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Vector Database Demo

Vector Database Demo is a small JavaScript/Node.js project that demonstrates local document storage and semantic-similarity retrieval with ChromaDB. Its script creates a collection, inserts seven example documents, and queries them in natural language.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Vector Database Demo — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to understand or demonstrate a small local semantic-search flow before adapting the pattern to a broader document-retrieval project.

Who it is for

  • Developers learning the basic ChromaDB collection and semantic-query workflow.
  • Anyone exploring a minimal local prototype for embedding-based retrieval over a fixed document set.

Documented uses

  • Install the demo's Node.js dependencies.
  • Start a local ChromaDB instance with a chosen data directory.
  • Insert the example documents into a collection and query them by natural language.
  • Observe a relevant result from a semantic-similarity search.
Before you choose

Requirements & limits

The repository is public and identifies the project as open-source under the MIT license. The documented workflow runs locally and requires Node.js.

  • ChromaDB runs locally, with data stored in the ./chroma directory.
  • The documented example has seven sample documents and uses the fixed query “What is India?”.
  • The README requires keeping the ChromaDB terminal running while executing the script in another terminal.
  • The project is JavaScript and Node.js based.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the local ChromaDB semantic-search demo

Install the Node.js dependencies, start a local ChromaDB instance, and run the example query against its sample documents.

Before you start

  • Node.js
  • A terminal for the ChromaDB process and a separate terminal for the demo script

Steps

  1. From the project directory, run the command below.

    Documented command
    npm install

  2. Start ChromaDB with the command below and leave that terminal running.

    Documented command
    npx chroma run --path ./chroma

  3. In a separate terminal, run the command below.

    Documented command
    node index.js

  4. The script creates my_collection, adds seven example documents, queries “What is India?”, and logs the most relevant result.

Expected outcome

A local demonstration of adding sample documents to ChromaDB and retrieving a semantically relevant result.

Guide limitations

  • The example uses seven sample documents and a fixed query.
  • The ChromaDB process must remain running while the script executes.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Vector Database DemoInstall the demo's Node.js dependencies.CLI, Self-hostedThe repository is public and identifies the project as open-source under the MIT license. The documented workflow runs locally and requires Node.js.
Chatbot with PDF for Semantic SearchPrepare document content as embeddings and save it to a supported vector database.WebThe repository is identified as open source and publicly visible on GitHub.
AI LLM PlaygroundAsk questions about PDF documents through an assistant interface.Web
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
eswar-7116/vector-db-demo · Official homepage title · retained in its source language
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