GenAI and LLMs with pgvector

Build LLM applications with pgvector vector store. Power your enterprise with AI-enabled applications with Tessell for PostgreSQL.

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Storage and retrieval of vector data

A dedicated data type, operators, and functions for efficiently storing and retrieving high-dimensional vector data within PostgreSQL.

Vector similarity search

Perform vector similarity searches, like nearest neighbor search, which is useful for recommendation systems, content similarity analysis, and fraud detection.

Advanced analytics and ML

Advanced analytics and machine learning directly within PostgreSQL, improving efficiency and reducing complexity by eliminating data movement between systems.

Integration with existing ecosystem

Seamlessly integrate with PostgreSQL, allowing companies to leverage existing infrastructure, tools, and expertise, making adopting vector data processing easier.

Use Cases

pgvector’s flexibility and performance make it a valuable tool for various applications and use cases such as:

Similarity Search: pgvector efficiently performs similarity searches in high-dimensional vector spaces, useful for recommendation systems, content similarity, and image recognition.

Natural Language Processing: Representing text as vectors, pgvector supports advanced NLP tasks like semantic search, sentiment analysis, and document clustering.

Anomaly Detection: pgvector detects anomalies in large datasets by comparing vector representations to identify outliers.

Time Series Analysis: Converting time series data into vectors, pgvector enables efficient pattern analysis, forecasting, anomaly detection, and trend analysis.

Machine Learning: pgvector integrates with machine learning workflows to store and process vector data, aiding in efficient model training and inference.