DocGPT is designed to simplify information extraction and Q&A from documents by providing a seamless conversational interface. Users can upload documents in PDF, Word, or PowerPoint formats, which are then processed using text parsers. The content is chunked and embedded with OpenAI's embedding models, stored in a vector database like FAISS or Pinecone. When a user submits a query, DocGPT retrieves the most relevant text chunks via similarity search and leverages ChatGPT to generate accurate, context-aware responses. It features interactive chat, document summarization, customizable prompts for domain-specific needs, and is built on Python with a Streamlit UI for easy deployment and extensibility.

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.

LangChain Chatbot for Customer Support leverages the LangChain framework and large language models to provide an intelligent conversational agent tailored for support scenarios. It integrates a vector store for storing and retrieving company-specific documents, ensuring accurate context-driven responses. The chatbot maintains multi-turn memory to handle follow-up questions naturally, and supports customizable prompt templates to align with brand tone. With built-in routines for API integration, users can connect to external systems like CRMs or knowledge bases. This open-source solution simplifies deploying a self-hosted support bot, enabling teams to reduce response times, standardize answers, and scale support operations without extensive AI expertise.