PostgresML

AI Lead Generation
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Introduction: Index, filter & rank vectors. Generate real-time, fact-based outputs.
Added on: Jan 20, 2025
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PostgresML

What is PostgresML

PostgresML is a platform designed to simplify the integration of machine learning and AI into PostgreSQL databases. It enables users to perform vector operations, generate embeddings, and train models directly within the database, reducing the complexity of managing multiple microservices and improving efficiency.

How to Use PostgresML

  1. Index, filter, and re-rank vector embeddings: Perform fast KNN and ANN searches using HNSW or IVFFlat indexing.
  2. Generate embeddings: Choose from state-of-the-art models and convert text to vector embeddings.
  3. Colocate data and compute: Embed, serve, and store data in one process for improved efficiency.
  4. Train, tune, and deploy models: Fine-tune LLMs on your own data and monitor deployments over time.
  5. Use open-source models: Leverage models like Mistral and LLama for NLP tasks.
  6. Deploy with SQL or SDKs: Use SQL or SDKs in JS and Python for seamless integration.

Use Cases of PostgresML

PostgresML is ideal for developers and organizations looking to integrate machine learning and AI capabilities directly into their PostgreSQL databases. It simplifies the architecture by combining data storage, embedding generation, and model training into a single platform, reducing latency, improving data privacy, and lowering costs.

Features of PostgresML

  • Index, filter and re-rank vector embeddings

    Perform fast KNN and ANN searches with 10x faster vector operations using HNSW or IVFFlat indexing.

  • Generate embeddings

    Convert text to vector embeddings using state-of-the-art models and built-in data preprocessors.

  • Colocate data and compute

    Embed, serve, and store data in one process, ensuring data privacy and security.

  • Train, tune and deploy

    Fine-tune LLMs on your own data and monitor model deployments over time.

  • Get the most of LLMs

    Use open-source models like Mistral and LLama for a range of NLP tasks.

  • Comprehensive platform

    Perform multiple AI and machine learning tasks using SQL or SDKs in JS and Python.

FAQs from PostgresML

1

What makes PostgresML faster than other solutions?
PostgresML is 4x faster than HuggingFace + Pinecone for RAG chatbots and 10x faster than OpenAI for embedding generation, thanks to its optimized architecture and GPU support.
2

How does PostgresML ensure data privacy?
PostgresML colocates data and compute in a single process, ensuring built-in data privacy and security without exposing data to multiple systems.
3

Can I use open-source models with PostgresML?
Yes, PostgresML supports open-source models like Mistral, LLama, and others for embedding generation and NLP tasks.