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
handles error when youtube video has transcripts disabled
```
youtube_transcript_api._errors.TranscriptsDisabled:
Could not retrieve a transcript for the video https://www.youtube.com/watch?v=<URL> This is most likely caused by:
Subtitles are disabled for this video
If you are sure that the described cause is not responsible for this error and that a transcript should be retrievable, please create an issue at https://github.com/jdepoix/youtube-transcript-api/issues. Please add which version of youtube_transcript_api you are using and provide the information needed to replicate the error. Also make sure that there are no open issues which already describe your problem!
```
Signed-off-by: Sertac Ozercan <sozercan@gmail.com>
### Description
Add Support for Lucene Filter. When you specify a Lucene filter for a
k-NN search, the Lucene algorithm decides whether to perform an exact
k-NN search with pre-filtering or an approximate search with modified
post-filtering. This filter is supported only for approximate search
with the indexes that are created using `lucene` engine.
OpenSearch Documentation -
https://opensearch.org/docs/latest/search-plugins/knn/filter-search-knn/#lucene-k-nn-filter-implementation
Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
Make it possible to control the HuggingFaceEmbeddings and HuggingFaceInstructEmbeddings client model kwargs. Additionally, the cache folder was added for HuggingFaceInstructEmbedding as the client inherits from SentenceTransformer (client of HuggingFaceEmbeddings).
It can be useful, especially to control the client device, as it will be defaulted to GPU by sentence_transformers if there is any.
---------
Co-authored-by: Yoann Poupart <66315201+Xmaster6y@users.noreply.github.com>
Currently `langchain.tools.sql_database.tool.QueryCheckerTool` has a
field `llm` with type `BaseLLM`. This breaks initialization for some
LLMs. For example, trying to use it with GPT4:
```python
from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
from langchain.tools.sql_database.tool import QueryCheckerTool
db = SQLDatabase.from_uri("some_db_uri")
llm = ChatOpenAI(model_name="gpt-4")
tool = QueryCheckerTool(db=db, llm=llm)
# pydantic.error_wrappers.ValidationError: 1 validation error for QueryCheckerTool
# llm
# Can't instantiate abstract class BaseLLM with abstract methods _agenerate, _generate, _llm_type (type=type_error)
```
Seems like much of the rest of the codebase has switched from `BaseLLM`
to `BaseLanguageModel`. This PR makes the change for QueryCheckerTool as
well
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
### Summary
Adds a loader for rich text files. Requires `unstructured>=0.5.12`.
### Testing
The following test uses the example RTF file from the [`unstructured`
repo](https://github.com/Unstructured-IO/unstructured/tree/main/example-docs).
```python
from langchain.document_loaders import UnstructuredRTFLoader
loader = UnstructuredRTFLoader("fake-doc.rtf", mode="elements")
docs = loader.load()
docs[0].page_content
```
While we work on solidifying the memory interfaces, handle common chat
history formats.
This may break linting on anyone who has been passing in
`get_chat_history` .
Somewhat handles #3077
Alternative to #3078 that updates the typing
First cut of a supabase vectorstore loosely patterned on the langchainjs
equivalent. Doesn't support async operations which is a limitation of
the supabase python client.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
