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<img src="logo.png" width=300 />
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Chromy is small and simple to use command-line utility for working with a local Chroma database. It lets you create collections, ingest files as chunked embeddings, and run similarity queries against stored documents. It integrates perfectly with agentic coding tools via simple skills.
Chromy is small and simple to use command-line utility for working with a local Chroma database. It lets you create collections, ingest files as chunked embeddings, and run similarity queries against stored documents. It integrates perfectly with agentic coding tools via simple skills (see an [example](./skills/chromy/SKILL.md) in the `skills` directory).
## What it does
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---
name: chromy
description: This skill provides access to a RAG-like context enhancer that uses Chromadb locally.
---
# Chromy
Whenever the user asks to "use chromy", you should invoke `chromy`, which is a cli tool to perform RAG search.
The tool should be available in the `$PATH` as `chromy`.
You have access to these commands:
- `$ chromy lc` -> Lists the existing collections.
- `$ chromy q <collection> <query>` -> Performs a query. Be sure to quote the `<query>` if this is composed by multiple words.
Then use the response from Chromy to enhance the context and give the user a refined response.
## A note on file sources
The Chromy 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 Chromy's metadatas) the information is coming.
## Example use case
**START**
User query:
> Search in Chromy information about lovecraft's Dunwich horror.
Step 1: Get the available collections with `chromy 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 `chromy 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**