Challenges and Considerations for Making RAG Production-Ready

🌟 Retrieval augmented generation (RAG) is a technique used to help large language models get relevant context to answer queries.

💡 RAG involves transforming queries based on provided context, using a retriever component to select relevant context, and injecting it into a prompt for a large language model.

🔎 Important building blocks for a production-ready RAG pipeline include retrieving relevant documents, choosing the right retrieval methodology (keyword, embedding, or hybrid), and optimizing diversity and context focus.

⭐️ LlamaIndex company's experience with implementing Rag in production

💡 Challenges faced when handling changes to documents and data distribution

🔑 Approaches to optimize the retrieval architecture and improve search results

🔍 Retrieval augmented generation (RAG) systems have two main steps: retrieval and generation.

⚡ Considerations for building RAG in production include performance, cost, latency, scalability, and security.

💡 Data syncing and ingestion can be complex and time-consuming, especially when dealing with large amounts of data.

🔑 The distinction between a demo and production is important, as indexing large amounts of data can be time-consuming and costly.

💡 Development in CPU-based inference and tooling is exciting, as it allows for cost estimations and efficient data storage and retrieval.

🌟 The combination of models, databases, and tooling is starting to come together, enabling advanced querying, data modification, and information retrieval.

🔑 When using large data sets, it is recommended to validate if the chosen model generates the desired results and build a pipeline to optimize for performance.

⏱️ Optimizing retrieval from the database and embedding generation can result in end-to-end latency of 20-30 milliseconds for big data sets.

📚 Consider the scalability and architectural setup needed for data ingestion, especially for continuously changing data sets.

🔍 The retrieval step in the RAG pipeline is often overlooked but is crucial for performance.

💡 Hybrid search and re-ranking can improve retrieval by combining keyword and embedding search.

📊 Adding metadata filters provides extra context and labeling information for more comprehensive answers.

🔑 Using metadata can provide context and explainability to generative models.

💡 Metadata can be used to generate content and create embeddings for effective search.

🔄 Re-ranking search results based on different criteria can improve user experience.