Efficient Chatbot Creation with RAG and Guardrails
This video explores different approaches to making RAG chatbots faster by using semantic similarity and guardrails.
00:00:00 This video explores different approaches to retrieval augmented generation using Nemo guardrails. The naive approach is quick but less powerful, while the agent-based approach is slower but potentially more effective.
🔍 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.
00:03:05 Learn how to create efficient chatbots using RAG and Guardrails, which allows for a middle-ground approach between heavy LM generations and slower retrieval 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.
00:06:08 This video explains how to make RAG chatbots faster by using semantic similarity to trigger the retrieval tool for generating responses. The approach is significantly faster than the traditional agent approach and allows for the use of multiple tools.
💡 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.
00:09:11 Learn how to create fast chatbots using RAG. Query an open AI to create embeddings and index them with Pinecone for efficient searching.
🔍 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.
00:12:17 Learn how to create efficient chatbots using RAG pipelines with guardrails, embedding queries and retrieving relevant items to generate responses.
⭐ 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.
00:15:22 How to create chatbots using semantically embedded vectors to provide quick and accurate responses without using Rag pipeline.
🔑 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.
00:18:25 A video demonstrates how to use guardrails to create an efficient chatbot that knows when to utilize its retrieval tools and when not to. This approach allows for faster response times without sacrificing functionality.
🤖 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.
Want to deep dive into this video?
