**Description:**
I traced the kwargs starting at `.invoke()` and it was not clear where
they go. it was clarified to two layers down. so I changed it to make it
more documented for the next person.
**Issue:**
No related issue.
**Dependencies:**
No dependency changes.
**Twitter handle:**
Nah. We're good.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
* New `reasoning` (bool) param to support toggling [Ollama
thinking](https://ollama.com/blog/thinking) (#31573, #31700). If
`reasoning=True`, Ollama's `thinking` content will be placed in the
model responses' `additional_kwargs.reasoning_content`.
* Supported by:
* ChatOllama (class level, invocation level TODO)
* OllamaLLM (TODO)
* Added tests to ensure streaming tool calls is successful (#29129)
* Refactored tests that relied on `extract_reasoning()`
* Myriad docs additions and consistency/typo fixes
* Improved type safety in some spots
Closes#29129
Addresses #31573 and #31700
Supersedes #31701
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: 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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
Integrate Bandit for security analysis, suppress warnings for specific issues, and address potential vulnerabilities such as hardcoded passwords and SQL injection risks. Adjust documentation and formatting for clarity.
* Ensure access to local model during `ChatOllama` instantiation
(#27720). This adds a new param `validate_model_on_init` (default:
`true`)
* Catch a few more errors from the Ollama client to assist users
## Summary
- Removes the `xslt_path` parameter from HTMLSectionSplitter to
eliminate XXE attack vector
- Hardens XML/HTML parsers with secure configurations to prevent XXE
attacks
- Adds comprehensive security tests to ensure the vulnerability is fixed
## Context
This PR addresses a critical XXE vulnerability discovered in the
HTMLSectionSplitter component. The vulnerability allowed attackers to:
- Read sensitive local files (SSH keys, passwords, configuration files)
- Perform Server-Side Request Forgery (SSRF) attacks
- Exfiltrate data to attacker-controlled servers
## Changes Made
1. **Removed `xslt_path` parameter** - This eliminates the primary
attack vector where users could supply malicious XSLT files
2. **Hardened XML parsers** - Added security configurations to prevent
XXE attacks even with the default XSLT:
- `no_network=True` - Blocks network access
- `resolve_entities=False` - Prevents entity expansion -
`load_dtd=False` - Disables DTD processing -
`XSLTAccessControl.DENY_ALL` - Blocks all file/network I/O in XSLT
transformations
3. **Added security tests** - New test file `test_html_security.py` with
comprehensive tests for various XXE attack vectors
4. **Updated existing tests** - Modified tests that were using the
removed `xslt_path` parameter
## Test Plan
- [x] All existing tests pass
- [x] New security tests verify XXE attacks are blocked
- [x] Code passes linting and formatting checks
- [x] Tested with both old and new versions of lxml
Twitter handle: @_colemurray
Recommend using context manager for FileCallbackHandler to avoid opening
too many file descriptors
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
- There was some ambiguous wording that has been updated to hopefully
clarify the functionality of `reasoning_format` in ChatGroq.
- Added support for `reasoning_effort`
- Added links to see models capable of `reasoning_format` and
`reasoning_effort`
- Other minor nits
- docs: for the Ollama notebooks, improve the specificity of some links,
add `homebrew` install info, update some wording
- tests: reduce number of local models needed to run in half from 4 → 2
(shedding 8gb of required installs)
- bump deps (non-breaking) in anticipation of upcoming "thinking" PR
Add additional hashing options to the indexing API, warn on SHA-1
Requires:
- Bumping langchain-core version
- bumping min langchain-core in langchain
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
`Runnable`'s `Input` is contravariant so we need to enumerate all
possible inputs and it's not possible to put them in a `Union`.
Also, it's better to only require a runnable that
accepts`list[BaseMessage]` instead of a broader `Sequence[BaseMessage]`
as internally the runnable is only called with a list.
**Description:**
Previously, when transitioning from a deeper Markdown header (e.g., ###)
to a shallower one (e.g., ##), the
ExperimentalMarkdownSyntaxTextSplitter retained the deeper header in the
metadata.
This commit updates the `_resolve_header_stack` method to remove headers
at the same or deeper levels before appending the current header. As a
result, each chunk now reflects only the active header context.
Fixes unexpected metadata leakage across sections in nested Markdown
documents.
Additionally, test cases have been updated to:
- Validate correct header resolution and metadata assignment.
- Cover edge cases with nested headers and horizontal rules.
**Issue:**
Fixes [#31596](https://github.com/langchain-ai/langchain/issues/31596)
**Dependencies:**
None
**Twitter handle:** -> [_RaghuKapur](https://twitter.com/_RaghuKapur)
**LinkedIn:** ->
[https://www.linkedin.com/in/raghukapur/](https://www.linkedin.com/in/raghukapur/)
## Description
<!-- What does this pull request accomplish? -->
- When parsing MistralAI chunk dicts to Langchain to `AIMessageChunk`
schemas via the `_convert_chunk_to_message_chunk` utility function, the
`finish_reason` was not being included in `response_metadata` as it is
for other providers.
- This PR adds a one-liner fix to include the finish reason.
- fixes: https://github.com/langchain-ai/langchain/issues/31666
* Simplified Pydantic handling since Pydantic v1 is not supported
anymore.
* Replace use of deprecated v1 methods by corresponding v2 methods.
* Remove use of other deprecated methods.
* Activate mypy errors on deprecated methods use.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
This PR fixes an `IndexError` that occurs when `LLMListwiseRerank` is
called with an empty list of documents.
Earlier, the code assumed the presence of at least one document and
attempted to construct the context string based on `len(documents) - 1`,
which raises an error when documents is an empty list.
The fix works with gpt-4o-mini if I make the list empty, but fails
occasionally with gpt-3.5-turbo. In case of empty list, setting the
string to "empty list" seems to have the expected response.
**Issue:** #31192
Description:
This pull request corrects minor spelling mistakes in the comments
within the `chat_models.py` file of the MistralAI partner integration.
Specifically, it fixes the spelling of "equivalent" and "compatibility"
in two separate comments. These changes improve code readability and
maintain professional documentation standards. No functional code
changes are included.
`uv lock --upgrade-package langsmith
`
Original issue: The lock file (uv.lock) was constraining
langsmith>=0.1.125,<0.4, preventing LangSmith 0.4.1 installation. Even
though the pyproject.toml wasn't restricting langchain core.
Issue:
https://langchain.slack.com/archives/C050X0VTN56/p1750107176007629
### Description
Add keep_separator arg to HTMLSemanticPreservingSplitter and pass value
to instance of RecursiveCharacterTextSplitter used under the hood.
### Issue
Documents returned by `HTMLSemanticPreservingSplitter.split_text(text)`
are defaulted to use separators at beginning of page_content. [See third
and fourth document in example output from how-to
guide](https://python.langchain.com/docs/how_to/split_html/#using-htmlsemanticpreservingsplitter):
```
[Document(metadata={'Header 1': 'Main Title'}, page_content='This is an introductory paragraph with some basic content.'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='This section introduces the topic'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content='. Below is a list: First item Second item Third item with bold text and a link Subsection 1.1: Details This subsection provides additional details'),
Document(metadata={'Header 2': 'Section 1: Introduction'}, page_content=". Here's a table: Header 1 Header 2 Header 3 Row 1, Cell 1 Row 1, Cell 2 Row 1, Cell 3 Row 2, Cell 1 Row 2, Cell 2 Row 2, Cell 3"),
Document(metadata={'Header 2': 'Section 2: Media Content'}, page_content='This section contains an image and a video:  '),
Document(metadata={'Header 2': 'Section 3: Code Example'}, page_content='This section contains a code block: <code:html> <div> <p>This is a paragraph inside a div.</p> </div> </code>'),
Document(metadata={'Header 2': 'Conclusion'}, page_content='This is the conclusion of the document.')]
```
### Dependencies
None
@ttrumper3
Thank you for contributing to LangChain!
[x] PR title: langchain_ollama: support custom headers for Ollama
partner APIs
Where "package" is whichever of langchain, core, etc. is being modified.
Use "docs: ..." for purely docs changes, "infra: ..." for CI changes.
Example: "core: add foobar LLM"
[x] PR message:
**Description: This PR adds support for passing custom HTTP headers to
Ollama models when used as a LangChain integration. This is especially
useful for enterprise users or partners who need to send authentication
tokens, API keys, or custom tracking headers when querying secured
Ollama servers.
Issue: N/A (new enhancement)
**Dependencies: No external dependencies introduced.
Twitter handle: @arunkumar_offl
[x] Add tests and docs: If you're adding a new integration, please
include
1.Added a unit test in test_chat_models.py to validate headers are
passed correctly.
2. Added an example notebook:
docs/docs/integrations/llms/ollama_custom_headers.ipynb showing how to
use custom headers.
[x] Lint and test: Ran make format, make lint, and make test to ensure
the code is clean and passing all checks.
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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
This MR is only for the docs. Added integration with Nebius AI Studio to
docs. The integration package is available at
[https://github.com/nebius/langchain-nebius](https://github.com/nebius/langchain-nebius).
---------
Co-authored-by: Akim Tsvigun <aktsvigun@nebius.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Remove proxy imports to langchain_experimental.
Previously, these imports would work if a user manually installed
langchain_experimental. However, we want to drop support even for that
as langchain_experimental is generally not recommended to be run in
production.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
OpenAI changed their API to require the `partial_images` parameter when
using image generation + streaming.
As described in https://github.com/langchain-ai/langchain/pull/31424, we
are ignoring partial images. Here, we accept the `partial_images`
parameter (as required by OpenAI), but emit a warning and continue to
ignore partial images.
**Description:**
`langchain_huggingface` has a very large installation size of around 600
MB (on a Mac with Python 3.11). This is due to its dependency on
`sentence-transformers`, which in turn depends on `torch`, which is 320
MB all by itself. Similarly, the depedency on `transformers` adds
another set of heavy dependencies. With those dependencies removed, the
installation of `langchain_huggingface` only takes up ~26 MB. This is
only 5 % of the full installation!
These libraries are not necessary to use `langchain_huggingface`'s API
wrapper classes, only for local inferences/embeddings. All import
statements for those two libraries already have import guards in place
(try/catch with a helpful "please install x" message).
This PR therefore moves those two libraries to an optional dependency
group `full`. So a `pip install langchain_huggingface` will only install
the lightweight version, and a `pip install
"langchain_huggingface[full]"` will install all dependencies.
I know this may break existing code, because `sentence-transformers` and
`transformers` are now no longer installed by default. Given that users
will see helpful error messages when that happens, and the major impact
of this small change, I hope that you will still consider this PR.
**Dependencies:** No new dependencies, but new optional grouping.
- **Description:**
- In _infer_arg_descriptions, the annotations dictionary contains string
representations of types instead of actual typing objects. This causes
_is_annotated_type to fail, preventing the correct description from
being generated.
- This is a simple fix using the get_type_hints method, which resolves
the annotations properly and is supported across all Python versions.
- **Issue:** #31051
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
https://github.com/langchain-ai/langchain/pull/31286 included an update
to the return type for `BaseChatModel.(a)stream`, from
`Iterator[BaseMessageChunk]` to `Iterator[BaseMessage]`.
This change is correct, because when streaming is disabled, the stream
methods return an iterator of `BaseMessage`, and the inheritance is such
that an `BaseMessage` is not a `BaseMessageChunk` (but the reverse is
true).
However, LangChain includes a pattern throughout its docs of [summing
BaseMessageChunks](https://python.langchain.com/docs/how_to/streaming/#llms-and-chat-models)
to accumulate a chat model stream. This pattern is implemented in tests
for most integration packages and appears in application code. So
https://github.com/langchain-ai/langchain/pull/31286 introduces mypy
errors throughout the ecosystem (or maybe more accurately, it reveals
that this pattern does not account for use of the `.stream` method when
streaming is disabled).
Here we revert just the change to the stream return type to unblock
things. A fix for this should address docs + integration packages (or if
we elect to just force people to update code, be explicit about that).
Does not support partial images during generation at the moment. Before
doing that I'd like to figure out how to specify the aggregation logic
without requiring changes in core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Llama-3.1 started failing consistently with
> groq.BadRequestError: Error code: 400 - ***'error': ***'message':
"Failed to call a function. Please adjust your prompt. See
'failed_generation' for more details.", 'type': 'invalid_request_error',
'code': 'tool_use_failed', 'failed_generation':
'<function=brave_search>***"query": "Hello!"***</function>'***
Release core 0.3.63
Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add image generation tool to the list of well known tools. This is needed for changes in the ChatOpenAI client.
TODO: Some of this logic needs to be moved from core directly into the client as changes in core should not be required to add a new tool to the openai chat client.
