### Adding the functionality to return the scores with retrieved
documents when using the max marginal relevance
- Description: Add the method
`max_marginal_relevance_search_with_score_by_vector` to the FAISS
wrapper. Functionality operates the same as
`similarity_search_with_score_by_vector` except for using the max
marginal relevance retrieval framework like is used in the
`max_marginal_relevance_search_by_vector` method.
- Dependencies: None
- Tag maintainer: @rlancemartin @eyurtsev
- Twitter handle: @RianDolphin
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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### Summary
This PR adds a LarkSuite (FeiShu) document loader.
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration
platform developed by ByteDance.
### Tests
- an integration test case is added
- an example notebook showing usage is added. [Notebook
preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)
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---------
Co-authored-by: Yaohui Wang <wangyaohui.01@bytedance.com>
Replace this comment with:
- Description: Add Async functionality to Zapier NLA Tools
- Issue: n/a
- Dependencies: n/a
- Tag maintainer:
Maintainer responsibilities:
- Agents / Tools / Toolkits: @vowelparrot
- 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
Description: When a 401 response is given back by Zapier, hint to the
end user why that may have occurred
- If an API Key was initialized with the wrapper, ask them to check
their API Key value
- if an access token was initialized with the wrapper, ask them to check
their access token or verify that it doesn't need to be refreshed.
Tag maintainer: @dev2049
#### Summary
A new approach to loading source code is implemented:
Each top-level function and class in the code is loaded into separate
documents. Then, an additional document is created with the top-level
code, but without the already loaded functions and classes.
This could improve the accuracy of QA chains over source code.
For instance, having this script:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
if __name__ == '__main__':
main()
```
The loader will create three documents with this content:
First document:
```
class MyClass:
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, {self.name}!")
```
Second document:
```
def main():
name = input("Enter your name: ")
obj = MyClass(name)
obj.greet()
```
Third document:
```
# Code for: class MyClass:
# Code for: def main():
if __name__ == '__main__':
main()
```
A threshold parameter is added to control whether small scripts are
split in this way or not.
At this moment, only Python and JavaScript are supported. The
appropriate parser is determined by examining the file extension.
#### Tests
This PR adds:
- Unit tests
- Integration tests
#### Dependencies
Only one dependency was added as optional (needed for the JavaScript
parser).
#### Documentation
A notebook is added showing how the loader can be used.
#### Who can review?
@eyurtsev @hwchase17
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
Description: Update documentation to
1) point to updated documentation links at Zapier.com (we've revamped
our help docs and paths), and
2) To provide clarity how to use the wrapper with an access token for
OAuth support
Demo:
Initializing the Zapier Wrapper with an OAuth Access Token
`ZapierNLAWrapper(zapier_nla_oauth_access_token="<redacted>")`
Using LangChain to resolve the current weather in Vancouver BC
leveraging Zapier NLA to lookup weather by coords.
```
> Entering new chain...
I need to use a tool to get the current weather.
Action: The Weather: Get Current Weather
Action Input: Get the current weather for Vancouver BC
Observation: {"coord__lon": -123.1207, "coord__lat": 49.2827, "weather": [{"id": 802, "main": "Clouds", "description": "scattered clouds", "icon": "03d", "icon_url": "http://openweathermap.org/img/wn/03d@2x.png"}], "weather[]icon_url": ["http://openweathermap.org/img/wn/03d@2x.png"], "weather[]icon": ["03d"], "weather[]id": [802], "weather[]description": ["scattered clouds"], "weather[]main": ["Clouds"], "base": "stations", "main__temp": 71.69, "main__feels_like": 71.56, "main__temp_min": 67.64, "main__temp_max": 76.39, "main__pressure": 1015, "main__humidity": 64, "visibility": 10000, "wind__speed": 3, "wind__deg": 155, "wind__gust": 11.01, "clouds__all": 41, "dt": 1687806607, "sys__type": 2, "sys__id": 2011597, "sys__country": "CA", "sys__sunrise": 1687781297, "sys__sunset": 1687839730, "timezone": -25200, "id": 6173331, "name": "Vancouver", "cod": 200, "summary": "scattered clouds", "_zap_search_was_found_status": true}
Thought: I now know the current weather in Vancouver BC.
Final Answer: The current weather in Vancouver BC is scattered clouds with a temperature of 71.69 and wind speed of 3
```
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
Adding Confluence to Jira tool. Can create a page in Confluence with
this PR. If accepted, will extend functionality to Bitbucket and
additional Confluence features.
---------
Co-authored-by: Ethan Bowen <ethan.bowen@slalom.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>
- Description: Updated regex to support a new format that was observed
when whatsapp chat was exported.
- Issue: #6654
- Dependencies: No new dependencies
- Tag maintainer: @rlancemartin, @eyurtsev
# 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>
- Description: The aviary integration has changed url link. This PR
provide fix for those changes and also it makes providing the input URL
optional to the API (since they can be set via env variables).
- Issue: N/A
- Dependencies: N/A
- Twitter handle: N/A
---------
Signed-off-by: Kourosh Hakhamaneshi <kourosh@anyscale.com>
Fixes#5456
This PR removes the `callbacks` argument from a tool's schema when
creating a `Tool` or `StructuredTool` with the `from_function` method
and `infer_schema` is set to `True`. The `callbacks` argument is now
removed in the `create_schema_from_function` and `_get_filtered_args`
methods. As suggested by @vowelparrot, this fix provides a
straightforward solution that minimally affects the existing
implementation.
A test was added to verify that this change enables the expected use of
`Tool` and `StructuredTool` when using a `CallbackManager` and inferring
the tool's schema.
- @hwchase17
A new implementation of `StreamlitCallbackHandler`. It formats Agent
thoughts into Streamlit expanders.
You can see the handler in action here:
https://langchain-mrkl.streamlit.app/
Per a discussion with Harrison, we'll be adding a
`StreamlitCallbackHandler` implementation to an upcoming
[Streamlit](https://github.com/streamlit/streamlit) release as well, and
will be updating it as we add new LLM- and LangChain-specific features
to Streamlit.
The idea with this PR is that the LangChain `StreamlitCallbackHandler`
will "auto-update" in a way that keeps it forward- (and backward-)
compatible with Streamlit. If the user has an older Streamlit version
installed, the LangChain `StreamlitCallbackHandler` will be used; if
they have a newer Streamlit version that has an updated
`StreamlitCallbackHandler`, that implementation will be used instead.
(I'm opening this as a draft to get the conversation going and make sure
we're on the same page. We're really excited to land this into
LangChain!)
#### Who can review?
@agola11, @hwchase17
# 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>
Since it seems like #6111 will be blocked for a bit, I've forked
@tyree731's fork and implemented the requested changes.
This change adds support to the base Embeddings class for two methods,
aembed_query and aembed_documents, those two methods supporting async
equivalents of embed_query and
embed_documents respectively. This ever so slightly rounds out async
support within langchain, with an initial implementation of this
functionality being implemented for openai.
Implements https://github.com/hwchase17/langchain/issues/6109
---------
Co-authored-by: Stephen Tyree <tyree731@gmail.com>
### Feature
Using FAISS on a retrievalQA task, I found myself wanting to allow in
multiple sources. From what I understood, the filter feature takes in a
dict of form {key: value} which then will check in the metadata for the
exact value linked to that key.
I added some logic to be able to pass a list which will be checked
against instead of an exact value. Passing an exact value will also
work.
Here's an example of how I could then use it in my own project:
```
pdfs_to_filter_in = ["file_A", "file_B"]
filter_dict = {
"source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in]
}
retriever = db.as_retriever()
retriever.search_kwargs = {"filter": filter_dict}
```
I added an integration test based on the other ones I found in
`tests/integration_tests/vectorstores/test_faiss.py` under
`test_faiss_with_metadatas_and_list_filter()`.
It doesn't feel like this is worthy of its own notebook or doc, but I'm
open to suggestions if needed.
Co-authored-by: Dev 2049 <dev.dev2049@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 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>
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>
Hello Folks,
Thanks for creating and maintaining this great project. I'm excited to
submit this PR to add Alibaba Cloud OpenSearch as a new vector store.
OpenSearch is a one-stop platform to develop intelligent search
services. OpenSearch was built based on the large-scale distributed
search engine developed by Alibaba. OpenSearch serves more than 500
business cases in Alibaba Group and thousands of Alibaba Cloud
customers. OpenSearch helps develop search services in different search
scenarios, including e-commerce, O2O, multimedia, the content industry,
communities and forums, and big data query in enterprises.
OpenSearch provides the vector search feature. In specific scenarios,
especially test question search and image search scenarios, you can use
the vector search feature together with the multimodal search feature to
improve the accuracy of search results.
This PR includes:
A AlibabaCloudOpenSearch class that can connect to the Alibaba Cloud
OpenSearch instance.
add embedings and metadata into a opensearch datasource.
querying by squared euclidean and metadata.
integration tests.
ipython notebook and docs.
I have read your contributing guidelines. And I have passed the tests
below
- [x] make format
- [x] make lint
- [x] make coverage
- [x] make test
---------
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
Already supported in the reverse operation in
`_convert_message_to_dict()`, this just provides parity.
@hwchase17
@agola11
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fix issue #6380
<!-- Remove if not applicable -->
Fixes#6380 (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on
network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests,
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#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: HubertKl <HubertKl>
1. Introduced new distance strategies support: **DOT_PRODUCT** and
**EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk"
usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code
reliability.
5. Updated the existing notebook with the usage example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
W.r.t recent changes, ChatPromptTemplate does not accepting partial
variables. This PR should fix that issue.
Fixes#6431
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Throwing ToolException when incorrect arguments are passed to tools so
that that agent can course correct them.
# Incorrect argument count handling
I was facing an error where the agent passed incorrect arguments to
tools. As per the discussions going around, I started throwing
ToolException to allow the model to course correct.
## Before submitting
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1. a test for the integration - favor unit tests that does not rely on
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-->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @vowelparrot
VectorStores / Retrievers / Memory
- @dev2049
-->
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
<!--
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<!-- Remove if not applicable -->
Fixes: ChatAnthropic was mutating the input message list during
formatting which isn't ideal bc you could be changing the behavior for
other chat models when using the same input
#### Before submitting
<!-- If you're adding a new integration, please include:
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#### Who can review?
Tag maintainers/contributors who might be interested:
#### Before submitting
Add memory support for `OpenAIFunctionsAgent` like
`StructuredChatAgent`.
#### Who can review?
@hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#5807 (issue)
#### Who can review?
Tag maintainers/contributors who might be interested: @dev2049
<!-- For a quicker response, figure out the right person to tag with @
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Tracing / Callbacks
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Async
- @agola11
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- @eyurtsev
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- @hwchase17
- @agola11
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-->
Similar as https://github.com/hwchase17/langchain/pull/5818
Added the functionality to save/load Graph Cypher QA Chain due to a user
reporting the following error
> raise NotImplementedError("Saving not supported for this chain
type.")\nNotImplementedError: Saving not supported for this chain
type.\n'
