Introduction to pg_vectorize: Enriching Postgres with Vector Search Capabilities
Overview
pg_vectorize is a powerful extension for Postgres designed to simplify the transformation of text into embeddings and facilitate seamless integration with popular large language models (LLMs). This tool enables users to perform vector searches and build applications utilizing LLMs on their existing data with as few as two function calls. pg_vectorize is built upon notable projects like pgvector for vector similarity search, pgmq for managing background tasks, and SentenceTransformers for embedding generation.
Core Features
- Automated Workflows: Supports both vector search and Retrieval-Augmented Generation (RAG) workflows.
- Integration with Major LLMs: Connect with OpenAI's embedding and chat-completion APIs, and utilize a self-hosted version of Hugging Face Sentence-Transformers.
- Postgres Trigger Automation: Automatically create triggers to keep your embeddings current with data modifications.
- User-Friendly API: Utilize simple function calls to initialize embedding transformations and execute searches.
Installation
Embarking with pg_vectorize is straightforward, especially with the use of Docker. By employing the Tembo Docker container, setting up involves executing a few Docker Compose commands. Once established, users can connect to Postgres, enable the pg_vectorize extension, and its requisite dependencies.
For existing Postgres installations, specific dependencies such as Rust's pgrx toolchain and PostgreSQL extensions like pgcron, pgmq, and pgvector are necessary. Additionally, shared library settings must be adjusted in the Postgres configuration.
Vector Search in Action
The process of converting text into embeddings is streamlined with pg_vectorize, leveraging either Hugging Face's Sentence-Transformers or OpenAI's embeddings. After setting up a table with example data, you can vectorize the table, specify columns for search, and execute a vector search query. This reveals the most relevant products, ranked by their similarity scores.
Retrieval-Augmented Generation (RAG)
pg_vectorize also enables the ability to query sample datasets, like a 'products' table, and receive responses from an OpenAI LLM. This process involves setting an OpenAI API key, initializing an appropriate table, and creating contextual columns. Queries can be run to receive AI-generated chat responses, with options to switch between different AI models.
Updating Embeddings
pg_vectorize offers options to update embeddings in two primary ways: scheduled updates using CRON-like intervals or real-time updates through table triggers. This ensures the embeddings reflect the most current state of the data whenever there are changes, either through new data insertions or existing data updates.
Direct LLM Interaction
Direct interaction with LLMs is possible, giving users advanced control over embedding handling and text generation. Users can execute SQL-based function calls to generate text or encode text into embeddings using various LLM models. This provides a flexible approach to experimenting and utilizing cutting-edge AI capabilities directly from Postgres.
Overall, pg_vectorize enriches PostgreSQL databases with advanced AI capabilities, making it easier for businesses and developers to harness the power of vector searches and large language models in their data processing and analysis workflows.