ragChatbot is a developer-centric framework designed to streamline the creation of Retrieval-Augmented Generation chatbots. It integrates LangChain pipelines with OpenAI or other LLM APIs to process queries against custom document corpora. Users can upload files in various formats (PDF, DOCX, TXT), automatically extract text, and compute embeddings using popular models. The framework supports multiple vector stores such as FAISS, Chroma, and Pinecone for efficient similarity search. It features a conversational memory layer for multi-turn interactions and a modular architecture for customizing prompt templates and retrieval strategies. With a simple CLI or web interface, you can ingest data, configure search parameters, and launch a chat server to answer user questions with contextual relevance and accuracy.

AChat.dev is a developer-centric platform that allows users to create, test and deploy AI chat agents with advanced capabilities. It supports persistent conversation memory so agents remember past interactions, dynamic function calls to external APIs for real-time data retrieval, and role-based multi-agent collaboration. Built on Python and Node.js SDKs, it includes templating for quick setup, plugin architecture for extensibility, and monitoring dashboards to track agent performance. AChat.dev ensures GDPR-compliant data handling and can scale across cloud and on-premise environments.