Description
PostgresML offers a streamlined approach to developing AI applications by embedding machine learning functionalities directly within your PostgreSQL database. This eliminates the need for complex microservice architectures, such as separate vector databases, embedding models, and LLM frameworks, thereby reducing operational overhead and potential production outages.
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The platform empowers developers to build AI-powered applications with ease, leveraging the familiarity of SQL. Key capabilities include indexing, filtering, and re-ranking vector embeddings up to 10 times faster than traditional methods, supporting fast KNN and ANN searches with HNSW or IVFFlat indexing. It also facilitates the generation of embeddings using a selection of state-of-the-art models, complete with built-in data preprocessors for text splitting and chunking.
PostgresML enables the co-location of data and compute, allowing you to embed, serve, and store terabytes of data on a single machine while ensuring built-in data privacy and security. Furthermore, it supports training, tuning, and deploying various ML models, including regression, classification, and clustering, as well as fine-tuning LLMs on your own data. Users can also monitor model deployments over time.
The platform integrates seamlessly with open-source LLMs like Mistral and Llama, enabling a range of NLP tasks and serving models with the same infrastructure. It provides a comprehensive platform with multiple deployment options and supports AI and machine learning tasks through SQL or SDKs in JavaScript and Python. This integrated approach leads to better price-performance, as it can replace multiple existing tools and minimize operational costs.
PostgresML is designed for engineers and developers who are familiar with PostgreSQL and want to incorporate advanced AI and ML capabilities without a steep learning curve or the management overhead of disparate systems. It addresses common pain points like microservice mayhem, increasing inefficiency, and excessive data exposure across multiple systems, offering a more secure, stable, and private solution.
- CategoryVector Databases & Retrieval
- Pricing ModelPay-as-you-go (based on models used)
- DeploymentCloud
- PlatformsWeb
PostgresML's Core Features
In-database machine learning and AI capabilities
Vector indexing, filtering, and re-ranking
Fast KNN and ANN search for embeddings
Support for HNSW and IVFFlat indexing
Generation of text embeddings using state-of-the-art models
Built-in data preprocessors for text splitting and chunking
Co-location of data and compute for embedding, serving, and storage
Support for terabytes of data on a single machine
Built-in data privacy and security features
Training, tuning, and deployment of ML models (regression, classification, clustering)
Fine-tuning of LLMs on custom data
Monitoring of model deployments
Integration with open-source LLMs (Mistral, Llama)
Support for SQL, JavaScript, and Python SDKs
Simplified AI stack reducing microservice complexity
How to use PostgresML?
Configure: Set up PostgresML within your PostgreSQL database.
Generate Embeddings: Use built-in models or integrate your own to convert text data into vector embeddings.
Index Vectors: Efficiently index your embeddings for fast retrieval.
Perform ML Tasks: Execute tasks like text generation, summarization, or translation using integrated models.
Deploy Models: Train, tune, and deploy custom machine learning models directly in the database.
Integrate: Utilize SQL or SDKs in Python and JavaScript to build AI-powered applications.
PostgresML's Use Cases
- RAG Chatbots
- Real-time AI Outputs
- Embedding Generation
- Data Analysis & Insights
- NLP Tasks
- Custom Model Deployment
- Streamlined AI Stacks
