- **Description:** Adding Structured Support for ChatPerplexity
- **Issue:** #29357
- This is implemented as per the Perplexity official docs:
https://docs.perplexity.ai/guides/structured-outputs
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:** Updated init_chat_model to support Granite models
deployed on IBM WatsonX
**Dependencies:**
[langchain-ibm](https://github.com/langchain-ai/langchain-ibm)
Tagging @baskaryan @efriis for review when you get a chance.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
community: langchain_community/vectorstore/oraclevs.py
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Refactored code to allow a connection or a connection
pool.
- **Issue:** Normally an idel connection is terminated by the server
side listener at timeout. A user thus has to re-instantiate the vector
store. The timeout in case of connection is not configurable. The
solution is to use a connection pool where a user can specify a user
defined timeout and the connections are managed by the pool.
- **Dependencies:** None
- **Twitter handle:**
- [ ] **Add tests and docs**: This is not a new integration. A user can
pass either a connection or a connection pool. The determination of what
is passed is made at run time. Everything should work as before.
- [ ] **Lint and test**: Already done.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
1. Make `_convert_chunk_to_generation_chunk` an instance method on
BaseChatOpenAI
2. Override on ChatDeepSeek to add `"reasoning_content"` to message
additional_kwargs.
Resolves https://github.com/langchain-ai/langchain/issues/29513
**Description:**
According to the [wikidata
documentation](https://www.wikidata.org/wiki/Wikidata_talk:REST_API),
Wikibase REST API version 1 (stable) is released from November 11, 2024.
Their guide is to use the new v1 API and, it just requires replacing v0
in the routes with v1 in almost all cases.
So I replaced WIKIDATA_REST_API_URL from v0 to v1 for stable usage.
Co-authored-by: ccurme <chester.curme@gmail.com>
**issue**
In Langchain, the original content is generally stored under the `text`
key. However, the `PineconeHybridSearchRetriever` searches the `context`
field in the metadata and cannot change this key. To address this, I
have modified the code to allow changing the key to something other than
context.
In my opinion, following Langchain's conventions, the `text` key seems
more appropriate than `context`. However, since I wasn't sure about the
author's intent, I have left the default value as `context`.
- Description: Adding getattr methods and set default value 500 to
cls.bulk_size, it can prevent the error below:
Error: type object 'OpenSearchVectorSearch' has no attribute 'bulk_size'
- Issue: https://github.com/langchain-ai/langchain/issues/29071
Description:
This PR fixes handling of null action_input in
[langchain.agents.output_parser]. Previously, passing null to
action_input could cause OutputParserException with unclear error
message which cause LLM don't know how to modify the action. The changes
include:
Added null-check validation before processing action_input
Implemented proper fallback behavior with default values
Maintained backward compatibility with existing implementations
Error Examples:
```
{
"action":"some action",
"action_input":null
}
```
Issue:
None
Dependencies:
None
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once. This specific part focuses on updating the
PyPDFium2 parser.
For more details, see
https://github.com/langchain-ai/langchain/pull/28970.
Same as https://github.com/langchain-ai/langchain/pull/29043 for the
langchain package.
**Dependencies:**
- blockbuster (test)
**Twitter handle:** cbornet_
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Add tests for respecting max_concurrency and
implement it for abatch_as_completed so that test passes
- **Issue:** #29425
- **Dependencies:** none
- **Twitter handle:** keenanpepper
Description:
The change allows you to use the overloaded `+` operator correctly when
`+`ing two BaseMessageChunk subclasses. Without this you *must*
instantiate a subclass for it to work.
Which feels... wrong. Base classes should be decoupled from sub classes
and should have in no way a dependency on them.
Issue:
You can't `+` a BaseMessageChunk with a BaseMessageChunk
e.g. this will explode
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import BaseMessageChunk
chunk1 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
# this will throw
new_chunk = chunk1 + chunk2
```
In case anyone ran into this issue themselves, it's probably best to use
the AIMessageChunk:
a la
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import AIMessageChunk
chunk1 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
# No explosion!
new_chunk = chunk1 + chunk2
```
Dependencies:
None!
Twitter handle:
`aaron_vogler`
Keeping these for later if need be:
```
baskaryan
efriis
eyurtsev
ccurme
vbarda
hwchase17
baskaryan
efriis
```
Co-authored-by: Erick Friis <erick@langchain.dev>
- This pull request includes various changes to add a `user_agent`
parameter to Azure OpenAI, Azure Search and Whisper in the Community and
Partner packages. This helps in identifying the source of API requests
so we can better track usage and help support the community better. I
will also be adding the user_agent to the new `langchain-azure` repo as
well.
- No issue connected or updated dependencies.
- Utilises existing tests and docs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
ONNX and OpenVINO models are available by specifying the `backend`
argument (the model is loaded using `optimum`
https://github.com/huggingface/optimum)
```python
from langchain_huggingface import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
model_name=model_id,
model_kwargs={"backend": "onnx"},
)
```
With this PR we also enable the IPEX backend
```python
from langchain_huggingface import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
model_name=model_id,
model_kwargs={"backend": "ipex"},
)
```
**Description**
Currently, when parsing a partial JSON, if a string ends with the escape
character, the whole key/value is removed. For example:
```
>>> from langchain_core.utils.json import parse_partial_json
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>>
>>> parse_partial_json(my_str)
{'foo': 'bar'}
```
My expectation (and with this fix) would be for `parse_partial_json()`
to return:
```
>>> from langchain_core.utils.json import parse_partial_json
>>>
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>> parse_partial_json(my_str)
{'foo': 'bar', 'baz': 'qux'}
```
Notes:
1. It could be argued that current behavior is still desired.
2. I have experienced this issue when the streaming output from an LLM
and the chunk happens to end with `\\`
3. I haven't included tests. Will do if change is accepted.
4. This is specially troublesome when this function is used by
187131c55c/libs/core/langchain_core/output_parsers/transform.py (L111)
since what happens is that, for example, if the received sequence of
chunks are: `{"foo": "b` , `ar\\` :
Then, the result of calling `self.parse_result()` is:
```
{"foo": "b"}
```
and the second time:
```
{}
```
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Before sending a completion chunk at the end of an
OpenAI stream, removing the tool_calls as those have already been sent
as chunks.
- **Issue:** -
- **Dependencies:** -
- **Twitter handle:** -
@ccurme as mentioned in another PR
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Added `similarity_search_with_score_by_vector()` function to the
`QdrantVectorStore` class.
It is required when we want to query multiple time with the same
embeddings. It was present in the now deprecated original `Qdrant`
vectorstore implementation, but was absent from the new one. It is also
implemented in a number of others `VectorStore` implementations
I have added tests for this new function
Note that I also argued in this discussion that it should be part of the
general `VectorStore`
https://github.com/langchain-ai/langchain/discussions/29638
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
I made a change to how was implemented the support for GPU in
`FastEmbedEmbeddings` to be more consistent with the existing
implementation `langchain-qdrant` sparse embeddings implementation
It is directly enabling to provide the list of ONNX execution providers:
https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py#L15
It is a bit less clear to a user that just wants to enable GPU, but
gives more capabilities to work with other execution providers that are
not the `CUDAExecutionProvider`, and is more future proof
Sorry for the disturbance @ccurme
> Nice to see you just moved to `uv`! It is so much nicer to run
format/lint/test! No need to manually rerun the `poetry install` with
all required extras now
These are set in Github workflows, but forgot to add them to most
makefiles for convenience when developing locally.
`uv run` will automatically sync the lock file. Because many of our
development dependencies are local installs, it will pick up version
changes and update the lock file. Passing `--frozen` or setting this
environment variable disables the behavior.
Motivation: dedicated structured output features are becoming more
common, such that integrations can support structured output without
supporting tool calling.
Here we make two changes:
1. Update the `has_structured_output` method to default to True if a
model supports tool calling (in addition to defaulting to True if
`with_structured_output` is overridden).
2. Update structured output tests to engage if `has_structured_output`
is True.
Deep Lake recently released version 4, which introduces significant
architectural changes, including a new on-disk storage format, enhanced
indexing mechanisms, and improved concurrency. However, LangChain's
vector store integration currently does not support Deep Lake v4 due to
breaking API changes.
Previously, the installation command was:
`pip install deeplake[enterprise]`
This installs the latest available version, which now defaults to Deep
Lake v4. Since LangChain's vector store integration is still dependent
on v3, this can lead to compatibility issues when using Deep Lake as a
vector database within LangChain.
To ensure compatibility, the installation command has been updated to:
`pip install deeplake[enterprise]<4.0.0`
This constraint ensures that pip installs the latest available version
of Deep Lake within the v3 series while avoiding the incompatible v4
update.
- **Description:** add a `gpu: bool = False` field to the
`FastEmbedEmbeddings` class which enables to use GPU (through ONNX CUDA
provider) when generating embeddings with any fastembed model. It just
requires the user to install a different dependency and we use a
different provider when instantiating `fastembed.TextEmbedding`
- **Issue:** when generating embeddings for a really large amount of
documents this drastically increase performance (honestly that is a must
have in some situations, you can't just use CPU it is way too slow)
- **Dependencies:** no direct change to dependencies, but internally the
users will need to install `fastembed-gpu` instead of `fastembed`, I
made all the changes to the init function to properly let the user know
which dependency they should install depending on if they enabled `gpu`
or not
cf. fastembed docs about GPU for more details:
https://qdrant.github.io/fastembed/examples/FastEmbed_GPU/
I did not added test because it would require access to a GPU in the
testing environment
### PR Title:
**community: add latest OpenAI models pricing**
### Description:
This PR updates the OpenAI model cost calculation mapping by adding the
latest OpenAI models, **o1 (non-preview)** and **o3-mini**, based on the
pricing listed on the [OpenAI pricing
page](https://platform.openai.com/docs/pricing).
### Changes:
- Added pricing for `o1`, `o1-2024-12-17`, `o1-cached`, and
`o1-2024-12-17-cached` for input tokens.
- Added pricing for `o1-completion` and `o1-2024-12-17-completion` for
output tokens.
- Added pricing for `o3-mini`, `o3-mini-2025-01-31`, `o3-mini-cached`,
and `o3-mini-2025-01-31-cached` for input tokens.
- Added pricing for `o3-mini-completion` and
`o3-mini-2025-01-31-completion` for output tokens.
### Issue:
N/A
### Dependencies:
None
### Testing & Validation:
- No functional changes outside of updating the cost mapping.
- No tests were added or modified.
**Description:**
The response from `tool.invoke()` is always a ToolMessage, with content
and artifact fields, not a tuple.
The tuple is converted to a ToolMessage here
b6ae7ca91d/libs/core/langchain_core/tools/base.py (L726)
**Issue:**
Currently `ToolsIntegrationTests` requires `invoke()` to return a tuple
and so standard tests fail for "content_and_artifact" tools. This fixes
that to check the returned ToolMessage.
This PR also adds a test that now passes.
Description: Fixes PreFilter value handling in Azure Cosmos DB NoSQL
vectorstore. The current implementation fails to handle numeric values
in filter conditions, causing an undefined value variable error. This PR
adds support for numeric, boolean, and NULL values while maintaining the
existing string and list handling.
Changes:
Added handling for numeric types (int/float)
Added boolean value support
Added NULL value handling
Added type validation for unsupported values
Fixed scope of value variable initialization
Issue:
Fixes#29610
Implementation Notes:
No changes to public API
Backwards compatible
Maintains consistent behavior with existing MongoDB-style filtering
Preserves SQL injection prevention through proper value handling
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once. This specific part focuses on updating the XXX
parser.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Failing with:
> ValueError: Provider page not found for databricks-langchain. Please
add one at docs/integrations/providers/databricks-langchain.{mdx,ipynb}
**PR title**: "community: Option to pass auth_file_location for
oci_generative_ai"
**Description:** Option to pass auth_file_location, to overwrite config
file default location "~/.oci/config" where profile name configs
present. This is not fixing any issues. Just added optional parameter
called "auth_file_location", which internally supported by any OCI
client including GenerativeAiInferenceClient.
- **Description:** Add to check pad_token_id and eos_token_id of model
config. It seems that this is the same bug as the HuggingFace TGI bug.
It's same bug as #29434
- **Issue:** #29431
- **Dependencies:** none
- **Twitter handle:** tell14
Example code is followings:
```python
from langchain_huggingface.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="meta-llama/Llama-3.2-3B-Instruct",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
from langchain_core.prompts import PromptTemplate
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
chain = prompt | hf
question = "What is electroencephalography?"
print(chain.invoke({"question": question}))
```
## Description:
This PR addresses issue #29429 by fixing the _wrap_query method in
langchain_community/graphs/age_graph.py. The method now correctly
handles Cypher queries with UNION and EXCEPT operators, ensuring that
the fields in the SQL query are ordered as they appear in the Cypher
query. Additionally, the method now properly handles cases where RETURN
* is not supported.
### Issue: #29429
### Dependencies: None
### Add tests and docs:
Added unit tests in tests/unit_tests/graphs/test_age_graph.py to
validate the changes.
No new integrations were added, so no example notebook is necessary.
Lint and test:
Ran make format, make lint, and make test to ensure code quality and
functionality.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR uses the [blockbuster](https://github.com/cbornet/blockbuster)
library in langchain-core to detect blocking calls made in the asyncio
event loop during unit tests.
Avoiding blocking calls is hard as these can be deeply buried in the
code or made in 3rd party libraries.
Blockbuster makes it easier to detect them by raising an exception when
a call is made to a known blocking function (eg: `time.sleep`).
Adding blockbuster allowed to find a blocking call in
`aconfig_with_context` (it ends up calling `get_function_nonlocals`
which loads function code).
**Dependencies:**
- blockbuster (test)
**Twitter handle:** cbornet_
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once.
This specific part focuses on updating the PyPDF parser.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
*Description:**
Updates the YahooFinanceNewsTool to handle the current yfinance news
data structure. The tool was failing with a KeyError due to changes in
the yfinance API's response format. This PR updates the code to
correctly extract news URLs from the new structure.
**Issue:** #29495
**Dependencies:**
No new dependencies required. Works with existing yfinance package.
The changes maintain backwards compatibility while fixing the KeyError
that users were experiencing.
The modified code properly handles the new data structure where:
- News type is now at `content.contentType`
- News URL is now at `content.canonicalUrl.url`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request addresses an issue with import statements in the
langchain_core/retrievers.py file. The following changes have been made:
Corrected the import for Document from langchain_core.documents.base.
Corrected the import for BaseRetriever from langchain_core.retrievers.
These changes ensure that the SimpleRetriever class can correctly
reference the Document and BaseRetriever classes, improving code
reliability and maintainability.
---------
Co-authored-by: Matheus Torquato <mtorquat@jaguarlandrover.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description
This PR adds support for MongoDB-style $in operator filtering in the
Supabase vectorstore implementation. Currently, filtering with $in
operators returns no results, even when matching documents exist. This
change properly translates MongoDB-style filters to PostgreSQL syntax,
enabling efficient multi-document filtering.
Changes
Modified similarity_search_by_vector_with_relevance_scores to handle
MongoDB-style $in operators
Added automatic conversion of $in filters to PostgreSQL IN clauses
Preserved original vector type handling and numpy array conversion
Maintained compatibility with existing postgrest filters
Added support for the same filtering in
similarity_search_by_vector_returning_embeddings
Issue
Closes#27932
Implementation Notes
No changes to public API or function signatures
Backwards compatible - behavior unchanged for non-$in filters
More efficient than multiple individual queries for multi-ID searches
Preserves all existing functionality including numpy array conversion
for vector types
Dependencies
None
Additional Notes
The implementation handles proper SQL escaping for filter values
Maintains consistent behavior with other vectorstore implementations
that support MongoDB-style operators
Future extensions could support additional MongoDB-style operators ($gt,
$lt, etc.)
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
allow any credential type in AzureAIDocumentInteligence, not only
`api_key`.
This allows to use any of the credentials types integrated with AD.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: Fix TypeError in AzureSearch similarity_search_with_score
by removing search_type from kwargs before passing to underlying
requests.
This resolves issue #29407 where search_type was being incorrectly
passed through to Session.request().
Issue: #29407
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: 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.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
- **Description:** Add to check pad_token_id and eos_token_id of model
config. It seems that this is the same bug as the HuggingFace TGI bug.
In addition, the source code of
libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
also requires similar changes.
- **Issue:** #29431
- **Dependencies:** none
- **Twitter handle:** tell14
TRY004 ("use TypeError rather than ValueError") existing errors are
marked as ignore to preserve backward compatibility.
LMK if you prefer to fix some of them.
Co-authored-by: Erick Friis <erick@langchain.dev>
We currently return string (and therefore no content blocks / citations)
if the response is of the form
```
[
{"text": "a claim", "citations": [...]},
]
```
There are other cases where we do return citations as-is:
```
[
{"text": "a claim", "citations": [...]},
{"text": "some other text"},
{"text": "another claim", "citations": [...]},
]
```
Here we update to return content blocks including citations in the first
case as well.
`RunnableLambda`'s `__repr__` may do costly OS operation by calling
`get_lambda_source`.
So it's better to cache it.
See #29043
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request removes the now-unused html_chunks_with_headers.xslt
file from the codebase. In a previous update ([PR
#27678](https://github.com/langchain-ai/langchain/pull/27678)), the
HTMLHeaderTextSplitter class was refactored to utilize BeautifulSoup
instead of lxml and XSLT for HTML processing. As a result, the
html_chunks_with_headers.xslt file is no longer necessary and can be
safely deleted to maintain code cleanliness and reduce potential
confusion.
Issue: N/A
Dependencies: N/A
- **Description:** the `delete` function of
AzureCosmosDBNoSqlVectorSearch is using
`self._container.delete_item(document_id)` which miss a mandatory
parameter `partition_key`
We use the class function `delete_document_by_id` to provide a default
`partition_key`
- **Issue:** #29372
- **Dependencies:** None
- **Twitter handle:** None
Co-authored-by: Loris Alexandre <loris.alexandre@boursorama.fr>
This pr, expands the gitlab url so it can also be set in a constructor,
instead of only through env variables.
This allows to do something like this.
```
# Create the GitLab API wrapper
gitlab_api = GitLabAPIWrapper(
gitlab_url=self.gitlab_url,
gitlab_personal_access_token=self.gitlab_personal_access_token,
gitlab_repository=self.gitlab_repository,
gitlab_branch=self.gitlab_branch,
gitlab_base_branch=self.gitlab_base_branch,
)
```
Where before you could not set the url in the constructor.
Co-authored-by: Tim Mallezie <tim.mallezie@dropsolid.com>
Why not Ollama ?
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…ore.py
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
Added docstring linting in the vectorstore.py file relating to issue
#25154
- [x] **Add tests and docs**: 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.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Siddhant Jain <sjain35@buffalo.edu>
Co-authored-by: Erick Friis <erick@langchain.dev>
Description: Added pipeshift integration. This integrates pipeshift LLM
and ChatModels APIs with langchain
Dependencies: none
Unit Tests & Integration tests are added
Documentation is added as well
This PR is w.r.t
[#27390](https://github.com/langchain-ai/langchain/pull/27390) and as
per request, a freshly minted `langchain-pipeshift` package is uploaded
to PYPI. Only changes to the docs & packages.yml are made in langchain
master branch
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- [ *] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Fix for pedantic model validator for GoogleApiHandler
- **Issue:** the issue #29165
- [ *] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified.
---------
Signed-off-by: Bhav Sardana <sardana.bhav@gmail.com>
- **Description:** The ValueError raised on certain structured-outputs
parsing errors, in langchain openai community integration, was missing a
f-string modifier and so didn't produce useful outputs. This is a
2-line, 2-character change.
- **Issue:** None open that this fixes
- **Dependencies:** Nothing changed
- **Twitter handle:** None
- [X] **Add tests and docs**: There's nothing to add for.
- [-] **Lint and test**: Happy to run this if you deem it necessary.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: 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.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
This pull request updates the `HTMLHeaderTextSplitter` by replacing the
`split_text_from_file` method's implementation. The original method used
`lxml` and XSLT for processing HTML files, which caused
`lxml.etree.xsltapplyerror maxhead` when handling large HTML documents
due to limitations in the XSLT processor. Fixes#13149
By switching to BeautifulSoup (`bs4`), we achieve:
- **Improved Performance and Reliability:** BeautifulSoup efficiently
processes large HTML files without the errors associated with `lxml` and
XSLT.
- **Simplified Dependencies:** Removes the dependency on `lxml` and
external XSLT files, relying instead on the widely used `beautifulsoup4`
library.
- **Maintained Functionality:** The new method replicates the original
behavior, ensuring compatibility with existing code and preserving the
extraction of content and metadata.
**Issue:**
This change addresses issues related to processing large HTML files with
the existing `HTMLHeaderTextSplitter` implementation. It resolves
problems where users encounter lxml.etree.xsltapplyerror maxhead due to
large HTML documents.
**Dependencies:**
- **BeautifulSoup (`beautifulsoup4`):** The `beautifulsoup4` library is
now used for parsing HTML content.
- Installation: `pip install beautifulsoup4`
**Code Changes:**
Updated the `split_text_from_file` method in `HTMLHeaderTextSplitter` as
follows:
```python
def split_text_from_file(self, file: Any) -> List[Document]:
"""Split HTML file using BeautifulSoup.
Args:
file: HTML file path or file-like object.
Returns:
List of Document objects with page_content and metadata.
"""
from bs4 import BeautifulSoup
from langchain.docstore.document import Document
import bs4
# Read the HTML content from the file or file-like object
if isinstance(file, str):
with open(file, 'r', encoding='utf-8') as f:
html_content = f.read()
else:
# Assuming file is a file-like object
html_content = file.read()
# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')
# Extract the header tags and their corresponding metadata keys
headers_to_split_on = [tag[0] for tag in self.headers_to_split_on]
header_mapping = dict(self.headers_to_split_on)
documents = []
# Find the body of the document
body = soup.body if soup.body else soup
# Find all header tags in the order they appear
all_headers = body.find_all(headers_to_split_on)
# If there's content before the first header, collect it
first_header = all_headers[0] if all_headers else None
if first_header:
pre_header_content = ''
for elem in first_header.find_all_previous():
if isinstance(elem, bs4.Tag):
text = elem.get_text(separator=' ', strip=True)
if text:
pre_header_content = text + ' ' + pre_header_content
if pre_header_content.strip():
documents.append(Document(
page_content=pre_header_content.strip(),
metadata={} # No metadata since there's no header
))
else:
# If no headers are found, return the whole content
full_text = body.get_text(separator=' ', strip=True)
if full_text.strip():
documents.append(Document(
page_content=full_text.strip(),
metadata={}
))
return documents
# Process each header and its associated content
for header in all_headers:
current_metadata = {}
header_name = header.name
header_text = header.get_text(separator=' ', strip=True)
current_metadata[header_mapping[header_name]] = header_text
# Collect all sibling elements until the next header of the same or higher level
content_elements = []
for sibling in header.find_next_siblings():
if sibling.name in headers_to_split_on:
# Stop at the next header
break
if isinstance(sibling, bs4.Tag):
content_elements.append(sibling)
# Get the text content of the collected elements
current_content = ''
for elem in content_elements:
text = elem.get_text(separator=' ', strip=True)
if text:
current_content += text + ' '
# Create a Document if there is content
if current_content.strip():
documents.append(Document(
page_content=current_content.strip(),
metadata=current_metadata.copy()
))
else:
# If there's no content, but we have metadata, still create a Document
documents.append(Document(
page_content='',
metadata=current_metadata.copy()
))
return documents
```
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
* Adds BlobParsers for images. These implementations can take an image
and produce one or more documents per image. This interface can be used
for exposing OCR capabilities.
* Update PyMuPDFParser and Loader to standardize metadata, handle
images, improve table extraction etc.
- **Twitter handle:** pprados
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once.
This specific part focuses to prepare the update of all parsers.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- [feat] **Added backwards compatibility for OllamaEmbeddings
initialization (migration from `langchain_community.embeddings` to
`langchain_ollama.embeddings`**: "langchain_ollama"
- **Description:** Given that `OllamaEmbeddings` from
`langchain_community.embeddings` is deprecated, code is being shifted to
``langchain_ollama.embeddings`. However, this does not offer backward
compatibility of initializing the parameters and `OllamaEmbeddings`
object.
- **Issue:** #29294
- **Dependencies:** None
- **Twitter handle:** @BaqarAbbas2001
## Additional Information
Previously, `OllamaEmbeddings` from `langchain_community.embeddings`
used to support the following options:
e9abe583b2/libs/community/langchain_community/embeddings/ollama.py (L125-L139)
However, in the new package `from langchain_ollama import
OllamaEmbeddings`, there is no method to set these options. I have added
these parameters to resolve this issue.
This issue was also discussed in
https://github.com/langchain-ai/langchain/discussions/29113
## Description
- Responding to `NCP API Key` changes.
- To fix `ChatClovaX` `astream` function to raise `SSEError` when an
error event occurs.
- To add `token length` and `ai_filter` to ChatClovaX's
`response_metadata`.
- To update document for apply NCP API Key changes.
cc. @efriis @vbarda
### Description:
This PR introduces Google-style docstring linting for the
ModelLaboratory class in libs/langchain/langchain/model_laboratory.py.
It also updates the pyproject.toml file to comply with the latest Ruff
configuration standards (deprecating top-level lint settings in favor of
lint).
### Changes include:
- [x] Added detailed Google-style docstrings to all methods in
ModelLaboratory.
- [x] Updated pyproject.toml to move select and pydocstyle settings
under the [tool.ruff.lint] section.
- [x] Ensured all files pass Ruff linting.
Issue:
Closes#25154
### Dependencies:
No additional dependencies are required for this change.
### Checklist
- [x] Files passes ruff linting.
- [x] Docstrings conform to the Google-style convention.
- [x] pyproject.toml updated to avoid deprecation warnings.
- [x] My PR is ready to review, please review.
- **Description:** Changed the Base Default Model and Base URL to
correct versions. Plus added a more explicit exception if user provides
an invalid API Key
- **Issue:** #29278
