Notebook shows preference scoring between two chains and reports wilson
score interval + p value
I think I'll add the option to insert ground truth labels but doesn't
have to be in this PR
allows for where filtering on collection via get
- Description: aligns langchain chroma vectorstore get with underlying
[chromadb collection
get](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py#L103)
allowing for where filtering, etc.
- Issue: NA
- Dependencies: none
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @pappanaka
#### Background
With the development of [structured
tools](https://blog.langchain.dev/structured-tools/), the LangChain team
expanded the platform's functionality to meet the needs of new
applications. The GMail tool, empowered by structured tools, now
supports multiple arguments and powerful search capabilities,
demonstrating LangChain's ability to interact with dynamic data sources
like email servers.
#### Challenge
The current GMail tool only supports GMail, while users often utilize
other email services like Outlook in Office365. Additionally, the
proposed calendar tool in PR
https://github.com/hwchase17/langchain/pull/652 only works with Google
Calendar, not Outlook.
#### Changes
This PR implements an Office365 integration for LangChain, enabling
seamless email and calendar functionality with a single authentication
process.
#### Future Work
With the core Office365 integration complete, future work could include
integrating other Office365 tools such as Tasks and Address Book.
#### Who can review?
@hwchase17 or @vowelparrot can review this PR
#### Appendix
@janscas, I utilized your [O365](https://github.com/O365/python-o365)
library extensively. Given the rising popularity of LangChain and
similar AI frameworks, the convergence of libraries like O365 and tools
like this one is likely. So, I wanted to keep you updated on our
progress.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
MHTML is a very interesting format since it's used both for emails but
also for archived webpages. Some scraping projects want to store pages
in disk to process them later, mhtml is perfect for that use case.
This is heavily inspired from the beautifulsoup html loader, but
extracting the html part from the mhtml file.
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
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- Description: Fix Typo in LangChain MyScale Integration Doc
@hwchase17
# Add caching to BaseChatModel
Fixes#1644
(Sidenote: While testing, I noticed we have multiple implementations of
Fake LLMs, used for testing. I consolidated them.)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
Models
- @hwchase17
- @agola11
Twitter: [@UmerHAdil](https://twitter.com/@UmerHAdil) | Discord:
RicChilligerDude#7589
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds a new LLM class for the Amazon API Gateway hosted LLM. The
PR also includes example notebooks for using the LLM class in an Agent
chain.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Just corrected a small inconsistency on a doc page (not exactly a
typo, per se)
- Description: There was inconsistency due to the use of single quotes
at one place on the [Squential
Chains](https://python.langchain.com/docs/modules/chains/foundational/sequential_chains)
page of the docs,
- Issue: NA,
- Dependencies: NA,
- Tag maintainer: @dev2049,
- Twitter handle: kambleakash0
This link for the notebook of OpenLLM is not migrated to the new format
Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
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If no one reviews your PR within a few days, feel free to @-mention the
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Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
We may want to process load all URLs under a root directory.
For example, let's look at the [LangChain JS
documentation](https://js.langchain.com/docs/).
This has many interesting child pages that we may want to read in bulk.
Of course, the `WebBaseLoader` can load a list of pages.
But, the challenge is traversing the tree of child pages and actually
assembling that list!
We do this using the `RecusiveUrlLoader`.
This also gives us the flexibility to exclude some children (e.g., the
`api` directory with > 800 child pages).
Many cities have open data portals for events like crime, traffic, etc.
Socrata provides an API for many, including SF (e.g., see
[here](https://dev.socrata.com/foundry/data.sfgov.org/tmnf-yvry)).
This is a new data loader for city data that uses Socrata API.
# Changes
This PR adds [Clarifai](https://www.clarifai.com/) integration to
Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user
the ability to use many types of LLM (OpenAI, cohere, ect and other open
source models). As well, a clarifai app can be treated as a vector
database to upload and retrieve data. The integrations includes:
- Clarifai LLM integration: Clarifai supports many types of language
model that users can utilize for their application
- Clarifai VectorDB: A Clarifai application can hold data and
embeddings. You can run semantic search with the embeddings
#### Before submitting
- [x] Added integration test for LLM
- [x] Added integration test for VectorDB
- [x] Added notebook for LLM
- [x] Added notebook for VectorDB
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Description
We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation
Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.
### Dependencies
No added dependencies were required for the change.
### Tests
Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.
### Notebook
A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.
### Twitters
[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)
---------
Co-authored-by: Matthew DeGuzman <91019033+matthewdeguzman@users.noreply.github.com>
Co-authored-by: prakharg-msft <75808410+prakharg-msft@users.noreply.github.com>
Everything needed to support sending messages over WhatsApp Business
Platform (GA), Facebook Messenger (Public Beta) and Google Business
Messages (Private Beta) was present. Just added some details on
leveraging it.
Just some grammar fixes: I found "retriver" instead of "retriever" in
several comments across the documentation and in the comments. I fixed
it.
Co-authored-by: andrey.vedishchev <andrey.vedishchev@rgigroup.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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#### Before submitting
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Here are some examples to use StarRocks as vectordb
```
from langchain.vectorstores import StarRocks
from langchain.vectorstores.starrocks import StarRocksSettings
embeddings = OpenAIEmbeddings()
# conifgure starrocks settings
settings = StarRocksSettings()
settings.port = 41003
settings.host = '127.0.0.1'
settings.username = 'root'
settings.password = ''
settings.database = 'zya'
# to fill new embeddings
docsearch = StarRocks.from_documents(split_docs, embeddings, config = settings)
# or to use already-built embeddings in database.
docsearch = StarRocks(embeddings, settings)
```
#### Who can review?
Tag maintainers/contributors who might be interested:
@dev2049
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@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
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- @eyurtsev
Models
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---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Integration of Infino with LangChain for Enhanced Observability
This PR aims to integrate [Infino](https://github.com/infinohq/infino),
an open source observability platform written in rust for storing
metrics and logs at scale, with LangChain, providing users with a
streamlined and efficient method of tracking and recording LangChain
experiments. By incorporating Infino into LangChain, users will be able
to gain valuable insights and easily analyze the behavior of their
language models.
#### Please refer to the following files related to integration:
- `InfinoCallbackHandler`: A [callback
handler](https://github.com/naman-modi/langchain/blob/feature/infino-integration/langchain/callbacks/infino_callback.py)
specifically designed for storing chain responses within Infino.
- Example `infino.ipynb` file: A comprehensive notebook named
[infino.ipynb](https://github.com/naman-modi/langchain/blob/feature/infino-integration/docs/extras/modules/callbacks/integrations/infino.ipynb)
has been included to guide users on effectively leveraging Infino for
tracking LangChain requests.
- [Integration
Doc](https://github.com/naman-modi/langchain/blob/feature/infino-integration/docs/extras/ecosystem/integrations/infino.mdx)
for Infino integration.
By integrating Infino, LangChain users will gain access to powerful
visualization and debugging capabilities. Infino enables easy tracking
of inputs, outputs, token usage, execution time of LLMs. This
comprehensive observability ensures a deeper understanding of individual
executions and facilitates effective debugging.
Co-authors: @vinaykakade @savannahar68
---------
Co-authored-by: Vinay Kakade <vinaykakade@gmail.com>
This PR adds Rockset as a vectorstore for langchain.
[Rockset](https://rockset.com/blog/introducing-vector-search-on-rockset/)
is a real time OLAP database which provides a fast and efficient vector
search functionality. Further since it is entirely schemaless, it can
store metadata in separate columns thereby allowing fast metadata
filters during vector similarity search (as opposed to storing the
entire metadata in a single JSON column). It currently supports three
distance functions: `COSINE_SIMILARITY`, `EUCLIDEAN_DISTANCE`, and
`DOT_PRODUCT`.
This PR adds `rockset` client as an optional dependency.
We would love a twitter shoutout, our handle is
https://twitter.com/RocksetCloud
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This pull request introduces a new feature to the LangChain QA Retrieval
Chains with Structures. The change involves adding a prompt template as
an optional parameter for the RetrievalQA chains that utilize the
recently implemented OpenAI Functions.
The main purpose of this enhancement is to provide users with the
ability to input a more customizable prompt to the chain. By introducing
a prompt template as an optional parameter, users can tailor the prompt
to their specific needs and context, thereby improving the flexibility
and effectiveness of the RetrievalQA chains.
## Changes Made
- Created a new optional parameter, "prompt", for the RetrievalQA with
structure chains.
- Added an example to the RetrievalQA with sources notebook.
My twitter handle is @El_Rey_Zero
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Added the functionality to leverage 3 new Codey models from Vertex AI:
- code-bison - Code generation using the existing LLM integration
- code-gecko - Code completion using the existing LLM integration
- codechat-bison - Code chat using the existing chat_model integration
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
This PR adds `KuzuGraph` and `KuzuQAChain` for interacting with [Kùzu
database](https://github.com/kuzudb/kuzu). Kùzu is an in-process
property graph database management system (GDBMS) built for query speed
and scalability. The `KuzuGraph` and `KuzuQAChain` provide the same
functionality as the existing integration with NebulaGraph and Neo4j and
enables query generation and question answering over Kùzu database.
A notebook example and a simple test case have also been added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
#### Fix
Added the mention of "store" amongst the tasks that the data connection
module can perform aside from the existing 3 (load, transform and
query). Particularly, this implies the generation of embeddings vectors
and the creation of vector stores.
This addresses #6291 adding support for using Cassandra (and compatible
databases, such as DataStax Astra DB) as a [Vector
Store](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes).
A new class `Cassandra` is introduced, which complies with the contract
and interface for a vector store, along with the corresponding
integration test, a sample notebook and modified dependency toml.
Dependencies: the implementation relies on the library `cassio`, which
simplifies interacting with Cassandra for ML- and LLM-oriented
workloads. CassIO, in turn, uses the `cassandra-driver` low-lever
drivers to communicate with the database. The former is added as
optional dependency (+ in `extended_testing`), the latter was already in
the project.
Integration testing relies on a locally-running instance of Cassandra.
[Here](https://cassio.org/more_info/#use-a-local-vector-capable-cassandra)
a detailed description can be found on how to compile and run it (at the
time of writing the feature has not made it yet to a release).
During development of the integration tests, I added a new "fake
embedding" class for what I consider a more controlled way of testing
the MMR search method. Likewise, I had to amend what looked like a
glitch in the behaviour of `ConsistentFakeEmbeddings` whereby an
`embed_query` call would have bypassed storage of the requested text in
the class cache for use in later repeated invocations.
@dev2049 might be the right person to tag here for a review. Thank you!
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
