- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Remove the client implementation (this breaks backwards compatibility
for existing testers. I could keep the stub in that file if we want, but
not many people are using it yet
- Add SDK as dependency
- Update the 'run_on_dataset' method to be a function that optionally
accepts a client as an argument
- Remove the langchain plus server implementation (you get it for free
with the SDK now)
We could make the SDK optional for now, but the plan is to use w/in the
tracer so it would likely become a hard dependency at some point.
Zep now supports persisting custom metadata with messages and hybrid
search across both message embeddings and structured metadata. This PR
implements custom metadata and enhancements to the
`ZepChatMessageHistory` and `ZepRetriever` classes to implement this
support.
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
# Support Qdrant filters
Qdrant has an [extensive filtering
system](https://qdrant.tech/documentation/concepts/filtering/) with rich
type support. This PR makes it possible to use the filters in Langchain
by passing an additional param to both the
`similarity_search_with_score` and `similarity_search` methods.
## Who can review?
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Adds ability to specify credentials when using Google BigQuery as a
data loader
Fixes#5465 . Adds ability to set credentials which must be of the
`google.auth.credentials.Credentials` type. This argument is optional
and will default to `None.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Update [psychicapi](https://pypi.org/project/psychicapi/) python package
dependency to the latest version 0.5. The newest python package version
addresses breaking changes in the Psychic http api.
# Added New Trello loader class and documentation
Simple Loader on top of py-trello wrapper.
With a board name you can pull cards and to do some field parameter
tweaks on load operation.
I included documentation and examples.
Included unit test cases using patch and a fixture for py-trello client
class.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add instructions to pyproject.toml
* Add instructions to pyproject.toml about how to handle optional
dependencies.
## Before submitting
## Who can review?
---------
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
# Add Momento as a standard cache and chat message history provider
This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.
[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.
## Before submitting
✅ We have added documentation, notebooks, and integration tests
demonstrating usage.
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
zep-python's sync methods no longer need an asyncio wrapper. This was
causing issues with FastAPI deployment.
Zep also now supports putting and getting of arbitrary message metadata.
Bump zep-python version to v0.30
Remove nest-asyncio from Zep example notebooks.
Modify tests to include metadata.
---------
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
# Bibtex integration
Wrap bibtexparser to retrieve a list of docs from a bibtex file.
* Get the metadata from the bibtex entries
* `page_content` get from the local pdf referenced in the `file` field
of the bibtex entry using `pymupdf`
* If no valid pdf file, `page_content` set to the `abstract` field of
the bibtex entry
* Support Zotero flavour using regex to get the file path
* Added usage example in
`docs/modules/indexes/document_loaders/examples/bibtex.ipynb`
---------
Co-authored-by: Sébastien M. Popoff <sebastien.popoff@espci.fr>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add AzureCognitiveServicesToolkit to call Azure Cognitive Services
API: achieve some multimodal capabilities
This PR adds a toolkit named AzureCognitiveServicesToolkit which bundles
the following tools:
- AzureCogsImageAnalysisTool: calls Azure Cognitive Services image
analysis API to extract caption, objects, tags, and text from images.
- AzureCogsFormRecognizerTool: calls Azure Cognitive Services form
recognizer API to extract text, tables, and key-value pairs from
documents.
- AzureCogsSpeech2TextTool: calls Azure Cognitive Services speech to
text API to transcribe speech to text.
- AzureCogsText2SpeechTool: calls Azure Cognitive Services text to
speech API to synthesize text to speech.
This toolkit can be used to process image, document, and audio inputs.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add a WhyLabs callback handler
* Adds a simple WhyLabsCallbackHandler
* Add required dependencies as optional
* protect against missing modules with imports
* Add docs/ecosystem basic example
based on initial prototype from @andrewelizondo
> this integration gathers privacy preserving telemetry on text with
whylogs and sends stastical profiles to WhyLabs platform to monitoring
these metrics over time. For more information on what WhyLabs is see:
https://whylabs.ai
After you run the notebook (if you have env variables set for the API
Keys, org_id and dataset_id) you get something like this in WhyLabs:
Co-authored-by: Andre Elizondo <andre@whylabs.ai>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add Mastodon toots loader.
Loader works either with public toots, or Mastodon app credentials. Toot
text and user info is loaded.
I've also added integration test for this new loader as it works with
public data, and a notebook with example output run now.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
tldr: The docarray [integration
PR](https://github.com/hwchase17/langchain/pull/4483) introduced a
pinned dependency to protobuf. This is a docarray dependency, not a
langchain dependency. Since this is handled by the docarray
dependencies, it is unnecessary here.
Further, as a pinned dependency, this quickly leads to incompatibilities
with application code that consumes the library. Much less with a
heavily used library like protobuf.
Detail: as we see in the [docarray
integration](https://github.com/hwchase17/langchain/pull/4483/files#diff-50c86b7ed8ac2cf95bd48334961bf0530cdc77b5a56f852c5c61b89d735fd711R81-R83),
the transitive dependencies of docarray were also listed as langchain
dependencies. This is unnecessary as the docarray project has an
appropriate
[extras](a01a05542d/pyproject.toml (L70)).
The docarray project also does not require this _pinned_ version of
protobuf, rather [a minimum
version](a01a05542d/pyproject.toml (L41)).
So this pinned version was likely in error.
To fix this, this PR reverts the explicit hnswlib and protobuf
dependencies and adds the hnswlib extras install for docarray (which
installs hnswlib and protobuf, as originally intended). Because version
`0.32.0`
of the docarray hnswlib extras added protobuf, we bump the docarray
dependency from `^0.31.0` to `^0.32.0`.
# revert docarray explicit transitive dependencies and use extras
instead
## Who can review?
@dev2049 -- reviewed the original PR
@eyurtsev -- bumped the pinned protobuf dependency a few days ago
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Improve Evernote Document Loader
When exporting from Evernote you may export more than one note.
Currently the Evernote loader concatenates the content of all notes in
the export into a single document and only attaches the name of the
export file as metadata on the document.
This change ensures that each note is loaded as an independent document
and all available metadata on the note e.g. author, title, created,
updated are added as metadata on each document.
It also uses an existing optional dependency of `html2text` instead of
`pypandoc` to remove the need to download the pandoc application via
`download_pandoc()` to be able to use the `pypandoc` python bindings.
Fixes#4493
Co-authored-by: Mike McGarry <mike.mcgarry@finbourne.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# TextLoader auto detect encoding and enhanced exception handling
- Add an option to enable encoding detection on `TextLoader`.
- The detection is done using `chardet`
- The loading is done by trying all detected encodings by order of
confidence or raise an exception otherwise.
### New Dependencies:
- `chardet`
Fixes#4479
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @eyurtsev
---------
Co-authored-by: blob42 <spike@w530>
Adds some basic unit tests for the ConfluenceLoader that can be extended
later. Ports this [PR from
llama-hub](https://github.com/emptycrown/llama-hub/pull/208) and adapts
it to `langchain`.
@Jflick58 and @zywilliamli adding you here as potential reviewers
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Fix Telegram API loader + add tests.
I was testing this integration and it was broken with next error:
```python
message_threads = loader._get_message_threads(df)
KeyError: False
```
Also, this particular loader didn't have any tests / related group in
poetry, so I added those as well.
@hwchase17 / @eyurtsev please take a look on this fix PR.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Cassandra support for chat history
### Description
- Store chat messages in cassandra
### Dependency
- cassandra-driver - Python Module
## Before submitting
- Added Integration Test
## Who can review?
@hwchase17
@agola11
# Your PR Title (What it does)
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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## 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: Jinto Jose <129657162+jj701@users.noreply.github.com>
# Add GraphQL Query Support
This PR introduces a GraphQL API Wrapper tool that allows LLM agents to
query GraphQL databases. The tool utilizes the httpx and gql Python
packages to interact with GraphQL APIs and provides a simple interface
for running queries with LLM agents.
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Adds a document loader for Docugami
Specifically:
1. Adds a data loader that talks to the [Docugami](http://docugami.com)
API to download processed documents as semantic XML
2. Parses the semantic XML into chunks, with additional metadata
capturing chunk semantics
3. Adds a detailed notebook showing how you can use additional metadata
returned by Docugami for techniques like the [self-querying
retriever](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html)
4. Adds an integration test, and related documentation
Here is an example of a result that is not possible without the
capabilities added by Docugami (from the notebook):
<img width="1585" alt="image"
src="https://github.com/hwchase17/langchain/assets/749277/bb6c1ce3-13dc-4349-a53b-de16681fdd5b">
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
Co-authored-by: Taqi Jaffri <tjaffri@gmail.com>
# Add `tiktoken` as dependency when installed as `langchain[openai]`
Fixes#4513 (issue)
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
@vowelparrot
<!-- 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
-->
Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made
few small changes to get it across the finish line
---------
Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Signed-off-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai>
Co-authored-by: jupyterjazz <saba.sturua@jina.ai>
Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>
# Add action to test with all dependencies installed
PR adds a custom action for setting up poetry that allows specifying a
cache key:
https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
This makes it possible to run 2 types of unit tests:
(1) unit tests with only core dependencies
(2) unit tests with extended dependencies (e.g., those that rely on an
optional pdf parsing library)
As part of this PR, we're moving some pdf parsing tests into the
unit-tests section and making sure that these unit tests get executed
when running with extended dependencies.
- added `Wikipedia` retriever. It is effectively a wrapper for
`WikipediaAPIWrapper`. It wrapps load() into get_relevant_documents()
- sorted `__all__` in the `retrievers/__init__`
- added integration tests for the WikipediaRetriever
- added an example (as Jupyter notebook) for the WikipediaRetriever
This PR adds:
* Option to show a tqdm progress bar when using the file system blob loader
* Update pytest run configuration to be stricter
* Adding a new marker that checks that required pkgs exist
This implements a loader of text passages in JSON format. The `jq`
syntax is used to define a schema for accessing the relevant contents
from the JSON file. This requires dependency on the `jq` package:
https://pypi.org/project/jq/.
---------
Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
Bump threshold to 1.4 from 1.3. Change import to be compatible
Resolves#4142 and #4129
---------
Co-authored-by: ndaugreal <ndaugreal@gmail.com>
Co-authored-by: Jeremy Lopez <lopez86@users.noreply.github.com>
Related to [this
issue.](https://github.com/hwchase17/langchain/issues/3655#issuecomment-1529415363)
The `Mapped` SQLAlchemy class is introduced in SQLAlchemy 1.4 but the
migration from 1.3 to 1.4 is quite challenging so, IMO, it's better to
keep backwards compatibility and not change the SQLAlchemy requirements
just because of type annotations.
During the import of langchain, SQLAlchemy was throeing an errror
`ImportError: cannot import name 'Mapped' from 'sqlalchemy.orm'`. This
is becaue the Mapped name was introduced in v1.4
Resolves#3664
Next PR will be to clean up CI to catch this earlier. Triaging this, it
looks like it wasn't caught because pexpect is a `poetry` dependency.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Alternate implementation of #3452 that relies on a generic query
constructor chain and language and then has vector store-specific
translation layer. Still refactoring and updating examples but general
structure is there and seems to work s well as #3452 on exampels
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Improvements
* set default num_workers for ingestion to 0
* upgraded notebooks for avoiding dataset creation ambiguity
* added `force_delete_dataset_by_path`
* bumped deeplake to 3.3.0
* creds arg passing to deeplake object that would allow custom S3
Notes
* please double check if poetry is not messed up (thanks!)
Asks
* Would be great to create a shared slack channel for quick questions
---------
Co-authored-by: Davit Buniatyan <d@activeloop.ai>
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.
Tested against the (limited) math dataset and got the same score as
before.
For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.
See https://github.com/hwchase17/langchain/issues/814
Note to self: Always run integration tests, even on "that last minute
change you thought would be safe" :)
---------
Co-authored-by: Mike Lambert <mike.lambert@anthropic.com>
Add more missed imports for integration tests. Bump `pytest` to the
current latest version.
Fix `tests/integration_tests/vectorstores/test_elasticsearch.py` to
update its cassette(easy fix).
Related PR: https://github.com/hwchase17/langchain/pull/2560
Almost all integration tests have failed, but we haven't encountered any
import errors yet. Some tests failed due to lazy import issues. It
doesn't seem like a problem to resolve some of these errors in the next
PR.
I have a headache from resolving conflicts with `deeplake` and `boto3`,
so I will temporarily comment out `boto3`.
fix https://github.com/hwchase17/langchain/issues/2426
Using `pytest-vcr` in integration tests has several benefits. Firstly,
it removes the need to mock external services, as VCR records and
replays HTTP interactions on the fly. Secondly, it simplifies the
integration test setup by eliminating the need to set up and tear down
external services in some cases. Finally, it allows for more reliable
and deterministic integration tests by ensuring that HTTP interactions
are always replayed with the same response.
Overall, `pytest-vcr` is a valuable tool for simplifying integration
test setup and improving their reliability
This commit adds the `pytest-vcr` package as a dependency for
integration tests in the `pyproject.toml` file. It also introduces two
new fixtures in `tests/integration_tests/conftest.py` files for managing
cassette directories and VCR configurations.
In addition, the
`tests/integration_tests/vectorstores/test_elasticsearch.py` file has
been updated to use the `@pytest.mark.vcr` decorator for recording and
replaying HTTP interactions.
Finally, this commit removes the `documents` fixture from the
`test_elasticsearch.py` file and replaces it with a new fixture defined
in `tests/integration_tests/vectorstores/conftest.py` that yields a list
of documents to use in any other tests.
This also includes my second attempt to fix issue :
https://github.com/hwchase17/langchain/issues/2386
Maybe related https://github.com/hwchase17/langchain/issues/2484
`AgentExecutor` already has support for limiting the number of
iterations. But the amount of time taken for each iteration can vary
quite a bit, so it is difficult to place limits on the execution time.
This PR adds a new field `max_execution_time` to the `AgentExecutor`
model. When called asynchronously, the agent loop is wrapped in an
`asyncio.timeout()` context which triggers the early stopping response
if the time limit is reached. When called synchronously, the agent loop
checks for both the max_iteration limit and the time limit after each
iteration.
When used asynchronously `max_execution_time` gives really tight control
over the max time for an execution chain. When used synchronously, the
chain can unfortunately exceed max_execution_time, but it still gives
more control than trying to estimate the number of max_iterations needed
to cap the execution time.
---------
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
