Here's a stepwise explanation of the procedure used in the code to build a chatbot for chat on multi-docs:
- Setting Up the Environment: • Import necessary libraries: Streamlit, streamlit_chat, Langchain, os, dotenv, tempfile. • Load environment variables (if any) using load_dotenv().
- Defining Chatbot Functions: • initialize_session_state(): Initializes session state to store chat history and responses. • conversation_chat(query, chain, history): Interacts with the conversational chain to generate a response to a query, considering chat history. • display_chat_history(chain): Displays the chat conversation using Streamlit's chat component. • create_conversational_chain(vector_store): Creates a conversational chain object, combining a large language model (LLM), retriever, and memory.
- Main Application Logic: • main() function: o Initializes session state. o Sets up the Streamlit app interface. o Creates a sidebar for document uploads. o Handles document uploads: Extracts text from various file formats (PDF, DOCX, TXT). Splits text into chunks using CharacterTextSplitter. Generates embeddings for text chunks using HuggingFaceEmbeddings. The Hugging Face model "sentence-transformers/all-MiniLM-L6-v2" is used for creating embeddings. Builds a vector store using FAISS for efficient retrieval. o Creates a conversational chain object. o Displays the chat interface.
- Chatbot Interaction: • User uploads documents through the sidebar. • Documents are processed and indexed for retrieval. • User asks questions in the chat interface. • Chatbot retrieves relevant information from the indexed documents using the vector store. • LLM generates a response based on the retrieved information and chat history. • Response is displayed in the chat interface. • Chat history is maintained for context. Key Points: • Langchain streamlines multi-document chatbot development. • LLM generates responses, vector store enables retrieval, and memory tracks conversation context. • Streamlit provides a user-friendly web interface.