**Description:**
Updated documentation for DeepLake init method.
Especially the exec_option docs needed improvement, but did a general
cleanup while I was looking at it.
**Issue:** n/a
**Dependencies:** None
---------
Co-authored-by: Nathan Voxland <nathan@voxland.net>
In this pull request, we introduce the add_images method to the
SingleStoreDB vector store class, expanding its capabilities to handle
multi-modal embeddings seamlessly. This method facilitates the
incorporation of image data into the vector store by associating each
image's URI with corresponding document content, metadata, and either
pre-generated embeddings or embeddings computed using the embed_image
method of the provided embedding object.
the change includes integration tests, validating the behavior of the
add_images. Additionally, we provide a notebook showcasing the usage of
this new method.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Hi, I'm from the LanceDB team.
Improves LanceDB integration by making it easier to use - now you aren't
required to create tables manually and pass them in the constructor,
although that is still backward compatible.
Bug fix - pandas was being used even though it's not a dependency for
LanceDB or langchain
PS - this issue was raised a few months ago but lost traction. It is a
feature improvement for our users kindly review this , Thanks !
Another PR will be done for the langchain-astradb package.
Note: for future PRs, devs will be done in the partner package only. This one is just to align with the rest of the components in the community package and it fixes a bunch of issues.
- **Description:** Addresses the bugs described in linked issue where an
import was erroneously removed and the rename of a keyword argument was
missed when migrating from beta --> stable of the azure-search-documents
package
- **Issue:** https://github.com/langchain-ai/langchain/issues/17598
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** This fixes an issue with working with RecordManager.
RecordManager was generating new hashes on documents because `add_texts`
was modifying the metadata directly. Additionally moved some tests to
unit tests since that was a more appropriate home.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
**Description:** This PR introduces a new "Astra DB" Partner Package.
So far only the vector store class is _duplicated_ there, all others
following once this is validated and established.
Along with the move to separate package, incidentally, the class name
will change `AstraDB` => `AstraDBVectorStore`.
The strategy has been to duplicate the module (with prospected removal
from community at LangChain 0.2). Until then, the code will be kept in
sync with minimal, known differences (there is a makefile target to
automate drift control. Out of convenience with this check, the
community package has a class `AstraDBVectorStore` aliased to `AstraDB`
at the end of the module).
With this PR several bugfixes and improvement come to the vector store,
as well as a reshuffling of the doc pages/notebooks (Astra and
Cassandra) to align with the move to a separate package.
**Dependencies:** A brand new pyproject.toml in the new package, no
changes otherwise.
**Twitter handle:** `@rsprrs`
---------
Co-authored-by: Christophe Bornet <cbornet@hotmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This pull request introduces support for various Approximate Nearest
Neighbor (ANN) vector index algorithms in the VectorStore class,
starting from version 8.5 of SingleStore DB. Leveraging this enhancement
enables users to harness the power of vector indexing, significantly
boosting search speed, particularly when handling large sets of vectors.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Users can provide an Elasticsearch connection with custom headers. This
PR makes sure these headers are preserved when adding the langchain user
agent header.
- **Description:** The from__xx methods of FAISS class have hardcoded
InMemoryStore implementation and thereby not let users pass a custom
DocStore implementation,
- **Issue:** no referenced issue,
- **Dependencies:** none,
- **Twitter handle:** ksachdeva
- **Description:** This adds a delete method so that rocksetdb can be
used with `RecordManager`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
---------
Co-authored-by: Rockset API Bot <admin@rockset.io>
**Description:** changed filtering so that failed filter doesn't add
document to results. Currently filtering is entirely broken and all
documents are returned whether or not they pass the filter.
fixes issue introduced in
https://github.com/langchain-ai/langchain/pull/16190
- **Description:** This PR adds support for `search_types="mmr"` and
`search_type="similarity_score_threshold"` to retrievers using
`DatabricksVectorSearch`,
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
Make some functions work with Milvus:
1. get_ids: Get primary keys by field in the metadata
2. delete: Delete one or more entities by ids
3. upsert: Update/Insert one or more entities
**Issue**
None
**Dependencies**
None
**Tag maintainer:**
@hwchase17
**Twitter handle:**
None
---------
Co-authored-by: HoaNQ9 <hoanq.1811@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:**
Embedding field name was hard-coded named "embedding".
So I suggest that change `res["embedding"]` into
`res[self._embedding_key]`.
- **Issue:** #17177,
- **Twitter handle:**
[@bagcheoljun17](https://twitter.com/bagcheoljun17)
**Description:**
Implemented unique ID validation in the FAISS component to ensure all
document IDs are distinct. This update resolves issues related to
non-unique IDs, such as inconsistent behavior during deletion processes.
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Previously, if this did not find a mypy cache then it wouldnt run
this makes it always run
adding mypy ignore comments with existing uncaught issues to unblock other prs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Description
In #16608, the calling `collection_name` was wrong.
I made a fix for it.
Sorry for the inconvenience!
## Issue
https://github.com/langchain-ai/langchain/issues/16962
## Dependencies
N/A
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whichever of langchain, community, core, experimental, etc. is being
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- **Issue:** the issue # it fixes if applicable,
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`docs/docs/integrations` directory.
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@baskaryan, @eyurtsev, @hwchase17.
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---------
Co-authored-by: Kumar Shivendu <kshivendu1@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description**: fully async versions are available for astrapy 0.7+.
For older astrapy versions or if the user provides a sync client without
an async one, the async methods will call the sync ones wrapped in
`run_in_executor`
- **Twitter handle:** cbornet_
- **Description:**
Filtering in a FAISS vectorstores is very inflexible and doesn't allow
that many use case. I think supporting callable like this enables a lot:
regular expressions, condition on multiple keys etc. **Note** I had to
manually alter a test. I don't understand if it was falty to begin with
or if there is something funky going on.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
Signed-off-by: thiswillbeyourgithub <26625900+thiswillbeyourgithub@users.noreply.github.com>
## Description
The PR is to return the ID and collection name from qdrant client to
metadata field in `Document` class.
## Issue
The motivation is almost same to
[11592](https://github.com/langchain-ai/langchain/issues/11592)
Returning ID is useful to update existing records in a vector store, but
we cannot know them if we use some retrievers.
In order to avoid any conflicts, breaking changes, the new fields in
metadata have a prefix `_`
## Dependencies
N/A
## Twitter handle
@kill_in_sun
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Replace this entire comment with:
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- **Issue:** the issue # it fixes if applicable,
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If you're adding a new integration, please include:
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Add missing async similarity_distance_threshold handling in
RedisVectorStoreRetriever
- **Description:** added method `_aget_relevant_documents` to
`RedisVectorStoreRetriever` that overrides parent method to add support
of `similarity_distance_threshold` in async mode (as for sync mode)
- **Issue:** #16099
- **Dependencies:** N/A
- **Twitter handle:** N/A
* Description: Fixed schema discrepancy in **from_texts** function for
weaviate vectorstore which created a redundant property "key" inside a
class.
* Issue: Fixed: https://github.com/langchain-ai/langchain/issues/16692
* Twitter handle: @pashvamehta1
**Description:** This update ensures that the user-defined embedding
function specified during vector store creation is applied during
queries. Previously, even if a custom embedding function was defined at
the time of store creation, Bagel DB would default to using the standard
embedding function during query execution. This pull request addresses
this issue by consistently using the user-defined embedding function for
queries if one has been specified earlier.
- **Description:**
This PR adds a VectorStore integration for SAP HANA Cloud Vector Engine,
which is an upcoming feature in the SAP HANA Cloud database
(https://blogs.sap.com/2023/11/02/sap-hana-clouds-vector-engine-announcement/).
- **Issue:** N/A
- **Dependencies:** [SAP HANA Python
Client](https://pypi.org/project/hdbcli/)
- **Twitter handle:** @sapopensource
Implementation of the integration:
`libs/community/langchain_community/vectorstores/hanavector.py`
Unit tests:
`libs/community/tests/unit_tests/vectorstores/test_hanavector.py`
Integration tests:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Example notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Access credentials for execution of the integration tests can be
provided to the maintainers.
---------
Co-authored-by: sascha <sascha.stoll@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds the ability to return similarity scores when using
`RetrievalQA.from_chain_type` with `MongoDBAtlasVectorSearch`. Requires
that `return_source_documents=True` is set.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vector_search.as_retriever(search_kwargs={"additional": ["similarity_score"]}),
return_source_documents=True
)
...
docs = qa({"query": "..."})
docs["source_documents"][0].metadata["score"] # score will be here
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a
vector search index.
Replace this entire comment with:
- **Description:** allow user to define tVector length in PGVector when
creating the embedding store, this allows for later indexing
- **Issue:** #16132
- **Dependencies:** None
