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White-Label RAG Chatbot Platform
[ ARCHIVED ]Platform for creating, customizing, and deploying retrieval-augmented chatbots with user-defined knowledge bases.
Developed a white-label chatbot platform that enables organizations to create and deploy their own retrieval-augmented generation (RAG) assistants using custom knowledge bases. The system allows users to upload documents, build vector indexes, configure LLM behavior, and deploy fully functional chatbots without building the underlying infrastructure.
Key Contributions
- Built an end-to-end RAG pipeline enabling document ingestion, embedding generation, vector indexing, retrieval, and LLM-based response generation.
- Implemented multi-tenant architecture allowing different users or organizations to create independent chatbots with isolated knowledge bases.
- Developed document ingestion workflows supporting PDF, text, and structured data sources, automatically chunking and embedding content for retrieval.
- Integrated vector database retrieval to surface relevant context during response generation.
- Designed backend services for chat session management, prompt orchestration, and retrieval pipelines.
- Built APIs enabling users to deploy and manage custom chatbots that can be embedded into applications or websites.
- Implemented configurable parameters for model selection, retrieval settings, and response behavior to support different use cases.
Tech Stack
Python, FastAPI, LLMs, Vector Database, RAG Pipelines, Embeddings, PostgreSQL
Links
Private – Developed for employer