Takder‑Ai is a sophisticated, AI-driven searchable knowledge base designed to transform static documents into dynamic, interactive assets. By leveraging cutting-edge Retrieval-Augmented Generation (RAG), Takder‑Ai allows users to upload PDFs and notes, creating an indexed repository that can be queried in natural language to extract precise, context-aware insights.
The application follows a robust RAG (Retrieval-Augmented Generation) pipeline to ensure accuracy and groundedness in every response:
- Ingestion: Files (PDFs, Markdown, Text) are uploaded and processed.
- Preprocessing & Chunking: Documents are split into semantic chunks to preserve context while fitting model constraints.
- Embedding: Each chunk is converted into a high-dimensional vector using Google Gemini's embedding models.
- Vector Store: Vectors are stored in a specialized database (PostgreSQL with
pgvectoror similar) for lightning-fast similarity searching. - Retrieval: When a query is asked, the system retrieves the most relevant document fragments based on vector similarity.
- Generation: The retrieved context + the original query are fed into Google Gemini Flash to generate a grounded, source-cited response.
- Next.js (React-based hybrid framework for speed and scalability)
- Tailwind CSS (Utility-first styling for a polished, modern interface)
- Lucide React (Clean, consistent iconography)
- Motion (Smooth, fluid UI transitions and micro-interactions)
- LangChain (The orchestration layer for RAG pipelines and LLM workflows)
- Google Gemini Flash (High-performance large language model for generation)
- Google GenAI SDK (Native integration for embeddings and multi-modal analysis)
- PostgreSQL / pgvector (Relational data + vector operations)
- Vite/Express (Development environment and server-side logic)
- Architecture Setup: Project initialization with Next.js & PostgreSQL schema design.
- Ingestion Engine: Implementation of the multi-format file ingestion logic (PDF/Notes).
- Embedding Pipeline: Integration of Google GenAI for vectorization of document chunks.
- Gemini Integration: Orchestrating LangChain with Google Gemini 1.5 Flash for RAG.
- Chat Interface UI: Developing the interactive natural language query interface.
- Vector Refinement: Optimizing similarity search and retrieval accuracy.
- Security & Access: Implementation of the authentication layer and role-based access.
- Edge Optimizations: Latency reduction and context window performance tuning.
- Production Ready: Full-scale testing, audit, and production deployment on Vercel/Cloud Run.
To run Takder‑Ai, you need to configure the following keys in your .env file:
# AI & LLM
GEMINI_API_KEY="your_google_gemini_api_key_here"
# Database
DATABASE_URL="postgresql://user:password@localhost:5432/takder_ai"
# Application
APP_URL="http://localhost:3000"
NODE_ENV="development"Ensure you have Node.js (v18+) and npm/yarn installed.
Clone the repository and install dependencies:
git clone https://github.com/your-username/takder-ai.git
cd takder-ai
npm installEnsure PostgreSQL is running with the pgvector extension enabled.
CREATE EXTENSION IF NOT EXISTS vector;Start the development server:
npm run devThe app will be available at http://localhost:3000.
"Turning data into dialogue." — The Takder‑Ai Team