Build a RAG Pipeline in Java (Text Vector LLM, No Paid APIs)
This article explains how to build a Retrieval-Augmented Generation (RAG) pipeline using Java, PostgreSQL, and the Ollama local LLM. The goal is to improve LLM responses by retrieving relevant data from a database and passing it to the LLM for context-aware generation.
Why it matters
This article provides a detailed, hands-on guide to building a powerful RAG system using open-source tools, which can be highly valuable for companies and developers looking to leverage LLMs while maintaining control over their data and infrastructure.
Key Points
- 1RAG combines a database, vector search, and LLM reasoning to provide more accurate and grounded responses
- 2The pipeline includes indexing text data and storing embeddings in PostgreSQL, then retrieving relevant data for a user query and passing it to the LLM
- 3The implementation uses Java, PostgreSQL with vector support, and the open-source Ollama LLM, avoiding paid APIs like OpenAI
Details
The article explains the Retrieval-Augmented Generation (RAG) architecture, which improves LLM responses by first retrieving relevant data from a knowledge source and then passing that data to the LLM to generate a final answer. This solves limitations of LLMs, which don't have access to private/company data and can hallucinate. The author walks through implementing a complete RAG pipeline in Java, using PostgreSQL with vector search capabilities to store and retrieve text embeddings, and the open-source Ollama LLM for generation. The goal is to build a practical, production-ready system without relying on paid APIs.
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