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The chatbot utilizes a retrieval-based approach to fetch relevant document segments before generating responses, making it highly effective for question-answering over custom documents.**\r\n\r\n## 🚀 Features\r\n\r\n- 📂 Upload multiple PDF documents\r\n- 🔍 Extracts and indexes document content for efficient retrieval\r\n- 💬 Conversational memory to maintain chat history\r\n- 🤖 Uses a powerful LLM (Gemma-2-9b-It via Groq API) for response generation\r\n- ⚡ Efficient vector search with ChromaDB\r\n- 🏗 Streamlit-based interactive UI\r\n  \r\n\r\n## 🛠️ Tech Stack\r\n\r\n- Python 🐍\r\n- LangChain 🔗 (for retrieval-augmented generation)\r\n- ChromaDB 📚 (for vector storage \u0026 retrieval)\r\n- Streamlit 🎨 (for building the UI)\r\n- Hugging Face Embeddings 🧠 (for text embedding)\r\n- Groq API ⚡ (for running the LLM)\r\n- PyPDFLoader 📄 (for processing PDFs)\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskrishna-7%2Fconversational-chatbot-with-pdf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskrishna-7%2Fconversational-chatbot-with-pdf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskrishna-7%2Fconversational-chatbot-with-pdf/lists"}