Data Storage
pg_vectorize: a VectorDB for Postgres
A Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls.
This project relies heavily on the work by pgvector... See more
A Postgres extension that automates the transformation and orchestration of text to embeddings and provides hooks into the most popular LLMs. This allows you to do vector search and build LLM applications on existing data with as little as two function calls.
This project relies heavily on the work by pgvector... See more
GitHub - tembo-io/pg_vectorize: The simplest way to orchestrate vector search on Postgres
Who will this data model serve? These are the stakeholders and users of the data model.
Why does this data model need to be built? What is the purpose and objective of the data model?
What are the data model’s core entities, attributes, and relationships? This is where you learn to see the business through the lens of data.
When is the timeframe for... See more
Why does this data model need to be built? What is the purpose and objective of the data model?
What are the data model’s core entities, attributes, and relationships? This is where you learn to see the business through the lens of data.
When is the timeframe for... See more
Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]
PGlite - Postgres in WASM
PGlite is a WASM Postgres build packaged into a TypeScript client library that enables you to run Postgres in the browser, Node.js and Bun, with no need to install any other dependencies. It is only 3.7mb gzipped.
import { PGlite } from "@electric-sql/pglite"
const db = new PGlite()
await db.query("select 'Hello world' as... See more
PGlite is a WASM Postgres build packaged into a TypeScript client library that enables you to run Postgres in the browser, Node.js and Bun, with no need to install any other dependencies. It is only 3.7mb gzipped.
import { PGlite } from "@electric-sql/pglite"
const db = new PGlite()
await db.query("select 'Hello world' as... See more
electric-sql • GitHub - electric-sql/pglite: Lightweight Postgres packaged as WASM into a TypeScript library for the browser, Node.js, Bun and Deno
Local database for development
Each table in the database had an accompanying script that would generate a subset of the data for use in local development, since the final database was too large to run on a developer's machine.
This let each developer work with a live, local, copy of the database and enabled efficient development of changes.
I highly... See more
Each table in the database had an accompanying script that would generate a subset of the data for use in local development, since the final database was too large to run on a developer's machine.
This let each developer work with a live, local, copy of the database and enabled efficient development of changes.
I highly... See more
Bill Mill • notes.billmill.org
Overview
pg_lakehouse is an extension that transforms Postgres into an analytical query engine over object stores like S3 and table formats like Delta Lake. Queries are pushed down to Apache DataFusion, which delivers excellent analytical performance. Combinations of the following object stores, table formats, and file formats are supported.
Object... See more
pg_lakehouse is an extension that transforms Postgres into an analytical query engine over object stores like S3 and table formats like Delta Lake. Queries are pushed down to Apache DataFusion, which delivers excellent analytical performance. Combinations of the following object stores, table formats, and file formats are supported.
Object... See more
https://github.com/paradedb/paradedb/tree/dev/pg_l...
memary: Open-Source Longterm Memory for Autonomous Agents
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more
memary demo
Why use memary?
Agents use LLMs that are currently constrained to finite context windows. memary overcomes this limitation by allowing your agents to store a large corpus of information in knowledge graphs, infer user knowledge through our memory modules, and only retrieve... See more
GitHub - kingjulio8238/memary: Longterm Memory for Autonomous Agents.
Data
pgmock
Demo — Discord
pgmock is an in-memory PostgreSQL mock server for unit and E2E tests. It requires no external dependencies and runs entirely within WebAssembly on both Node.js and the browser.
Installation
npm install pgmock
If you'd like to run pgmock in a browser, see the Browser support section for detailed instructions.
Demo — Discord
pgmock is an in-memory PostgreSQL mock server for unit and E2E tests. It requires no external dependencies and runs entirely within WebAssembly on both Node.js and the browser.
Installation
npm install pgmock
If you'd like to run pgmock in a browser, see the Browser support section for detailed instructions.
stackframe-projects • GitHub - stackframe-projects/pgmock: In-memory Postgres for unit/E2E tests
filesystem_spec
A specification for pythonic filesystems.
Install
pip install fsspec
would install the base fsspec. Various optionally supported features might require specification of custom extra require, e.g. pip install fsspec[ssh] will install dependencies for ssh backends support. Use pip install fsspec[full] for installation of all known... See more
A specification for pythonic filesystems.
Install
pip install fsspec
would install the base fsspec. Various optionally supported features might require specification of custom extra require, e.g. pip install fsspec[ssh] will install dependencies for ssh backends support. Use pip install fsspec[full] for installation of all known... See more
fsspec • GitHub - fsspec/filesystem_spec: A specification that python filesystems should adhere to.
With Quary, engineers can:
View the documentation.
- 🔌 Connect to their Database
- 📖 Write SQL queries to transform, organize, and document tables in a database
- 📊 Create charts, dashboards and reports (in development)
- 🧪 Test, collaborate & refactor iteratively through version control
- 🚀 Deploy the organised, documented model back up to the database
View the documentation.