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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Local semantic search. Stupidly simple.
| Date | Stars |
|---|---|
| 2026-07-31 | 462 |
| 2026-08-04 | 462 |
| 2026-08-13 | 463 |
| 2026-08-18 | 464 |
| 2026-09-03 | 463 |
| 2026-09-06 | 462 |
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| 2026-09-20 | 461 |
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# AI Filesystem
[](https://colab.research.google.com/drive/1QdXPchTDnzW6I_3HTZFpSeak_XoH81v5?usp=sharing)
Local semantic search over folders. Why didn't this exist?
```shell
pip install aifs
pip install "unstructured[all-docs]" # If you want to parse all doc types. Includes large packages!
```
```python
from aifs import search
search("How does AI Filesystem work?", path="/path/to/folder")
search("It's not unlike how Spotlight works.") # Path defaults to CWD
```
# How it works
<br>
Running `aifs.search` will chunk and embed all nested supported files (`.txt`, `.py`, `.sh`, `.docx`, `.pptx`, `.jpg`, `.png`, `.eml`, `.html`, and `.pdf`) in `path`. It will then store these embeddings into an `_.aifs` file in `path`.
By storing the index, you only have to chunk/embed once. This makes semantic search **very** fast after the first time you search a path.
If a file has changed or been added, `aifs.search` will update or add those chunks. We still need to handle file deletions (we welcome PRs).
### In detail:
1. If a folder hasn't been indexed, we first use [`unstructured`](https://github.com/Unstructured-IO/unstructured/tree/main) to parse and chunk every file in the `path`.
2. Then we use [`chroma`](https://github.com/chroma-core/chroma) to embed the chunks locally and save them to a `_.aifs` file in `path`.
3. Finally, `chroma` is used again to semantically search the embeddings.
If an `_.aifs` file _is_ found in a directory, it uses that instead of indexing it again. If some files have been updated, it will re-index those.
# Goals
- We should always have SOTA parsing and chunking. The logic for this should be swapped out as new methods arise.
- Chunking should be semantic — as in, `python` and `markdown` files should have _different_ chunking algorithms based on the expected content of those filetypes. Who has this solution?
- For parsing, I think Unstructured is the best of the best. Is this true?
- We should always have SOTA embedding. If a better local embedding model is found, we should automatically download and use it.
- I think Chroma will always do this (is this true?) so we depend on Chroma.
- This project should stay **minimally scoped** — we want `aifs` to be the best local semantic search in the universe.
# Why?
We built this to let [`open-interpreter`](https://openinterpreter.com/) quickly semantically search files/folders.
Excerpt of 2,571 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b571efd590020ba7, llm:description: 'Local semantic search. Stupidly simple.'; language: Python
matched fp:b571efd590020ba7, llm:description: 'Local semantic search. Stupidly simple.'; language: Python
matched fp:b571efd590020ba7, llm:description: 'Local semantic search. Stupidly simple.'; language: Python