# 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>
1. upgrade the version of AwaDB
2. add some new interfaces
3. fix bug of packing page content error
@dev2049 please review, thanks!
---------
Co-authored-by: vincent <awadb.vincent@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>
# Provider the latest duckduckgo_search API
The Git commit contents involve two files related to some DuckDuckGo
query operations, and an upgrade of the DuckDuckGo module to version
3.8.3. A suitable commit message could be "Upgrade DuckDuckGo module to
version 3.8.3, including query operations". Specifically, in the
duckduckgo_search.py file, a DDGS() class instance is newly added to
replace the previous ddg() function, and the time parameter name in the
get_snippets() and results() methods is changed from "time" to
"timelimit" to accommodate recent changes. In the pyproject.toml file,
the duckduckgo-search module is upgraded to version 3.8.3.
[duckduckgo_search readme
attention](https://github.com/deedy5/duckduckgo_search): Versions before
v2.9.4 no longer work as of May 12, 2023
## Who can review?
@vowelparrot
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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>
In LangChain, all module classes are enumerated in the `__init__.py`
file of the correspondent module. But some classes were missed and were
not included in the module `__init__.py`
This PR:
- added the missed classes to the module `__init__.py` files
- `__init__.py:__all_` variable value (a list of the class names) was
sorted
- `langchain.tools.sql_database.tool.QueryCheckerTool` was renamed into
the `QuerySQLCheckerTool` because it conflicted with
`langchain.tools.spark_sql.tool.QueryCheckerTool`
- changes to `pyproject.toml`:
- added `pgvector` to `pyproject.toml:extended_testing`
- added `pandas` to
`pyproject.toml:[tool.poetry.group.test.dependencies]`
- commented out the `streamlit` from `collbacks/__init__.py`, It is
because now the `streamlit` requires Python >=3.7, !=3.9.7
- fixed duplicate names in `tools`
- fixed correspondent ut-s
#### Who can review?
@hwchase17
@dev2049
1. Changed the implementation of add_texts interface for the AwaDB
vector store in order to improve the performance
2. Upgrade the AwaDB from 0.3.2 to 0.3.3
---------
Co-authored-by: vincent <awadb.vincent@gmail.com>
Added AwaDB vector store, which is a wrapper over the AwaDB, 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
Delete a unneeded empty file and resolve the
conflict(https://github.com/hwchase17/langchain/pull/5886)
Please check, Thanks!
@dev2049
@hwchase17
---------
<!--
Thank you for contributing to LangChain! Your PR will appear in our
release under the title you set. Please make sure it highlights your
valuable contribution.
Replace this with a description of the change, the issue it fixes (if
applicable), and relevant context. List any dependencies required for
this change.
After you're done, someone will review your PR. They may suggest
improvements. If no one reviews your PR within a few days, feel free to
@-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd
like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (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,
lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
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: ljeagle <vincent_jieli@yeah.net>
Co-authored-by: vincent <awadb.vincent@gmail.com>
# Expose full params in Qdrant
There were many questions regarding supporting some additional
parameters in Qdrant integration. Qdrant supports many vector search
optimizations that were impossible to use directly in Qdrant before.
That includes:
1. Possibility to manipulate collection params while using
`Qdrant.from_texts`. The PR allows setting things such as quantization,
HNWS config, optimizers config, etc. That makes it consistent with raw
`QdrantClient`.
2. Extended options while searching. It includes HNSW options, exact
search, score threshold filtering, and read consistency in distributed
mode.
After merging that PR, #4858 might also be closed.
## Who can review?
VectorStores / Retrievers / Memory
@dev2049 @hwchase17
- 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>
# 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 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:
![Screenshot
(443)](https://github.com/hwchase17/langchain/assets/88007022/6bdb3e1c-4243-4ae8-b974-23a8bb12edac)
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>