Motivation, it seems that when dealing with a long context and "big"
number of relevant documents we must avoid using out of the box score
ordering from vector stores.
See: https://arxiv.org/pdf/2306.01150.pdf
So, I added an additional parameter that allows you to reorder the
retrieved documents so we can work around this performance degradation.
The relevance respect the original search score but accommodates the
lest relevant document in the middle of the context.
Extract from the paper (one image speaks 1000 tokens):
This seems to be common to all diff arquitectures. SO I think we need a
good generic way to implement this reordering and run some test in our
already running retrievers.
It could be that my approach is not the best one from the architecture
point of view, happy to have a discussion about that.
For me this was the best place to introduce the change and start
retesting diff implementations.
@rlancemartin, @eyurtsev
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Still don't have good "how to's", and the guides / examples section
could be further pruned and improved, but this PR adds a couple examples
for each of the common evaluator interfaces.
- [x] Example docs for each implemented evaluator
- [x] "how to make a custom evalutor" notebook for each low level APIs
(comparison, string, agent)
- [x] Move docs to modules area
- [x] Link to reference docs for more information
- [X] Still need to finish the evaluation index page
- ~[ ] Don't have good data generation section~
- ~[ ] Don't have good how to section for other common scenarios / FAQs
like regression testing, testing over similar inputs to measure
sensitivity, etc.~
Some docstring / small nits to #6003
---------
Co-authored-by: BoazWasserman <49598618+boazwasserman@users.noreply.github.com>
Co-authored-by: HippoTerrific <49598618+HippoTerrific@users.noreply.github.com>
Co-authored-by: Or Raz <orraz1994@gmail.com>
This new version fixes the"Verified Sources" display that got broken.
Instead of displaying the full URL, it shows the title of the page the
source is from.
- 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
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- 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.
