๐ Retrieval augmented generation (RAG) is a technique used to help large language models get relevant context to answer queries.
๐ก 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.
๐ 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.
โญ๏ธ LlamaIndex company's experience with implementing Rag in production
๐ก Challenges faced when handling changes to documents and data distribution
๐ Approaches to optimize the retrieval architecture and improve search results
๐ Retrieval augmented generation (RAG) systems have two main steps: retrieval and generation.
โก Considerations for building RAG in production include performance, cost, latency, scalability, and security.
๐ก Data syncing and ingestion can be complex and time-consuming, especially when dealing with large amounts of data.
๐ The distinction between a demo and production is important, as indexing large amounts of data can be time-consuming and costly.
๐ก Development in CPU-based inference and tooling is exciting, as it allows for cost estimations and efficient data storage and retrieval.
๐ The combination of models, databases, and tooling is starting to come together, enabling advanced querying, data modification, and information retrieval.
๐ 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.
โฑ๏ธ Optimizing retrieval from the database and embedding generation can result in end-to-end latency of 20-30 milliseconds for big data sets.
๐ Consider the scalability and architectural setup needed for data ingestion, especially for continuously changing data sets.
๐ The retrieval step in the RAG pipeline is often overlooked but is crucial for performance.
๐ก Hybrid search and re-ranking can improve retrieval by combining keyword and embedding search.
๐ Adding metadata filters provides extra context and labeling information for more comprehensive answers.
๐ Using metadata can provide context and explainability to generative models.
๐ก Metadata can be used to generate content and create embeddings for effective search.
๐ Re-ranking search results based on different criteria can improve user experience.
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