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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:

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.

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);