| ## PDF Q&A Chatbot | |
| This project is a PDF-based chatbot that allows users to upload a PDF, process its content, and ask questions related to the document using the Langchain library and OllamaLLM (gemma2:2b). The chatbot utilizes Streamlit for the user interface and Chroma for vector storage, enabling efficient document processing and retrieval-based question-answering. | |
| ## Features | |
| * PDF Upload: Users can upload a PDF file which will be processed and split into smaller chunks for efficient retrieval. | |
| * Persisted Vector Store: The PDF is processed once and stored in a vector database, allowing for fast responses to user queries without reprocessing the document. | |
| * Q&A: Users can ask questions related to the uploaded PDF, and the chatbot will provide concise answers based on the document content. | |
| * Fast PDF Processing: The use of vector storage ensures that once the PDF is processed, users can query it efficiently. | |
| Tech Stack | |
| * Langchain: For building the Q&A chatbot pipeline and vector-based document retrieval. | |
| * OllamaLLM: The LLM used for generating answers to user queries. | |
| * Chroma: Vector store for document embeddings, enabling fast document retrieval. | |
| ### Create and activate a virtual environment: | |
| ```bash | |
| python3 -m venv ven | |
| ``` | |
| ### Install the required libraries: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Run the application: | |
| ```bash | |
| streamlit run app.py | |
| ``` |