> For the complete documentation index, see [llms.txt](https://docs.telys.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.telys.ai/resources/faq.md).

# FAQ

Quick answers to common questions. Each links to the page with the full story.

## Is my data private?

Yes. Telys is on-device: collections live in a local directory you choose, and your documents, vectors, and metadata do not leave the machine as a side effect of using Telys. `telys serve` makes **no outbound network connections**, and over TCP it refuses anonymous clients (an access token is required and compared in constant time). The only traffic Telys initiates is control-plane sign-in, license/key exchange, and the signed runtime download — never your memory contents. See [What leaves the machine](/security/what-leaves-the-machine.md) and [Data residency & privacy](/security/data-residency-privacy.md).

## Does it work offline?

Yes, after you sign in once. `telys login` does a device-code sign-in and installs a verified runtime and license; from then on, collection operations run locally with no network, and the license is verified **offline**. You need connectivity again only to sign in on a new device, renew a license, or download a runtime update. See [On-device & offline-first](/understand-telys/on-device-offline.md) and [Sign in & activate a license](/start-here/sign-in.md).

## Do I need a GPU or a model download?

No — not with the lexical embedders. `AlgentaBigramEmbedder` and `AlgentaMultigramEmbedder` hash character n-grams into a normalized vector deterministically, using the native kernel. No model weights to download and no accelerator required.

```python
from telys import Telys
from telys.embedding import AlgentaMultigramEmbedder

engine = Telys("./memory")
emb = AlgentaMultigramEmbedder()   # no GPU, no model download
col = engine.create_collection(
    "docs", dim=emb.profile.dimension, partition_by="scope",
    embedder=emb,
)
```

You need a GPU or downloaded weights only if you *choose* a dense model through bring-your-own embeddings. See [Lexical vs dense embeddings](/understand-telys/lexical-vs-dense.md) and [Choosing an embedder](/embeddings/choosing-an-embedder.md).

## Can I use my own embeddings?

Yes. `CallableEmbedder` wraps any function `list[str] -> [N, D] float32` behind an `EmbeddingProfile`, so you can plug in OpenAI, BGE / sentence-transformers, or any other model — dense or lexical.

```python
from telys.embedding import CallableEmbedder, EmbeddingProfile

profile = EmbeddingProfile(provider="openai", model_id="text-embedding-3-small",
                           model_version="1", dimension=1536)
embedder = CallableEmbedder(my_embed_fn, profile)   # my_embed_fn: list[str] -> [N, 1536]
```

The collection binds to that space via `profile.space_id()`, so reopening it re-attaches the right embedder. See [CallableEmbedder (bring your own)](/embeddings/callable-byo.md) and the [OpenAI embeddings recipe](/embeddings/recipe-openai.md).

## Does Telys run on Windows?

Through WSL2. The runtime installer supports `macos-arm64`, `linux-x86_64`, and `linux-arm64` (experimental); on Windows, install and run Telys inside a WSL2 Linux environment. Intel macOS is not yet supported. See [Supported platforms](/operations/supported-platforms.md).

## Which Python versions are supported?

`requires-python >= 3.10`. The package is pure-Python (`py3-none-any`) with classifiers through 3.13; because the runtime loads via `ctypes`, it also runs on 3.14. See [Python version support](/operations/python-version-support.md).

## Is it free? What does it cost?

There is a free tier. `telys_developer` is free for one device — sign in with `telys login`. Paid tiers are `telys_pro` (commercial), `telys_enterprise` (contact sales), and `telys_team` (self-host / serve). See [Plans & licensing](/licensing/tiers-and-pricing.md); current prices are on [telys.ai/pricing](https://telys.ai/pricing).

## How do I know my install is trustworthy?

Verify it. The runtime is signed and checked offline before it is trusted, and you can re-run the check any time:

```bash
telys runtime verify
```

See [Verify your install](/start-here/verify-install.md) and [Verify the runtime signature](/security/runtime-signature-verification.md).

## Next steps

* [Support & contact](/resources/support.md) — where to ask a question that isn't answered here.
* [What is Telys](/start-here/what-is-telys.md) — the full picture in one page.
* [Troubleshooting](/troubleshooting/overview.md) — fixes for common errors.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.telys.ai/resources/faq.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.