- **Description:** add a ValidationError handler as a field of
[`BaseTool`](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/tools.py#L101)
and add unit tests for the code change.
- **Issue:** #12721#13662
- **Dependencies:** None
- **Tag maintainer:**
- **Twitter handle:** @hmdev3
- **NOTE:**
- I'm wondering if the update of document is required.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
We didn't override the namespace of the ImagePromptTemplate, so it is
listed as being in langchain.schema
This updates the mapping to let the loader deserialize.
Alternatively, we could make a slight breaking change and update the
namespace of the ImagePromptTemplate since we haven't broadly
publicized/documented it yet..
All models should be calling the callback for new token prior to
yielding the token.
Not doing this can cause callbacks for downstream steps to be called
prior to the callback for the new token; causing issues in
astream_events APIs and other things that depend in callback ordering
being correct.
We need to make this change for all chat models.
The `langchain.prompts.example_selector` [still holds several
artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.prompts)
that belongs to `community`. If they moved to
`langchain_community.example_selectors`, the `langchain.prompts`
namespace would be effectively removed which is great.
- moved a class and afunction to `langchain_community`
Note:
- Previously, the `langchain.prompts.example_selector` artifacts were
moved into the `langchain_core.exampe_selectors`. See the flattened
namespace (`.prompts` was removed)!
Similar flattening was implemented for the `langchain_core` as the
`langchain_core.exampe_selectors`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
* Adds `AstraDBEnvironment` class and use it in `AstraDBLoader`,
`AstraDBCache`, `AstraDBSemanticCache`, `AstraDBBaseStore` and
`AstraDBChatMessageHistory`
* Create an `AsyncAstraDB` if we only have an `AstraDB` and vice-versa
so:
* we always have an instance of `AstraDB`
* we always have an instance of `AsyncAstraDB` for recent versions of
astrapy
* Create collection if not exists in `AstraDBBaseStore`
* Some typing improvements
Note: `AstraDB` `VectorStore` not using `AstraDBEnvironment` at the
moment. This will be done after the `langchain-astradb` package is out.
- **Description:**
The BaseStore methods are currently blocking. Some implementations
(AstraDBStore, RedisStore) would benefit from having async methods.
Also once we have async methods for BaseStore, we can implement the
async `aembed_documents` in CacheBackedEmbeddings to cache the
embeddings asynchronously.
* adds async methods amget, amset, amedelete and ayield_keys to
BaseStore
* implements the async methods for InMemoryStore
* adds tests for InMemoryStore async methods
- **Twitter handle:** cbornet_
* Add bulk add_messages method to the interface.
* Update documentation for add_ai_message and add_human_message to
denote them as being marked for deprecation. We should stop using them
as they create more incorrect (inefficient) ways of doing things
Adds:
* methods `aload()` and `alazy_load()` to interface `BaseLoader`
* implementation for class `MergedDataLoader `
* support for class `BaseLoader` in async function `aindex()` with unit
tests
Note: this is compatible with existing `aload()` methods that some
loaders already had.
**Twitter handle:** @cbornet_
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description:** the existing AssemblyAI API allows to pass a path or
an url to transcribe an audio file and turn in into Langchain Documents,
this PR allows to get existing transcript by their transcript id and
turn them into Documents.
- **Issue:** not related to an existing issue
- **Dependencies:** requests
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The current implementation leaves it up to the particular file loader
implementation to report the file on which an error was encountered - in
my case pdfminer was simply saying it could not parse a file as a PDF,
but I didn't know which of my hundreds of files it was failing on.
No reason not to log the particular item on which an error was
encountered, and it should be an immense debugging assistant.
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Description: Added the parameter for a possibility to change a language
model in SpacyEmbeddings. The default value is still the same:
"en_core_web_sm", so it shouldn't affect a code which previously did not
specify this parameter, but it is not hard-coded anymore and easy to
change in case you want to use it with other languages or models.
Issue: At Barcelona Supercomputing Center in Aina project
(https://github.com/projecte-aina), a project for Catalan Language
Models and Resources, we would like to use Langchain for one of our
current projects and we would like to comment that Langchain, while
being a very powerful and useful open-source tool, is pretty much
focused on English language. We would like to contribute to make it a
bit more adaptable for using with other languages.
Dependencies: This change requires the Spacy library and a language
model, specified in the model parameter.
Tag maintainer: @dev2049
Twitter handle: @projecte_aina
---------
Co-authored-by: Marina Pliusnina <marina.pliusnina@bsc.es>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description**: fully async versions are available for astrapy 0.7+.
For older astrapy versions or if the user provides a sync client without
an async one, the async methods will call the sync ones wrapped in
`run_in_executor`
- **Twitter handle:** cbornet_
Replace this entire comment with:
- **Description:** Add Baichuan LLM to integration/llm, also updated
related docs.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
- **Description:**
Filtering in a FAISS vectorstores is very inflexible and doesn't allow
that many use case. I think supporting callable like this enables a lot:
regular expressions, condition on multiple keys etc. **Note** I had to
manually alter a test. I don't understand if it was falty to begin with
or if there is something funky going on.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
Signed-off-by: thiswillbeyourgithub <26625900+thiswillbeyourgithub@users.noreply.github.com>
Adjusted deprecate decorator to make sure decorated async functions are
still recognized as "coroutinefunction" by inspect
Addresses #16402
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
The PR is to return the ID and collection name from qdrant client to
metadata field in `Document` class.
## Issue
The motivation is almost same to
[11592](https://github.com/langchain-ai/langchain/issues/11592)
Returning ID is useful to update existing records in a vector store, but
we cannot know them if we use some retrievers.
In order to avoid any conflicts, breaking changes, the new fields in
metadata have a prefix `_`
## Dependencies
N/A
## Twitter handle
@kill_in_sun
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network access,
2. an example notebook showing its use. It lives in
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Use the real "history" provided by the original program instead of
putting "None" in the history.
- **Description:** I change one line in the code to make it return the
"history" of the chat model.
- **Issue:** At the moment it returns only the answers of the chat
model. However the chat model himself provides a history more complet
with the questions of the user.
- **Dependencies:** no dependencies required for this change,
This PR includes updates for OctoAI integrations:
- The LLM class was updated to fix a bug that occurs with multiple
sequential calls
- The Embedding class was updated to support the new GTE-Large endpoint
released on OctoAI lately
- The documentation jupyter notebook was updated to reflect using the
new LLM sdk
Thank you!
## Summary
This PR implements the "Connery Action Tool" and "Connery Toolkit".
Using them, you can integrate Connery actions into your LangChain agents
and chains.
Connery is an open-source plugin infrastructure for AI.
With Connery, you can easily create a custom plugin with a set of
actions and seamlessly integrate them into your LangChain agents and
chains. Connery will handle the rest: runtime, authorization, secret
management, access management, audit logs, and other vital features.
Additionally, Connery and our community offer a wide range of
ready-to-use open-source plugins for your convenience.
Learn more about Connery:
- GitHub: https://github.com/connery-io/connery-platform
- Documentation: https://docs.connery.io
- Twitter: https://twitter.com/connery_io
## TODOs
- [x] API wrapper
- [x] Integration tests
- [x] Connery Action Tool
- [x] Docs
- [x] Example
- [x] Integration tests
- [x] Connery Toolkit
- [x] Docs
- [x] Example
- [x] Formatting (`make format`)
- [x] Linting (`make lint`)
- [x] Testing (`make test`)
- **Description:** To adapt more parameters related to
MemorySearchPayload for the search method of ZepChatMessageHistory,
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
