This Streamlit application is designed to upload, process, and store documents in ChromaDB, and query these documents using a LLAMA language model. It utilizes transformers, PyPDF2, Chroma, and other libraries for processing and querying documents.

• Document Processing: Upload PDF documents, extract their text, and store them in ChromaDB.
• ChromaDB Integration: Initialize and interact with ChromaDB for document persistence and retrieval.
• LLAMA Integration: Utilize LLAMA, a powerful causal language model, to generate answers based on the context from the documents stored in ChromaDB.
• Interactive UI: Easy-to-use Streamlit interface for uploading documents and querying.

• Python 3
• Streamlit (streamlit)
• Transformers (transformers)
• PyPDF2 (PyPDF2)
• LangChain (langchain)
• ChromaDB (chromadb)
• Other dependencies as required by these packages

1. Install Dependencies: Use pip install streamlit transformers PyPDF2 langchain chromadb.
2. Run the Application: Execute streamlit run main.py to start the Streamlit application.

• Upload Documents: Use the file uploader to add PDF documents to ChromaDB.
• Query with LLAMA: Enter your query, and the application will search ChromaDB for relevant documents and use LLAMA to generate context-based answers.

• ChromaDB Directory: Set the directory for ChromaDB storage. Default is ./chromadb.
• Model for Embeddings: The application uses all-MiniLM-L6-v2 for sentence embeddings. This can be customized in the code.

• Ensure that ChromaDB is initialized and has documents before querying.
• The application handles basic error scenarios like empty ChromaDB directories. Nothing too crazy of an error handling is going here otherwise.