> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hymalaia.com/llms.txt
> Use this file to discover all available pages before exploring further.
# System Overview
> High-level explanation of Hymalaia system components and data flows.
This page gives you a high-level overview of how Hymalaia works. It’s designed to provide clarity and transparency into the system design so you can use it with confidence.
If you're looking to **customize** Hymalaia or become an **open source contributor**, this is a great place to begin.
***
## System Architecture
Hymalaia can be deployed on a **single instance** or a **container orchestration platform** (e.g., Kubernetes). Regardless of where it's deployed, the **data flow remains consistent**.
* Documents are pulled and processed via **connectors**.
* These are then stored in **Vespa** or **Postgres**, running in containers within your setup.
### LLM Communication
The **only time-sensitive data that leaves** your system is when Hymalaia makes a request to a **Large Language Model (LLM)** for generating a response.
* All communication with the LLM is **encrypted**.
* Data persistence policies depend on your **LLM hosting provider**.
> 🕵️ Hymalaia also includes minimal, **anonymous telemetry** to help improve the platform and monitor flaky connectors.\
> You can **disable telemetry** by setting the following environment variable:
```env theme={null}
DISABLE_TELEMETRY=True
```
***
## Embedding Flow
Each document is **split into chunks** (smaller sections) for processing.
Benefits of chunking:
* Only relevant parts are passed to the LLM → **less noise**.
* **Reduced cost**: LLMs charge per token.
* **Improved detail retention**: Embedding vectors have limits on how much info they can store.
### Mini-Chunks
Mini-chunks go one step further:
* Create **multiple embedding sizes**.
* Improve retrieval of both **high-level context** and **fine-grained details**.
* Can be toggled using environment variables.
> ⚠️ Note: Mini-chunks may **slow down indexing** on low-performance hardware.
### Embedding Model
Hymalaia uses a **state-of-the-art biencoder**, optimized for:
* Running on **CPUs**
* **Subsecond** document retrieval
***
## Query Flow
The **query pipeline** is under constant improvement, adapting new research and open-source techniques.
Everything is configurable:
* Number of documents to retrieve
* Number of reranked documents
* Embedding and reranking models
* Chunk selection passed to the LLM
> ❓Have questions or ideas? Don’t hesitate to reach out to the maintainers.
***
Ready to dive deeper? Explore the [Multilingual Setup](./multilingual_setup) or the [Connector Guide](./connectors) to further customize your Hymalaia deployment.