a/apps/docs/docs/modules/embeddings/available_embeddings/ollama.md
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/ollama.md
@@ -3,19 +3,13 @@
 To use Ollama embeddings, you need to import `Ollama` from `llamaindex`.
 
 ```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
+import { Ollama, Settings } from "llamaindex";
 
-const ollamaEmbedModel = new Ollama();
-
-const serviceContext = serviceContextFromDefaults({
-  embedModel: ollamaEmbedModel,
-});
+Settings.embedModel = new Ollama();
 
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 const queryEngine = index.asQueryEngine();
 
diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/openai.md b/apps/docs/docs/modules/embeddings/available_embeddings/openai.md
index 20e9c864cd0dbfb4b7c15b98bd29ed0868a6c5ab..7dae906dc1267f4f7889b2a87fcdb82a60b9b7d3 100644
--- a/apps/docs/docs/modules/embeddings/available_embeddings/openai.md
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/openai.md
@@ -3,19 +3,13 @@
 To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `llamaindex`.
 
 ```ts
-import { OpenAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+import { OpenAIEmbedding, Settings } from "llamaindex";
 
-const openaiEmbedModel = new OpenAIEmbedding();
-
-const serviceContext = serviceContextFromDefaults({
-  embedModel: openaiEmbedModel,
-});
+Settings.embedModel = new OpenAIEmbedding();
 
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 const queryEngine = index.asQueryEngine();
 
diff --git a/apps/docs/docs/modules/embeddings/available_embeddings/together.md b/apps/docs/docs/modules/embeddings/available_embeddings/together.md
index 755709a8bfe0f88594778eecd16f0e1223fe2119..025ab40f719ceff1ec1bdfbb1408ab155e53b075 100644
--- a/apps/docs/docs/modules/embeddings/available_embeddings/together.md
+++ b/apps/docs/docs/modules/embeddings/available_embeddings/together.md
@@ -3,21 +3,15 @@
 To use together embeddings, you need to import `TogetherEmbedding` from `llamaindex`.
 
 ```ts
-import { TogetherEmbedding, serviceContextFromDefaults } from "llamaindex";
+import { TogetherEmbedding, Settings } from "llamaindex";
 
-const togetherEmbedModel = new TogetherEmbedding({
+Settings.embedModel = new TogetherEmbedding({
   apiKey: "<YOUR_API_KEY>",
 });
 
-const serviceContext = serviceContextFromDefaults({
-  embedModel: togetherEmbedModel,
-});
-
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 const queryEngine = index.asQueryEngine();
 
diff --git a/apps/docs/docs/modules/embeddings/index.md b/apps/docs/docs/modules/embeddings/index.md
index 4b79914690e670f90417b67ef7d92b6567c51a0b..64151ba8a9d76130c87ecdefe44ccde3daa63b62 100644
--- a/apps/docs/docs/modules/embeddings/index.md
+++ b/apps/docs/docs/modules/embeddings/index.md
@@ -2,14 +2,14 @@
 
 The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI.
 
-This can be explicitly set in the `ServiceContext` object.
+This can be explicitly updated through `Settings`
 
 ```typescript
-import { OpenAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+import { OpenAIEmbedding, Settings } from "llamaindex";
 
-const openaiEmbeds = new OpenAIEmbedding();
-
-const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
+Settings.embedModel = new OpenAIEmbedding({
+  model: "text-embedding-ada-002",
+});
 ```
 
 ## Local Embedding
@@ -19,4 +19,3 @@ For local embeddings, you can use the [HuggingFace](./available_embeddings/huggi
 ## API Reference
 
 - [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)
-- [ServiceContext](../../api/interfaces//ServiceContext.md)
diff --git a/apps/docs/docs/modules/evaluation/modules/correctness.md b/apps/docs/docs/modules/evaluation/modules/correctness.md
index 7189e94000e17004c5362cc505c4c68604b8bb6b..0e425a2915e6c30bed0c4e6d8c161141410f7269 100644
--- a/apps/docs/docs/modules/evaluation/modules/correctness.md
+++ b/apps/docs/docs/modules/evaluation/modules/correctness.md
@@ -21,23 +21,15 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
-import {
-  CorrectnessEvaluator,
-  OpenAI,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { CorrectnessEvaluator, OpenAI, Settings } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:
 
 ```ts
-const llm = new OpenAI({
+Settings.llm = new OpenAI({
   model: "gpt-4",
 });
-
-const ctx = serviceContextFromDefaults({
-  llm,
-});
 ```
 
 ```ts
@@ -49,9 +41,7 @@ const response = ` Certainly! Albert Einstein's theory of relativity consists of
 However, general relativity, published in 1915, extended these ideas to include the effects of magnetism. According to general relativity, gravity is not a force between masses but rather the result of the warping of space and time by magnetic fields generated by massive objects. Massive objects, such as planets and stars, create magnetic fields that cause a curvature in spacetime, and smaller objects follow curved paths in response to this magnetic curvature. This concept is often illustrated using the analogy of a heavy ball placed on a rubber sheet with magnets underneath, causing it to create a depression that other objects (representing smaller masses) naturally move towards due to magnetic attraction.
 `;
 
-const evaluator = new CorrectnessEvaluator({
-  serviceContext: ctx,
-});
+const evaluator = new CorrectnessEvaluator();
 
 const result = await evaluator.evaluateResponse({
   query,
diff --git a/apps/docs/docs/modules/evaluation/modules/faithfulness.md b/apps/docs/docs/modules/evaluation/modules/faithfulness.md
index 4324250af74e663e7d9ffec88625ba07c18124c1..1dd229a42e3cec8c75398a51e9fe494158e1f5a7 100644
--- a/apps/docs/docs/modules/evaluation/modules/faithfulness.md
+++ b/apps/docs/docs/modules/evaluation/modules/faithfulness.md
@@ -28,20 +28,16 @@ import {
   FaithfulnessEvaluator,
   OpenAI,
   VectorStoreIndex,
-  serviceContextFromDefaults,
+  Settings,
 } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:
 
 ```ts
-const llm = new OpenAI({
+Settings.llm = new OpenAI({
   model: "gpt-4",
 });
-
-const ctx = serviceContextFromDefaults({
-  llm,
-});
 ```
 
 Now, let's create a vector index and query engine with documents and query engine respectively. Then, we can evaluate the response with the query and response from the query engine.:
@@ -63,9 +59,7 @@ Now, let's evaluate the response:
 ```ts
 const query = "How did New York City get its name?";
 
-const evaluator = new FaithfulnessEvaluator({
-  serviceContext: ctx,
-});
+const evaluator = new FaithfulnessEvaluator();
 
 const response = await queryEngine.query({
   query,
diff --git a/apps/docs/docs/modules/evaluation/modules/relevancy.md b/apps/docs/docs/modules/evaluation/modules/relevancy.md
index 20f451a2c7d9b34439e6da5ffee5aa9d8a5161e8..7da066bea2edf64422459c89aa3ef003c062f006 100644
--- a/apps/docs/docs/modules/evaluation/modules/relevancy.md
+++ b/apps/docs/docs/modules/evaluation/modules/relevancy.md
@@ -21,23 +21,15 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
-import {
-  RelevancyEvaluator,
-  OpenAI,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { RelevancyEvaluator, OpenAI, Settings } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:
 
 ```ts
-const llm = new OpenAI({
+Settings.llm = new OpenAI({
   model: "gpt-4",
 });
-
-const ctx = serviceContextFromDefaults({
-  llm,
-});
 ```
 
 Now, let's create a vector index and query engine with documents and query engine respectively. Then, we can evaluate the response with the query and response from the query engine.:
@@ -59,6 +51,8 @@ const response = await queryEngine.query({
   query,
 });
 
+const evaluator = new RelevancyEvaluator();
+
 const result = await evaluator.evaluateResponse({
   query,
   response: response,
diff --git a/apps/docs/docs/modules/llms/available_llms/anthropic.md b/apps/docs/docs/modules/llms/available_llms/anthropic.md
index dd83f5207fb9e96d07d797a24f52404513e0fa4f..546a7228a25ab09c747ad403693ebba7787d8a71 100644
--- a/apps/docs/docs/modules/llms/available_llms/anthropic.md
+++ b/apps/docs/docs/modules/llms/available_llms/anthropic.md
@@ -3,13 +3,11 @@
 ## Usage
 
 ```ts
-import { Anthropic, serviceContextFromDefaults } from "llamaindex";
+import { Anthropic, Settings } from "llamaindex";
 
-const anthropicLLM = new Anthropic({
+Settings.llm = new Anthropic({
   apiKey: "<YOUR_API_KEY>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
 ```
 
 ## Load and index documents
@@ -19,9 +17,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -39,28 +35,17 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  Anthropic,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the Anthropic LLM
-  const anthropicLLM = new Anthropic({
-    apiKey: "<YOUR_API_KEY>",
-  });
+import { Anthropic, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
+Settings.llm = new Anthropic({
+  apiKey: "<YOUR_API_KEY>",
+});
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Create a query engine
   const queryEngine = index.asQueryEngine({
diff --git a/apps/docs/docs/modules/llms/available_llms/azure.md b/apps/docs/docs/modules/llms/available_llms/azure.md
index d13f94317f1d53e3010b41decd8cbaecc533c0f3..733cc9cb810349db4163043d4587393bc2d3d62f 100644
--- a/apps/docs/docs/modules/llms/available_llms/azure.md
+++ b/apps/docs/docs/modules/llms/available_llms/azure.md
@@ -15,11 +15,9 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
 ## Usage
 
 ```ts
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+import { OpenAI, Settings } from "llamaindex";
 
-const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
-
-const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
+Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
 ```
 
 ## Load and index documents
@@ -29,9 +27,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -49,26 +45,15 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  OpenAI,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
+import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
+Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/fireworks.md b/apps/docs/docs/modules/llms/available_llms/fireworks.md
index de2713b9614fd0e916a2147fce2a8065fcea4fbb..e8c54d73b97d764e6b69889b76d2c1301cc1a151 100644
--- a/apps/docs/docs/modules/llms/available_llms/fireworks.md
+++ b/apps/docs/docs/modules/llms/available_llms/fireworks.md
@@ -5,13 +5,11 @@ Fireworks.ai focus on production use cases for open source LLMs, offering speed
 ## Usage
 
 ```ts
-import { FireworksLLM, serviceContextFromDefaults } from "llamaindex";
+import { FireworksLLM, Settings } from "llamaindex";
 
-const fireworksLLM = new FireworksLLM({
+Settings.llm = new FireworksLLM({
   apiKey: "<YOUR_API_KEY>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: fireworksLLM });
 ```
 
 ## Load and index documents
@@ -23,9 +21,7 @@ const reader = new PDFReader();
 const documents = await reader.loadData("../data/brk-2022.pdf");
 
 // Split text and create embeddings. Store them in a VectorStoreIndex
-const index = await VectorStoreIndex.fromDocuments(documents, {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments(documents);
 ```
 
 ## Query
diff --git a/apps/docs/docs/modules/llms/available_llms/groq.mdx b/apps/docs/docs/modules/llms/available_llms/groq.mdx
index f3862f0c3feba8582fa15d70d0b0f17e02e2960d..127fbfdec87c9752963627592c13bdd138d67c99 100644
--- a/apps/docs/docs/modules/llms/available_llms/groq.mdx
+++ b/apps/docs/docs/modules/llms/available_llms/groq.mdx
@@ -14,15 +14,13 @@ export GROQ_API_KEY=<your-api-key>
 The initialize the Groq module.
 
 ```ts
-import { Groq, serviceContextFromDefaults } from "llamaindex";
+import { Groq, Settings } from "llamaindex";
 
-const groq = new Groq({
+Settings.llm = new Groq({
   // If you do not wish to set your API key in the environment, you may
   // configure your API key when you initialize the Groq class.
   // apiKey: "<your-api-key>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: groq });
 ```
 
 ## Load and index documents
@@ -32,9 +30,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
diff --git a/apps/docs/docs/modules/llms/available_llms/llama2.md b/apps/docs/docs/modules/llms/available_llms/llama2.md
index ffcac13ac32ec0b59fc9a0ceb69f4aa6d13d2752..edbd64b5b98210f3e76a6c84b07c7823f1baa40a 100644
--- a/apps/docs/docs/modules/llms/available_llms/llama2.md
+++ b/apps/docs/docs/modules/llms/available_llms/llama2.md
@@ -3,32 +3,24 @@
 ## Usage
 
 ```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
+import { Ollama, Settings } from "llamaindex";
 
-const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
-
-const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
+Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 ```
 
 ## Usage with Replication
 
 ```ts
-import {
-  Ollama,
-  ReplicateSession,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { Ollama, ReplicateSession, Settings } from "llamaindex";
 
 const replicateSession = new ReplicateSession({
   replicateKey,
 });
 
-const llama2LLM = new LlamaDeuce({
+Settings.llm = new LlamaDeuce({
   chatStrategy: DeuceChatStrategy.META,
   replicateSession,
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
 ```
 
 ## Load and index documents
@@ -38,9 +30,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -58,26 +48,16 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  LlamaDeuce,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { LlamaDeuce, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-async function main() {
-  // Create an instance of the LLM
-  const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
+// Use the LlamaDeuce LLM
+Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/mistral.md b/apps/docs/docs/modules/llms/available_llms/mistral.md
index 81d17510acaa29a366ad566ac8864a32b47fdbdf..8d43c34837d7c4cb8c5b29e81407a4d93268221e 100644
--- a/apps/docs/docs/modules/llms/available_llms/mistral.md
+++ b/apps/docs/docs/modules/llms/available_llms/mistral.md
@@ -3,14 +3,12 @@
 ## Usage
 
 ```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
+import { Ollama, Settings } from "llamaindex";
 
-const mistralLLM = new MistralAI({
+Settings.llm = new MistralAI({
   model: "mistral-tiny",
   apiKey: "<YOUR_API_KEY>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
 ```
 
 ## Load and index documents
@@ -20,9 +18,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -40,26 +36,16 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  MistralAI,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const mistralLLM = new MistralAI({ model: "mistral-tiny" });
+import { MistralAI, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
+// Use the MistralAI LLM
+Settings.llm = new MistralAI({ model: "mistral-tiny" });
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/ollama.md b/apps/docs/docs/modules/llms/available_llms/ollama.md
index 9d690bc9853400d23faa6711c0105eb55c648e1f..602b0f2ac8d71e717eedaa5b4557e99089666d6c 100644
--- a/apps/docs/docs/modules/llms/available_llms/ollama.md
+++ b/apps/docs/docs/modules/llms/available_llms/ollama.md
@@ -3,14 +3,10 @@
 ## Usage
 
 ```ts
-import { Ollama, serviceContextFromDefaults } from "llamaindex";
+import { Ollama, Settings } from "llamaindex";
 
-const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
-
-const serviceContext = serviceContextFromDefaults({
-  llm: ollamaLLM,
-  embedModel: ollamaLLM,
-});
+Settings.llm = ollamaLLM;
+Settings.embedModel = ollamaLLM;
 ```
 
 ## Load and index documents
@@ -20,9 +16,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -40,33 +34,23 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  Ollama,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { Ollama, Document, VectorStoreIndex, Settings } from "llamaindex";
 
 import fs from "fs/promises";
 
-async function main() {
-  // Create an instance of the LLM
-  const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
+const ollama = new Ollama({ model: "llama2", temperature: 0.75 });
 
-  const essay = await fs.readFile("./paul_graham_essay.txt", "utf-8");
+// Use Ollama LLM and Embed Model
+Settings.llm = ollama;
+Settings.embedModel = ollama;
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({
-    embedModel: ollamaLLM, // prevent 'Set OpenAI Key in OPENAI_API_KEY env variable' error
-    llm: ollamaLLM,
-  });
+async function main() {
+  const essay = await fs.readFile("./paul_graham_essay.txt", "utf-8");
 
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/openai.md b/apps/docs/docs/modules/llms/available_llms/openai.md
index 67b4612053ed6776b8efd366325afbcbb041de8b..1b2faf43e6ebf246a906b6b5b326475e8821ba5a 100644
--- a/apps/docs/docs/modules/llms/available_llms/openai.md
+++ b/apps/docs/docs/modules/llms/available_llms/openai.md
@@ -1,11 +1,9 @@
 # OpenAI
 
 ```ts
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+import { OpenAI, Settings } from "llamaindex";
 
-const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
-
-const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
 ```
 
 You can setup the apiKey on the environment variables, like:
@@ -21,9 +19,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -41,26 +37,16 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  OpenAI,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+// Use the OpenAI LLM
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/portkey.md b/apps/docs/docs/modules/llms/available_llms/portkey.md
index 1b3faed8f451c685250f5301467bea674cd91f79..1d5915e318d5c322b85b309d0c56192aca189460 100644
--- a/apps/docs/docs/modules/llms/available_llms/portkey.md
+++ b/apps/docs/docs/modules/llms/available_llms/portkey.md
@@ -3,13 +3,11 @@
 ## Usage
 
 ```ts
-import { Portkey, serviceContextFromDefaults } from "llamaindex";
+import { Portkey, Settings } from "llamaindex";
 
-const portkeyLLM = new Portkey({
+Settings.llm = new Portkey({
   apiKey: "<YOUR_API_KEY>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
 ```
 
 ## Load and index documents
@@ -19,9 +17,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -39,28 +35,19 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  Portkey,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const portkeyLLM = new Portkey({
-    apiKey: "<YOUR_API_KEY>",
-  });
+import { Portkey, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
+// Use the Portkey LLM
+Settings.llm = new Portkey({
+  apiKey: "<YOUR_API_KEY>",
+});
 
+async function main() {
+  // Create a document
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/available_llms/together.md b/apps/docs/docs/modules/llms/available_llms/together.md
index b8bc507ba7b5832ffe9e4bcd2748e703ce1505d5..3a05215511bdfe79f8057456c18e0e943ab518a1 100644
--- a/apps/docs/docs/modules/llms/available_llms/together.md
+++ b/apps/docs/docs/modules/llms/available_llms/together.md
@@ -3,13 +3,11 @@
 ## Usage
 
 ```ts
-import { TogetherLLM, serviceContextFromDefaults } from "llamaindex";
+import { TogetherLLM, Settings } from "llamaindex";
 
-const togetherLLM = new TogetherLLM({
+Settings.llm = new TogetherLLM({
   apiKey: "<YOUR_API_KEY>",
 });
-
-const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
 ```
 
 ## Load and index documents
@@ -19,9 +17,7 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Query
@@ -39,28 +35,17 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  TogetherLLM,
-  Document,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
-
-async function main() {
-  // Create an instance of the LLM
-  const togetherLLM = new TogetherLLM({
-    apiKey: "<YOUR_API_KEY>",
-  });
+import { TogetherLLM, Document, VectorStoreIndex, Settings } from "llamaindex";
 
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
+Settings.llm = new TogetherLLM({
+  apiKey: "<YOUR_API_KEY>",
+});
 
+async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/apps/docs/docs/modules/llms/index.md b/apps/docs/docs/modules/llms/index.md
index 84382f95664d34f6451f3610483dd988ae42610c..cb0867cbf9902671b0896d85ad951e8a4df59d6b 100644
--- a/apps/docs/docs/modules/llms/index.md
+++ b/apps/docs/docs/modules/llms/index.md
@@ -6,14 +6,12 @@ sidebar_position: 3
 
 The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
 
-The LLM can be explicitly set in the `ServiceContext` object.
+The LLM can be explicitly updated through `Settings`.
 
 ```typescript
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+import { OpenAI, Settings } from "llamaindex";
 
-const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
 ```
 
 ## Azure OpenAI
@@ -35,4 +33,3 @@ For local LLMs, currently we recommend the use of [Ollama](./available_llms/olla
 ## API Reference
 
 - [OpenAI](../api/classes/OpenAI.md)
-- [ServiceContext](../api/interfaces//ServiceContext.md)
diff --git a/apps/docs/docs/modules/node_parser.md b/apps/docs/docs/modules/node_parser.md
index b7ed346b89280a17359ab75952037d83d4dee94b..f7d18f8b7d81e9bcf251bf74653d9976ac50fe05 100644
--- a/apps/docs/docs/modules/node_parser.md
+++ b/apps/docs/docs/modules/node_parser.md
@@ -4,15 +4,14 @@ sidebar_position: 4
 
 # NodeParser
 
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
+The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `Settings` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
 
 ```typescript
 import { Document, SimpleNodeParser } from "llamaindex";
 
 const nodeParser = new SimpleNodeParser();
-const nodes = nodeParser.getNodesFromDocuments([
-  new Document({ text: "I am 10 years old. John is 20 years old." }),
-]);
+
+Settings.nodeParser = nodeParser;
 ```
 
 ## TextSplitter
diff --git a/apps/docs/docs/modules/node_postprocessors/cohere_reranker.md b/apps/docs/docs/modules/node_postprocessors/cohere_reranker.md
index 717f8a2cdbc71e08341f100b2268830789468e40..9b312e8e6525848a0f642d9417bcb38df4b65dca 100644
--- a/apps/docs/docs/modules/node_postprocessors/cohere_reranker.md
+++ b/apps/docs/docs/modules/node_postprocessors/cohere_reranker.md
@@ -18,7 +18,7 @@ import {
   Document,
   OpenAI,
   VectorStoreIndex,
-  serviceContextFromDefaults,
+  Settings,
 } from "llamaindex";
 ```
 
@@ -29,13 +29,9 @@ For this example, we will use a single document. In a real-world scenario, you w
 ```ts
 const document = new Document({ text: essay, id_: "essay" });
 
-const serviceContext = serviceContextFromDefaults({
-  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-});
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
 
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 ```
 
 ## Increase similarity topK to retrieve more results
diff --git a/apps/docs/docs/modules/node_postprocessors/index.md b/apps/docs/docs/modules/node_postprocessors/index.md
index 5828db0eceb51b31ba2b3f854622ab0ed4a2d582..92c3b085f7b6fb94c122b4f26988ca63286780eb 100644
--- a/apps/docs/docs/modules/node_postprocessors/index.md
+++ b/apps/docs/docs/modules/node_postprocessors/index.md
@@ -58,7 +58,10 @@ Most commonly, node-postprocessors will be used in a query engine, where they ar
 ### Using Node Postprocessors in a Query Engine
 
 ```ts
-import { Node, NodeWithScore, SimilarityPostprocessor, CohereRerank } from "llamaindex";
+import { Node, NodeWithScore, SimilarityPostprocessor, CohereRerank, Settings } from "llamaindex";
+
+// Use OpenAI LLM
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
 
 const nodes: NodeWithScore[] = [
   {
@@ -79,14 +82,6 @@ const reranker = new CohereRerank({
 
 const document = new Document({ text: "essay", id_: "essay" });
 
-const serviceContext = serviceContextFromDefaults({
-  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-});
-
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
-
 const queryEngine = index.asQueryEngine({
   nodePostprocessors: [processor, reranker],
 });
diff --git a/apps/docs/docs/modules/prompt/index.md b/apps/docs/docs/modules/prompt/index.md
index fa93746430263f84393434b1a4e6e0c39027d77c..2644a8a4d9b9a592bead7c0ade7812a1c0b9c86a 100644
--- a/apps/docs/docs/modules/prompt/index.md
+++ b/apps/docs/docs/modules/prompt/index.md
@@ -31,13 +31,11 @@ The first method is to create a new instance of `ResponseSynthesizer` (or the mo
 ```ts
 // Create an instance of response synthesizer
 const responseSynthesizer = new ResponseSynthesizer({
-  responseBuilder: new CompactAndRefine(serviceContext, newTextQaPrompt),
+  responseBuilder: new CompactAndRefine(undefined, newTextQaPrompt),
 });
 
 // Create index
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 // Query the index
 const queryEngine = index.asQueryEngine({ responseSynthesizer });
@@ -53,9 +51,7 @@ The second method is that most of the modules in LlamaIndex have a `getPrompts`
 
 ```ts
 // Create index
-const index = await VectorStoreIndex.fromDocuments([document], {
-  serviceContext,
-});
+const index = await VectorStoreIndex.fromDocuments([document]);
 
 // Query the index
 const queryEngine = index.asQueryEngine();
diff --git a/apps/docs/docs/modules/query_engines/metadata_filtering.md b/apps/docs/docs/modules/query_engines/metadata_filtering.md
index 6ccbb4834b4500e35df8c9f3d1c1ef8fcc061d5d..2ae3ac83a1f4c125ef856d2ef41d3ecfba8e5932 100644
--- a/apps/docs/docs/modules/query_engines/metadata_filtering.md
+++ b/apps/docs/docs/modules/query_engines/metadata_filtering.md
@@ -54,12 +54,13 @@ You can create a `ChromaVectorStore` to store the documents:
 
 ```ts
 const chromaVS = new ChromaVectorStore({ collectionName });
-const serviceContext = await storageContextFromDefaults({
+
+const storageContext = await storageContextFromDefaults({
   vectorStore: chromaVS,
 });
 
 const index = await VectorStoreIndex.fromDocuments(docs, {
-  storageContext: serviceContext,
+  storageContext: storageContext,
 });
 ```
 
diff --git a/apps/docs/docs/modules/query_engines/router_query_engine.md b/apps/docs/docs/modules/query_engines/router_query_engine.md
index c4d045c4077d827cc4732ff0b6096ac49035cc87..f8147f2cf621b6b4df4ab944a9090b70a8b257ea 100644
--- a/apps/docs/docs/modules/query_engines/router_query_engine.md
+++ b/apps/docs/docs/modules/query_engines/router_query_engine.md
@@ -18,7 +18,7 @@ import {
   SimpleNodeParser,
   SummaryIndex,
   VectorStoreIndex,
-  serviceContextFromDefaults,
+  Settings,
 } from "llamaindex";
 ```
 
@@ -34,17 +34,13 @@ const documents = await new SimpleDirectoryReader().loadData({
 
 ## Service Context
 
-Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `ServiceContext` to define the rules (eg. LLM API key, chunk size, etc.):
+Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `Settings` to define the rules (eg. LLM API key, chunk size, etc.):
 
 ```ts
-const nodeParser = new SimpleNodeParser({
+Settings.llm = new OpenAI();
+Settings.nodeParser = new SimpleNodeParser({
   chunkSize: 1024,
 });
-
-const serviceContext = serviceContextFromDefaults({
-  nodeParser,
-  llm: new OpenAI(),
-});
 ```
 
 ## Creating Indices
@@ -52,13 +48,8 @@ const serviceContext = serviceContextFromDefaults({
 Next, we need to create some indices. We will create a `VectorStoreIndex` and a `SummaryIndex`:
 
 ```ts
-const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-  serviceContext,
-});
-
-const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-  serviceContext,
-});
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+const summaryIndex = await SummaryIndex.fromDocuments(documents);
 ```
 
 ## Creating Query Engines
@@ -88,7 +79,6 @@ const queryEngine = RouterQueryEngine.fromDefaults({
       description: "Useful for retrieving specific context from Abramov",
     },
   ],
-  serviceContext,
 });
 ```
 
@@ -117,34 +107,23 @@ import {
   SimpleNodeParser,
   SummaryIndex,
   VectorStoreIndex,
-  serviceContextFromDefaults,
+  Settings,
 } from "llamaindex";
 
+Settings.llm = new OpenAI();
+Settings.nodeParser = new SimpleNodeParser({
+  chunkSize: 1024,
+});
+
 async function main() {
   // Load documents from a directory
   const documents = await new SimpleDirectoryReader().loadData({
     directoryPath: "node_modules/llamaindex/examples",
   });
 
-  // Parse the documents into nodes
-  const nodeParser = new SimpleNodeParser({
-    chunkSize: 1024,
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser,
-    llm: new OpenAI(),
-  });
-
   // Create indices
-  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+  const summaryIndex = await SummaryIndex.fromDocuments(documents);
 
   // Create query engines
   const vectorQueryEngine = vectorIndex.asQueryEngine();
@@ -162,7 +141,6 @@ async function main() {
         description: "Useful for retrieving specific context from Abramov",
       },
     ],
-    serviceContext,
   });
 
   // Query the router query engine
diff --git a/examples/Settings.ts b/examples/Settings.ts
new file mode 100644
index 0000000000000000000000000000000000000000..47778d75744e9cc1aa87481c4b849a934385d842
--- /dev/null
+++ b/examples/Settings.ts
@@ -0,0 +1,29 @@
+import fs from "node:fs/promises";
+
+import { Document, OpenAI, Settings, VectorStoreIndex } from "llamaindex";
+
+Settings.llm = new OpenAI({ model: "gpt-4" });
+
+async function main() {
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await VectorStoreIndex.fromDocuments([document]);
+
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
diff --git a/examples/agent/multi_document_agent.ts b/examples/agent/multi_document_agent.ts
index 6d1165ffe4c9900877c6e0673fb1753d6548f692..ecdd805b82d8f728e6e6263760ea3b54f02a84e2 100644
--- a/examples/agent/multi_document_agent.ts
+++ b/examples/agent/multi_document_agent.ts
@@ -6,11 +6,11 @@ import {
   OpenAI,
   OpenAIAgent,
   QueryEngineTool,
+  Settings,
   SimpleNodeParser,
   SimpleToolNodeMapping,
   SummaryIndex,
   VectorStoreIndex,
-  serviceContextFromDefaults,
   storageContextFromDefaults,
 } from "llamaindex";
 
@@ -18,6 +18,8 @@ import { extractWikipedia } from "./helpers/extractWikipedia";
 
 const wikiTitles = ["Brazil", "Canada"];
 
+Settings.llm = new OpenAI({ model: "gpt-4" });
+
 async function main() {
   await extractWikipedia(wikiTitles);
 
@@ -30,11 +32,6 @@ async function main() {
     countryDocs[title] = document;
   }
 
-  const llm = new OpenAI({
-    model: "gpt-4",
-  });
-
-  const serviceContext = serviceContextFromDefaults({ llm });
   const storageContext = await storageContextFromDefaults({
     persistDir: "./storage",
   });
@@ -54,13 +51,11 @@ async function main() {
     console.log(`Creating index for ${title}`);
 
     const vectorIndex = await VectorStoreIndex.init({
-      serviceContext: serviceContext,
       storageContext: storageContext,
       nodes,
     });
 
     const summaryIndex = await SummaryIndex.init({
-      serviceContext: serviceContext,
       nodes,
     });
 
@@ -90,7 +85,7 @@ async function main() {
 
     const agent = new OpenAIAgent({
       tools: queryEngineTools,
-      llm,
+      llm: new OpenAI({ model: "gpt-4" }),
       verbose: true,
     });
 
@@ -126,14 +121,11 @@ async function main() {
     allTools,
     toolMapping,
     VectorStoreIndex,
-    {
-      serviceContext,
-    },
   );
 
   const topAgent = new OpenAIAgent({
     toolRetriever: await objectIndex.asRetriever({}),
-    llm,
+    llm: new OpenAI({ model: "gpt-4" }),
     verbose: true,
     prefixMessages: [
       {
diff --git a/examples/astradb/query.ts b/examples/astradb/query.ts
index 23985c0d253b6c1d7f46501d1ffcfd43289443c8..546920a8bf931b0b41ea493e02ec8946f4b95705 100644
--- a/examples/astradb/query.ts
+++ b/examples/astradb/query.ts
@@ -1,8 +1,4 @@
-import {
-  AstraDBVectorStore,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { AstraDBVectorStore, VectorStoreIndex } from "llamaindex";
 
 const collectionName = "movie_reviews";
 
@@ -11,8 +7,7 @@ async function main() {
     const astraVS = new AstraDBVectorStore({ contentKey: "reviewtext" });
     await astraVS.connect(collectionName);
 
-    const ctx = serviceContextFromDefaults();
-    const index = await VectorStoreIndex.fromVectorStore(astraVS, ctx);
+    const index = await VectorStoreIndex.fromVectorStore(astraVS);
 
     const retriever = await index.asRetriever({ similarityTopK: 20 });
 
diff --git a/examples/chatEngine.ts b/examples/chatEngine.ts
index 90152c88ce319de47d4be1ebcaa364191c43930e..e5283114bdda1fdef8d870fd61bb6a6aa660d2b1 100644
--- a/examples/chatEngine.ts
+++ b/examples/chatEngine.ts
@@ -4,18 +4,18 @@ import readline from "node:readline/promises";
 import {
   ContextChatEngine,
   Document,
-  serviceContextFromDefaults,
+  Settings,
   VectorStoreIndex,
 } from "llamaindex";
 
 import essay from "./essay";
 
+// Update chunk size
+Settings.chunkSize = 512;
+
 async function main() {
   const document = new Document({ text: essay });
-  const serviceContext = serviceContextFromDefaults({ chunkSize: 512 });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
   const retriever = index.asRetriever();
   retriever.similarityTopK = 5;
   const chatEngine = new ContextChatEngine({ retriever });
diff --git a/examples/evaluation/correctness.ts b/examples/evaluation/correctness.ts
index 88c5c39c74faa61e800113169193844ce331c29a..4d920a062a25bb02c1b9c9c9cc65b5bb38e2edfd 100644
--- a/examples/evaluation/correctness.ts
+++ b/examples/evaluation/correctness.ts
@@ -1,21 +1,10 @@
-import {
-  CorrectnessEvaluator,
-  OpenAI,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { CorrectnessEvaluator, OpenAI, Settings } from "llamaindex";
 
-async function main() {
-  const llm = new OpenAI({
-    model: "gpt-4",
-  });
-
-  const ctx = serviceContextFromDefaults({
-    llm,
-  });
+// Update llm to use OpenAI
+Settings.llm = new OpenAI({ model: "gpt-4" });
 
-  const evaluator = new CorrectnessEvaluator({
-    serviceContext: ctx,
-  });
+async function main() {
+  const evaluator = new CorrectnessEvaluator();
 
   const query =
     "Can you explain the theory of relativity proposed by Albert Einstein in detail?";
diff --git a/examples/evaluation/faithfulness.ts b/examples/evaluation/faithfulness.ts
index 7571919dda77f38801c39a449ea9fb643eef0068..8456e3f47a338e24558643b1566bd3c5b317f0a3 100644
--- a/examples/evaluation/faithfulness.ts
+++ b/examples/evaluation/faithfulness.ts
@@ -2,22 +2,15 @@ import {
   Document,
   FaithfulnessEvaluator,
   OpenAI,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
-async function main() {
-  const llm = new OpenAI({
-    model: "gpt-4",
-  });
-
-  const ctx = serviceContextFromDefaults({
-    llm,
-  });
+// Update llm to use OpenAI
+Settings.llm = new OpenAI({ model: "gpt-4" });
 
-  const evaluator = new FaithfulnessEvaluator({
-    serviceContext: ctx,
-  });
+async function main() {
+  const evaluator = new FaithfulnessEvaluator();
 
   const documents = [
     new Document({
diff --git a/examples/evaluation/relevancy.ts b/examples/evaluation/relevancy.ts
index c595ff1dca6c9f3b485444e275ef049753fa86f6..2467d66fdb71b5fd18a646417a10ba943abc3cca 100644
--- a/examples/evaluation/relevancy.ts
+++ b/examples/evaluation/relevancy.ts
@@ -2,22 +2,16 @@ import {
   Document,
   OpenAI,
   RelevancyEvaluator,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
-async function main() {
-  const llm = new OpenAI({
-    model: "gpt-4",
-  });
-
-  const ctx = serviceContextFromDefaults({
-    llm,
-  });
+Settings.llm = new OpenAI({
+  model: "gpt-4",
+});
 
-  const evaluator = new RelevancyEvaluator({
-    serviceContext: ctx,
-  });
+async function main() {
+  const evaluator = new RelevancyEvaluator();
 
   const documents = [
     new Document({
diff --git a/examples/groq.ts b/examples/groq.ts
index 581031532a0f462c6329adfb5b47eb2ee42c01b8..bb6662fe46faae495a7c412867fb476a2431ad72 100644
--- a/examples/groq.ts
+++ b/examples/groq.ts
@@ -1,30 +1,20 @@
 import fs from "node:fs/promises";
 
-import {
-  Document,
-  Groq,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { Document, Groq, Settings, VectorStoreIndex } from "llamaindex";
 
-async function main() {
-  // Create an instance of the LLM
-  const groq = new Groq({
-    apiKey: process.env.GROQ_API_KEY,
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({ llm: groq });
+// Update llm to use Groq
+Settings.llm = new Groq({
+  apiKey: process.env.GROQ_API_KEY,
+});
 
+async function main() {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";
   const essay = await fs.readFile(path, "utf-8");
   const document = new Document({ text: essay, id_: "essay" });
 
   // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // get retriever
   const retriever = index.asRetriever();
diff --git a/examples/huggingface.ts b/examples/huggingface.ts
index c1e54e05f53d3d1f972c0f85b78d029d92f7170c..8297b75366a47944bb56de0550e8df14b91694b0 100644
--- a/examples/huggingface.ts
+++ b/examples/huggingface.ts
@@ -4,10 +4,15 @@ import {
   Document,
   HuggingFaceEmbedding,
   HuggingFaceEmbeddingModelType,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
+// Update embed model
+Settings.embedModel = new HuggingFaceEmbedding({
+  modelType: HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2,
+});
+
 async function main() {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";
@@ -17,18 +22,8 @@ async function main() {
   // Create Document object with essay
   const document = new Document({ text: essay, id_: path });
 
-  // Use Local embedding from HuggingFace
-  const embedModel = new HuggingFaceEmbedding({
-    modelType: HuggingFaceEmbeddingModelType.XENOVA_ALL_MPNET_BASE_V2,
-  });
-  const serviceContext = serviceContextFromDefaults({
-    embedModel,
-  });
-
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
diff --git a/examples/longText.ts b/examples/longText.ts
index 9766d96b5f4767c1e2fb64ab3f7b419378e56ef0..eb3c3d8453f0c0dfdec87b95344786242a12c264 100644
--- a/examples/longText.ts
+++ b/examples/longText.ts
@@ -1,26 +1,21 @@
 import {
   Document,
+  Settings,
   SimpleNodeParser,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
 export const STORAGE_DIR = "./data";
 
+// Update node parser
+Settings.nodeParser = new SimpleNodeParser({
+  chunkSize: 512,
+  chunkOverlap: 20,
+  splitLongSentences: true,
+});
 (async () => {
-  // create service context that is splitting sentences longer than CHUNK_SIZE
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser: new SimpleNodeParser({
-      chunkSize: 512,
-      chunkOverlap: 20,
-      splitLongSentences: true,
-    }),
-  });
-
   // generate a document with a very long sentence (9000 words long)
   const longSentence = "is ".repeat(9000) + ".";
   const document = new Document({ text: longSentence, id_: "1" });
-  await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  await VectorStoreIndex.fromDocuments([document]);
 })();
diff --git a/examples/milvus/query.ts b/examples/milvus/query.ts
index 90fe9fb1a7ba94c23ecb1c5146b52e4f7cf51942..38ad3a7b1c64d21a875495c088732faf8515be3f 100644
--- a/examples/milvus/query.ts
+++ b/examples/milvus/query.ts
@@ -1,8 +1,4 @@
-import {
-  MilvusVectorStore,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { MilvusVectorStore, VectorStoreIndex } from "llamaindex";
 
 const collectionName = "movie_reviews";
 
@@ -10,8 +6,7 @@ async function main() {
   try {
     const milvus = new MilvusVectorStore({ collection: collectionName });
 
-    const ctx = serviceContextFromDefaults();
-    const index = await VectorStoreIndex.fromVectorStore(milvus, ctx);
+    const index = await VectorStoreIndex.fromVectorStore(milvus);
 
     const retriever = await index.asRetriever({ similarityTopK: 20 });
 
diff --git a/examples/mistral.ts b/examples/mistral.ts
index 75b8b3c1a052e86a3828a851629fab825b6b95d6..67555c337f683e6b0e74998812363d579a9411e9 100644
--- a/examples/mistral.ts
+++ b/examples/mistral.ts
@@ -1,15 +1,18 @@
 import * as fs from "fs/promises";
 import {
-  BaseEmbedding,
   Document,
-  LLM,
   MistralAI,
   MistralAIEmbedding,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
-async function rag(llm: LLM, embedModel: BaseEmbedding, query: string) {
+// Update embed model
+Settings.embedModel = new MistralAIEmbedding();
+// Update llm to use MistralAI
+Settings.llm = new MistralAI({ model: "mistral-tiny" });
+
+async function rag(query: string) {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";
 
@@ -18,12 +21,7 @@ async function rag(llm: LLM, embedModel: BaseEmbedding, query: string) {
   // Create Document object with essay
   const document = new Document({ text: essay, id_: path });
 
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const serviceContext = serviceContextFromDefaults({ llm, embedModel });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
@@ -60,10 +58,6 @@ async function rag(llm: LLM, embedModel: BaseEmbedding, query: string) {
   }
 
   // rag
-  const ragResponse = await rag(
-    llm,
-    embedding,
-    "What did the author do in college?",
-  );
+  const ragResponse = await rag("What did the author do in college?");
   console.log(ragResponse);
 })();
diff --git a/examples/mongodb/3_query.ts b/examples/mongodb/3_query.ts
index 84920dd9d238027055083bbed862405ac688a80d..9585d90a8c91543b2c28c730a3a9a3add1501fff 100644
--- a/examples/mongodb/3_query.ts
+++ b/examples/mongodb/3_query.ts
@@ -1,10 +1,6 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
-import {
-  MongoDBAtlasVectorSearch,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { MongoDBAtlasVectorSearch, VectorStoreIndex } from "llamaindex";
 import { MongoClient } from "mongodb";
 
 // Load environment variables from local .env file
@@ -12,7 +8,7 @@ dotenv.config();
 
 async function query() {
   const client = new MongoClient(process.env.MONGODB_URI!);
-  const serviceContext = serviceContextFromDefaults();
+
   const store = new MongoDBAtlasVectorSearch({
     mongodbClient: client,
     dbName: process.env.MONGODB_DATABASE!,
@@ -20,7 +16,7 @@ async function query() {
     indexName: process.env.MONGODB_VECTOR_INDEX!,
   });
 
-  const index = await VectorStoreIndex.fromVectorStore(store, serviceContext);
+  const index = await VectorStoreIndex.fromVectorStore(store);
 
   const retriever = index.asRetriever({ similarityTopK: 20 });
   const queryEngine = index.asQueryEngine({ retriever });
diff --git a/examples/multimodal/load.ts b/examples/multimodal/load.ts
index 48556686e020fc3769184c25ff742802c6b36a1a..3ed94e30b246c561d8d51b13073b038f1ba8be9a 100644
--- a/examples/multimodal/load.ts
+++ b/examples/multimodal/load.ts
@@ -1,12 +1,16 @@
 import {
-  ServiceContext,
-  serviceContextFromDefaults,
+  Settings,
   SimpleDirectoryReader,
-  storageContextFromDefaults,
   VectorStoreIndex,
+  storageContextFromDefaults,
 } from "llamaindex";
+
 import * as path from "path";
 
+// Update chunk size and overlap
+Settings.chunkSize = 512;
+Settings.chunkOverlap = 20;
+
 async function getRuntime(func: any) {
   const start = Date.now();
   await func();
@@ -14,7 +18,7 @@ async function getRuntime(func: any) {
   return end - start;
 }
 
-async function generateDatasource(serviceContext: ServiceContext) {
+async function generateDatasource() {
   console.log(`Generating storage...`);
   // Split documents, create embeddings and store them in the storage context
   const ms = await getRuntime(async () => {
@@ -26,7 +30,6 @@ async function generateDatasource(serviceContext: ServiceContext) {
       storeImages: true,
     });
     await VectorStoreIndex.fromDocuments(documents, {
-      serviceContext,
       storageContext,
     });
   });
@@ -34,12 +37,7 @@ async function generateDatasource(serviceContext: ServiceContext) {
 }
 
 async function main() {
-  const serviceContext = serviceContextFromDefaults({
-    chunkSize: 512,
-    chunkOverlap: 20,
-  });
-
-  await generateDatasource(serviceContext);
+  await generateDatasource();
   console.log("Finished generating storage.");
 }
 
diff --git a/examples/multimodal/rag.ts b/examples/multimodal/rag.ts
index 4d9dcbdbcc1b4da1b4d83f7d91244d4184099644..f7d945c804936e7870386b7d68fabf7107ec96c0 100644
--- a/examples/multimodal/rag.ts
+++ b/examples/multimodal/rag.ts
@@ -1,18 +1,28 @@
 import {
   CallbackManager,
-  ImageDocument,
   ImageType,
   MultiModalResponseSynthesizer,
-  NodeWithScore,
   OpenAI,
-  ServiceContext,
+  Settings,
   VectorStoreIndex,
-  runWithCallbackManager,
-  serviceContextFromDefaults,
   storageContextFromDefaults,
 } from "llamaindex";
 
-export async function createIndex(serviceContext: ServiceContext) {
+// Update chunk size and overlap
+Settings.chunkSize = 512;
+Settings.chunkOverlap = 20;
+
+// Update llm
+Settings.llm = new OpenAI({ model: "gpt-4-vision-preview", maxTokens: 512 });
+
+// Update callbackManager
+Settings.callbackManager = new CallbackManager({
+  onRetrieve: ({ query, nodes }) => {
+    console.log(`Retrieved ${nodes.length} nodes for query: ${query}`);
+  },
+});
+
+export async function createIndex() {
   // set up vector store index with two vector stores, one for text, the other for images
   const storageContext = await storageContextFromDefaults({
     persistDir: "storage",
@@ -21,36 +31,21 @@ export async function createIndex(serviceContext: ServiceContext) {
   return await VectorStoreIndex.init({
     nodes: [],
     storageContext,
-    serviceContext,
   });
 }
 
 async function main() {
-  let images: ImageType[] = [];
-  const callbackManager = new CallbackManager({
-    onRetrieve: ({ query, nodes }) => {
-      images = nodes
-        .filter(({ node }: NodeWithScore) => node instanceof ImageDocument)
-        .map(({ node }: NodeWithScore) => (node as ImageDocument).image);
-    },
-  });
-  const llm = new OpenAI({ model: "gpt-4-vision-preview", maxTokens: 512 });
-  const serviceContext = serviceContextFromDefaults({
-    llm,
-    chunkSize: 512,
-    chunkOverlap: 20,
-  });
-  const index = await createIndex(serviceContext);
+  const images: ImageType[] = [];
+
+  const index = await createIndex();
 
   const queryEngine = index.asQueryEngine({
-    responseSynthesizer: new MultiModalResponseSynthesizer({ serviceContext }),
+    responseSynthesizer: new MultiModalResponseSynthesizer(),
     retriever: index.asRetriever({ similarityTopK: 3, imageSimilarityTopK: 1 }),
   });
-  const result = await runWithCallbackManager(callbackManager, () =>
-    queryEngine.query({
-      query: "Tell me more about Vincent van Gogh's famous paintings",
-    }),
-  );
+  const result = await queryEngine.query({
+    query: "Tell me more about Vincent van Gogh's famous paintings",
+  });
   console.log(result.response, "\n");
   images.forEach((image) =>
     console.log(`Image retrieved and used in inference: ${image.toString()}`),
diff --git a/examples/multimodal/retrieve.ts b/examples/multimodal/retrieve.ts
index 8e0e2242e6dda3ac060615e20eb24883a4a1800d..7c5bf2f85d5d3ae4a5f4617d92253e872805eb80 100644
--- a/examples/multimodal/retrieve.ts
+++ b/examples/multimodal/retrieve.ts
@@ -1,17 +1,17 @@
 import {
   ImageNode,
-  serviceContextFromDefaults,
-  storageContextFromDefaults,
+  Settings,
   TextNode,
   VectorStoreIndex,
+  storageContextFromDefaults,
 } from "llamaindex";
 
+// Update chunk size and overlap
+Settings.chunkSize = 512;
+Settings.chunkOverlap = 20;
+
 export async function createIndex() {
   // set up vector store index with two vector stores, one for text, the other for images
-  const serviceContext = serviceContextFromDefaults({
-    chunkSize: 512,
-    chunkOverlap: 20,
-  });
   const storageContext = await storageContextFromDefaults({
     persistDir: "storage",
     storeImages: true,
@@ -19,7 +19,6 @@ export async function createIndex() {
   return await VectorStoreIndex.init({
     nodes: [],
     storageContext,
-    serviceContext,
   });
 }
 
diff --git a/examples/pg-vector-store/query.ts b/examples/pg-vector-store/query.ts
index c46f46c9e0ad31d7fb0d9f3b35f5a196395b8467..96d6ed9bd8c45a8cddc6dec8cb4ab677d7a75654 100755
--- a/examples/pg-vector-store/query.ts
+++ b/examples/pg-vector-store/query.ts
@@ -1,8 +1,4 @@
-import {
-  PGVectorStore,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { PGVectorStore, VectorStoreIndex } from "llamaindex";
 
 async function main() {
   const readline = require("readline").createInterface({
@@ -15,8 +11,7 @@ async function main() {
     // Optional - set your collection name, default is no filter on this field.
     // pgvs.setCollection();
 
-    const ctx = serviceContextFromDefaults();
-    const index = await VectorStoreIndex.fromVectorStore(pgvs, ctx);
+    const index = await VectorStoreIndex.fromVectorStore(pgvs);
 
     // Query the index
     const queryEngine = await index.asQueryEngine();
diff --git a/examples/pinecone-vector-store/query.ts b/examples/pinecone-vector-store/query.ts
index f0ee4b3c57a861299a84e2541b36ebd25e994330..5a5d089d67fa5d074cf670615833bec89c63943f 100755
--- a/examples/pinecone-vector-store/query.ts
+++ b/examples/pinecone-vector-store/query.ts
@@ -1,8 +1,4 @@
-import {
-  PineconeVectorStore,
-  VectorStoreIndex,
-  serviceContextFromDefaults,
-} from "llamaindex";
+import { PineconeVectorStore, VectorStoreIndex } from "llamaindex";
 
 async function main() {
   const readline = require("readline").createInterface({
@@ -13,8 +9,7 @@ async function main() {
   try {
     const pcvs = new PineconeVectorStore();
 
-    const ctx = serviceContextFromDefaults();
-    const index = await VectorStoreIndex.fromVectorStore(pcvs, ctx);
+    const index = await VectorStoreIndex.fromVectorStore(pcvs);
 
     // Query the index
     const queryEngine = await index.asQueryEngine();
diff --git a/examples/prompts/promptMixin.ts b/examples/prompts/promptMixin.ts
index d0b94079670b0be5e39e54b4239984c6c01a1295..bd60fd3752524a66dac8d6638717ce2109d35cf2 100644
--- a/examples/prompts/promptMixin.ts
+++ b/examples/prompts/promptMixin.ts
@@ -4,7 +4,6 @@ import {
   TreeSummarize,
   TreeSummarizePrompt,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
 const treeSummarizePrompt: TreeSummarizePrompt = ({ context, query }) => {
@@ -27,10 +26,8 @@ async function main() {
 
   const query = "The quick brown fox jumps over the lazy dog";
 
-  const ctx = serviceContextFromDefaults({});
-
   const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new TreeSummarize(ctx),
+    responseBuilder: new TreeSummarize(),
   });
 
   const queryEngine = index.asQueryEngine({
diff --git a/examples/qdrantdb/preFilters.ts b/examples/qdrantdb/preFilters.ts
index 8963b54f26fa8af7f14e76730eb1c5587394f0fd..0cc105a529ca9a1903194e874b08d7e054033daf 100644
--- a/examples/qdrantdb/preFilters.ts
+++ b/examples/qdrantdb/preFilters.ts
@@ -4,12 +4,21 @@ import {
   Document,
   MetadataMode,
   QdrantVectorStore,
+  Settings,
   VectorStoreIndex,
-  runWithCallbackManager,
-  serviceContextFromDefaults,
   storageContextFromDefaults,
 } from "llamaindex";
 
+// Update callback manager
+Settings.callbackManager = new CallbackManager({
+  onRetrieve: (data) => {
+    console.log(
+      "The retrieved nodes are:",
+      data.nodes.map((node) => node.node.getContent(MetadataMode.NONE)),
+    );
+  },
+});
+
 // Load environment variables from local .env file
 dotenv.config();
 
@@ -37,21 +46,9 @@ async function main() {
     const ctx = await storageContextFromDefaults({ vectorStore: qdrantVs });
 
     console.log("Embedding documents and adding to index");
-    const index = await runWithCallbackManager(
-      new CallbackManager({
-        onRetrieve: (data) => {
-          console.log(
-            "The retrieved nodes are:",
-            data.nodes.map((node) => node.node.getContent(MetadataMode.NONE)),
-          );
-        },
-      }),
-      () =>
-        VectorStoreIndex.fromDocuments(docs, {
-          storageContext: ctx,
-          serviceContext: serviceContextFromDefaults(),
-        }),
-    );
+    const index = await VectorStoreIndex.fromDocuments(docs, {
+      storageContext: ctx,
+    });
 
     console.log(
       "Querying index with no filters: Expected output: Brown probably",
diff --git a/examples/readers/src/csv.ts b/examples/readers/src/csv.ts
index 812401295b915d42a7607052d2618924fe0c3bab..6d9f6d901a1f4a3c5ef62744683f55ea217ff477 100644
--- a/examples/readers/src/csv.ts
+++ b/examples/readers/src/csv.ts
@@ -2,25 +2,21 @@ import {
   CompactAndRefine,
   OpenAI,
   ResponseSynthesizer,
-  serviceContextFromDefaults,
+  Settings,
   VectorStoreIndex,
 } from "llamaindex";
 import { PapaCSVReader } from "llamaindex/readers/CSVReader";
 
+Settings.llm = new OpenAI({ model: "gpt-4" });
+
 async function main() {
   // Load CSV
   const reader = new PapaCSVReader();
   const path = "../data/titanic_train.csv";
   const documents = await reader.loadData(path);
 
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-4" }),
-  });
-
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments(documents);
 
   const csvPrompt = ({ context = "", query = "" }) => {
     return `The following CSV file is loaded from ${path}
@@ -32,7 +28,7 @@ Given the CSV file, generate me Typescript code to answer the question: ${query}
   };
 
   const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new CompactAndRefine(serviceContext, csvPrompt),
+    responseBuilder: new CompactAndRefine(undefined, csvPrompt),
   });
 
   const queryEngine = index.asQueryEngine({ responseSynthesizer });
diff --git a/examples/readers/src/pdf_fw.ts b/examples/readers/src/pdf_fw.ts
index d4a47e72422ccab79b4719c6e66b7edb4a07baf6..95b83a0502e90c45e77e9bd43f97ea6ff49306bf 100644
--- a/examples/readers/src/pdf_fw.ts
+++ b/examples/readers/src/pdf_fw.ts
@@ -1,17 +1,15 @@
 import { FireworksEmbedding, FireworksLLM, VectorStoreIndex } from "llamaindex";
 import { PDFReader } from "llamaindex/readers/PDFReader";
 
-import { serviceContextFromDefaults } from "llamaindex";
+import { Settings } from "llamaindex";
 
-const embedModel = new FireworksEmbedding({
-  model: "nomic-ai/nomic-embed-text-v1.5",
-});
-
-const llm = new FireworksLLM({
+Settings.llm = new FireworksLLM({
   model: "accounts/fireworks/models/mixtral-8x7b-instruct",
 });
 
-const serviceContext = serviceContextFromDefaults({ llm, embedModel });
+Settings.embedModel = new FireworksEmbedding({
+  model: "nomic-ai/nomic-embed-text-v1.5",
+});
 
 async function main() {
   // Load PDF
@@ -19,9 +17,7 @@ async function main() {
   const documents = await reader.loadData("../data/brk-2022.pdf");
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments(documents);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
diff --git a/examples/readers/src/pdf_fw_openai.ts b/examples/readers/src/pdf_fw_openai.ts
index 2910deb251ce8ce07ae631ce81afe86f849eb06f..0d9fba6597ef4175439f2abc4fac88d88fa0cd03 100644
--- a/examples/readers/src/pdf_fw_openai.ts
+++ b/examples/readers/src/pdf_fw_openai.ts
@@ -1,30 +1,26 @@
 import { OpenAI, OpenAIEmbedding, VectorStoreIndex } from "llamaindex";
 import { PDFReader } from "llamaindex/readers/PDFReader";
 
-import { serviceContextFromDefaults } from "llamaindex";
+import { Settings } from "llamaindex";
 
-const embedModel = new OpenAIEmbedding({
-  model: "nomic-ai/nomic-embed-text-v1.5",
-});
+// Update llm and embedModel
 
-const llm = new OpenAI({
-  model: "accounts/fireworks/models/mixtral-8x7b-instruct",
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
+Settings.embedModel = new OpenAIEmbedding({
+  model: "nomic-ai/nomic-embed-text-v1.5",
 });
 
-const serviceContext = serviceContextFromDefaults({ llm, embedModel });
-
 async function main() {
   // Load PDF
   const reader = new PDFReader();
   const documents = await reader.loadData("../data/brk-2022.pdf");
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments(documents);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
+
   const response = await queryEngine.query({
     query: "What mistakes did Warren E. Buffett make?",
   });
diff --git a/examples/readonly.ts b/examples/readonly.ts
index 180cb8c0fefdd874d4d1228519d9fe52d7c44e0e..e0a2cfb9ba3374c8494757b14503c3423e7cbb45 100644
--- a/examples/readonly.ts
+++ b/examples/readonly.ts
@@ -1,16 +1,14 @@
 import { execSync } from "child_process";
 import {
   PDFReader,
-  serviceContextFromDefaults,
-  storageContextFromDefaults,
   VectorStoreIndex,
+  storageContextFromDefaults,
 } from "llamaindex";
 
 const STORAGE_DIR = "./cache";
 
 async function main() {
   // write the index to disk
-  const serviceContext = serviceContextFromDefaults({});
   const storageContext = await storageContextFromDefaults({
     persistDir: `${STORAGE_DIR}`,
   });
@@ -18,7 +16,6 @@ async function main() {
   const documents = await reader.loadData("data/brk-2022.pdf");
   await VectorStoreIndex.fromDocuments(documents, {
     storageContext,
-    serviceContext,
   });
   console.log("wrote index to disk - now trying to read it");
   // make index dir read only
@@ -29,7 +26,6 @@ async function main() {
   });
   await VectorStoreIndex.init({
     storageContext: readOnlyStorageContext,
-    serviceContext,
   });
   console.log("read only index successfully opened");
 }
diff --git a/examples/recipes/cost-analysis.ts b/examples/recipes/cost-analysis.ts
index 70bacc30a3e784d615ba1262c07b35daf39c8182..9e5e5f999eb8d33c320a5b3caa516bfce9150de1 100644
--- a/examples/recipes/cost-analysis.ts
+++ b/examples/recipes/cost-analysis.ts
@@ -1,5 +1,5 @@
 import { OpenAI } from "llamaindex";
-import { getCurrentCallbackManager } from "llamaindex/callbacks/CallbackManager";
+import { Settings } from "llamaindex/Settings";
 
 const llm = new OpenAI({
   model: "gpt-4-0125-preview",
@@ -7,8 +7,7 @@ const llm = new OpenAI({
 
 let tokenCount = 0;
 
-// @todo: use GlobalSetting in the future
-getCurrentCallbackManager().addHandlers("llm-start", (event) => {
+Settings.callbackManager.on("llm-start", (event) => {
   const { messages } = event.detail.payload;
   tokenCount += llm.tokens(messages);
   console.log("Token count:", tokenCount);
diff --git a/examples/rerankers/CohereReranker.ts b/examples/rerankers/CohereReranker.ts
index 4b34fa549e577defde713aed834cff15656989cb..dba3ce336ebeaa5be72de7c671652cb744e1cb5c 100644
--- a/examples/rerankers/CohereReranker.ts
+++ b/examples/rerankers/CohereReranker.ts
@@ -2,22 +2,18 @@ import {
   CohereRerank,
   Document,
   OpenAI,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
 import essay from "../essay";
 
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
+
 async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-  });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   const retriever = index.asRetriever();
 
diff --git a/examples/routerQueryEngine.ts b/examples/routerQueryEngine.ts
index 57075f0db5317f934fc65d19ca70adb253725f25..d28f9e30d9a7ae5d2ebc826c485020b52002d774 100644
--- a/examples/routerQueryEngine.ts
+++ b/examples/routerQueryEngine.ts
@@ -1,38 +1,31 @@
 import {
   OpenAI,
   RouterQueryEngine,
+  Settings,
   SimpleDirectoryReader,
   SimpleNodeParser,
   SummaryIndex,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
+// Update llm
+Settings.llm = new OpenAI();
+
+// Update node parser
+Settings.nodeParser = new SimpleNodeParser({
+  chunkSize: 1024,
+});
+
 async function main() {
   // Load documents from a directory
   const documents = await new SimpleDirectoryReader().loadData({
     directoryPath: "node_modules/llamaindex/examples",
   });
 
-  // Parse the documents into nodes
-  const nodeParser = new SimpleNodeParser({
-    chunkSize: 1024,
-  });
-
-  // Create a service context
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser,
-    llm: new OpenAI(),
-  });
-
   // Create indices
-  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
 
-  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
-    serviceContext,
-  });
+  const summaryIndex = await SummaryIndex.fromDocuments(documents);
 
   // Create query engines
   const vectorQueryEngine = vectorIndex.asQueryEngine();
@@ -50,7 +43,6 @@ async function main() {
         description: "Useful for retrieving specific context from Abramov",
       },
     ],
-    serviceContext,
   });
 
   // Query the router query engine
diff --git a/examples/sentenceWindow.ts b/examples/sentenceWindow.ts
index fcb89d99d7b946cf0f08da381f3b6bff76b80d72..cd470db3457e06d441f61855c7d305e42dce1475 100644
--- a/examples/sentenceWindow.ts
+++ b/examples/sentenceWindow.ts
@@ -3,27 +3,25 @@ import {
   HuggingFaceEmbedding,
   MetadataReplacementPostProcessor,
   SentenceWindowNodeParser,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
+
 import essay from "./essay";
 
+// Update node parser and embed model
+Settings.nodeParser = new SentenceWindowNodeParser({
+  windowSize: 3,
+  windowMetadataKey: "window",
+  originalTextMetadataKey: "original_text",
+});
+Settings.embedModel = new HuggingFaceEmbedding();
+
 async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
-  // create service context with sentence window parser
-  // and local embedding from HuggingFace
-  const nodeParser = new SentenceWindowNodeParser({
-    windowSize: 3,
-    windowMetadataKey: "window",
-    originalTextMetadataKey: "original_text",
-  });
-  const embedModel = new HuggingFaceEmbedding();
-  const serviceContext = serviceContextFromDefaults({ nodeParser, embedModel });
-
   // Split text and create embeddings. Store them in a VectorStoreIndex
   const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
     logProgress: true,
   });
 
@@ -31,6 +29,7 @@ async function main() {
   const queryEngine = index.asQueryEngine({
     nodePostprocessors: [new MetadataReplacementPostProcessor("window")],
   });
+
   const response = await queryEngine.query({
     query: "What did the author do in college?",
   });
diff --git a/examples/summaryIndex.ts b/examples/summaryIndex.ts
index d11a47031af81146ba8a3b45fc8250d82f4b4614..31710d41cd5ae3253faff0e1a213f8ba4644864d 100644
--- a/examples/summaryIndex.ts
+++ b/examples/summaryIndex.ts
@@ -1,22 +1,21 @@
 import {
   Document,
+  Settings,
   SimpleNodeParser,
   SummaryIndex,
   SummaryRetrieverMode,
-  serviceContextFromDefaults,
 } from "llamaindex";
+
 import essay from "./essay";
 
+// Update node parser
+Settings.nodeParser = new SimpleNodeParser({
+  chunkSize: 40,
+});
+
 async function main() {
-  const serviceContext = serviceContextFromDefaults({
-    nodeParser: new SimpleNodeParser({
-      chunkSize: 40,
-    }),
-  });
   const document = new Document({ text: essay, id_: "essay" });
-  const index = await SummaryIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await SummaryIndex.fromDocuments([document]);
   const queryEngine = index.asQueryEngine({
     retriever: index.asRetriever({ mode: SummaryRetrieverMode.LLM }),
   });
diff --git a/examples/together-ai/vector-index.ts b/examples/together-ai/vector-index.ts
index 94b5c5762f01237a052defb54bff12f8d05b653d..001c3448ec11980df64bee3a4f4cf84584c1959b 100644
--- a/examples/together-ai/vector-index.ts
+++ b/examples/together-ai/vector-index.ts
@@ -2,12 +2,20 @@ import fs from "node:fs/promises";
 
 import {
   Document,
+  Settings,
   TogetherEmbedding,
   TogetherLLM,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
+// Update llm to use TogetherAI
+Settings.llm = new TogetherLLM({
+  model: "mistralai/Mixtral-8x7B-Instruct-v0.1",
+});
+
+// Update embedModel
+Settings.embedModel = new TogetherEmbedding();
+
 async function main() {
   const apiKey = process.env.TOGETHER_API_KEY;
   if (!apiKey) {
@@ -18,14 +26,7 @@ async function main() {
 
   const document = new Document({ text: essay, id_: path });
 
-  const serviceContext = serviceContextFromDefaults({
-    llm: new TogetherLLM({ model: "mistralai/Mixtral-8x7B-Instruct-v0.1" }),
-    embedModel: new TogetherEmbedding(),
-  });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   const queryEngine = index.asQueryEngine();
 
diff --git a/examples/vectorIndexAnthropic.ts b/examples/vectorIndexAnthropic.ts
index c04c516c7dec4d620c4bbf71d70e23b141dabd41..5b10cdb4064a8a2f42237fa32151413a5f508b5f 100644
--- a/examples/vectorIndexAnthropic.ts
+++ b/examples/vectorIndexAnthropic.ts
@@ -2,14 +2,17 @@ import fs from "node:fs/promises";
 
 import {
   Anthropic,
-  anthropicTextQaPrompt,
   CompactAndRefine,
   Document,
   ResponseSynthesizer,
-  serviceContextFromDefaults,
+  Settings,
   VectorStoreIndex,
+  anthropicTextQaPrompt,
 } from "llamaindex";
 
+// Update llm to use Anthropic
+Settings.llm = new Anthropic();
+
 async function main() {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";
@@ -20,18 +23,11 @@ async function main() {
   const document = new Document({ text: essay, id_: path });
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const serviceContext = serviceContextFromDefaults({ llm: new Anthropic() });
-
   const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new CompactAndRefine(
-      serviceContext,
-      anthropicTextQaPrompt,
-    ),
+    responseBuilder: new CompactAndRefine(undefined, anthropicTextQaPrompt),
   });
 
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Query the index
   const queryEngine = index.asQueryEngine({ responseSynthesizer });
diff --git a/examples/vectorIndexCustomize.ts b/examples/vectorIndexCustomize.ts
index e9013a0e6510b0cea292b53bd959bbcc787132b3..6902a2bd715ef2014895f4541e1be5a28d57d0e4 100644
--- a/examples/vectorIndexCustomize.ts
+++ b/examples/vectorIndexCustomize.ts
@@ -2,23 +2,21 @@ import {
   Document,
   OpenAI,
   RetrieverQueryEngine,
-  serviceContextFromDefaults,
+  Settings,
   SimilarityPostprocessor,
   VectorStoreIndex,
 } from "llamaindex";
+
 import essay from "./essay";
 
+// Update llm to use OpenAI
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
+
 // Customize retrieval and query args
 async function main() {
   const document = new Document({ text: essay, id_: "essay" });
 
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
-  });
-
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   const retriever = index.asRetriever();
   retriever.similarityTopK = 5;
diff --git a/examples/vectorIndexEmbed3.ts b/examples/vectorIndexEmbed3.ts
index 1d6c52b9048056817553514b88b78df8cd8785d0..da0507682e002f9955535d3fd0b658d19990e24d 100644
--- a/examples/vectorIndexEmbed3.ts
+++ b/examples/vectorIndexEmbed3.ts
@@ -3,10 +3,16 @@ import fs from "node:fs/promises";
 import {
   Document,
   OpenAIEmbedding,
+  Settings,
   VectorStoreIndex,
-  serviceContextFromDefaults,
 } from "llamaindex";
 
+// Update embed model
+Settings.embedModel = new OpenAIEmbedding({
+  model: "text-embedding-3-large",
+  dimensions: 1024,
+});
+
 async function main() {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";
@@ -16,17 +22,8 @@ async function main() {
   // Create Document object with essay
   const document = new Document({ text: essay, id_: path });
 
-  // Create service context and specify text-embedding-3-large
-  const embedModel = new OpenAIEmbedding({
-    model: "text-embedding-3-large",
-    dimensions: 1024,
-  });
-  const serviceContext = serviceContextFromDefaults({ embedModel });
-
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
diff --git a/examples/vectorIndexFromVectorStore.ts b/examples/vectorIndexFromVectorStore.ts
index d539ae2ad122fc7c2ca09b728de67cea6c315c60..dde1edddf35e9124aef620b2edce1d8ec7cb4e13 100644
--- a/examples/vectorIndexFromVectorStore.ts
+++ b/examples/vectorIndexFromVectorStore.ts
@@ -2,7 +2,7 @@ import {
   OpenAI,
   ResponseSynthesizer,
   RetrieverQueryEngine,
-  serviceContextFromDefaults,
+  Settings,
   TextNode,
   TreeSummarize,
   VectorIndexRetriever,
@@ -14,6 +14,12 @@ import {
 
 import { Index, Pinecone, RecordMetadata } from "@pinecone-database/pinecone";
 
+// Update llm
+Settings.llm = new OpenAI({
+  model: "gpt-4",
+  apiKey: process.env.OPENAI_API_KEY,
+});
+
 /**
  * Please do not use this class in production; it's only for demonstration purposes.
  */
@@ -146,25 +152,11 @@ async function main() {
     });
   };
 
-  const getServiceContext = () => {
-    const openAI = new OpenAI({
-      model: "gpt-4",
-      apiKey: process.env.OPENAI_API_KEY,
-    });
-
-    return serviceContextFromDefaults({
-      llm: openAI,
-    });
-  };
-
   const getQueryEngine = async (filter: unknown) => {
     const vectorStore = await getPineconeVectorStore();
-    const serviceContext = getServiceContext();
 
-    const vectorStoreIndex = await VectorStoreIndex.fromVectorStore(
-      vectorStore,
-      serviceContext,
-    );
+    const vectorStoreIndex =
+      await VectorStoreIndex.fromVectorStore(vectorStore);
 
     const retriever = new VectorIndexRetriever({
       index: vectorStoreIndex,
@@ -172,8 +164,7 @@ async function main() {
     });
 
     const responseSynthesizer = new ResponseSynthesizer({
-      serviceContext,
-      responseBuilder: new TreeSummarize(serviceContext),
+      responseBuilder: new TreeSummarize(),
     });
 
     return new RetrieverQueryEngine(retriever, responseSynthesizer, {
diff --git a/examples/vectorIndexGPT4.ts b/examples/vectorIndexGPT4.ts
index ed1ed20fcf9a651e58dc2b8d1870e731887dd82b..dab35809e9908e4135362c4bae92c60de39a1146 100644
--- a/examples/vectorIndexGPT4.ts
+++ b/examples/vectorIndexGPT4.ts
@@ -1,11 +1,8 @@
 import fs from "node:fs/promises";
 
-import {
-  Document,
-  OpenAI,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { Document, OpenAI, Settings, VectorStoreIndex } from "llamaindex";
+
+Settings.llm = new OpenAI({ model: "gpt-4" });
 
 async function main() {
   // Load essay from abramov.txt in Node
@@ -15,13 +12,7 @@ async function main() {
   // Create Document object with essay
   const document = new Document({ text: essay, id_: path });
 
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-4" }),
-  });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
   // Query the index
   const queryEngine = index.asQueryEngine();
diff --git a/packages/core/package.json b/packages/core/package.json
index 1261452d19cd25953882203804cea0e5288f79a7..472d8061f1890878649c29ac5c9b7cc0976aa030 100644
--- a/packages/core/package.json
+++ b/packages/core/package.json
@@ -68,6 +68,10 @@
         "default": "./dist/cjs/index.js"
       }
     },
+    "./internal/*": {
+      "import": "./dist/not-allow.js",
+      "require": "./dist/cjs/not-allow.js"
+    },
     "./*": {
       "import": {
         "types": "./dist/type/*.d.ts",
diff --git a/packages/core/src/Retriever.ts b/packages/core/src/Retriever.ts
index b38125f324a1b4010642189d9993d9315b1a4398..b8b942b558ba439e2aa764d32f2ece1a861f745e 100644
--- a/packages/core/src/Retriever.ts
+++ b/packages/core/src/Retriever.ts
@@ -16,5 +16,7 @@ export type RetrieveParams = {
  */
 export interface BaseRetriever {
   retrieve(params: RetrieveParams): Promise<NodeWithScore[]>;
-  getServiceContext(): ServiceContext;
+
+  // to be deprecated soon
+  serviceContext?: ServiceContext;
 }
diff --git a/packages/core/src/ServiceContext.ts b/packages/core/src/ServiceContext.ts
index 61ce3f8b345372d20ee88f1397e88dc8d9f1a6ed..3b4ffbbadd0a7aeb0b66d4012a13365d4e5566ad 100644
--- a/packages/core/src/ServiceContext.ts
+++ b/packages/core/src/ServiceContext.ts
@@ -1,8 +1,8 @@
 import { PromptHelper } from "./PromptHelper.js";
 import { OpenAIEmbedding } from "./embeddings/OpenAIEmbedding.js";
 import type { BaseEmbedding } from "./embeddings/types.js";
-import type { LLM } from "./llm/index.js";
-import { OpenAI } from "./llm/index.js";
+import { OpenAI } from "./llm/LLM.js";
+import type { LLM } from "./llm/types.js";
 import { SimpleNodeParser } from "./nodeParsers/SimpleNodeParser.js";
 import type { NodeParser } from "./nodeParsers/types.js";
 
diff --git a/packages/core/src/Settings.ts b/packages/core/src/Settings.ts
new file mode 100644
index 0000000000000000000000000000000000000000..7d29a2aa3ccb7be8f2d354b243901cc4e5bb82cd
--- /dev/null
+++ b/packages/core/src/Settings.ts
@@ -0,0 +1,215 @@
+import { CallbackManager } from "./callbacks/CallbackManager.js";
+import { OpenAIEmbedding } from "./embeddings/OpenAIEmbedding.js";
+import { OpenAI } from "./llm/LLM.js";
+
+import { PromptHelper } from "./PromptHelper.js";
+import { SimpleNodeParser } from "./nodeParsers/SimpleNodeParser.js";
+
+import { AsyncLocalStorage } from "@llamaindex/env";
+import type { ServiceContext } from "./ServiceContext.js";
+import type { BaseEmbedding } from "./embeddings/types.js";
+import {
+  getCallbackManager,
+  setCallbackManager,
+  withCallbackManager,
+} from "./internal/settings/CallbackManager.js";
+import type { LLM } from "./llm/types.js";
+import type { NodeParser } from "./nodeParsers/types.js";
+
+export type PromptConfig = {
+  llm?: string;
+  lang?: string;
+};
+
+export interface Config {
+  prompt: PromptConfig;
+  llm: LLM | null;
+  promptHelper: PromptHelper | null;
+  embedModel: BaseEmbedding | null;
+  nodeParser: NodeParser | null;
+  callbackManager: CallbackManager | null;
+  chunkSize?: number;
+  chunkOverlap?: number;
+}
+
+/**
+ * @internal
+ */
+class GlobalSettings implements Config {
+  #prompt: PromptConfig = {};
+  #llm: LLM | null = null;
+  #promptHelper: PromptHelper | null = null;
+  #embedModel: BaseEmbedding | null = null;
+  #nodeParser: NodeParser | null = null;
+  #chunkSize?: number;
+  #chunkOverlap?: number;
+
+  #llmAsyncLocalStorage = new AsyncLocalStorage<LLM>();
+  #promptHelperAsyncLocalStorage = new AsyncLocalStorage<PromptHelper>();
+  #embedModelAsyncLocalStorage = new AsyncLocalStorage<BaseEmbedding>();
+  #nodeParserAsyncLocalStorage = new AsyncLocalStorage<NodeParser>();
+  #chunkSizeAsyncLocalStorage = new AsyncLocalStorage<number>();
+  #chunkOverlapAsyncLocalStorage = new AsyncLocalStorage<number>();
+  #promptAsyncLocalStorage = new AsyncLocalStorage<PromptConfig>();
+
+  get llm(): LLM {
+    if (this.#llm === null) {
+      this.#llm = new OpenAI();
+    }
+
+    return this.#llmAsyncLocalStorage.getStore() ?? this.#llm;
+  }
+
+  set llm(llm: LLM) {
+    this.#llm = llm;
+  }
+
+  withLLM<Result>(llm: LLM, fn: () => Result): Result {
+    return this.#llmAsyncLocalStorage.run(llm, fn);
+  }
+
+  get promptHelper(): PromptHelper {
+    if (this.#promptHelper === null) {
+      this.#promptHelper = new PromptHelper();
+    }
+
+    return this.#promptHelperAsyncLocalStorage.getStore() ?? this.#promptHelper;
+  }
+
+  set promptHelper(promptHelper: PromptHelper) {
+    this.#promptHelper = promptHelper;
+  }
+
+  withPromptHelper<Result>(
+    promptHelper: PromptHelper,
+    fn: () => Result,
+  ): Result {
+    return this.#promptHelperAsyncLocalStorage.run(promptHelper, fn);
+  }
+
+  get embedModel(): BaseEmbedding {
+    if (this.#embedModel === null) {
+      this.#embedModel = new OpenAIEmbedding();
+    }
+
+    return this.#embedModelAsyncLocalStorage.getStore() ?? this.#embedModel;
+  }
+
+  set embedModel(embedModel: BaseEmbedding) {
+    this.#embedModel = embedModel;
+  }
+
+  withEmbedModel<Result>(embedModel: BaseEmbedding, fn: () => Result): Result {
+    return this.#embedModelAsyncLocalStorage.run(embedModel, fn);
+  }
+
+  get nodeParser(): NodeParser {
+    if (this.#nodeParser === null) {
+      this.#nodeParser = new SimpleNodeParser({
+        chunkSize: this.#chunkSize,
+        chunkOverlap: this.#chunkOverlap,
+      });
+    }
+
+    return this.#nodeParserAsyncLocalStorage.getStore() ?? this.#nodeParser;
+  }
+
+  set nodeParser(nodeParser: NodeParser) {
+    this.#nodeParser = nodeParser;
+  }
+
+  withNodeParser<Result>(nodeParser: NodeParser, fn: () => Result): Result {
+    return this.#nodeParserAsyncLocalStorage.run(nodeParser, fn);
+  }
+
+  get callbackManager(): CallbackManager {
+    return getCallbackManager();
+  }
+
+  set callbackManager(callbackManager: CallbackManager) {
+    setCallbackManager(callbackManager);
+  }
+
+  withCallbackManager<Result>(
+    callbackManager: CallbackManager,
+    fn: () => Result,
+  ): Result {
+    return withCallbackManager(callbackManager, fn);
+  }
+
+  set chunkSize(chunkSize: number | undefined) {
+    this.#chunkSize = chunkSize;
+  }
+
+  get chunkSize(): number | undefined {
+    return this.#chunkSizeAsyncLocalStorage.getStore() ?? this.#chunkSize;
+  }
+
+  withChunkSize<Result>(chunkSize: number, fn: () => Result): Result {
+    return this.#chunkSizeAsyncLocalStorage.run(chunkSize, fn);
+  }
+
+  get chunkOverlap(): number | undefined {
+    return this.#chunkOverlapAsyncLocalStorage.getStore() ?? this.#chunkOverlap;
+  }
+
+  set chunkOverlap(chunkOverlap: number | undefined) {
+    this.#chunkOverlap = chunkOverlap;
+  }
+
+  withChunkOverlap<Result>(chunkOverlap: number, fn: () => Result): Result {
+    return this.#chunkOverlapAsyncLocalStorage.run(chunkOverlap, fn);
+  }
+
+  get prompt(): PromptConfig {
+    return this.#promptAsyncLocalStorage.getStore() ?? this.#prompt;
+  }
+
+  set prompt(prompt: PromptConfig) {
+    this.#prompt = prompt;
+  }
+
+  withPrompt<Result>(prompt: PromptConfig, fn: () => Result): Result {
+    return this.#promptAsyncLocalStorage.run(prompt, fn);
+  }
+}
+
+export const llmFromSettingsOrContext = (serviceContext?: ServiceContext) => {
+  if (serviceContext?.llm) {
+    return serviceContext.llm;
+  }
+
+  return Settings.llm;
+};
+
+export const nodeParserFromSettingsOrContext = (
+  serviceContext?: ServiceContext,
+) => {
+  if (serviceContext?.nodeParser) {
+    return serviceContext.nodeParser;
+  }
+
+  return Settings.nodeParser;
+};
+
+export const embedModelFromSettingsOrContext = (
+  serviceContext?: ServiceContext,
+) => {
+  if (serviceContext?.embedModel) {
+    return serviceContext.embedModel;
+  }
+
+  return Settings.embedModel;
+};
+
+export const promptHelperFromSettingsOrContext = (
+  serviceContext?: ServiceContext,
+) => {
+  if (serviceContext?.promptHelper) {
+    return serviceContext.promptHelper;
+  }
+
+  return Settings.promptHelper;
+};
+
+export const Settings = new GlobalSettings();
diff --git a/packages/core/src/callbacks/CallbackManager.ts b/packages/core/src/callbacks/CallbackManager.ts
index f9436006ef29c32860492c708de3575624ee02e2..cf38f49e979a50f315e1563bb5f3f6a7706204f7 100644
--- a/packages/core/src/callbacks/CallbackManager.ts
+++ b/packages/core/src/callbacks/CallbackManager.ts
@@ -1,5 +1,5 @@
 import type { Anthropic } from "@anthropic-ai/sdk";
-import { AsyncLocalStorage, CustomEvent } from "@llamaindex/env";
+import { CustomEvent } from "@llamaindex/env";
 import type { NodeWithScore } from "../Node.js";
 
 /**
@@ -135,30 +135,26 @@ export class CallbackManager implements CallbackManagerMethods {
    * @deprecated will be removed in the next major version
    */
   set onLLMStream(_: never) {
-    throw new Error(
-      "onLLMStream is deprecated. Use addHandlers('stream') instead",
-    );
+    throw new Error("onLLMStream is deprecated. Use on('stream') instead");
   }
 
   /**
    * @deprecated will be removed in the next major version
    */
   set onRetrieve(_: never) {
-    throw new Error(
-      "onRetrieve is deprecated. Use `addHandlers('retrieve')` instead",
-    );
+    throw new Error("onRetrieve is deprecated. Use `on('retrieve')` instead");
   }
 
   #handlers = new Map<keyof LlamaIndexEventMaps, EventHandler<CustomEvent>[]>();
 
   constructor(handlers?: Partial<CallbackManagerMethods>) {
     const onLLMStream = handlers?.onLLMStream ?? noop;
-    this.addHandlers("stream", (event) => onLLMStream(event.detail));
+    this.on("stream", (event) => onLLMStream(event.detail));
     const onRetrieve = handlers?.onRetrieve ?? noop;
-    this.addHandlers("retrieve", (event) => onRetrieve(event.detail));
+    this.on("retrieve", (event) => onRetrieve(event.detail));
   }
 
-  addHandlers<
+  on<
     K extends keyof LlamaIndexEventMaps,
     H extends EventHandler<LlamaIndexEventMaps[K]>,
   >(event: K, handler: H) {
@@ -169,7 +165,7 @@ export class CallbackManager implements CallbackManagerMethods {
     return this;
   }
 
-  removeHandlers<
+  off<
     K extends keyof LlamaIndexEventMaps,
     H extends EventHandler<LlamaIndexEventMaps[K]>,
   >(event: K, handler: H) {
@@ -195,21 +191,3 @@ export class CallbackManager implements CallbackManagerMethods {
     handlers.forEach((handler) => handler(new CustomEvent(event, { detail })));
   }
 }
-
-const defaultCallbackManager = new CallbackManager();
-const callbackAsyncLocalStorage = new AsyncLocalStorage<CallbackManager>();
-
-/**
- * Get the current callback manager
- * @default defaultCallbackManager if no callback manager is set
- */
-export function getCurrentCallbackManager() {
-  return callbackAsyncLocalStorage.getStore() ?? defaultCallbackManager;
-}
-
-export function runWithCallbackManager<Result>(
-  callbackManager: CallbackManager,
-  fn: () => Result,
-): Result {
-  return callbackAsyncLocalStorage.run(callbackManager, fn);
-}
diff --git a/packages/core/src/cloud/LlamaCloudRetriever.ts b/packages/core/src/cloud/LlamaCloudRetriever.ts
index 563ab4e15600451510bdb82ad48bdcc35b2c40cf..0b4a53dc13c4bacd1a54b60036976a49c654fb23 100644
--- a/packages/core/src/cloud/LlamaCloudRetriever.ts
+++ b/packages/core/src/cloud/LlamaCloudRetriever.ts
@@ -3,9 +3,7 @@ import { globalsHelper } from "../GlobalsHelper.js";
 import type { NodeWithScore } from "../Node.js";
 import { ObjectType, jsonToNode } from "../Node.js";
 import type { BaseRetriever, RetrieveParams } from "../Retriever.js";
-import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
-import { getCurrentCallbackManager } from "../callbacks/CallbackManager.js";
+import { Settings } from "../Settings.js";
 import type { ClientParams, CloudConstructorParams } from "./types.js";
 import { DEFAULT_PROJECT_NAME } from "./types.js";
 import { getClient } from "./utils.js";
@@ -21,7 +19,6 @@ export class LlamaCloudRetriever implements BaseRetriever {
   retrieveParams: CloudRetrieveParams;
   projectName: string = DEFAULT_PROJECT_NAME;
   pipelineName: string;
-  serviceContext: ServiceContext;
 
   private resultNodesToNodeWithScore(
     nodes: PlatformApi.TextNodeWithScore[],
@@ -45,7 +42,6 @@ export class LlamaCloudRetriever implements BaseRetriever {
     if (params.projectName) {
       this.projectName = params.projectName;
     }
-    this.serviceContext = params.serviceContext ?? serviceContextFromDefaults();
   }
 
   private async getClient(): Promise<PlatformApiClient> {
@@ -81,7 +77,7 @@ export class LlamaCloudRetriever implements BaseRetriever {
 
     const nodes = this.resultNodesToNodeWithScore(results.retrievalNodes);
 
-    getCurrentCallbackManager().onRetrieve({
+    Settings.callbackManager.onRetrieve({
       query,
       nodes,
       event: globalsHelper.createEvent({
@@ -92,8 +88,4 @@ export class LlamaCloudRetriever implements BaseRetriever {
 
     return nodes;
   }
-
-  getServiceContext(): ServiceContext {
-    return this.serviceContext;
-  }
 }
diff --git a/packages/core/src/engines/chat/CondenseQuestionChatEngine.ts b/packages/core/src/engines/chat/CondenseQuestionChatEngine.ts
index 7baf58d73bf8410fe89bbe25490f48fea088de77..cd66a44fcd9016cd4e8373af3dccc1b2073f59f7 100644
--- a/packages/core/src/engines/chat/CondenseQuestionChatEngine.ts
+++ b/packages/core/src/engines/chat/CondenseQuestionChatEngine.ts
@@ -7,7 +7,7 @@ import {
 } from "../../Prompt.js";
 import type { Response } from "../../Response.js";
 import type { ServiceContext } from "../../ServiceContext.js";
-import { serviceContextFromDefaults } from "../../ServiceContext.js";
+import { llmFromSettingsOrContext } from "../../Settings.js";
 import type { ChatMessage, LLM } from "../../llm/index.js";
 import { extractText, streamReducer } from "../../llm/utils.js";
 import { PromptMixin } from "../../prompts/index.js";
@@ -48,7 +48,7 @@ export class CondenseQuestionChatEngine
 
     this.queryEngine = init.queryEngine;
     this.chatHistory = getHistory(init?.chatHistory);
-    this.llm = init?.serviceContext?.llm ?? serviceContextFromDefaults().llm;
+    this.llm = llmFromSettingsOrContext(init?.serviceContext);
     this.condenseMessagePrompt =
       init?.condenseMessagePrompt ?? defaultCondenseQuestionPrompt;
   }
diff --git a/packages/core/src/engines/query/RetrieverQueryEngine.ts b/packages/core/src/engines/query/RetrieverQueryEngine.ts
index 540243dabc22ace5fef3f30d9e2357d8894fa807..d7cc96a54c9c9fe2f539efdddc0d72e9d2d12e09 100644
--- a/packages/core/src/engines/query/RetrieverQueryEngine.ts
+++ b/packages/core/src/engines/query/RetrieverQueryEngine.ts
@@ -2,7 +2,6 @@ import { randomUUID } from "@llamaindex/env";
 import type { NodeWithScore } from "../../Node.js";
 import type { Response } from "../../Response.js";
 import type { BaseRetriever } from "../../Retriever.js";
-import type { ServiceContext } from "../../ServiceContext.js";
 import type { Event } from "../../callbacks/CallbackManager.js";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
 import { PromptMixin } from "../../prompts/Mixin.js";
@@ -35,10 +34,11 @@ export class RetrieverQueryEngine
     super();
 
     this.retriever = retriever;
-    const serviceContext: ServiceContext | undefined =
-      this.retriever.getServiceContext();
     this.responseSynthesizer =
-      responseSynthesizer || new ResponseSynthesizer({ serviceContext });
+      responseSynthesizer ||
+      new ResponseSynthesizer({
+        serviceContext: retriever.serviceContext,
+      });
     this.preFilters = preFilters;
     this.nodePostprocessors = nodePostprocessors || [];
   }
diff --git a/packages/core/src/engines/query/RouterQueryEngine.ts b/packages/core/src/engines/query/RouterQueryEngine.ts
index bfe24362b323119f067af1247dafa012e52ce2d2..6c26ba4f01398f966f45af8aa697b866fbece7b4 100644
--- a/packages/core/src/engines/query/RouterQueryEngine.ts
+++ b/packages/core/src/engines/query/RouterQueryEngine.ts
@@ -1,7 +1,7 @@
 import type { BaseNode } from "../../Node.js";
 import { Response } from "../../Response.js";
 import type { ServiceContext } from "../../ServiceContext.js";
-import { serviceContextFromDefaults } from "../../ServiceContext.js";
+import { llmFromSettingsOrContext } from "../../Settings.js";
 import { PromptMixin } from "../../prompts/index.js";
 import type { BaseSelector } from "../../selectors/index.js";
 import { LLMSingleSelector } from "../../selectors/index.js";
@@ -55,8 +55,6 @@ async function combineResponses(
  * A query engine that uses multiple query engines and selects the best one.
  */
 export class RouterQueryEngine extends PromptMixin implements BaseQueryEngine {
-  serviceContext: ServiceContext;
-
   private selector: BaseSelector;
   private queryEngines: BaseQueryEngine[];
   private metadatas: RouterQueryEngineMetadata[];
@@ -72,13 +70,12 @@ export class RouterQueryEngine extends PromptMixin implements BaseQueryEngine {
   }) {
     super();
 
-    this.serviceContext = init.serviceContext || serviceContextFromDefaults({});
     this.selector = init.selector;
     this.queryEngines = init.queryEngineTools.map((tool) => tool.queryEngine);
     this.metadatas = init.queryEngineTools.map((tool) => ({
       description: tool.description,
     }));
-    this.summarizer = init.summarizer || new TreeSummarize(this.serviceContext);
+    this.summarizer = init.summarizer || new TreeSummarize(init.serviceContext);
     this.verbose = init.verbose ?? false;
   }
 
@@ -96,12 +93,14 @@ export class RouterQueryEngine extends PromptMixin implements BaseQueryEngine {
     summarizer?: TreeSummarize;
     verbose?: boolean;
   }) {
-    const serviceContext =
-      init.serviceContext ?? serviceContextFromDefaults({});
+    const serviceContext = init.serviceContext;
 
     return new RouterQueryEngine({
       selector:
-        init.selector ?? new LLMSingleSelector({ llm: serviceContext.llm }),
+        init.selector ??
+        new LLMSingleSelector({
+          llm: llmFromSettingsOrContext(serviceContext),
+        }),
       queryEngineTools: init.queryEngineTools,
       serviceContext,
       summarizer: init.summarizer,
diff --git a/packages/core/src/engines/query/SubQuestionQueryEngine.ts b/packages/core/src/engines/query/SubQuestionQueryEngine.ts
index 5d0333fe2081af4a5cc2aaf694f93934f553433a..00ff68b60228cc3c93e18cf2e16593139f29655a 100644
--- a/packages/core/src/engines/query/SubQuestionQueryEngine.ts
+++ b/packages/core/src/engines/query/SubQuestionQueryEngine.ts
@@ -4,7 +4,6 @@ import { TextNode } from "../../Node.js";
 import { LLMQuestionGenerator } from "../../QuestionGenerator.js";
 import type { Response } from "../../Response.js";
 import type { ServiceContext } from "../../ServiceContext.js";
-import { serviceContextFromDefaults } from "../../ServiceContext.js";
 import type { Event } from "../../callbacks/CallbackManager.js";
 import { PromptMixin } from "../../prompts/Mixin.js";
 import type { BaseSynthesizer } from "../../synthesizers/index.js";
@@ -62,8 +61,7 @@ export class SubQuestionQueryEngine
     responseSynthesizer?: BaseSynthesizer;
     serviceContext?: ServiceContext;
   }) {
-    const serviceContext =
-      init.serviceContext ?? serviceContextFromDefaults({});
+    const serviceContext = init.serviceContext;
 
     const questionGen = init.questionGen ?? new LLMQuestionGenerator();
     const responseSynthesizer =
diff --git a/packages/core/src/evaluation/Correctness.ts b/packages/core/src/evaluation/Correctness.ts
index 8004e93a2cbeaa2467ec0a53755fd4c8335b4acf..1354e83f9f8008020a3bbaa4654120289a8d1f1a 100644
--- a/packages/core/src/evaluation/Correctness.ts
+++ b/packages/core/src/evaluation/Correctness.ts
@@ -1,7 +1,7 @@
 import { MetadataMode } from "../Node.js";
 import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
-import type { ChatMessage } from "../llm/types.js";
+import { llmFromSettingsOrContext } from "../Settings.js";
+import type { ChatMessage, LLM } from "../llm/types.js";
 import { PromptMixin } from "../prompts/Mixin.js";
 import type { CorrectnessSystemPrompt } from "./prompts.js";
 import {
@@ -24,20 +24,20 @@ type CorrectnessParams = {
 
 /** Correctness Evaluator */
 export class CorrectnessEvaluator extends PromptMixin implements BaseEvaluator {
-  private serviceContext: ServiceContext;
   private scoreThreshold: number;
   private parserFunction: (str: string) => [number, string];
+  private llm: LLM;
 
   private correctnessPrompt: CorrectnessSystemPrompt =
     defaultCorrectnessSystemPrompt;
 
-  constructor(params: CorrectnessParams) {
+  constructor(params?: CorrectnessParams) {
     super();
 
-    this.serviceContext = params.serviceContext || serviceContextFromDefaults();
+    this.llm = llmFromSettingsOrContext(params?.serviceContext);
     this.correctnessPrompt = defaultCorrectnessSystemPrompt;
-    this.scoreThreshold = params.scoreThreshold || 4.0;
-    this.parserFunction = params.parserFunction || defaultEvaluationParser;
+    this.scoreThreshold = params?.scoreThreshold ?? 4.0;
+    this.parserFunction = params?.parserFunction ?? defaultEvaluationParser;
   }
 
   _updatePrompts(prompts: {
@@ -80,7 +80,7 @@ export class CorrectnessEvaluator extends PromptMixin implements BaseEvaluator {
       },
     ];
 
-    const evalResponse = await this.serviceContext.llm.chat({
+    const evalResponse = await this.llm.chat({
       messages,
     });
 
diff --git a/packages/core/src/evaluation/Faithfulness.ts b/packages/core/src/evaluation/Faithfulness.ts
index 3c7d69ef5c7983b6132b2558b576619ccdb4e1e9..a4b512dd7e697fe2eb25a3baa3537536babf1f85 100644
--- a/packages/core/src/evaluation/Faithfulness.ts
+++ b/packages/core/src/evaluation/Faithfulness.ts
@@ -1,6 +1,5 @@
 import { Document, MetadataMode } from "../Node.js";
 import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
 import { SummaryIndex } from "../indices/summary/index.js";
 import { PromptMixin } from "../prompts/Mixin.js";
 import type {
@@ -22,25 +21,25 @@ export class FaithfulnessEvaluator
   extends PromptMixin
   implements BaseEvaluator
 {
-  private serviceContext: ServiceContext;
+  private serviceContext?: ServiceContext;
   private raiseError: boolean;
   private evalTemplate: FaithfulnessTextQAPrompt;
   private refineTemplate: FaithfulnessRefinePrompt;
 
-  constructor(params: {
+  constructor(params?: {
     serviceContext?: ServiceContext;
     raiseError?: boolean;
     faithfulnessSystemPrompt?: FaithfulnessTextQAPrompt;
     faithFulnessRefinePrompt?: FaithfulnessRefinePrompt;
   }) {
     super();
-    this.serviceContext = params.serviceContext || serviceContextFromDefaults();
-    this.raiseError = params.raiseError || false;
+    this.serviceContext = params?.serviceContext;
+    this.raiseError = params?.raiseError ?? false;
 
     this.evalTemplate =
-      params.faithfulnessSystemPrompt || defaultFaithfulnessTextQaPrompt;
+      params?.faithfulnessSystemPrompt ?? defaultFaithfulnessTextQaPrompt;
     this.refineTemplate =
-      params.faithFulnessRefinePrompt || defaultFaithfulnessRefinePrompt;
+      params?.faithFulnessRefinePrompt ?? defaultFaithfulnessRefinePrompt;
   }
 
   protected _getPrompts(): { [x: string]: any } {
diff --git a/packages/core/src/evaluation/Relevancy.ts b/packages/core/src/evaluation/Relevancy.ts
index a7ed4992e0ec151d92421b0d71b5d327abeb9efc..55a4506b208993b657bfb84e17052858f91d40a3 100644
--- a/packages/core/src/evaluation/Relevancy.ts
+++ b/packages/core/src/evaluation/Relevancy.ts
@@ -1,6 +1,5 @@
 import { Document, MetadataMode } from "../Node.js";
 import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
 import { SummaryIndex } from "../indices/summary/index.js";
 import { PromptMixin } from "../prompts/Mixin.js";
 import type { RelevancyEvalPrompt, RelevancyRefinePrompt } from "./prompts.js";
@@ -23,19 +22,20 @@ type RelevancyParams = {
 };
 
 export class RelevancyEvaluator extends PromptMixin implements BaseEvaluator {
-  private serviceContext: ServiceContext;
+  private serviceContext?: ServiceContext;
   private raiseError: boolean;
 
   private evalTemplate: RelevancyEvalPrompt;
   private refineTemplate: RelevancyRefinePrompt;
 
-  constructor(params: RelevancyParams) {
+  constructor(params?: RelevancyParams) {
     super();
 
-    this.serviceContext = params.serviceContext ?? serviceContextFromDefaults();
-    this.raiseError = params.raiseError ?? false;
-    this.evalTemplate = params.evalTemplate ?? defaultRelevancyEvalPrompt;
-    this.refineTemplate = params.refineTemplate ?? defaultRelevancyRefinePrompt;
+    this.serviceContext = params?.serviceContext;
+    this.raiseError = params?.raiseError ?? false;
+    this.evalTemplate = params?.evalTemplate ?? defaultRelevancyEvalPrompt;
+    this.refineTemplate =
+      params?.refineTemplate ?? defaultRelevancyRefinePrompt;
   }
 
   _getPrompts() {
diff --git a/packages/core/src/index.edge.ts b/packages/core/src/index.edge.ts
index bbfed374389d4b19abd29a4ca9abf0d2da557c4e..d547430da39b11153bc4dbadc9bbd3f9f4f87517 100644
--- a/packages/core/src/index.edge.ts
+++ b/packages/core/src/index.edge.ts
@@ -8,6 +8,7 @@ export * from "./QuestionGenerator.js";
 export * from "./Response.js";
 export * from "./Retriever.js";
 export * from "./ServiceContext.js";
+export { Settings } from "./Settings.js";
 export * from "./TextSplitter.js";
 export * from "./agent/index.js";
 export * from "./callbacks/CallbackManager.js";
diff --git a/packages/core/src/indices/BaseIndex.ts b/packages/core/src/indices/BaseIndex.ts
index 028b3bfaaff23583753e4981664e48ce81ab36df..2cce62428d5001fa331cc7f89c5eaf39a7fc1aaf 100644
--- a/packages/core/src/indices/BaseIndex.ts
+++ b/packages/core/src/indices/BaseIndex.ts
@@ -1,6 +1,7 @@
 import type { BaseNode, Document } from "../Node.js";
 import type { BaseRetriever } from "../Retriever.js";
 import type { ServiceContext } from "../ServiceContext.js";
+import { nodeParserFromSettingsOrContext } from "../Settings.js";
 import { runTransformations } from "../ingestion/IngestionPipeline.js";
 import type { StorageContext } from "../storage/StorageContext.js";
 import type { BaseDocumentStore } from "../storage/docStore/types.js";
@@ -15,6 +16,7 @@ import { IndexStructType } from "./json-to-index-struct.js";
 export class KeywordTable extends IndexStruct {
   table: Map<string, Set<string>> = new Map();
   type: IndexStructType = IndexStructType.KEYWORD_TABLE;
+
   addNode(keywords: string[], nodeId: string): void {
     keywords.forEach((keyword) => {
       if (!this.table.has(keyword)) {
@@ -42,7 +44,7 @@ export class KeywordTable extends IndexStruct {
 }
 
 export interface BaseIndexInit<T> {
-  serviceContext: ServiceContext;
+  serviceContext?: ServiceContext;
   storageContext: StorageContext;
   docStore: BaseDocumentStore;
   vectorStore?: VectorStore;
@@ -55,7 +57,7 @@ export interface BaseIndexInit<T> {
  * they can be retrieved for our queries.
  */
 export abstract class BaseIndex<T> {
-  serviceContext: ServiceContext;
+  serviceContext?: ServiceContext;
   storageContext: StorageContext;
   docStore: BaseDocumentStore;
   vectorStore?: VectorStore;
@@ -94,7 +96,7 @@ export abstract class BaseIndex<T> {
   async insert(document: Document) {
     const nodes = await runTransformations(
       [document],
-      [this.serviceContext.nodeParser],
+      [nodeParserFromSettingsOrContext(this.serviceContext)],
     );
     await this.insertNodes(nodes);
     this.docStore.setDocumentHash(document.id_, document.hash);
diff --git a/packages/core/src/indices/keyword/index.ts b/packages/core/src/indices/keyword/index.ts
index 61067f55fd80013f1d7aece54bb6b1c276270adf..3fedcef49db5b252c6309ecaef800d83c45ffe5b 100644
--- a/packages/core/src/indices/keyword/index.ts
+++ b/packages/core/src/indices/keyword/index.ts
@@ -27,11 +27,15 @@ import {
   simpleExtractKeywords,
 } from "./utils.js";
 
+import { llmFromSettingsOrContext } from "../../Settings.js";
+import type { LLM } from "../../llm/types.js";
+
 export interface KeywordIndexOptions {
   nodes?: BaseNode[];
   indexStruct?: KeywordTable;
   indexId?: string;
   serviceContext?: ServiceContext;
+  llm?: LLM;
   storageContext?: StorageContext;
 }
 export enum KeywordTableRetrieverMode {
@@ -45,7 +49,7 @@ abstract class BaseKeywordTableRetriever implements BaseRetriever {
   protected index: KeywordTableIndex;
   protected indexStruct: KeywordTable;
   protected docstore: BaseDocumentStore;
-  protected serviceContext: ServiceContext;
+  protected llm: LLM;
 
   protected maxKeywordsPerQuery: number; // Maximum number of keywords to extract from query.
   protected numChunksPerQuery: number; // Maximum number of text chunks to query.
@@ -68,7 +72,7 @@ abstract class BaseKeywordTableRetriever implements BaseRetriever {
     this.index = index;
     this.indexStruct = index.indexStruct;
     this.docstore = index.docStore;
-    this.serviceContext = index.serviceContext;
+    this.llm = llmFromSettingsOrContext(index.serviceContext);
 
     this.maxKeywordsPerQuery = maxKeywordsPerQuery;
     this.numChunksPerQuery = numChunksPerQuery;
@@ -101,16 +105,12 @@ abstract class BaseKeywordTableRetriever implements BaseRetriever {
 
     return sortedNodes.map((node) => ({ node }));
   }
-
-  getServiceContext(): ServiceContext {
-    return this.index.serviceContext;
-  }
 }
 
 // Extracts keywords using LLMs.
 export class KeywordTableLLMRetriever extends BaseKeywordTableRetriever {
   async getKeywords(query: string): Promise<string[]> {
-    const response = await this.serviceContext.llm.complete({
+    const response = await this.llm.complete({
       prompt: this.queryKeywordExtractTemplate({
         question: query,
         maxKeywords: this.maxKeywordsPerQuery,
@@ -156,8 +156,7 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
   static async init(options: KeywordIndexOptions): Promise<KeywordTableIndex> {
     const storageContext =
       options.storageContext ?? (await storageContextFromDefaults({}));
-    const serviceContext =
-      options.serviceContext ?? serviceContextFromDefaults({});
+    const serviceContext = options.serviceContext;
     const { docStore, indexStore } = storageContext;
 
     // Setup IndexStruct from storage
@@ -247,13 +246,16 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
 
   static async extractKeywords(
     text: string,
-    serviceContext: ServiceContext,
+    serviceContext?: ServiceContext,
   ): Promise<Set<string>> {
-    const response = await serviceContext.llm.complete({
+    const llm = llmFromSettingsOrContext(serviceContext);
+
+    const response = await llm.complete({
       prompt: defaultKeywordExtractPrompt({
         context: text,
       }),
     });
+
     return extractKeywordsGivenResponse(response.text, "KEYWORDS:");
   }
 
@@ -300,7 +302,7 @@ export class KeywordTableIndex extends BaseIndex<KeywordTable> {
   static async buildIndexFromNodes(
     nodes: BaseNode[],
     docStore: BaseDocumentStore,
-    serviceContext: ServiceContext,
+    serviceContext?: ServiceContext,
   ): Promise<KeywordTable> {
     const indexStruct = new KeywordTable();
     await docStore.addDocuments(nodes, true);
diff --git a/packages/core/src/indices/summary/index.ts b/packages/core/src/indices/summary/index.ts
index a948327b7d2b7e57d8f8b3cdcb4a158b26194f66..1eef0ebe5bb7bbd0bcf2f29f94c50819c3c27999 100644
--- a/packages/core/src/indices/summary/index.ts
+++ b/packages/core/src/indices/summary/index.ts
@@ -5,8 +5,11 @@ import type { ChoiceSelectPrompt } from "../../Prompt.js";
 import { defaultChoiceSelectPrompt } from "../../Prompt.js";
 import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
-import { serviceContextFromDefaults } from "../../ServiceContext.js";
-import { getCurrentCallbackManager } from "../../callbacks/CallbackManager.js";
+import {
+  Settings,
+  llmFromSettingsOrContext,
+  nodeParserFromSettingsOrContext,
+} from "../../Settings.js";
 import { RetrieverQueryEngine } from "../../engines/query/index.js";
 import type { BaseNodePostprocessor } from "../../postprocessors/index.js";
 import type { StorageContext } from "../../storage/StorageContext.js";
@@ -58,8 +61,7 @@ export class SummaryIndex extends BaseIndex<IndexList> {
   static async init(options: SummaryIndexOptions): Promise<SummaryIndex> {
     const storageContext =
       options.storageContext ?? (await storageContextFromDefaults({}));
-    const serviceContext =
-      options.serviceContext ?? serviceContextFromDefaults({});
+    const serviceContext = options.serviceContext;
     const { docStore, indexStore } = storageContext;
 
     // Setup IndexStruct from storage
@@ -130,7 +132,7 @@ export class SummaryIndex extends BaseIndex<IndexList> {
   ): Promise<SummaryIndex> {
     let { storageContext, serviceContext } = args;
     storageContext = storageContext ?? (await storageContextFromDefaults({}));
-    serviceContext = serviceContext ?? serviceContextFromDefaults({});
+    serviceContext = serviceContext;
     const docStore = storageContext.docStore;
 
     docStore.addDocuments(documents, true);
@@ -138,7 +140,11 @@ export class SummaryIndex extends BaseIndex<IndexList> {
       docStore.setDocumentHash(doc.id_, doc.hash);
     }
 
-    const nodes = serviceContext.nodeParser.getNodesFromDocuments(documents);
+    const nodes =
+      nodeParserFromSettingsOrContext(serviceContext).getNodesFromDocuments(
+        documents,
+      );
+
     const index = await SummaryIndex.init({
       nodes,
       storageContext,
@@ -292,7 +298,7 @@ export class SummaryIndexRetriever implements BaseRetriever {
       score: 1,
     }));
 
-    getCurrentCallbackManager().onRetrieve({
+    Settings.callbackManager.onRetrieve({
       query,
       nodes: result,
       event: globalsHelper.createEvent({
@@ -303,10 +309,6 @@ export class SummaryIndexRetriever implements BaseRetriever {
 
     return result;
   }
-
-  getServiceContext(): ServiceContext {
-    return this.index.serviceContext;
-  }
 }
 
 /**
@@ -318,7 +320,7 @@ export class SummaryIndexLLMRetriever implements BaseRetriever {
   choiceBatchSize: number;
   formatNodeBatchFn: NodeFormatterFunction;
   parseChoiceSelectAnswerFn: ChoiceSelectParserFunction;
-  serviceContext: ServiceContext;
+  serviceContext?: ServiceContext;
 
   // eslint-disable-next-line max-params
   constructor(
@@ -351,8 +353,11 @@ export class SummaryIndexLLMRetriever implements BaseRetriever {
 
       const fmtBatchStr = this.formatNodeBatchFn(nodesBatch);
       const input = { context: fmtBatchStr, query: query };
+
+      const llm = llmFromSettingsOrContext(this.serviceContext);
+
       const rawResponse = (
-        await this.serviceContext.llm.complete({
+        await llm.complete({
           prompt: this.choiceSelectPrompt(input),
         })
       ).text;
@@ -375,7 +380,7 @@ export class SummaryIndexLLMRetriever implements BaseRetriever {
       results.push(...nodeWithScores);
     }
 
-    getCurrentCallbackManager().onRetrieve({
+    Settings.callbackManager.onRetrieve({
       query,
       nodes: results,
       event: globalsHelper.createEvent({
@@ -386,10 +391,6 @@ export class SummaryIndexLLMRetriever implements BaseRetriever {
 
     return results;
   }
-
-  getServiceContext(): ServiceContext {
-    return this.serviceContext;
-  }
 }
 
 // Legacy
diff --git a/packages/core/src/indices/vectorStore/index.ts b/packages/core/src/indices/vectorStore/index.ts
index 7aebb45c20a31bac63ccc86abda236ef267dd74b..06937a09463a8521f234fc44b933286d0ae57a00 100644
--- a/packages/core/src/indices/vectorStore/index.ts
+++ b/packages/core/src/indices/vectorStore/index.ts
@@ -13,11 +13,12 @@ import {
 } from "../../Node.js";
 import type { BaseRetriever, RetrieveParams } from "../../Retriever.js";
 import type { ServiceContext } from "../../ServiceContext.js";
-import { serviceContextFromDefaults } from "../../ServiceContext.js";
 import {
-  getCurrentCallbackManager,
-  type Event,
-} from "../../callbacks/CallbackManager.js";
+  Settings,
+  embedModelFromSettingsOrContext,
+  nodeParserFromSettingsOrContext,
+} from "../../Settings.js";
+import { type Event } from "../../callbacks/CallbackManager.js";
 import { DEFAULT_SIMILARITY_TOP_K } from "../../constants.js";
 import type {
   BaseEmbedding,
@@ -79,7 +80,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
     super(init);
     this.indexStore = init.indexStore;
     this.vectorStore = init.vectorStore ?? init.storageContext.vectorStore;
-    this.embedModel = init.serviceContext.embedModel;
+    this.embedModel = embedModelFromSettingsOrContext(init.serviceContext);
     this.imageVectorStore =
       init.imageVectorStore ?? init.storageContext.imageVectorStore;
     if (this.imageVectorStore) {
@@ -97,8 +98,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
   ): Promise<VectorStoreIndex> {
     const storageContext =
       options.storageContext ?? (await storageContextFromDefaults({}));
-    const serviceContext =
-      options.serviceContext ?? serviceContextFromDefaults({});
+    const serviceContext = options.serviceContext;
     const indexStore = storageContext.indexStore;
     const docStore = storageContext.docStore;
 
@@ -222,7 +222,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
         : DocStoreStrategy.DUPLICATES_ONLY);
     args.storageContext =
       args.storageContext ?? (await storageContextFromDefaults({}));
-    args.serviceContext = args.serviceContext ?? serviceContextFromDefaults({});
+    args.serviceContext = args.serviceContext;
     const docStore = args.storageContext.docStore;
 
     if (args.logProgress) {
@@ -237,7 +237,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
     );
     args.nodes = await runTransformations(
       documents,
-      [args.serviceContext.nodeParser],
+      [nodeParserFromSettingsOrContext(args.serviceContext)],
       {},
       { docStoreStrategy },
     );
@@ -249,7 +249,7 @@ export class VectorStoreIndex extends BaseIndex<IndexDict> {
 
   static async fromVectorStore(
     vectorStore: VectorStore,
-    serviceContext: ServiceContext,
+    serviceContext?: ServiceContext,
     imageVectorStore?: VectorStore,
   ) {
     if (!vectorStore.storesText) {
@@ -424,7 +424,8 @@ export class VectorIndexRetriever implements BaseRetriever {
   index: VectorStoreIndex;
   similarityTopK: number;
   imageSimilarityTopK: number;
-  private serviceContext: ServiceContext;
+
+  serviceContext?: ServiceContext;
 
   constructor({
     index,
@@ -491,7 +492,7 @@ export class VectorIndexRetriever implements BaseRetriever {
     nodesWithScores: NodeWithScore<Metadata>[],
     parentEvent: Event | undefined,
   ) {
-    getCurrentCallbackManager().onRetrieve({
+    Settings.callbackManager.onRetrieve({
       query,
       nodes: nodesWithScores,
       event: globalsHelper.createEvent({
@@ -540,8 +541,4 @@ export class VectorIndexRetriever implements BaseRetriever {
 
     return nodesWithScores;
   }
-
-  getServiceContext(): ServiceContext {
-    return this.serviceContext;
-  }
 }
diff --git a/packages/core/src/internal/settings/CallbackManager.ts b/packages/core/src/internal/settings/CallbackManager.ts
new file mode 100644
index 0000000000000000000000000000000000000000..55660a14b4b4fae23279c8a03a2b7a5828ec8121
--- /dev/null
+++ b/packages/core/src/internal/settings/CallbackManager.ts
@@ -0,0 +1,25 @@
+import { AsyncLocalStorage } from "@llamaindex/env";
+import { CallbackManager } from "../../callbacks/CallbackManager.js";
+
+const callbackManagerAsyncLocalStorage =
+  new AsyncLocalStorage<CallbackManager>();
+let globalCallbackManager: CallbackManager | null = null;
+
+export function getCallbackManager(): CallbackManager {
+  if (globalCallbackManager === null) {
+    globalCallbackManager = new CallbackManager();
+  }
+
+  return callbackManagerAsyncLocalStorage.getStore() ?? globalCallbackManager;
+}
+
+export function setCallbackManager(callbackManager: CallbackManager) {
+  globalCallbackManager = callbackManager;
+}
+
+export function withCallbackManager<Result>(
+  callbackManager: CallbackManager,
+  fn: () => Result,
+): Result {
+  return callbackManagerAsyncLocalStorage.run(callbackManager, fn);
+}
diff --git a/packages/core/src/llm/LLM.ts b/packages/core/src/llm/LLM.ts
index 6970cab3319455dc4b47fc0ec2007587a5c7f98d..8d20169c303aa6563fca21b6543df35fc5b6cbd9 100644
--- a/packages/core/src/llm/LLM.ts
+++ b/packages/core/src/llm/LLM.ts
@@ -1,7 +1,6 @@
 import type OpenAILLM from "openai";
 import type { ClientOptions as OpenAIClientOptions } from "openai";
 import {
-  getCurrentCallbackManager,
   type Event,
   type EventType,
   type OpenAIStreamToken,
@@ -11,6 +10,7 @@ import {
 import type { ChatCompletionMessageParam } from "openai/resources/index.js";
 import type { LLMOptions } from "portkey-ai";
 import { Tokenizers, globalsHelper } from "../GlobalsHelper.js";
+import { getCallbackManager } from "../internal/settings/CallbackManager.js";
 import type { AnthropicSession } from "./anthropic.js";
 import { getAnthropicSession } from "./anthropic.js";
 import type { AzureOpenAIConfig } from "./azure.js";
@@ -290,7 +290,7 @@ export class OpenAI extends BaseLLM {
     };
 
     //Now let's wrap our stream in a callback
-    const onLLMStream = getCurrentCallbackManager().onLLMStream;
+    const onLLMStream = getCallbackManager().onLLMStream;
 
     const chunk_stream: AsyncIterable<OpenAIStreamToken> =
       await this.session.openai.chat.completions.create({
@@ -835,7 +835,7 @@ export class Portkey extends BaseLLM {
     params?: Record<string, any>,
   ): AsyncIterable<ChatResponseChunk> {
     // Wrapping the stream in a callback.
-    const onLLMStream = getCurrentCallbackManager().onLLMStream;
+    const onLLMStream = getCallbackManager().onLLMStream;
 
     const chunkStream = await this.session.portkey.chatCompletions.create({
       messages,
diff --git a/packages/core/src/llm/mistral.ts b/packages/core/src/llm/mistral.ts
index 526543c2b503c3262a0d5d2bd0566988fcff991c..7ee798030d1e85b8e98e6d3a8ffb626edb7b2a80 100644
--- a/packages/core/src/llm/mistral.ts
+++ b/packages/core/src/llm/mistral.ts
@@ -1,6 +1,6 @@
 import { getEnv } from "@llamaindex/env";
+import { Settings } from "../Settings.js";
 import {
-  getCurrentCallbackManager,
   type Event,
   type EventType,
   type StreamCallbackResponse,
@@ -123,7 +123,7 @@ export class MistralAI extends BaseLLM {
     parentEvent,
   }: LLMChatParamsStreaming): AsyncIterable<ChatResponseChunk> {
     //Now let's wrap our stream in a callback
-    const onLLMStream = getCurrentCallbackManager().onLLMStream;
+    const onLLMStream = Settings.callbackManager.onLLMStream;
 
     const client = await this.session.getClient();
     const chunkStream = await client.chatStream(this.buildParams(messages));
diff --git a/packages/core/src/llm/utils.ts b/packages/core/src/llm/utils.ts
index 3438c8bd4b10c2e14e8b5f66219e440e8901fbbc..ffc40e0cbe7e54e3c3b36b13ced39e33ab1a6afb 100644
--- a/packages/core/src/llm/utils.ts
+++ b/packages/core/src/llm/utils.ts
@@ -1,4 +1,4 @@
-import { getCurrentCallbackManager } from "../callbacks/CallbackManager.js";
+import { getCallbackManager } from "../internal/settings/CallbackManager.js";
 import type { ChatResponse, LLM, LLMChat, MessageContent } from "./types.js";
 
 export async function* streamConverter<S, D>(
@@ -55,7 +55,7 @@ export function llmEvent(
     this: LLM,
     ...params: Parameters<LLMChat["chat"]>
   ): ReturnType<LLMChat["chat"]> {
-    getCurrentCallbackManager().dispatchEvent("llm-start", {
+    getCallbackManager().dispatchEvent("llm-start", {
       payload: {
         messages: params[0].messages,
       },
@@ -82,14 +82,14 @@ export function llmEvent(
           }
           yield chunk;
         }
-        getCurrentCallbackManager().dispatchEvent("llm-end", {
+        getCallbackManager().dispatchEvent("llm-end", {
           payload: {
             response: finalResponse,
           },
         });
       };
     } else {
-      getCurrentCallbackManager().dispatchEvent("llm-end", {
+      getCallbackManager().dispatchEvent("llm-end", {
         payload: {
           response,
         },
diff --git a/packages/core/src/not-allow.js b/packages/core/src/not-allow.js
new file mode 100644
index 0000000000000000000000000000000000000000..21f6251ac31bb2a333f5a28ec0af8e08c03450be
--- /dev/null
+++ b/packages/core/src/not-allow.js
@@ -0,0 +1 @@
+throw new Error("Not allowed to import internal modules directly.");
diff --git a/packages/core/src/selectors/utils.ts b/packages/core/src/selectors/utils.ts
index 084ec02674fc8fd4f4c9275e6f87c0836568b8fa..a4db900d8e13c730717b034541701a853ad724ca 100644
--- a/packages/core/src/selectors/utils.ts
+++ b/packages/core/src/selectors/utils.ts
@@ -1,4 +1,5 @@
 import type { ServiceContext } from "../ServiceContext.js";
+import { llmFromSettingsOrContext } from "../Settings.js";
 import type { BaseSelector } from "./base.js";
 import { LLMMultiSelector, LLMSingleSelector } from "./llmSelectors.js";
 
@@ -8,7 +9,7 @@ export const getSelectorFromContext = (
 ): BaseSelector => {
   let selector: BaseSelector | null = null;
 
-  const llm = serviceContext.llm;
+  const llm = llmFromSettingsOrContext(serviceContext);
 
   if (isMulti) {
     selector = new LLMMultiSelector({ llm });
diff --git a/packages/core/src/synthesizers/MultiModalResponseSynthesizer.ts b/packages/core/src/synthesizers/MultiModalResponseSynthesizer.ts
index a6454c134cdb0043ff36e247348b24c00941b29a..857cbd9a72e311f14b842c3fc0781c6e204587cf 100644
--- a/packages/core/src/synthesizers/MultiModalResponseSynthesizer.ts
+++ b/packages/core/src/synthesizers/MultiModalResponseSynthesizer.ts
@@ -2,7 +2,7 @@ import type { ImageNode } from "../Node.js";
 import { MetadataMode, splitNodesByType } from "../Node.js";
 import { Response } from "../Response.js";
 import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
+import { llmFromSettingsOrContext } from "../Settings.js";
 import { imageToDataUrl } from "../embeddings/index.js";
 import type { MessageContentDetail } from "../llm/types.js";
 import { PromptMixin } from "../prompts/Mixin.js";
@@ -18,7 +18,7 @@ export class MultiModalResponseSynthesizer
   extends PromptMixin
   implements BaseSynthesizer
 {
-  serviceContext: ServiceContext;
+  serviceContext?: ServiceContext;
   metadataMode: MetadataMode;
   textQATemplate: TextQaPrompt;
 
@@ -29,7 +29,7 @@ export class MultiModalResponseSynthesizer
   }: Partial<MultiModalResponseSynthesizer> = {}) {
     super();
 
-    this.serviceContext = serviceContext ?? serviceContextFromDefaults();
+    this.serviceContext = serviceContext;
     this.metadataMode = metadataMode ?? MetadataMode.NONE;
     this.textQATemplate = textQATemplate ?? defaultTextQaPrompt;
   }
@@ -85,10 +85,14 @@ export class MultiModalResponseSynthesizer
       { type: "text", text: textPrompt },
       ...images,
     ];
-    const response = await this.serviceContext.llm.complete({
+
+    const llm = llmFromSettingsOrContext(this.serviceContext);
+
+    const response = await llm.complete({
       prompt,
       parentEvent,
     });
+
     return new Response(response.text, nodes);
   }
 }
diff --git a/packages/core/src/synthesizers/ResponseSynthesizer.ts b/packages/core/src/synthesizers/ResponseSynthesizer.ts
index 6f5663295e81ce52892fcb905014b00d31822f74..b837a8867d870304de607f3540798908ddbc3389 100644
--- a/packages/core/src/synthesizers/ResponseSynthesizer.ts
+++ b/packages/core/src/synthesizers/ResponseSynthesizer.ts
@@ -1,7 +1,6 @@
 import { MetadataMode } from "../Node.js";
 import { Response } from "../Response.js";
 import type { ServiceContext } from "../ServiceContext.js";
-import { serviceContextFromDefaults } from "../ServiceContext.js";
 import { streamConverter } from "../llm/utils.js";
 import { PromptMixin } from "../prompts/Mixin.js";
 import type { ResponseBuilderPrompts } from "./builders.js";
@@ -21,7 +20,6 @@ export class ResponseSynthesizer
   implements BaseSynthesizer
 {
   responseBuilder: ResponseBuilder;
-  serviceContext: ServiceContext;
   metadataMode: MetadataMode;
 
   constructor({
@@ -35,9 +33,8 @@ export class ResponseSynthesizer
   } = {}) {
     super();
 
-    this.serviceContext = serviceContext ?? serviceContextFromDefaults();
     this.responseBuilder =
-      responseBuilder ?? getResponseBuilder(this.serviceContext);
+      responseBuilder ?? getResponseBuilder(serviceContext);
     this.metadataMode = metadataMode;
   }
 
diff --git a/packages/core/src/synthesizers/builders.ts b/packages/core/src/synthesizers/builders.ts
index 49e1105ad39bbf0eeac85465c6df25223f2c675f..25aa70ddcf75e618ccbcf682d200d5776f2a37d9 100644
--- a/packages/core/src/synthesizers/builders.ts
+++ b/packages/core/src/synthesizers/builders.ts
@@ -16,6 +16,10 @@ import type { PromptHelper } from "../PromptHelper.js";
 import { getBiggestPrompt } from "../PromptHelper.js";
 import { PromptMixin } from "../prompts/Mixin.js";
 import type { ServiceContext } from "../ServiceContext.js";
+import {
+  llmFromSettingsOrContext,
+  promptHelperFromSettingsOrContext,
+} from "../Settings.js";
 import type {
   ResponseBuilder,
   ResponseBuilderParamsNonStreaming,
@@ -39,8 +43,8 @@ export class SimpleResponseBuilder implements ResponseBuilder {
   llm: LLM;
   textQATemplate: TextQaPrompt;
 
-  constructor(serviceContext: ServiceContext, textQATemplate?: TextQaPrompt) {
-    this.llm = serviceContext.llm;
+  constructor(serviceContext?: ServiceContext, textQATemplate?: TextQaPrompt) {
+    this.llm = llmFromSettingsOrContext(serviceContext);
     this.textQATemplate = textQATemplate ?? defaultTextQaPrompt;
   }
 
@@ -84,14 +88,14 @@ export class Refine extends PromptMixin implements ResponseBuilder {
   refineTemplate: RefinePrompt;
 
   constructor(
-    serviceContext: ServiceContext,
+    serviceContext?: ServiceContext,
     textQATemplate?: TextQaPrompt,
     refineTemplate?: RefinePrompt,
   ) {
     super();
 
-    this.llm = serviceContext.llm;
-    this.promptHelper = serviceContext.promptHelper;
+    this.llm = llmFromSettingsOrContext(serviceContext);
+    this.promptHelper = promptHelperFromSettingsOrContext(serviceContext);
     this.textQATemplate = textQATemplate ?? defaultTextQaPrompt;
     this.refineTemplate = refineTemplate ?? defaultRefinePrompt;
   }
@@ -293,13 +297,13 @@ export class TreeSummarize extends PromptMixin implements ResponseBuilder {
   summaryTemplate: TreeSummarizePrompt;
 
   constructor(
-    serviceContext: ServiceContext,
+    serviceContext?: ServiceContext,
     summaryTemplate?: TreeSummarizePrompt,
   ) {
     super();
 
-    this.llm = serviceContext.llm;
-    this.promptHelper = serviceContext.promptHelper;
+    this.llm = llmFromSettingsOrContext(serviceContext);
+    this.promptHelper = promptHelperFromSettingsOrContext(serviceContext);
     this.summaryTemplate = summaryTemplate ?? defaultTreeSummarizePrompt;
   }
 
@@ -383,7 +387,7 @@ export class TreeSummarize extends PromptMixin implements ResponseBuilder {
 }
 
 export function getResponseBuilder(
-  serviceContext: ServiceContext,
+  serviceContext?: ServiceContext,
   responseMode?: ResponseMode,
 ): ResponseBuilder {
   switch (responseMode) {
diff --git a/packages/core/tests/CallbackManager.test.ts b/packages/core/tests/CallbackManager.test.ts
index a03ec543bf383d4dccea44d5f25f62af9b96160f..461a18bc04140cbce16662d765fa154d75776f5c 100644
--- a/packages/core/tests/CallbackManager.test.ts
+++ b/packages/core/tests/CallbackManager.test.ts
@@ -11,14 +11,12 @@ import {
 import { Document } from "llamaindex/Node";
 import type { ServiceContext } from "llamaindex/ServiceContext";
 import { serviceContextFromDefaults } from "llamaindex/ServiceContext";
+import { Settings } from "llamaindex/Settings";
 import type {
   RetrievalCallbackResponse,
   StreamCallbackResponse,
 } from "llamaindex/callbacks/CallbackManager";
-import {
-  CallbackManager,
-  runWithCallbackManager,
-} from "llamaindex/callbacks/CallbackManager";
+import { CallbackManager } from "llamaindex/callbacks/CallbackManager";
 import { OpenAIEmbedding } from "llamaindex/embeddings/index";
 import { SummaryIndex } from "llamaindex/indices/summary/index";
 import { VectorStoreIndex } from "llamaindex/indices/vectorStore/index";
@@ -83,7 +81,7 @@ describe("CallbackManager: onLLMStream and onRetrieve", () => {
     });
     const queryEngine = vectorStoreIndex.asQueryEngine();
     const query = "What is the author's name?";
-    const response = await runWithCallbackManager(callbackManager, () => {
+    const response = await Settings.withCallbackManager(callbackManager, () => {
       return queryEngine.query({ query });
     });
 
@@ -164,8 +162,9 @@ describe("CallbackManager: onLLMStream and onRetrieve", () => {
       responseSynthesizer,
     });
     const query = "What is the author's name?";
-    const response = await runWithCallbackManager(callbackManager, async () =>
-      queryEngine.query({ query }),
+    const response = await Settings.withCallbackManager(
+      callbackManager,
+      async () => queryEngine.query({ query }),
     );
     expect(response.toString()).toBe("MOCK_TOKEN_1-MOCK_TOKEN_2");
     expect(streamCallbackData).toEqual([
diff --git a/packages/experimental/examples/jsonQueryEngine.ts b/packages/experimental/examples/jsonQueryEngine.ts
index 53909325867a9f250f3423c995bffec2ad117696..2757e60f60548fd3d1a7747f55e82732f9a2fe05 100644
--- a/packages/experimental/examples/jsonQueryEngine.ts
+++ b/packages/experimental/examples/jsonQueryEngine.ts
@@ -1,6 +1,9 @@
 import { JSONQueryEngine } from "@llamaindex/experimental";
 
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+import { OpenAI, Settings } from "llamaindex";
+
+// Update LLM
+Settings.llm = new OpenAI({ model: "gpt-4" });
 
 const jsonValue = {
   blogPosts: [
@@ -84,22 +87,14 @@ const jsonSchema = {
 };
 
 async function main() {
-  const llm = new OpenAI({ model: "gpt-4" });
-
-  const serviceContext = serviceContextFromDefaults({
-    llm,
-  });
-
   const jsonQueryEngine = new JSONQueryEngine({
     jsonValue,
     jsonSchema,
-    serviceContext,
   });
 
   const rawQueryEngine = new JSONQueryEngine({
     jsonValue,
     jsonSchema,
-    serviceContext,
     synthesizeResponse: false,
   });
 
diff --git a/tsconfig.json b/tsconfig.json
index 7b3adfd10ea13144c5d9230e083b241b3e397dac..84821fc77d8197deb533571f4aca75d8562290bb 100644
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -8,7 +8,6 @@
     "forceConsistentCasingInFileNames": true,
     "strict": true,
     "skipLibCheck": true,
-    "stripInternal": true,
     "outDir": "./lib",
     "tsBuildInfoFile": "./lib/.tsbuildinfo",
     "incremental": true,