Compare the Top Retrieval-Augmented Generation (RAG) Software that integrates with PostgreSQL as of June 2025

This a list of Retrieval-Augmented Generation (RAG) software that integrates with PostgreSQL. Use the filters on the left to add additional filters for products that have integrations with PostgreSQL. View the products that work with PostgreSQL in the table below.

What is Retrieval-Augmented Generation (RAG) Software for PostgreSQL?

Retrieval-Augmented Generation (RAG) tools are advanced AI systems that combine information retrieval with text generation to produce more accurate and contextually relevant outputs. These tools first retrieve relevant data from a vast corpus or database, and then use that information to generate responses or content, enhancing the accuracy and detail of the generated text. RAG tools are particularly useful in applications requiring up-to-date information or specialized knowledge, such as customer support, content creation, and research. By leveraging both retrieval and generation capabilities, RAG tools improve the quality of responses in tasks like question-answering and summarization. This approach bridges the gap between static knowledge bases and dynamic content generation, providing more reliable and context-aware results. Compare and read user reviews of the best Retrieval-Augmented Generation (RAG) software for PostgreSQL currently available using the table below. This list is updated regularly.

  • 1
    Airbyte

    Airbyte

    Airbyte

    Airbyte is an open-source data integration platform designed to help businesses synchronize data from various sources to their data warehouses, lakes, or databases. The platform provides over 550 pre-built connectors and enables users to easily create custom connectors using low-code or no-code tools. Airbyte's solution is optimized for large-scale data movement, enhancing AI workflows by seamlessly integrating unstructured data into vector databases like Pinecone and Weaviate. It offers flexible deployment options, ensuring security, compliance, and governance across all models.
    Starting Price: $2.50 per credit
  • 2
    Pathway

    Pathway

    Pathway

    Pathway is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. Pathway comes with an easy-to-use Python API, allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: you can use it in both development and production environments, handling both batch and streaming data effectively. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a scalable Rust engine based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with Docker and Kubernetes.
  • 3
    eRAG

    eRAG

    GigaSpaces

    GigaSpaces eRAG (Enterprise Retrieval Augmented Generation) is an AI-powered platform designed to enhance enterprise decision-making by enabling natural language interactions with structured data sources such as relational databases. Unlike traditional generative AI models that may produce inaccurate or "hallucinated" responses when dealing with structured data, eRAG employs deep semantic reasoning to accurately translate user queries into SQL, retrieve relevant data, and generate precise, context-aware answers. This approach ensures that responses are grounded in real-time, authoritative data, mitigating the risks associated with unverified AI outputs.​ eRAG seamlessly integrates with various data sources, allowing organizations to unlock the full potential of their existing data infrastructure. eRAG offers built-in governance features that monitor interactions to ensure compliance with regulations.
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