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Workplace AI Assistant and Search Platform

730 starsRust

Omni – Open-source workplace search and chat, built on Postgres

by prvnsmpth·Feb 24, 2026·1 point·0 comments
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AI Analysis

●●●BangerSolve My ProblemSlick

Glean alternative on pure Postgres; hybrid BM25+vector search, no Elasticsearch or external DB.

Strengths
  • •Genuinely bold architecture choice: ParadeDB (Postgres) + pgvector eliminates need for dedicated vector DB or Elasticsearch—simpler ops.
  • •Hybrid search combining BM25 and HNSW, with LLM tools for multi-step reasoning, not just basic RAG.
  • •Self-hosted with permission inheritance from source systems, Docker Compose to production Terraform—deployment story is real.
Weaknesses
  • •Early-stage with only ~42 stars; unclear production maturity or multi-tenant reliability at scale.
  • •Integrations WIP for MS 365, GitHub, Notion; core use case (Google/Slack/Confluence) works but ecosystem is incomplete.
Category
SaaS
Target Audience

Mid-market teams, enterprises needing self-hosted search; cost-conscious orgs seeking Glean alternative.

Similar To

Glean · Perplexity Teams · Sourcegraph Cody

Post Description

Hey HN!

Over the past few months, I've been working on building Omni - a workplace search and chat platform that connects to apps like Google Drive/Gmail, Slack, Confluence, etc. Essentially an open-source alternative to Glean, fully self-hosted.

I noticed that some orgs find Glean to be expensive and not very extensible. I wanted to build something that small to mid-size teams could run themselves, so I decided to build it all on Postgres (ParadeDB to be precise) and pgvector. No Elasticsearch, or dedicated vector databases. I figured Postgres is more than capable of handling the level of scale required.

To bring up Omni on your own infra, all it takes is a single `docker compose up`, and some basic configuration to connect your apps and LLMs.

What it does:

- Syncs data from all connected apps and builds a BM25 index (ParadeDB) and HNSW vector index (pgvector) - Hybrid search combines results from both - Chat UI where the LLM has tools to search the index - not just basic RAG - Traditional search UI - Users bring their own LLM provider (OpenAI/Anthropic/Gemini) - Connectors for Google Workspace, Slack, Confluence, Jira, HubSpot, and more - Connector SDK to build your own custom connectors

Omni is in beta right now, and I'd love your feedback, especially on the following:

- Has anyone tried self-hosting workplace search and/or AI tools, and what was your experience like? - Any concerns with the Postgres-only approach at larger scales?

Happy to answer any questions!

The code: https://github.com/getomnico/omni (Apache 2.0 licensed)

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