---
name: chroma
description: This skill provides access to a RAG-like context enhancer that uses Chroma locally.
---

# Chroma

Whenever the user asks to "use chroma", you should perform a search on Chroma, which runs locally via a Python script.
The working directory you must use is located in `~/code/chroma` and the entrypoint is `main.py`. The script **MUST** be invoked using `uv`.

You have access to these commands:

- `$ uv run main.py lc` -> Lists the existing collections.
- `$ uv run main.py q <collection> <query>` -> Performs a query. Be sure to quote the `<query>` if this is composed by multiple words.

Then use the response from Chroma to enhance the context and give the user a refined response.

## A note on file sources

The Chroma response returns the metadatas for the chunks it finds. Among these metadatas, there is `file_name`, which refers to the original file that was chunked and imported. **DO NOT ATTEMPT** to find or fetch these files. They most likely do not exist in the filesystem. You **SHOULD ALWAYS** however cite correctly from which files (**ONLY** from Chroma's metadatas) the information is coming.

## Example use case

**START**

User query:
> Search in chroma information about lovecraft's Dunwich horror.

Step 1: Get the available collections with `uv run main.py lc`. The output is:

```
lovecraft
documents
```

Most likely our information is in the `lovecraft` collection. We will use that for the query.

Step 2: Query using `uv run main.py q lovecraft <query>`. The query *is up to you*, create one keeping into account that this is a raw query on a vector DB. Be concise, extract keywords, avoid noise.

Step 3: Get the results, enhance the context, and respond to the user.

**END**