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This is a node.js project leveraging the OpenAI API for vector embedding, seamlessly integrating with MongoDB to store embedded data and facilitating efficient query-based retrieval for enhanced knowledge management

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Swoyam1/KnowledgeBase-RAG

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Node.js PDF Knowledge Base

Tech Stack

  • JavaScript
  • Node.js
  • Express.js
  • MongoDB
  • pdfReader
  • Embedding API (OpenAI)
  • Thunder Client (API CLIENT)

Local Development

Start developing locally.

Step-1

clone this repo

git clone https://github.com/Swoyam1/KnowledgeBase-RAG.git

Step-2

Install all dependencies

# install server side deps
npm install

Step-3:

Create a SEARCH INDEX in MONGODB ATLAS

Step-4:

Create a .env file in root folder and write these code

MONGO_URL = "PROVIDE YOUR MONGODB ATLAS URL"
OPENAI_API_KEY = "PROVIDE YOUR OPENAI API KEY"

Step-5: Starting the server

Finally to start the server execute this script

npm run dev

After starting the server it should be running on http://localhost:7000

API

/document

  • POST : Post query to add vector embedding of PDF to the database

/query

  • POST : Post query and get the answer to the query in response
# post query body element
{
  "query" : "PROVIDE YOUR QUERY"
}

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This is a node.js project leveraging the OpenAI API for vector embedding, seamlessly integrating with MongoDB to store embedded data and facilitating efficient query-based retrieval for enhanced knowledge management

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