GITHUB: https://github.com/ronidas39/LLMtutorial/tree/main/tutorial58 TELEGRAM: https://t.me/ttyoutubediscussion The aim is to create a system that retrieves information from embedded vectors of text data but can also query an external search engine like Google when the information is not available in the local dataset. Here’s a detailed breakdown of the steps and concepts discussed: 1. **Overview of the RAG System**: - **Purpose**: To develop a conditional RAG system that initially tries to fetch information from a locally embedded database (Vector DB). If the information isn't available locally, it defaults to querying an external search engine. - **Context Usage**: The system utilizes documents (such as PDFs) as its knowledge base. These documents are processed to extract and store information in a format that can be quickly accessed through vector embeddings. 2. **Workflow Description**: - **Document Loading**: PDF documents are loaded into the system. - **Chunking**: The loaded documents are split into manageable chunks. This is likely done to simplify processing and embedding. - **Vector Embedding**: These chunks are then embedded into vectors using OpenAI's models and stored in a Vector DB. - **Querying**: When a query is made, the system first searches the Vector DB to find the most relevant document chunk based on the embeddings. - **Fallback Mechanism**: If the query cannot be resolved with the local data, the system is designed to use an external search engine to attempt to find an answer. 3. **Development Steps**: - **Environment Setup**: The presenter plans to use a programming environment suitable for handling files and processing text, evidenced by the imports from Langchain (for document loading and text splitting) and the setup of directory structures in the OS module. - **PDF Processing**: The tutorial likely includes detailed code for loading PDF files, parsing them into text, and then processing this text into chunks. - **Embedding and Vector Storage**: The chunks are embedded into vectors using an AI model, and these vectors are stored in a specialized database (Vector DB) designed for quick retrieval based on vector similarity. - **Conditional Logic Implementation**: The system is programmed to default to a Google search if the local Vector DB does not return any relevant results. 4. **Testing and Debugging**: - **Local Testing**: The system is first tested with queries that should fetch results from the local database. - **Fallback Testing**: Queries that are known not to have answers in the local database are used to test the fallback to Google search functionality. 5. **Practical Application and Considerations**: - **Client Requirement**: The need for such a system arose from a client requirement, indicating a real-world application where information retrieval needs to be robust and comprehensive, covering both local and extensive internet resources. - **Technical Details**: Throughout the script, there are technical specifics regarding the use of Python libraries and modules, file handling, text processing, and AI-driven text embedding, demonstrating a complex and technically rich implementation. This tutorial aims to educate viewers on integrating advanced text processing and retrieval techniques using modern AI technologies and traditional programming tools.