This program implements a Retrieval-Augmented Generation (RAG) system, utilizing advanced natural language processing techniques to enhance the generation of text. It's built on the langchain framework and integrates various components for document loading, embeddings, and conversational interfaces.
This file handles the embedding functionalities required for the RAG system. It imports necessary modules from langchain, such as OpenAIEmbeddings and Chroma for embedding computations. It also includes text splitters and document loaders for various file formats (PDF, DOCX, TXT), which are crucial for processing input documents.
This is the main executable file for the RAG system. It sets up a web interface using Streamlit, configuring the necessary callbacks, memories, and runnable configurations for the RAG system. It includes integration with the langchain and langsmith libraries for operational flow and user interface elements.
- Python 3.x
- Dependencies:
langchain,streamlit,langsmith, and other supporting libraries. - Installation: Use
pip install -r requirements.txtto install the necessary packages.
To run the program, navigate to the directory containing main.py and execute the command streamlit run main.py. This will start a local web server and the interface can be accessed through a web browser.
This project builds upon the original work by Charly Wargnier, which can be found at Charly Wargnier's LangchainRAG-Trubrics-Langsmith repository on GitHub.