{"id":16385056,"url":"https://github.com/vedanti-u/dbsense-ai","last_synced_at":"2026-04-12T15:03:02.628Z","repository":{"id":223516604,"uuid":"751971569","full_name":"vedanti-u/DbSense-AI","owner":"vedanti-u","description":"A quick and lightweight library to chat with databases. 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Yes!](https://badgen.net/badge/Open%20Source%20%3F/Yes%21/blue?icon=github)](https://github.com/Naereen/badges/)\n[![Npm package version](https://badgen.net/npm/v/dbsense-ai)](https://npmjs.com/package/dbsense-ai)\n[![Npm package monthly downloads](https://badgen.net/npm/dm/dbsense-ai)](https://www.npmjs.com/package/dbsense-ai)\n[![Npm package total downloads](https://badgen.net/npm/dt/dbsense-ai)](https://www.npmjs.com/package/dbsense-ai)\n[![GitHub contributors](https://badgen.net/github/contributors/vedanti-u/DbSense-AI)](https://GitHub.com/vedanti-u/DbSense-AI/graphs/contributors/)\n\n\u003c/div\u003e\n\n\u003c/div align=\"left\" \u003e\n\n\n\n\nhttps://github.com/user-attachments/assets/66115d65-1015-4875-a8a3-131095df8a80\n\n\n### 📦 NPM Downloads\n\n\u003e **[`dbsense-ai`](https://www.npmjs.com/package/dbsense-ai)** has crossed **1,000+ downloads** on NPM! 🎉  \n\u003e Thank you to the amazing developer community for the love and support! 🙌  \n\n\n## What is DbSenseAi\n\n**DbSenseAI** is a fast and lightweight library that simplifies chatting with your database. Unlike traditional methods, it doesn't need to embed all your database data. Instead, it focuses only on the schema, making it efficient and quick.\n\u003c/br\u003e\n\n**Features**:\n\n- _**Efficient Schema Embedding**: Only embeds schema, skipping the need to embed all database data._\n\n- _**Fast Database Interaction**: Enables quick communication with your database._\n\n- _**Resource Optimization**: Saves resources by avoiding unnecessary data embedding._\n\n- _**Versatile Integration**: Works well with various database systems._\n  \u003c/br\u003e\n\n## How DbSenseAi works ?\n\n```mermaid\n  sequenceDiagram\n    participant User\n    participant App\n    participant LLM_Model\n    participant Database\n\n    User-\u003e\u003eApp: Provides data (Schema of tables)\n    App-\u003e\u003eLLM_Model: Sends schema\n    LLM_Model-\u003e\u003eApp: Creates Vector Embedding\n    App-\u003e\u003eApp: Stores Vector Embedding in Local File\n    App-\u003e\u003eUser: Acknowledgement\n\n    User-\u003e\u003eApp: Asks query: \"All students passed with above 80 marks\"\n    App-\u003e\u003eLLM_Model: Sends query with Vector Embedding\n    LLM_Model-\u003e\u003eLLM_Model: Converts to SQL\n    LLM_Model-\u003e\u003eApp: Sends SQL\n    App-\u003e\u003eDatabase: Sends SQL\n    Database-\u003e\u003eDatabase: Processes SQL\n    Database--\u003e\u003eApp: Returns response\n    App--\u003e\u003eUser: Sends response\n\n```\n\nThe sequence diagram illustrates the process flow of a system where a user provides data to DBSenseAi, which includes schema information of tables. DBSenseAi forwards this schema to the Language Model (LLM_Model), which generates Vector Embeddings. These embeddings are stored locally by DBSenseAi. When the user queries for students who passed with above 80 marks, DBSenseAi sends this query along with the embeddings to the LLM_Model, which converts it into SQL. The SQL is then forwarded to the Database, processed, and the response is sent back to DBSenseAi, which in turn delivers it to the user.\n\n## Class Diagram\n\n```mermaid\n\nclassDiagram\n    class User {\n        +ProvidesData()\n        +AsksQuery()\n    }\n    class LLMService {\n        -vectorStore: HNSWLib | undefined\n        -model: OpenAI\n        -vectorStorePath: string\n        -openAIEmbeddings: OpenAIEmbeddings\n        +createTable(sqlQueryForTable: string): void\n        +updateTable(sqlQueryForTable: string): void\n        -extractTableNameFromCreateQuery(sqlQueryForTable: string): string | null\n        -extractTableNameFromUpdateQuery(sqlQueryForTable: string): string | null\n        +createVectorEmbeddings(tableString: string): void\n        -checkFileExists(filePath: string): Promise\u003cboolean\u003e\n        -deleteFile(filePath: string): Promise\u003cvoid\u003e\n    }\n    class PromptService {\n        -vectorStore: HNSWLib | undefined\n        -model: OpenAI\n        -vectorStorePath: string\n        -openAIEmbeddings: OpenAIEmbeddings\n        -rawData: string\n        -jsonData: any\n        +createSqlQuery(question: string): void\n        +checkFileExists(filePath: string): Promise\u003cboolean\u003e\n        +summarizeResponse(question: string, answer: any): void\n        +parseMessage(unformatedPrompt: string, ...args: string[]): string | undefined\n    }\n    class DBService {\n        -connection: dbconfig\n        -client: Client\n        +queryDatabase(inputQuery: string): Promise\u003cQueryResult\u003e\n        +connect(): Promise\u003cvoid\u003e\n    }\n    class DbSenseAi {\n        -dbService: DBService\n        -promptService: PromptService\n        -llmService: LLMService\n        +createTable(createQuery: string): Promise\u003cboolean\u003e\n        +updateTable(updateQuery: string): Promise\u003cboolean\u003e\n        +ask(question: string): Promise\u003cQuestionResponse\u003e\n    }\n\n    class OpenAIEmbeddings {\n        // properties and methods\n    }\n    class OpenAI {\n        // properties and methods\n    }\n    class HNSWLib {\n        // properties and methods\n    }\n    class dbconfig {\n        // properties\n    }\n    class Client {\n        // properties and methods\n    }\n    class QueryResult {\n        // properties and methods\n    }\n    class QuestionResponse {\n        // properties and methods\n    }\n\n    User --\u003e DbSenseAi : Uses\n    DbSenseAi --\u003e LLMService : Uses\n    DbSenseAi --\u003e PromptService : Uses\n    DbSenseAi --\u003e DBService : Uses\n    PromptService --\u003e OpenAIEmbeddings : Uses\n    PromptService --\u003e OpenAI : Uses\n    LLMService --\u003e OpenAIEmbeddings : Uses\n    LLMService --\u003e OpenAI : Uses\n    LLMService --\u003e HNSWLib : Uses\n    DBService --\u003e dbconfig : Contains\n    DBService --\u003e Client : Contains\n    DBService --\u003e QueryResult : Returns\n    DbSenseAi --\u003e QuestionResponse : Returns\n\n\n\n\n```\n\n## ⚡ Try DbSenseAi\n\n## Prerequisites\n\n- **Make**\n\n  \u003e Install make on Linux\n\n  ```bash\n  sudo apt install make\n  ```\n\n  _Check version_\n\n  ```bash\n  make -version\n  ```\n\n- ### **G++**\n\n  \u003e Install G++ on Linux\n\n  ```bash\n  sudo apt install g++\n  ```\n\n  _Check version_\n\n  ```bash\n  g++ --version\n  ```\n\n  ## Installation\n\n  ```bash\n  npm i dbsense-ai\n  ```\n\n## Setting-up `.env` file\n\nYour `.env` file should include\n\n```bash\nexport OPENAI_API_KEY=\u003cYOUR_OPENAI_KEY\u003e\nDB_DATABASE=\u003cYOUR_DATABASE_NAME\u003e\nDB_HOST=\u003cYOUR_DATABASE_HOST\u003e\nDB_PORT=\u003cYOUR_DATABASE_PORT\u003e\nDB_USER=\u003cYOUR_DATABASE_USER\u003e\nDB_PASSWORD=\u003cYOUR_DATABASE_PASSWORD\u003e\n```\n\n_Once the package is installed, you can import the library using import or require approach:_\n\n```javascript\nvar DbSenseAi = require(\"dbsense-ai\");\n```\n\n##### Instanciate the DbSenseAi class\n\n```javascript\nconst dbsenseai = new DbSenseAi();\n```\n\n## Usage\n\nAdd your _create table query_ inside the createTable() function\n\n```javascript\nawait dbsenseai.createTable(\n  \"CREATE TABLE cosmetics (brand VARCHAR(100) NOT NULL,product_type VARCHAR(100) NOT NULL,product_price NUMERIC(10, 2));\"\n);\n```\n\nAdd _your prompt_ inside the ask() function\n\n```javascript\nlet response = await dbsenseai.ask(\n  \"Give me name of all brands sorted in ascending order of price\"\n);\n```\n\n_You can get the response as table and summary_\n\n```javascript\nconsole.table(response.table);\nconsole.log(response.summary);\n```\n\n\u003c/br\u003e\n\n# 🤝 Contributing to Library\n\n\u003e [!NOTE]\n\u003e Contributing Guidelines\n\n### Dependencies\n\n![NPM Version](https://img.shields.io/badge/npm-v10.2.4-red?style=For-the-badge)\n![Node Version](https://img.shields.io/badge/node-v^20.11.17-blue?style=For-the-badge) ![NVM Version](https://img.shields.io/badge/nvm-v0.39.1-green?style=For-the-badge)\n![TypeScript](https://img.shields.io/badge/typescript-v^5.3.3-yellow?style=sqaure-flat\u0026logo=typescript\u0026logoColor=white) ![Postgres](https://img.shields.io/badge/postgres-^8.11.0-purple?style=square-flat\u0026logo=postgresql\u0026logoColor=white)\n\n### Prerequisites\n\n\u003e If you don't have git on your machine, [install it](https://docs.github.com/en/get-started/quickstart/set-up-git).\n\n- #### **make**\n\n  \u003cdetails open\u003e\n    \u003csummary\u003eInstall make on Linux\u003c/summary\u003e\n\n  ```bash\n  $ sudo apt install make\n  ```\n\n  _Check version_\n\n  ```bash\n  $ make -version\n  ```\n\n  \u003c/details\u003e\n\n- #### **G++**\n\n    \u003cdetails open\u003e\n      \u003csummary\u003eInstall G++ on Linux\u003c/summary\u003e\n      \n    ```bash\n    $ sudo apt install g++\n    ```\n\n  _Check version_\n\n  ```bash\n  $ g++ --version\n  ```\n\n    \u003c/details\u003e\n\n### Fork this repository\n\n\u003cdetails close\u003e\n  \u003csummary\u003eForking\u003c/summary\u003e\n  \u003cimg align=\"right\" width=\"400\" src=\"https://github.com/vedanti-u/readme-assets/blob/main/fork-the-repo.png\" alt=\"fork this repository\" /\u003e\n  \u003ch4\u003eFork this repository by clicking on the fork button on the top of this page. This will create a copy of this repository in your account.\n  \u003c/h4\u003e\n\u003c/details\u003e\n\n### Clone the repository\n\n\u003cdetails close\u003e\n  \u003csummary\u003eCloning\u003c/summary\u003e\n  \u003c/br\u003e\n  \u003cimg align=\"right\" width=\"200\" src=\"https://github.com/vedanti-u/readme-assets/blob/main/copy-cloning-url.png\" alt=\"fork this repository\" /\u003e\n  \u003cimg align=\"right\" width=\"300\" src=\"https://github.com/vedanti-u/readme-assets/blob/main/clone-button.png\" /\u003e\n\n  \u003ch4\u003eNow clone the forked repository to your machine. Go to your GitHub account, open the forked repository, click on the code button and then click the _copy to clipboard_ icon, this is the COPIED_URL.\u003c/h4\u003e\n\n\u003c/br\u003e\n\u003c/br\u003e\n\u003c/br\u003e\n\u003c/br\u003e\n\u003c/br\u003e\n\n\u003e _Open a terminal and run the following git command:_\n\n```git\ngit clone \"COPIED_URL\"\n```\n\ne.g : `git clone https://github.com/vedanti-u/db.ai.git`\n\u003c/br\u003e\n\n\u003c/details\u003e\n\n### Install dependencies\n\n```bash\nnpm install\n```\n\n---\n\n### Create a branch\n\n\u003cdetails\u003e\n  \u003csummary\u003eBranch naming conviction\u003c/summary\u003e\n  Change to the repository directory on your computer (if you are not already there):\n\n```bash\n$ cd dbsense-ai\n```\n\nNow create a branch using the `git checkout` command:\n\n```bash\n$ git checkout -b new-branch-name\n```\n\ne.g : `git checkout -b llm-prompt-support`\n\n**Name your branch according to the feature you are working on :**\n\ne.g : you want to work on creating more llm prompt support, name your branch like `llm-prompt-support`\n\n_(follow this naming convention i.e using \"-\" in between)_\n\n\u003c/details\u003e\n\n### Make contribution to _Code_\n\n#### Create a `.env` File with format\n\n```bash\nexport OPENAI_API_KEY=\u003cYOUR_OPENAI_KEY\u003e\nDB_DATABASE=\u003cYOUR_DATABASE_NAME\u003e\nDB_HOST=\u003cYOUR_DATABASE_HOST\u003e\nDB_PORT=\u003cYOUR_DATABASE_PORT\u003e\nDB_USER=\u003cYOUR_DATABASE_USER\u003e\nDB_PASSWORD=\u003cYOUR_DATABASE_PASSWORD\u003e\n```\n\n### Linking the library locally\n\n```bash\nrm -rf dist\ntsc\nnpm link\nnpm link dbsense-ai\n```\n\n---\n\n## Testing the library locally\n\n```bash\nnode test/localLibrary.test.ts --env=.env\n```\n\n### Create a pull request\n\n  \u003cdetails\u003e\n   \u003csummary\u003eCreating pull requests\u003c/summary\u003e\n  \u003c/br\u003e\n  Once you have modified an existing file or added a new file to the project of your choice, you can stage it to your local repository, which we can do with the `git add` command. In our example, `filename.md`, we will type the following command.\n\n\u003ccode\u003e$ git add filename.md\u003c/code\u003e\n\nwhere filename is the file you have modified or created\n\nIf you are looking to add all the files you have modified in a particular directory, you can stage them all with the following command:\n`git add .`\n\nOr, alternatively, you can type `git add -all` for all new files to be staged.\n\n\u003ch3\u003eCommiting the changes\u003c/h3\u003e\n\u003ccode\u003egit commit -m \"Added a new prompt in prompts.json file\"\u003c/code\u003e\n\n\u003ch3\u003eTo PUSH your branch to your remote main\u003c/h3\u003e\n\u003ccode\u003e$ git push --set-upstream origin your-branch-name\u003c/code\u003e\n\u003c/br\u003e\n\ne.g : `$ git push --set-upstream origin optimise-binding`\n\n\u003ch4\u003eOpen Github\u003c/h4\u003e\n\u003cimg align=\"right\" width=\"300\" src=\"https://github.com/vedanti-u/readme-assets/blob/main/compare-and-pulll-request.png\" alt=\"compare and pull request\" /\u003e\nclick on compare \u0026 pull request\n\u003c/br\u003e\n\u003c/br\u003e\n\u003c/br\u003e\n\u003c/br\u003e\n\u003cimg align=\"right\" width=\"300\" src=\"https://github.com/vedanti-u/readme-assets/blob/main/create-pull-request.png\" alt=\"create pull request\" /\u003e\nwrite a description for your pull request specifing the changes you have made, title it and then, Click on create pull request\n\n_your branch will be merged on code review_\n\n  \u003c/details\u003e\n\n\u003c/br\u003e\n\n### :octocat: Statistics\n\n[![Open Source Love svg2](https://badges.frapsoft.com/os/v2/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/)\n![stars](https://img.shields.io/github/stars/vedanti-u/DbSense-AI.svg)\n![forks](https://img.shields.io/github/forks/vedanti-u/DbSense-AI.svg)\n![watchers](https://img.shields.io/github/watchers/vedanti-u/DbSense-AI.svg)\n[![Open Source? 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