diff --git a/apps/docs/docs/modules/data_index.md b/apps/docs/docs/modules/data_index.md
index 2855e4e4081fd87c4cb308f2d406272aa6ed0881..ad5a4dcd8932b7cc004665d4ef7aaa5bf6da7bc4 100644
--- a/apps/docs/docs/modules/data_index.md
+++ b/apps/docs/docs/modules/data_index.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 3
+sidebar_position: 4
---
# Index
diff --git a/apps/docs/docs/modules/data_loader.md b/apps/docs/docs/modules/data_loader.md
index f1b1aa97a557ca580f74e0648abcc76d637dee10..1e3043b60212294d5a039b3d539f68140789f58a 100644
--- a/apps/docs/docs/modules/data_loader.md
+++ b/apps/docs/docs/modules/data_loader.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 2
+sidebar_position: 3
---
# Reader / Loader
diff --git a/apps/docs/docs/modules/embedding.md b/apps/docs/docs/modules/embedding.md
index bf8e0baccbfabb0d1179916ad401c3db96f3a163..b3ca243f9ba8e587080902f95391e4966721227e 100644
--- a/apps/docs/docs/modules/embedding.md
+++ b/apps/docs/docs/modules/embedding.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 2
+sidebar_position: 3
---
# Embedding
diff --git a/apps/docs/docs/modules/ingestion_pipeline/_category_.yml b/apps/docs/docs/modules/ingestion_pipeline/_category_.yml
new file mode 100644
index 0000000000000000000000000000000000000000..1152ca7235caacda6100f64c010ebb33e57b4bf7
--- /dev/null
+++ b/apps/docs/docs/modules/ingestion_pipeline/_category_.yml
@@ -0,0 +1,2 @@
+label: "Ingestion Pipeline"
+position: 2
diff --git a/apps/docs/docs/modules/ingestion_pipeline/index.md b/apps/docs/docs/modules/ingestion_pipeline/index.md
new file mode 100644
index 0000000000000000000000000000000000000000..4de6ee5fba687d281c129aab49c10ab2356511ae
--- /dev/null
+++ b/apps/docs/docs/modules/ingestion_pipeline/index.md
@@ -0,0 +1,99 @@
+# Ingestion Pipeline
+
+An `IngestionPipeline` uses a concept of `Transformations` that are applied to input data.
+These `Transformations` are applied to your input data, and the resulting nodes are either returned or inserted into a vector database (if given).
+
+## Usage Pattern
+
+The simplest usage is to instantiate an IngestionPipeline like so:
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+ Document,
+ IngestionPipeline,
+ MetadataMode,
+ OpenAIEmbedding,
+ TitleExtractor,
+ SimpleNodeParser,
+} from "llamaindex";
+
+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 pipeline = new IngestionPipeline({
+ transformations: [
+ new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+ new TitleExtractor(),
+ new OpenAIEmbedding(),
+ ],
+ });
+
+ // run the pipeline
+ const nodes = await pipeline.run({ documents: [document] });
+
+ // print out the result of the pipeline run
+ for (const node of nodes) {
+ console.log(node.getContent(MetadataMode.NONE));
+ }
+}
+
+main().catch(console.error);
+```
+
+## Connecting to Vector Databases
+
+When running an ingestion pipeline, you can also chose to automatically insert the resulting nodes into a remote vector store.
+
+Then, you can construct an index from that vector store later on.
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+ Document,
+ IngestionPipeline,
+ MetadataMode,
+ OpenAIEmbedding,
+ TitleExtractor,
+ SimpleNodeParser,
+ QdrantVectorStore,
+ VectorStoreIndex,
+} from "llamaindex";
+
+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 vectorStore = new QdrantVectorStore({
+ host: "http://localhost:6333",
+ });
+
+ // Create Document object with essay
+ const document = new Document({ text: essay, id_: path });
+ const pipeline = new IngestionPipeline({
+ transformations: [
+ new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+ new TitleExtractor(),
+ new OpenAIEmbedding(),
+ ],
+ vectorStore,
+ });
+
+ // run the pipeline
+ const nodes = await pipeline.run({ documents: [document] });
+
+ // create an index
+ const index = VectorStoreIndex.fromVectorStore(vectorStore);
+}
+
+main().catch(console.error);
+```
diff --git a/apps/docs/docs/modules/ingestion_pipeline/transformations.md b/apps/docs/docs/modules/ingestion_pipeline/transformations.md
new file mode 100644
index 0000000000000000000000000000000000000000..3d947628e279fbac4af4ef313f0e4f3037e80f5f
--- /dev/null
+++ b/apps/docs/docs/modules/ingestion_pipeline/transformations.md
@@ -0,0 +1,77 @@
+# Transformations
+
+A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
+
+Currently, the following components are Transformation objects:
+
+- [SimpleNodeParser](../../api/classes/SimpleNodeParser.md)
+- [MetadataExtractor](../documents_and_nodes/metadata_extraction.md)
+- Embeddings
+
+## Usage Pattern
+
+While transformations are best used with with an IngestionPipeline, they can also be used directly.
+
+```ts
+import { SimpleNodeParser, TitleExtractor, Document } from "llamaindex";
+
+async function main() {
+ let nodes = new SimpleNodeParser().getNodesFromDocuments([
+ new Document({ text: "I am 10 years old. John is 20 years old." }),
+ ]);
+
+ const titleExtractor = new TitleExtractor();
+
+ nodes = await titleExtractor.transform(nodes);
+
+ for (const node of nodes) {
+ console.log(node.getContent(MetadataMode.NONE));
+ }
+}
+
+main().catch(console.error);
+```
+
+## Custom Transformations
+
+You can implement any transformation yourself by implementing the `TransformerComponent`.
+
+The following custom transformation will remove any special characters or punctutaion in text.
+
+```ts
+import { TransformerComponent, Node } from "llamaindex";
+
+class RemoveSpecialCharacters extends TransformerComponent {
+ async transform(nodes: Node[]): Promise<Node[]> {
+ for (const node of nodes) {
+ node.text = node.text.replace(/[^\w\s]/gi, "");
+ }
+
+ return nodes;
+ }
+}
+```
+
+These can then be used directly or in any IngestionPipeline.
+
+```ts
+import { IngestionPipeline, Document } from "llamaindex";
+
+async function main() {
+ const pipeline = new IngestionPipeline({
+ transformations: [new RemoveSpecialCharacters()],
+ });
+
+ const nodes = await pipeline.run({
+ documents: [
+ new Document({ text: "I am 10 years old. John is 20 years old." }),
+ ],
+ });
+
+ for (const node of nodes) {
+ console.log(node.getContent(MetadataMode.NONE));
+ }
+}
+
+main().catch(console.error);
+```
diff --git a/apps/docs/docs/modules/llm.md b/apps/docs/docs/modules/llm.md
index 7f69f13db4b1d4e70161558eb3d94be26cc7fc90..92bae09ca51748b476cd43ca009aaef962d4b035 100644
--- a/apps/docs/docs/modules/llm.md
+++ b/apps/docs/docs/modules/llm.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 2
+sidebar_position: 3
---
# LLM
diff --git a/apps/docs/docs/modules/query_engine.md b/apps/docs/docs/modules/query_engine.md
index 853641a17ea6a0c61061c31be5f6dd2144e67bf5..65cc742f0ac13711a3580ae4f18af7e4af6eb878 100644
--- a/apps/docs/docs/modules/query_engine.md
+++ b/apps/docs/docs/modules/query_engine.md
@@ -1,5 +1,5 @@
---
-sidebar_position: 3
+sidebar_position: 4
---
# QueryEngine