Building RAG with Postgres
The article outlines building a Retrieval-Augmented Generation system using Postgres, highlighting its architecture, ingestion and retrieval processes, and the importance of evaluation and iterative refinement for optimal performance.
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This article provides a comprehensive guide on building a Retrieval-Augmented Generation (RAG) system using Postgres. It emphasizes the advantages of using Postgres due to its robustness and familiarity, which can enhance productivity. The architecture of the RAG system is outlined, detailing components such as the ingestion pipeline, Postgres database, and the RAG pipeline itself. Key elements include the creation of tables for documents and document chunks, which facilitate efficient data storage and retrieval. The ingestion process involves extracting, cleaning, chunking, and embedding data, while the retrieval process focuses on querying and reranking relevant chunks based on user input. The article also discusses the importance of evaluation, feedback loops, and observability in refining the system. It suggests that building an effective RAG system is iterative, requiring continuous experimentation and adjustment based on user interactions and data insights. The conclusion reiterates the need to understand specific use cases, evaluate components independently, and maintain a balance between performance and accuracy.
- Postgres is a powerful tool for building RAG systems due to its versatility and robustness.
- The architecture includes an ingestion pipeline, a database for documents, and a retrieval system.
- Evaluation and feedback loops are crucial for refining the RAG system.
- The process involves chunking, embedding, and reranking data to enhance retrieval accuracy.
- Building a RAG system is an iterative process that requires continuous measurement and adjustment.
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