> ## Documentation Index
> Fetch the complete documentation index at: https://unch.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# How it works

> A conceptual overview of how unch parses, indexes, and searches code symbols.

## 1. File discovery

`unch index` walks the repository, applies `.gitignore` plus explicit `--exclude` patterns, and decides which source files should be processed.

## 2. File hashing

Each candidate file is hashed. The active file-hash state in `.semsearch/filehashes.db` is used to decide whether the file can be reused or needs to be reindexed.

## 3. Symbol extraction

For supported languages, `unch` uses Tree-sitter to extract top-level symbols and attached docs.

That currently covers:

* Go
* Rust
* TypeScript
* JavaScript
* Python

For unsupported files or parser failures, `index` can still use the legacy prefix fallback.

## 4. Embedding generation

Each extracted symbol is flattened into an indexed document and embedded with the selected provider.

Provider options today:

* `llama.cpp` for local GGUF models through `yzma`
* `openrouter` for remote embedding APIs

Known model ids today:

* `embeddinggemma`
* `qwen3`

## 5. Snapshot activation

Embeddings and symbols are written into provider-scoped and model-scoped snapshots in `.semsearch/index.db`.

When indexing succeeds, the new snapshot becomes active for that provider/model pair. Other provider/model snapshots are left untouched.

Embedding vector tables are also separated by dimension, so models with different embedding sizes can coexist in one `.semsearch` directory.

## 6. Search

`unch search` supports:

* `semantic`
* `lexical`
* `auto`

`auto` stays semantic-first, but can prefer lexical results when the query looks more like a symbol name or code fragment.

## 7. Optional remote restore

If the manifest is bound to remote CI, `unch search` can restore the latest compatible published state before executing the query.