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If you're adding a new integration, please include:
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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
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- Async: @agola11
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---------
Co-authored-by: Jiří Moravčík <jiri.moravcik@gmail.com>
Co-authored-by: Jan Čurn <jan.curn@gmail.com>
- Description: Added the ability to define the open AI model.
- Issue: Currently the Doctran instance uses gpt-4 by default, this does
not work if the user has no access to gpt -4.
- rlancemartin, @eyurtsev, @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
BedrockEmbeddings does not have endpoint_url so that switching to custom
endpoint is not possible. I have access to Bedrock custom endpoint and
cannot use BedrockEmbeddings
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Added a parameter in VectorStoreRetrieverMemory which
filters the input given by the key when constructing the buffering the
document for Vector. This feature is helpful if you have certain inputs
apart from the VectorMemory's own memory_key that needs to be ignored
e.g when using combined memory, we might need to filter the memory_key
of the other memory, Please see the issue.
- Issue: #7695
- Tag maintainer: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Golden Query is a wrapper on top of the [Golden Query
API](https://docs.golden.com/reference/query-api) which enables
programmatic access to query results on entities across Golden's
Knowledge Base. For more information about Golden API, please see the
[Golden API Getting
Started](https://docs.golden.com/reference/getting-started) page.
**Issue:** None
**Dependencies:** requests(already present in project)
**Tag maintainer:** @hinthornw
Signed-off-by: Constantin Musca <constantin.musca@gmail.com>
- Description: Adding code to set pandas dataframe to display all the
columns. Otherwise, some data get truncated (it puts a "..." in the
middle and just shows the first 4 and last 4 columns) and the LLM
doesn't realize it isn't getting the full data. Default value is 8, so
this helps Dataframes larger than that.
- Issue: none
- Dependencies: none
- Tag maintainer: @hinthornw
- Twitter handle: none
## Background
With the addition on email and calendar tools, LangChain is continuing
to complete its functionality to automate business processes.
## Challenge
One of the pieces of business functionality that LangChain currently
doesn't have is the ability to search for flights and travel in order to
book business travel.
## Changes
This PR implements an integration with the
[Amadeus](https://developers.amadeus.com/) travel search API for
LangChain, enabling seamless search for flights with a single
authentication process.
## Who can review?
@hinthornw
## Appendix
@tsolakoua and @minjikarin, I utilized your
[amadeus-python](https://github.com/amadeus4dev/amadeus-python) library
extensively. Given the rising popularity of LangChain and similar AI
frameworks, the convergence of libraries like amadeus-python and tools
like this one is likely. So, I wanted to keep you updated on our
progress.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Add verbose support for the extraction_chain
- Issue: Fixes#7982
- Dependencies: NA
- Twitter handle: sheikirfanbasha
@hwchase17 and @agola11
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Added a doc about the [Datadog APM integration for
LangChain](https://github.com/DataDog/dd-trace-py/pull/6137).
Note that the integration is on `ddtrace`'s end and so no code is
introduced/required by this integration into the langchain library. For
that reason I've refrained from adding an example notebook (although
I've added setup instructions for enabling the integration in the doc)
as no code is technically required to enable the integration.
Tagging @baskaryan as reviewer on this PR, thank you very much!
## Dependencies
Datadog APM users will need to have `ddtrace` installed, but the
integration is on `ddtrace` end and so does not introduce any external
dependencies to the LangChain project.
Co-authored-by: Bagatur <baskaryan@gmail.com>
Work in Progress.
WIP
Not ready...
Adds Document Loader support for
[Geopandas.GeoDataFrames](https://geopandas.org/)
Example:
- [x] stub out `GeoDataFrameLoader` class
- [x] stub out integration tests
- [ ] Experiment with different geometry text representations
- [ ] Verify CRS is successfully added in metadata
- [ ] Test effectiveness of searches on geometries
- [ ] Test with different geometry types (point, line, polygon with
multi-variants).
- [ ] Add documentation
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Lance Martin <122662504+rlancemartin@users.noreply.github.com>
Removing **kwargs argument from add_texts method in DeepLake vectorstore
as it confuses users and doesn't fail when user is typing incorrect
parameters.
Also added small test to ensure the change is applies correctly.
Guys could pls take a look: @rlancemartin, @eyurtsev, this is a small
PR.
Thx so much!
<!-- 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
-->
- Adds integration for MLflow AI Gateway (this will be shipped in MLflow
2.5 this week).
Manual testing:
```sh
# Move to mlflow repo
cd /path/to/mlflow
# install langchain
pip install git+https://github.com/harupy/langchain.git@gateway-integration
# launch gateway service
mlflow gateway start --config-path examples/gateway/openai/config.yaml
# Then, run the examples in this PR
```
Fixed missing "content" field in azure.
Added a check for "content" in _dict (missing for azure
api=2023-07-01-preview)
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: VectorStoreRetriever->similarity_score_threshold with
search_type of "similarity_score_threshold" not working with the
following two minor issues,
- Issue: 1. In line 237 of `vectorstores/base.py`, "score_threshold" is
passed to `_similarity_search_with_relevance_scores` as in the kwargs,
while score_threshold is not a valid argument of this method. As a fix,
before calling `_similarity_search_with_relevance_scores`,
score_threshold is popped from kwargs. 2. In line 596 to 607 of
`vectorstores/pgvector.py`, it's checking the distance_strategy against
the string in Enum. However, self.distance_strategy will get the
property of distance_strategy from line 316, where the callable function
is passed. To solve this issue, self.distance_strategy is changed to
self._distance_strategy to avoid calling the property method.,
- Dependencies: No,
- Tag maintainer: @rlancemartin, @eyurtsev,
- Twitter handle: No
---------
Co-authored-by: Bin Wang <bin@arcanum.ai>
- Description: exposes the ResultItem DocumentAttributes as document
metadata with key 'document_attributes' and refactors
AmazonKendraRetriever by providing a ResultItem base class in order to
avoid duplicate code;
- Tag maintainer: @3coins @hupe1980 @dev2049 @baskaryan
- Twitter handle: wilsonleao
### Why?
Some use cases depend on specific document attributes returned by the
retriever in order to improve the quality of the overall completion and
adjust what will be displayed to the user. For the sake of consistency,
we need to expose the DocumentAttributes as document metadata so we are
sure that we are using the values returned by the kendra request issued
by langchain.
I would appreciate your review @3coins @hupe1980 @dev2049. Thank you in
advance!
### References
- [Amazon Kendra
DocumentAttribute](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DocumentAttribute.html)
- [Amazon Kendra
DocumentAttributeValue](https://docs.aws.amazon.com/kendra/latest/APIReference/API_DocumentAttributeValue.html)
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
- Description: check title and excerpt separately for page_content so
that if title is empty but excerpt is present, the page_content will
only contain the excerpt
- Issue: #7782
- Tag maintainer: @3coins @baskaryan
- Twitter handle: wilsonleao
- Description: It allows to use chat models that do not return token
usage
- Issue: [#7900](https://github.com/hwchase17/langchain/issues/7900)
- Dependencies: None
- Tag maintainer: @agola11 @hwchase17
- Twitter handle: @alonsosilva
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
** This should land Monday the 17th **
Chroma is upgrading from `0.3.29` to `0.4.0`. `0.4.0` is easier to
build, more durable, faster, smaller, and more extensible. This comes
with a few changes:
1. A simplified and improved client setup. Instead of having to remember
weird settings, users can just do `EphemeralClient`, `PersistentClient`
or `HttpClient` (the underlying direct `Client` implementation is also
still accessible)
2. We migrated data stores away from `duckdb` and `clickhouse`. This
changes the api for the `PersistentClient` that used to reference
`chroma_db_impl="duckdb+parquet"`. Now we simply set
`is_persistent=true`. `is_persistent` is set for you to `true` if you
use `PersistentClient`.
3. Because we migrated away from `duckdb` and `clickhouse` - this also
means that users need to migrate their data into the new layout and
schema. Chroma is committed to providing extension notification and
tooling around any schema and data migrations (for example - this PR!).
After upgrading to `0.4.0` - if users try to access their data that was
stored in the previous regime, the system will throw an `Exception` and
instruct them how to use the migration assistant to migrate their data.
The migration assitant is a pip installable CLI: `pip install
chroma_migrate`. And is runnable by calling `chroma_migrate`
-- TODO ADD here is a short video demonstrating how it works.
Please reference the readme at
[chroma-core/chroma-migrate](https://github.com/chroma-core/chroma-migrate)
to see a full write-up of our philosophy on migrations as well as more
details about this particular migration.
Please direct any users facing issues upgrading to our Discord channel
called
[#get-help](https://discord.com/channels/1073293645303795742/1129200523111841883).
We have also created a [email
listserv](https://airtable.com/shrHaErIs1j9F97BE) to notify developers
directly in the future about breaking changes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: version check to make sure chromadb >=0.4.0 does not
throw an error, and uses the default sqlite persistence engine when the
directory is set,
- Issue: the issue #7887
For attention of
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR
- fixes the `similarity_search_by_vector` example, makes the code run
and adds the example to mirror `similarity_search`
- reverts back to chroma from faiss to remove sharp edges / create a
happy path for new developers. (1) real metadata filtering, (2) expected
functionality like `update`, `delete`, etc to serve beyond the most
trivial use cases
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
