Using Embedding

What is an embedding?

An embedding is a numerical vector representation that captures the semantic meaning of a text.

To embed a text is the process of converting a text into an embedding.

This is useful for many NLP (Natural Language Processing) tasks, such as classification, clustering, and similarity search.

This is often used for searching for similar texts based on their meaning, rather than verbatim text matching.

When you have a lot of data, processing all of it using inference (by feeding it into a model and asking it questions about the data) is slow and can be expensive. Using inference for processing provides the most high-quality results, but it's not always necessary.

For example, assuming that we have 10K documents and want to find the most relevant ones to a given query, using inference for all of those documents can take a long time, and even if done in parallel, it can be expensive (in terms of compute resource usage costs).

Instead, we can embed all the documents once and then search for the most similar ones to the query based on the embeddings. To do that, we embed all the documents in advance and store the embeddings in a database. Then, when a query comes in, we embed the query and search for the most similar embeddings in the database, and return the corresponding documents.

Finding Relevant Documents

Let's see an example of how we can embed 10 texts and then search for the most relevant one to a given query:

NOTE

Always make sure you only compare embeddings created using the exact same model file.

Comparing embeddings created using different models can lead to incorrect results and may even cause errors.

typescript

import {fileURLToPath
} from "url";
import path
 from "path";
import {getLlama
, LlamaEmbedding
} from "node-llama-cpp";

const __dirname
 = path
.dirname
(
    fileURLToPath
(import.meta.url
)
);

const llama
 = await getLlama
();
const model
 = await llama
.loadModel
({
    modelPath
: path
.join
(__dirname
, "Meta-Llama-3.1-8B-Instruct.Q4_K_M.gguf")
});
const context
 = await model
.createEmbeddingContext
();

async function embedDocuments
(documents
: readonly string[]) {
    const embeddings
 = new Map
<string, LlamaEmbedding
>();

    await Promise
.all
(
        documents
.map
(async (document
) => {
            const embedding
 = await context
.getEmbeddingFor
(document
);
            embeddings
.set
(document
, embedding
);

            console
.debug
(
                `${embeddings
.size
}/${documents
.length
} documents embedded`
            );
        })
    );

    return embeddings
;
}

function findSimilarDocuments
(
    embedding
: LlamaEmbedding
,
    documentEmbeddings
: Map
<string, LlamaEmbedding
>
) {
    const similarities
 = new Map
<string, number>();
    for (const [otherDocument
, otherDocumentEmbedding
] of documentEmbeddings
)
        similarities
.set
(
            otherDocument
,
            embedding
.calculateCosineSimilarity
(otherDocumentEmbedding
)
        );

    return Array
.from
(similarities
.keys
())
        .sort
((a
, b
) => similarities
.get
(b
)! - similarities
.get
(a
)!);
}

const documentEmbeddings
 = await embedDocuments
([
    "The sky is clear and blue today",
    "I love eating pizza with extra cheese",
    "Dogs love to play fetch with their owners",
    "The capital of France is Paris",
    "Drinking water is important for staying hydrated",
    "Mount Everest is the tallest mountain in the world",
    "A warm cup of tea is perfect for a cold winter day",
    "Painting is a form of creative expression",
    "Not all the things that shine are made of gold",
    "Cleaning the house is a good way to keep it tidy"
]);


const query
 = "What is the tallest mountain on Earth?";
const queryEmbedding
 = await context
.getEmbeddingFor
(query
);

const similarDocuments
 = findSimilarDocuments
(
    queryEmbedding
,
    documentEmbeddings

);
const topSimilarDocument
 = similarDocuments
[0];

console
.log
("query:", query
);
console
.log
("Document:", topSimilarDocument
);

This example will produce this output:

query: What is the tallest mountain on Earth?
Document: Mount Everest is the tallest mountain in the world

Getting Raw Vectors

To get the raw embedding vectors, you can use the vector property of the LlamaEmbedding object:

typescript

import {fileURLToPath
} from "url";
import path
 from "path";
import {getLlama
} from "node-llama-cpp";

const __dirname
 = path
.dirname
(
    fileURLToPath
(import.meta.url
)
);

const llama
 = await getLlama
();
const model
 = await llama
.loadModel
({
    modelPath
: path
.join
(__dirname
, "my-model.gguf")
});
const context
 = await model
.createEmbeddingContext
();


const text
 = "Hello world";
console
.log
("Text:", text
);

const embedding
 = await context
.getEmbeddingFor
(text
);
console
.log
("Embedding vector:", embedding
.vector
);

Using External Databases

When you have a large number of documents you want to use with embedding, it's often more efficient to store them with their embedding in an external database and search for the most similar embeddings there.

You can use node-llama-cpp to create an embedding and then store the embedding vector in an external database that supports vector search.

Vector databases

Here is a list of some vector databases you can use:

Embedded databases

LanceDB (GitHub | npm | Quick start) - Serverless vector database you can embed inside your application. No server required.
Written in
Rust
License
Apache-2.0
Vectra (GitHub | npm) - local vector database using local files
Written in
TypeScript
License
MIT

Open Source

Qdrant (GitHub | npm | Quick start) - High-performance, massive-scale vector database
Written in
Rust
License
Apache-2.0
Milvus (GitHub | npm | Quick start) - A cloud-native vector database
Written in
Go, C++
License
Apache-2.0
Chroma (GitHub | npm | Guide)
Written in
Python, Rust
License
Apache-2.0
Apache Cassandra (GitHub | npm | Quickstart | Vector search quickstart) - Highly-scalable distributed NoSQL database with vector search support
Written in
Java
License
Apache-2.0

Proprietary

Redis via the Redis Search module (Vector Search docs) - High-performance vector search. Useful if you already use Redis Stack or Redis Enterprise.
Written in
C
License
Custom
Not open source
Source available
Self hosting price
Free
ElasticSearch - native vector search support. Useful is you already use ElasticSearch.
Written in
Java
License
Custom
Partially open source
Source available
Self hosting price
Free

Does this list miss your favorite vector database? Open a PR to add it!

Last edited 18 days agoView full history

Using Embedding ​

Finding Relevant Documents ​

Getting Raw Vectors ​

Using External Databases ​

Vector databases ​

Embedded databases ​

Open Source ​

Proprietary ​