AI_RAG delivers a modular retrieval-augmented generation solution that combines document indexing, vector search, embedding generation, and LLM-driven response composition. Users prepare corpora of text documents, connect a vector store like FAISS or Pinecone, configure embedding and LLM endpoints, and run the indexing process. When a query arrives, AI_RAG retrieves the most relevant passages, feeds them alongside the prompt into the chosen language model, and returns a contextually grounded answer. Its extensible design allows custom connectors, multi-model support, and fine-grained control over retrieval and generation parameters, ideal for knowledge bases and advanced conversational agents.

ChatGPT for Google is an innovative extension designed to streamline your online search experience. This tool allows users to access AI-generated responses from ChatGPT, Claude, and Bard right next to traditional search results. By providing contextually relevant answers, this extension enhances the way you retrieve and engage with information online. It supports multiple search engines, making it a versatile addition to your browsing arsenal and improving your productivity.

ExplainIt is a powerful AI-driven tool designed to enhance the accessibility and usability of your documentation. By understanding the context of your documents, ExplainIt delivers accurate, contextually relevant answers to user queries. This helps in reducing the time spent searching for information and improves overall efficiency. It's an essential tool for organizations looking to streamline their documentation process and enhance user engagement.

LangChain RAG Agent Chatbot sets up a pipeline that ingests documents, converts them into embeddings with OpenAI models, and stores them in a FAISS vector database. When a user query arrives, the LangChain retrieval chain fetches relevant passages, and the agent executor orchestrates between retrieval and generation tools to produce contextually rich answers. This modular architecture supports custom prompt templates, multiple LLM providers, and configurable vector stores, making it ideal for building knowledge-driven chatbots.