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PromptlicityAI is a platform for storing and managing custom content, enabling users to retrieve knowledge-based responses via API calls. It simplifies AI integration by allowing structured content storage and retrieval without requiring expertise in Retrieval-Augmented Generation (RAG) or complex frameworks.

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PromptlicityAI

Description

PromptlicityAI is a platform that enables users to store and manage custom content, which can then be used to generate knowledge-based responses through API calls. With PromptlicityAI, you can easily store content in a structured way and retrieve relevant knowledge chunks based on prompts—without needing to understand the underlying architecture of Retrieval-Augmented Generation (RAG) applications or how frameworks like work. PromptlicityAI simplifies the process of managing and querying knowledge content, enabling seamless AI integration without the complexity of underlying systems.

Tech Stack

  • Application: Flask, LangChain,
  • Embedding and LLM: Gemini
  • Vector DB: pgvector
  • Deployment: Docker

Features

  • Content Storage: Store and manage custom content with ease.
  • API Access: Generate API keys to retrieve knowledge-based responses using the stored content.
  • User-Friendly Interface: A simple dashboard where users can manage their content and API keys, making it easy to integrate and retrieve responses from stored knowledge.
  • No Technical Overhead: Users don’t need to know the complex processes of how knowledge is stored, chunks are retrieved, or how to work with large language models (LLMs) for generating responses.
  • Knowledge-Based Responses: Get relevant responses from your content based on the prompts you provide.

System Diagram

System Diagram

DB Schema

DB Schema

Documentation

Author

Dev Shah

About

PromptlicityAI is a platform for storing and managing custom content, enabling users to retrieve knowledge-based responses via API calls. It simplifies AI integration by allowing structured content storage and retrieval without requiring expertise in Retrieval-Augmented Generation (RAG) or complex frameworks.

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