The Pandas agent fails to pass callback_manager forward, making it
impossible to use custom callbacks with it. Fix that.
Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
Improved `arxiv/tool.py` by adding more specific information to the
`description`. It would help with selecting `arxiv` tool between other
tools.
Improved `arxiv.ipynb` with more useful descriptions.
Apart from being unnecessary, postgresql is run on its default port,
which means that the langchain-server will fail to start if there is
already a postgresql server running on the host. This is obviously less
than ideal.
(Yeah, I don't understand why "expose" is the syntax that does not
expose the ports to the host...)
Tested by running langchain-server and trying out debugging on a host
that already has postgresql bound to the port 5432.
Co-authored-by: Sami Liedes <sami.liedes@rocket-science.ch>
So, this is basically fixing the same things as #1517 but for GCS.
### Problem
When loading GCS Objects with `/` in the object key (eg.
folder/some-document.txt) using `GCSFileLoader`, the objects are
downloaded into a temporary directory and saved as a file.
This errors out when the parent directory does not exist within the
temporary directory.
### What this pr does
Creates parent directories based on object key.
This also works with deeply nested keys:
folder/subfolder/some-document.txt
Fix for: [Changed regex to cover new line before action
serious.](https://github.com/hwchase17/langchain/issues/3365)
---
This PR fixes the issue where `ValueError: Could not parse LLM output:`
was thrown on seems to be valid input.
Changed regex to cover new lines before action serious (after the
keywords "Action:" and "Action Input:").
regex101: https://regex101.com/r/CXl1kB/1
---------
Co-authored-by: msarskus <msarskus@cisco.com>
Rewrite of #3368
Mainly an issue for when people are just getting started, but still nice
to not throw an error if the number of docs is < k.
Add a little decorator utility to block mutually exclusive keyword
arguments
At present, the method of generating `point` in qdrant is to use random
`uuid`. The problem with this approach is that even documents with the
same content will be inserted repeatedly instead of updated. Using `md5`
as the `ID` of `point` to insert text can achieve true `update or
insert`.
Co-authored-by: mayue <mayue05@qiyi.com>
This fixes the below mentioned issue. Instead of simply passing the text
to `tensorflow_hub`, we convert it to a list and then pass it.
https://github.com/hwchase17/langchain/issues/3282
Co-authored-by: Prakhar Agarwal <i.prakhar-agarwal@devrev.ai>
First PR, let me know if this needs anything like unit tests,
reformatting, etc. Seemed pretty straightforward to implement. Only
hitch was that mmap needs to be disabled when loading LoRAs or else you
segfault.
This PR adds support for providing a Weaviate API Key to the VectorStore
methods `from_documents` and `from_texts`. With this addition, users can
authenticate to Weaviate and make requests to private Weaviate servers
when using these methods.
## Motivation
Currently, LangChain's VectorStore methods do not provide a way to
authenticate to Weaviate. This limits the functionality of the library
and makes it more difficult for users to take advantage of Weaviate's
features.
This PR addresses this issue by adding support for providing a Weaviate
API Key as extra parameter used in the `from_texts` method.
## Contributing Guidelines
I have read the [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md)
and the PR code passes the following tests:
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
### Background
Continuing to implement all the interface methods defined by the
`VectorStore` class. This PR pertains to implementation of the
`max_marginal_relevance_search_by_vector` method.
### Changes
- a `max_marginal_relevance_search_by_vector` method implementation has
been added in `weaviate.py`
- tests have been added to the the new method
- vcr cassettes have been added for the weaviate tests
### Test Plan
Added tests for the `max_marginal_relevance_search_by_vector`
implementation
### Change Safety
- [x] I have added tests to cover my changes
kwargs shoud be passed into cls so that opensearch client can be
properly initlized in __init__(). Otherwise logic like below will not
work. as auth will not be passed into __init__
```python
docsearch = OpenSearchVectorSearch.from_documents(docs, embeddings, opensearch_url="http://localhost:9200")
query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)
```
Co-authored-by: EC2 Default User <ec2-user@ip-172-31-28-97.ec2.internal>
- Proactively raise error if a tool subclasses BaseTool, defines its
own schema, but fails to add the type-hints
- fix the auto-inferred schema of the decorator to strip the
unneeded virtual kwargs from the schema dict
Helps avoid silent instances of #3297
Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3
Notes
* please double check if poetry is not messed up (thanks!)
Asks
* Would be great to create a shared slack channel for quick questions
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
This PR addresses several improvements:
- Previously it was not possible to load spaces of more than 100 pages.
The `limit` was being used both as an overall page limit *and* as a per
request pagination limit. This, in combination with the fact that
atlassian seem to use a server-side hard limit of 100 when page content
is expanded, meant it wasn't possible to download >100 pages. Now
`limit` is used *only* as a per-request pagination limit and `max_pages`
is introduced as the way to limit the total number of pages returned by
the paginator.
- Document metadata now includes `source` (the source url), making it
compatible with `RetrievalQAWithSourcesChain`.
- It is now possible to include inline and footer comments.
- It is now possible to pass `verify_ssl=False` and other parameters to
the confluence object for use cases that require it.
Small improvements for the YouTube loader:
a) use the YouTube API permission scope instead of Google Drive
b) bugfix: allow transcript loading for single videos
c) an additional parameter "continue_on_failure" for cases when videos
in a playlist do not have transcription enabled.
d) support automated translation for all languages, if available.
---------
Co-authored-by: Johann-Peter Hartmann <johann-peter.hartmann@mayflower.de>
This pull request adds a ChatGPT document loader to the document loaders
module in `langchain/document_loaders/chatgpt.py`. Additionally, it
includes an example Jupyter notebook in
`docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
which uses fake sample data based on the original structure of the
`conversations.json` file.
The following files were added/modified:
- `langchain/document_loaders/__init__.py`
- `langchain/document_loaders/chatgpt.py`
- `docs/modules/indexes/document_loaders/examples/chatgpt_loader.ipynb`
-
`docs/modules/indexes/document_loaders/examples/example_data/fake_conversations.json`
This pull request was made in response to the recent release of ChatGPT
data exports by email:
https://help.openai.com/en/articles/7260999-how-do-i-export-my-chatgpt-history
