For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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  • Getting started
    • Introduction
    • Quickstart
    • How it works
  • Installation Options
    • Package Install
    • Docker
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  • Configuration
    • CLI
    • Settings & Profiles
    • Model Configuration
  • Inference Providers
    • Overview
    • Ollama
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  • Integrations
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  • Built-in Tools
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  • Storage Providers
    • Vector Store
    • Object Storage
  • User Interface
    • Workbench
  • Observability
    • Observability
  • Reference
    • Troubleshooting
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On this page
  • Remote Qdrant
  • Embedding dimensions
Storage Providers

Vector Store

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Object Storage

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Built with

PrivateGPT uses Qdrant as the default vector store. By default it runs in local disk mode — no separate Qdrant server needed.

Local disk mode is not recommended for production. Use a remote Qdrant server for any persistent or multi-instance deployment.

1qdrant:
2 path: local_data/private_gpt/qdrant

Remote Qdrant

To connect to an existing Qdrant server instead of the local disk instance:

1qdrant:
2 url: http://localhost:6333
3 api_key: your-api-key # only needed for Qdrant Cloud

Embedding dimensions

If you switch embedding models, the vector dimensions change. Re-ingest your documents after changing the model and update embed_dim to match:

1vectorstore:
2 embed_dim: 1024 # must match the output dimension of your embedding model

Run make wipe before re-ingesting to avoid a dimension mismatch error.