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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If no one reviews your PR within a few days, please @-mention one of
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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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whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
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- **Issue:** the issue # it fixes if applicable,
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network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
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@baskaryan, @eyurtsev, @hwchase17.
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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!
Description: One too many set of triple-ticks in a sample code block in
the QuickStart doc was causing "\`\`\`shell" to appear in the shell
command that was being demonstrated. I just deleted the extra "```".
Issue: Didn't see one
Dependencies: None
## 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
**Description:**
Updated the retry.ipynb notebook, it contains the illustrations of
RetryOutputParser in LangChain. But the notebook lacks to explain the
compatibility of RetryOutputParser with existing chains. This changes
adds some code to illustrate the workflow of using RetryOutputParser
with the user chain.
Changes:
1. Changed RetryWithErrorOutputParser with RetryOutputParser, as the
markdown text says so.
2. Added code at the last of the notebook to define a chain which passes
the LLM completions to the retry parser, which can be customised for
user needs.
**Issue:**
Since RetryOutputParser/RetryWithErrorOutputParser does not implement
the parse function it cannot be used with LLMChain directly like
[this](https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser#prompttemplate-llm-outputparser).
This also raised various issues #15133#12175#11719 still open, instead
of adding new features/code changes its best to explain the "how to
integrate LLMChain with retry parsers" clearly with an example in the
corresponding notebook.
Inspired from:
https://github.com/langchain-ai/langchain/issues/15133#issuecomment-1868972580
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add missing async similarity_distance_threshold handling in
RedisVectorStoreRetriever
- **Description:** added method `_aget_relevant_documents` to
`RedisVectorStoreRetriever` that overrides parent method to add support
of `similarity_distance_threshold` in async mode (as for sync mode)
- **Issue:** #16099
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** This is a template for creating shopping assistant
chat bots
- **Issue:** Example for creating a shopping assistant with OpenAI Tools
Agent
- **Dependencies:** Ionic
https://github.com/ioniccommerce/ionic_langchain
- **Twitter handle:** @ioniccommerce
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
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whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
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Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
**Description** : This PR updates the documentation for installing
llama-cpp-python on Windows.
- Updates install command to support pyproject.toml
- Makes CPU/GPU install instructions clearer
- Adds reinstall with GPU support command
**Issue**: Existing
[documentation](https://python.langchain.com/docs/integrations/llms/llamacpp#compiling-and-installing)
lists the following commands for installing llama-cpp-python
```
python setup.py clean
python setup.py install
````
The current version of the repo does not include a `setup.py` and uses a
`pyproject.toml` instead.
This can be replaced with
```
python -m pip install -e .
```
As explained in
https://github.com/abetlen/llama-cpp-python/issues/965#issuecomment-1837268339
**Dependencies**: None
**Twitter handle**: None
---------
Co-authored-by: blacksmithop <angstycoder101@gmaii.com>
- **Description:** The current pubmed tool documentation is referencing
the path to langchain core not the path to the tool in community. The
old tool redirects anyways, but for efficiency of using the more direct
path, just adding this documentation so it references the new path
- **Issue:** doesn't fix an issue
- **Dependencies:** no dependencies
- **Twitter handle:** rooftopzen
* Description: Fixed schema discrepancy in **from_texts** function for
weaviate vectorstore which created a redundant property "key" inside a
class.
* Issue: Fixed: https://github.com/langchain-ai/langchain/issues/16692
* Twitter handle: @pashvamehta1
- **Description:** Syntax correction according to langchain version
update in 'Retry Parser' tutorial example,
- **Issue:** #16698
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Adds Wikidata support to langchain. Can read out
documents from Wikidata.
- **Issue:** N/A
- **Dependencies:** Adds implicit dependencies for
`wikibase-rest-api-client` (for turning items into docs) and
`mediawikiapi` (for hitting the search endpoint)
- **Twitter handle:** @derenrich
You can see an example of this tool used in a chain
[here](https://nbviewer.org/urls/d.erenrich.net/upload/Wikidata_Langchain.ipynb)
or
[here](https://nbviewer.org/urls/d.erenrich.net/upload/Wikidata_Lars_Kai_Hansen.ipynb)
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2. an example notebook showing its use. It lives in
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URL : https://python.langchain.com/docs/use_cases/extraction
Desc:
<b> While the following statement executes successfully, it throws an
error which is described below when we use the imported packages</b>
```py
from pydantic import BaseModel, Field, validator
```
Code:
```python
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import (
PromptTemplate,
)
from langchain_openai import OpenAI
from pydantic import BaseModel, Field, validator
# Define your desired data structure.
class Joke(BaseModel):
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator("setup")
def question_ends_with_question_mark(cls, field):
if field[-1] != "?":
raise ValueError("Badly formed question!")
return field
```
Error:
```md
PydanticUserError: The `field` and `config` parameters are not available
in Pydantic V2, please use the `info` parameter instead.
For further information visit
https://errors.pydantic.dev/2.5/u/validator-field-config-info
```
Solution:
Instead of doing:
```py
from pydantic import BaseModel, Field, validator
```
We should do:
```py
from langchain_core.pydantic_v1 import BaseModel, Field, validator
```
Thanks.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This update ensures that the user-defined embedding
function specified during vector store creation is applied during
queries. Previously, even if a custom embedding function was defined at
the time of store creation, Bagel DB would default to using the standard
embedding function during query execution. This pull request addresses
this issue by consistently using the user-defined embedding function for
queries if one has been specified earlier.
- **Description:** This change allows the `_fetch` method in the
`WebBaseLoader` class to utilize cookies from an existing
`requests.Session`. It ensures that when the `fetch` method is used, any
cookies in the provided session are included in the request. This
enhancement maintains compatibility with existing functionality while
extending the utility of the `fetch` method for scenarios where cookie
persistence is necessary.
- **Issue:** Not applicable (new feature),
- **Dependencies:** Requires `aiohttp` and `requests` libraries (no new
dependencies introduced),
- **Twitter handle:** N/A
Co-authored-by: Joao Almeida <joao.almeida@mercedes-benz.io>
