- Description: This is an update to a previously published notebook.
Sales Agent now has access to tools, and this notebook shows how to use
a Product Knowledge base
to reduce hallucinations and act as a better sales person!
- Issue: N/A
- Dependencies: `chromadb openai tiktoken`
- Tag maintainer: @baskaryan @hinthornw
- Twitter handle: @FilipMichalsky
Moving to the latest non-preview Azure OpenAI API version=2023-05-15.
The previous 2023-03-15-preview doesn't have support, SLA etc. For
instance, OpenAI SDK has moved to this version
https://github.com/openai/openai-python/releases/tag/v0.27.7
@baskaryan
Description:
Currently, Zilliz only support dedicated clusters using a pair of
username and password for connection. Regarding serverless clusters,
they can connect to them by using API keys( [ see official note
detail](https://docs.zilliz.com/docs/manage-cluster-credentials)), so I
add API key(token) description in Zilliz docs to make it more obvious
and convenient for this group of users to better utilize Zilliz. No
changes done to code.
---------
Co-authored-by: Robin.Wang <3Jg$94sbQ@q1>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Azure GPT-4 models can't be accessed via LLM model. It's easy to miss
that and a lot of discussions about that are on the Internet. Therefore
I added a comment in Azure LLM docs that mentions that and points to
Azure Chat OpenAI docs.
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: This PR adds the option to retrieve scores and explanations
in the WeaviateHybridSearchRetriever. This feature improves the
usability of the retriever by allowing users to understand the scoring
logic behind the search results and further refine their search queries.
Issue: This PR is a solution to the issue #7855
Dependencies: This PR does not introduce any new dependencies.
Tag maintainer: @rlancemartin, @eyurtsev
I have included a unit test for the added feature, ensuring that it
retrieves scores and explanations correctly. I have also included an
example notebook demonstrating its use.
Here I am adding documentation for the `PromptLayerCallbackHandler`.
When we created the initial PR for the callback handler the docs were
causing issues, so we merged without the docs.
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.~
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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- 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
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.
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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,
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Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
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- Models / Prompts: @hwchase17, @baskaryan
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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>
- 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)
# Browserless
Added support for Browserless' `/content` endpoint as a document loader.
### About Browserless
Browserless is a cloud service that provides access to headless Chrome
browsers via a REST API. It allows developers to automate Chromium in a
serverless fashion without having to configure and maintain their own
Chrome infrastructure.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Lance Martin <lance@langchain.dev>
This PR is aimed at enhancing the clarity of the documentation in the
langchain project.
**Description**:
In the graphql.ipynb file, I have removed the unnecessary 'llm' argument
from the initialization process of the GraphQL tool (of type
_EXTRA_OPTIONAL_TOOLS). The 'llm' argument is not required for this
process. Its presence could potentially confuse users. This modification
simplifies the understanding of tool initialization and minimizes
potential confusion.
**Issue**: Not applicable, as this is a documentation improvement.
**Dependencies**: None.
**I kindly request a review from the following maintainer**: @hinthornw,
who is responsible for Agents / Tools / Toolkits.
No new integration is being added in this PR, hence no need for a test
or an example notebook.
Please see the changes for more detail and let me know if any further
modification is necessary.
- Migrate from deprecated langchainplus_sdk to `langsmith` package
- Update the `run_on_dataset()` API to use an eval config
- Update a number of evaluators, as well as the loading logic
- Update docstrings / reference docs
- Update tracer to share single HTTP session
Added fix to avoid irrelevant attributes being returned plus an example
of extracting unrelated entities and an exampe of using an 'extra_info'
attribute to extract unstructured data for an entity.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
