Efficient Chatbot Creation with RAG and Guardrails

🔍 Retrieval augmented generation (RAG) is a powerful tool within Nemo guard rails that utilizes a vector database and an embedding model.

⚡️ The naive approach of RAG involves taking a query and embedding it to retrieve relevant information quickly.

🔁 The more complex approach of RAG involves using an agent to process queries over time and access external tools.

🔑 The video discusses the process of creating chatbots using an external knowledge tool and an embedding model.

⏳ The use of multiple LM Generations in the process makes it slower, but using guardrails allows for a more efficient approach.

🛠️ Guardrails provide a middle ground solution that utilizes a different embedding model to create vector representations of queries.

💡 The video discusses how to make RAG chatbots faster by using retrieval-based methods.

🔍 A key technique is to check if a user query is semantically similar to predefined topics and trigger the retrieval tool if necessary.

🔧 Multiple tools can be used to generate responses, and the unique approach of using guardrails allows for faster generation.

🔍 The video discusses querying data from an open AI API to create embeddings and index them using Vex databases.

🧩 The presenter demonstrates the process of creating unique IDs and selecting relevant fields from a dataset.

💻 An API key from Pinecone is used to initialize a vector index and create the index if it doesn't already exist.

⭐ Initializing and populating the index with data.

🔧 Creating rag pipelines with guardrails using executable functions.

💬 Using prompt templates to generate responses and setting up guardrails criteria.

🔑 Semantically embedded vectors are used to compare user queries and trigger specific flows.

👩‍💻 Retrieval augmented generation is used to create context-based answers.

🤖 Guardrails helps register actions and allows easy integration of functions.

🤖 Red teaming is a technique used to identify risks and measure the robustness of a model.

📘 Red teaming provides quality insights by recognizing and targeting specific patterns.

⚡ Using guardrails allows for faster execution of tools that only need to be triggered.