https://github.com/tigrisdata-archive/tigris-vector-ts
Tigris Vector Database client for TypeScript
https://github.com/tigrisdata-archive/tigris-vector-ts
database javascript nodejs search typescript vector
Last synced: 12 months ago
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Tigris Vector Database client for TypeScript
- Host: GitHub
- URL: https://github.com/tigrisdata-archive/tigris-vector-ts
- Owner: tigrisdata-archive
- License: apache-2.0
- Created: 2023-05-06T05:22:38.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-09T19:04:58.000Z (about 3 years ago)
- Last Synced: 2025-06-05T11:02:07.084Z (about 1 year ago)
- Topics: database, javascript, nodejs, search, typescript, vector
- Language: TypeScript
- Homepage: https://www.tigrisdata.com/
- Size: 97.7 KB
- Stars: 5
- Watchers: 7
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Tigris Vector Database Client Library for TypeScript
[](https://github.com/tigrisdata/tigris-vector-ts/actions/workflows/ts-ci.yaml)
[](https://codecov.io/gh/tigrisdata/tigris-vector-ts)
[](https://github.com/tigrisdata/tigris-vector-ts/blob/main/LICENSE)
[](https://tigris.dev/discord)
[](https://twitter.com/tigrisdata)
# Getting Started
Tigris makes it easy to build AI applications with vector embeddings.
It is a fully managed cloud-native database that allows you store and index
documents and vector embeddings for fast and scalable vector search.
## 1. Install the client library
```shell
npm install @tigrisdata/vector
```
## 2. Fetch Tigris API credentials
You can sign up for a free Tigris account [here](https://console.preview.tigrisdata.cloud/signup).
Once you have signed up for the Tigris account, create a new project called `vectordemo`.
Next, make a note of the `clientId` and `clientSecret`, which you can get from the
**Application Keys** section of the project.
## 3. Create the Vector Database client
```ts
import { VectorDocumentStore } from "@tigrisdata/vector";
const vectorDocStore = new VectorDocumentStore({
connection: {
serverUrl: "api.preview.tigrisdata.cloud",
projectName: "vectordemo",
clientId: "clientId_here",
clientSecret: "clientSecret_here",
},
indexName: "my_index",
numDimensions: 3,
});
```
Here, we have created a new `VectorDocumentStore` instance that connects to the
Tigris Vector Database. The `indexName` is the name of the index that will store
your embeddings, documents, and any additional metadata. You can use any name
you like for the index. The `numDimensions` is the number of dimensions of the
vector embeddings.
## 4. Add documents to the index
```ts
await vectorDocStore.addDocumentsWithVectors({
ids: ["id1", "id2"],
embeddings: [
[1.2, 2.3, 4.5],
[6.7, 8.2, 9.2],
],
documents: [
{
content: "This is a document",
metadata: {
title: "Document 1",
},
},
{
content: "This is another document",
metadata: {
title: "Document 2",
},
},
],
});
```
Here, we have added two documents to the index. The `ids` are the unique
identifiers for the documents. The `embeddings` are the vector embeddings for
the documents. The `documents` are the actual documents that you want to store
in the index. The `content` is the text content of the document. The `metadata`
is any additional metadata that you want to store for the document.
## 5. Query the index
You can query the index for the top `k` most similar documents to a given
vector. It's that simple!
```ts
const results = await vectorDocStore.similaritySearchVectorWithScore({
query: [1.0, 2.1, 3.2],
k: 10,
});
console.log(JSON.stringify(results, null, 2));
```
## 6. Enhance the query with attribute filtering
You can enhance the query with attribute filtering. For example, you can
filter the results by a given metadata field.
```ts
const results2 = await vectorDocStore.similaritySearchVectorWithScore({
query: [1.0, 2.1, 3.2],
k: 10,
filter: {
"metadata.title": "Document 1",
},
});
console.log(JSON.stringify(results2, null, 2));
```
## Next Steps
Tigris Vector Database is simple enough to get started with, but powerful
enough to build real-world applications.
Tigris provides the following features:
- High performance: Tigris is built from the ground up to provide fast
vector search. It is optimized for low-latency vector search and high
throughput.
- Real-time: Tigris provides real-time updates to the index. You can add,
update, and delete documents and embeddings in real-time.
- Hybrid search: Tigris supports hybrid search, which allows you to
combine vector search with attribute filtering for more relevant search
results.
- Fully managed: Tigris is a fully managed cloud-native database. You
can get started with Tigris in minutes and scale as needed with ease.
Check out the docs at [https://www.tigrisdata.com/docs/concepts/vector-search/](https://www.tigrisdata.com/docs/concepts/vector-search/).
# Contributing
## Building
```
# clean the dev env
npm run clean
# build
npm run build
# test
npm run test
# lint
npm run lint
```
## Code Quality
### 1. Linting
The coding style rules are defined by [Prettier](https://prettier.io/) and
enforced by [Eslint](https://eslint.org)
### 2. Git Hooks
We use [pre-commit](https://pre-commit.com/index.html) to automatically
setup and run git hooks.
Install the pre-commit hooks as follows:
```shell
pre-commit install
```
On every `git commit` we check the code quality using prettier and eslint.
# License
This software is licensed under the [Apache 2.0](LICENSE).
# Contributors
Thanks to all the people who contributed!
