- Description: Add a BM25 Retriever that do not need Elastic search
- Dependencies: rank_bm25(if it is not installed it will be install by
using pip, just like TFIDFRetriever do)
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: DayuanJian21687
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
Add LLM for ChatGLM-6B & ChatGLM2-6B API
Related Issue:
Will the langchain support ChatGLM? #4766
Add support for selfhost models like ChatGLM or transformer models #1780
Dependencies:
No extra library install required.
It wraps api call to a ChatGLM(2)-6B server(start with api.py), so api
endpoint is required to run.
Tag maintainer: @mlot
Any comments on this PR would be appreciated.
---------
Co-authored-by: mlot <limpo2000@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Support Redis Sentinel database connections
This PR adds the support to connect not only to Redis standalone servers
but High Availability Replication sets too
(https://redis.io/docs/management/sentinel/)
Redis Replica Sets have on Master allowing to write data and 2+ replicas
with read-only access to the data. The additional Redis Sentinel
instances monitor all server and reconfigure the RW-Master on the fly if
it comes unavailable.
Therefore all connections must be made through the Sentinels the query
the current master for a read-write connection. This PR adds basic
support to also allow a redis connection url specifying a Sentinel as
Redis connection.
Redis documentation and Jupyter notebook with Redis examples are updated
to mention how to connect to a redis Replica Set with Sentinels
-
Remark - i did not found test cases for Redis server connections to add
new cases here. Therefor i tests the new utility class locally with
different kind of setups to make sure different connection urls are
working as expected. But no test case here as part of this PR.
- [Xorbits](https://doc.xorbits.io/en/latest/) is an open-source
computing framework that makes it easy to scale data science and machine
learning workloads in parallel. Xorbits can leverage multi cores or GPUs
to accelerate computation on a single machine, or scale out up to
thousands of machines to support processing terabytes of data.
- This PR added support for the Xorbits agent, which allows langchain to
interact with Xorbits Pandas dataframe and Xorbits Numpy array.
- Dependencies: This change requires the Xorbits library to be installed
in order to be used.
`pip install xorbits`
- Request for review: @hinthornw
- Twitter handle: https://twitter.com/Xorbitsio
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Update the negative criterion descriptions to prevent bad predictions
- Add support for normalizing the string distance
- Fix potential json deserializing into float issues in the example
mapper
Starting over from #5654 because I utterly borked the poetry.lock file.
Adds new paramerters for to the MWDumpLoader class:
* skip_redirecst (bool) Tells the loader to skip articles that redirect
to other articles. False by default.
* stop_on_error (bool) Tells the parser to skip any page that causes a
parse error. True by default.
* namespaces (List[int]) Tells the parser which namespaces to parse.
Contains namespaces from -2 to 15 by default.
Default values are chosen to preserve backwards compatibility.
Sample dump XML and full unit test coverage (with extended tests that
pass!) also included!
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Issue**
When I use conda to install langchain, a dependency error throwed -
"ModuleNotFoundError: No module named 'langsmith'"
**Updated**
Run `pip install langsmith` when install langchain with conda
Co-authored-by: xaver.xu <xavier.xu@batechworks.com>
- New pin-to-side (button). This functionality allows you to search the
docs while asking the AI for questions
- Fixed the search bar in Firefox that won't detect a mouse click
- Fixes and improvements overall in the model's performance
Description: Added debugging output in DirectoryLoader to identify the
file being processed.
Issue: [Need a trace or debug feature in Lanchain DirectoryLoader
#7725](https://github.com/hwchase17/langchain/issues/7725)
Dependencies: No additional dependencies are required.
Tag maintainer: @rlancemartin, @eyurtsev
This PR enhances the DirectoryLoader with debugging output to help
diagnose issues when loading documents. This new feature does not add
any dependencies and has been tested on a local machine.
Inspired by #5550, I implemented full async API support in Qdrant. The
docs were extended to mention the existence of asynchronous operations
in Langchain. I also used that chance to restructure the tests of Qdrant
and provided a suite of tests for the async version. Async API requires
the GRPC protocol to be enabled. Thus, it doesn't work on local mode
yet, but we're considering including the support to be consistent.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Integrate [Rockset](https://rockset.com/docs/) as a document loader.
Issue: None
Dependencies: Nothing new (rockset's dependency was already added
[here](https://github.com/hwchase17/langchain/pull/6216))
Tag maintainer: @rlancemartin
I have added a test for the integration and an example notebook showing
its use. I ran `make lint` and everything looks good.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This pull request adds a ElasticsearchDatabaseChain chain for
interacting with analytics database, in the manner of the
SQLDatabaseChain.
Maintainer: @samber
Twitter handler: samuelberthe
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: This allows passing auth objects in request wrappers.
Currently, we can handle auth by editing headers in the
RequestsWrappers, but more complex auth methods, such as Kerberos, could
be handled better by using existing functionality within the requests
library. There are many authentication options supported both natively
and by extensions, such as requests-kerberos or requests-ntlm.
- Issue: Fixes#7542
- Dependencies: none
Co-authored-by: eric.speidel@de.bosch.com <eric.speidel@de.bosch.com>
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
- Add langchain.llms.Tonyi for text completion, in examples into the
Tonyi Text API,
- Add system tests.
Note async completion for the Text API is not yet supported and will be
included in a future PR.
Dependencies: dashscope. It will be installed manually cause it is not
need by everyone.
Happy for feedback on any aspect of this PR @hwchase17 @baskaryan.
Multiple people have asked in #5081 for a way to limit the documents
returned from an AzureCognitiveSearchRetriever. This PR adds the `top_n`
parameter to allow that.
Twitter handle:
[@UmerHAdil](twitter.com/umerHAdil)
Fix for Serializable class to include name, used in FileCallbackHandler
as same issue #7524
Description: Fixes the Serializable class to include 'name' attribute
(class_name) in the dict created,
This is used in Callbacks, specifically the StdOutCallbackHandler,
FileCallbackHandler.
Issue: As described in issue #7524
Dependencies: None
Tag maintainer: SInce this is related to the callback module, tagging
@agola11 @idoru
Comments:
Glad to see issue #7524 fixed in pull #6124, but you forget to change
the same place in FileCallbackHandler
When a custom Embeddings object is set, embed all given texts in a batch
instead of passing them through individually. Any code calling add_texts
can then appropriately size the chunks of texts that are passed through
to take full advantage of the hardware it's running on.
Fixes#6198
ElasticKnnSearch.from_texts is actually ElasticVectorSearch.from_texts
and throws because it calls ElasticKnnSearch constructor with the wrong
arguments.
Now ElasticKnnSearch has its own from_texts, which constructs a proper
ElasticKnnSearch.
---------
Co-authored-by: Charles Parker <charlesparker@FiltaMacbook.local>
Co-authored-by: Bagatur <baskaryan@gmail.com>
