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>
Updated `Getting Started` page of `Prompt Templates` to showcase more
features provided by the class. Might need some proof reading because
apparently English is not my first language.
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
Now it is hard to search for the integration points between
data_loaders, retrievers, tools, etc.
I've placed links to all groups of providers and integrations on the
`ecosystem` page.
So, it is easy to navigate between all integrations from a single
location.
### 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>
The detailed walkthrough of the Weaviate wrapper was pointing to the
getting-started notebook. Fixed it to point to the Weaviable notebook in
the examples folder.
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
Hi there！
I'm excited to open this PR to add support for using a fully Postgres
syntax compatible database 'AnalyticDB' as a vector.
As AnalyticDB has been proved can be used with AutoGPT,
ChatGPT-Retrieve-Plugin, and LLama-Index, I think it is also good for
you.
AnalyticDB is a distributed Alibaba Cloud-Native vector database. It
works better when data comes to large scale. The PR includes:
- [x] A new memory: AnalyticDBVector
- [x] A suite of integration tests verifies the AnalyticDB integration
I have read your [contributing
guidelines](72b7d76d79/.github/CONTRIBUTING.md).
And I have passed the tests below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
