re
https://github.com/hwchase17/langchain/issues/439#issuecomment-1510442791
I think it's not polite for a library to use the root logger
both of these forms are also used:
```
logger = logging.getLogger(__name__)
logger = logging.getLogger(__file__)
```
I am not sure if there is any reason behind one vs the other? (...I am
guessing maybe just contributed by different people)
it seems to me it'd be better to consistently use
`logging.getLogger(__name__)`
this makes it easier for consumers of the library to set up log
handlers, e.g. for everything with `langchain.` prefix
Same as similarity_search, allows child classes to add vector
store-specific args (this was technically already happening in couple
places but now typing is correct).
Minor cosmetic changes
- Activeloop environment cred authentication in notebooks with
`getpass.getpass` (instead of CLI which not always works)
- much faster tests with Deep Lake pytest mode on
- Deep Lake kwargs pass
Notes
- I put pytest environment creds inside `vectorstores/conftest.py`, but
feel free to suggest a better location. For context, if I put in
`test_deeplake.py`, `ruff` doesn't let me to set them before import
deeplake
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
Improvements to Deep Lake Vector Store
- much faster view loading of embeddings after filters with
`fetch_chunks=True`
- 2x faster ingestion
- use np.float32 for embeddings to save 2x storage, LZ4 compression for
text and metadata storage (saves up to 4x storage for text data)
- user defined functions as filters
Docs
- Added retriever full example for analyzing twitter the-algorithm
source code with GPT4
- Added a use case for code analysis (please let us know your thoughts
how we can improve it)
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
### Features include
- Metadata based embedding search
- Choice of distance metric function (`L2` for Euclidean, `L1` for
Nuclear, `max` L-infinity distance, `cos` for cosine similarity, 'dot'
for dot product. Defaults to `L2`
- Returning scores
- Max Marginal Relevance Search
- Deleting samples from the dataset
### Notes
- Added numerous tests, let me know if you would like to shorten them or
make smarter
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
I'm using Deeplake as a vector store for a Q&A application. When several
questions are being processed at the same time for the same dataset, the
2nd one triggers the following error:
> LockedException: This dataset cannot be open for writing as it is
locked by another machine. Try loading the dataset with
`read_only=True`.
Answering questions doesn't require writing new embeddings so it's ok to
open the dataset in read only mode at that time.
This pull request thus adds the `read_only` option to the Deeplake
constructor and to its subsequent `deeplake.load()` call.
The related Deeplake documentation is
[here](https://docs.deeplake.ai/en/latest/deeplake.html#deeplake.load).
I've tested this update on my local dev environment. I don't know if an
integration test and/or additional documentation are expected however.
Let me know if it is, ideally with some guidance as I'm not particularly
experienced in Python.
Paul Garner