- Description: run the poetry dependencies
- Issue: #7329
- Dependencies: any dependencies required for this change,
- Tag maintainer: @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Summary
Updates the `unstructured` install instructions. For
`unstructured>=0.9.0`, dependencies are broken out by document type and
the base `unstructured` package includes fewer dependencies. `pip
install "unstructured[local-inference]"` has been replace by `pip
install "unstructured[all-docs]"`, though the `local-inference` extra is
still supported for the time being.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
Description:
This PR adds support for loading documents from Huawei OBS (Object
Storage Service) in Langchain. OBS is a cloud-based object storage
service provided by Huawei Cloud. With this enhancement, Langchain users
can now easily access and load documents stored in Huawei OBS directly
into the system.
Key Changes:
- Added a new document loader module specifically for Huawei OBS
integration.
- Implemented the necessary logic to authenticate and connect to Huawei
OBS using access credentials.
- Enabled the loading of individual documents from a specified bucket
and object key in Huawei OBS.
- Provided the option to specify custom authentication information or
obtain security tokens from Huawei Cloud ECS for easy access.
How to Test:
1. Ensure the required package "esdk-obs-python" is installed.
2. Configure the endpoint, access key, secret key, and bucket details
for Huawei OBS in the Langchain settings.
3. Load documents from Huawei OBS using the updated document loader
module.
4. Verify that documents are successfully retrieved and loaded into
Langchain for further processing.
Please review this PR and let us know if any further improvements are
needed. Your feedback is highly appreciated!
@rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Works just like the GenericLoader but concurrently for those who choose
to optimize their workflow.
@rlancemartin @eyurtsev
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Added a new tool to the Github toolkit called **Create Pull Request.**
Now we can make our own langchain contributor in langchain 😁
In order to have somewhere to pull from, I also added a new env var,
"GITHUB_BASE_BRANCH." This will allow the existing env var,
"GITHUB_BRANCH," to be a working branch for the bot (so that it doesn't
have to always commit on the main/master). For example, if you want the
bot to work in a branch called `bot_dev` and your repo base is `main`,
you would set up the vars like:
```
GITHUB_BASE_BRANCH = "main"
GITHUB_BRANCH = "bot_dev"
```
Maintainer responsibilities:
- Agents / Tools / Toolkits: @hinthornw
@rlancemartin
The modification includes:
* etherscanLoader
* test_etherscan
* document ipynb
I have run the test, lint, format, and spell check. I do encounter a
linting error on ipynb, I am not sure how to address that.
```
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:55: error: Name "null" is not defined [name-defined]
docs/extras/modules/data_connection/document_loaders/integrations/Etherscan.ipynb:76: error: Name "null" is not defined [name-defined]
Found 2 errors in 1 file (checked 1 source file)
```
- Description: The Etherscan loader uses etherscan api to load
transaction histories under specific accounts on Ethereum Mainnet.
- No dependency is introduced by this PR.
- Twitter handle: glazecl
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**PR Description:**
This pull request introduces several enhancements and new features to
the `CubeSemanticLoader`. The changes include the following:
1. Added imports for the `json` and `time` modules.
2. Added new constructor parameters: `load_dimension_values`,
`dimension_values_limit`, `dimension_values_max_retries`, and
`dimension_values_retry_delay`.
3. Updated the class documentation with descriptions for the new
constructor parameters.
4. Added a new private method `_get_dimension_values()` to retrieve
dimension values from Cube's REST API.
5. Modified the `load()` method to load dimension values for string
dimensions if `load_dimension_values` is set to `True`.
6. Updated the API endpoint in the `load()` method from the base URL to
the metadata endpoint.
7. Refactored the code to retrieve metadata from the response JSON.
8. Added the `column_member_type` field to the metadata dictionary to
indicate if a column is a measure or a dimension.
9. Added the `column_values` field to the metadata dictionary to store
the dimension values retrieved from Cube's API.
10. Modified the `page_content` construction to include the column title
and description instead of the table name, column name, data type,
title, and description.
These changes improve the functionality and flexibility of the
`CubeSemanticLoader` class by allowing the loading of dimension values
and providing more detailed metadata for each document.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
