This pull request includes a change to the `TavilySearchResults` class
in the `tool.py` file, which updates the code block format in the
documentation.
Documentation update:
*
[`libs/community/langchain_community/tools/tavily_search/tool.py`](diffhunk://#diff-e3b6a980979268b639c6a86e9b182756b0f7c7e9e5605e613bc0a72ea6aa5301L54-R59):
Changed the code block format from Python to JSON in the example
provided in the docstring.Thank you for contributing to LangChain!
## **Description:**
When using the Tavily retriever with include_raw_content=True, the
retriever occasionally fails with a Pydantic ValidationError because
raw_content can be None.
The Document model in langchain_core/documents/base.py requires
page_content to be a non-None value, but the Tavily API sometimes
returns None for raw_content.
This PR fixes the issue by ensuring that even when raw_content is None,
an empty string is used instead:
```python
page_content=result.get("content", "")
if not self.include_raw_content
else (result.get("raw_content") or ""),
This pull request includes updates to the
`libs/community/langchain_community/callbacks/bedrock_anthropic_callback.py`
file to add a new model version to the list of supported models.
Updates to supported models:
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.003` for 1000 input tokens.
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.015` for 1000 output tokens.
AWS Bedrock pricing reference : https://aws.amazon.com/bedrock/pricing
- [x] **PR title**:
- [x] **PR message**:
- Added a new section for how to set up and use Milvus with Docker, and
added an example of how to instantiate Milvus for hybrid retrieval
- Fixed the documentation setup to run `make lint` and `make format`
- [x] **Add tests and docs**: If you're adding a new integration, please
include
N/A
- [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: Mark Perfect <mark.anthony.perfect1@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## PyMuPDF4LLM integration to LangChain for PDF content extraction in
Markdown format
### Description
[PyMuPDF4LLM](https://github.com/pymupdf/RAG) makes it easier to extract
PDF content in Markdown format, needed for LLM & RAG applications.
(License: GNU Affero General Public License v3.0)
[langchain-pymupdf4llm](https://github.com/lakinduboteju/langchain-pymupdf4llm)
integrates PyMuPDF4LLM to LangChain as a Document Loader.
(License: MIT License)
This pull request introduces the integration of
[PyMuPDF4LLM](https://pymupdf.readthedocs.io/en/latest/pymupdf4llm) into
the LangChain project as an integration package:
[`langchain-pymupdf4llm`](https://github.com/lakinduboteju/langchain-pymupdf4llm).
The most important changes include adding new Jupyter notebooks to
document the integration and updating the package configuration file to
include the new package.
### Documentation:
* `docs/docs/integrations/providers/pymupdf4llm.ipynb`: Added a new
Jupyter notebook to document the integration of `PyMuPDF4LLM` with
LangChain, including installation instructions and class imports.
* `docs/docs/integrations/document_loaders/pymupdf4llm.ipynb`: Added a
new Jupyter notebook to document the usage of `langchain-pymupdf4llm` as
a LangChain integration package in detail.
### Package registration:
* `libs/packages.yml`: Updated the package configuration file to include
the `langchain-pymupdf4llm` package.
### Additional information
* Related to: https://github.com/langchain-ai/langchain/pull/29848
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Same changes as #26593 but for FileCallbackHandler
- **Issue:** Fixes#29941
- **Dependencies:** None
- **Twitter handle:** None
- [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/
See https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
Some fixes done for TC001,TC002 and TC003 but these rules are excluded
since they don't play well with Pydantic.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue**: This trigger can only be used by the first table created.
Cannot create additional triggers for other tables.
**fixed**: Update the trigger name so that it can be used for new
tables.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Tavily search results returned from API include useful information like
title, score and (optionally) raw_content that is missed in wrapper
although it's documented there properly. Add this data to the result
structure.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Instead of Github it was mentioned that Gitlab which causing confusion
while refering the documentation
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>
Resolves https://github.com/langchain-ai/langchain/issues/29951
Was able to reproduce the issue with Anthropic installing from pydantic
`main` and correct it with the fix recommended in the issue.
Thanks very much @Viicos for finding the bug and the detailed writeup!
Resolves https://github.com/langchain-ai/langchain/issues/29003,
https://github.com/langchain-ai/langchain/issues/27264
Related: https://github.com/langchain-ai/langchain-redis/issues/52
```python
from langchain.chat_models import init_chat_model
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from pydantic import BaseModel
cache = SQLiteCache()
set_llm_cache(cache)
class Temperature(BaseModel):
value: int
city: str
llm = init_chat_model("openai:gpt-4o-mini")
structured_llm = llm.with_structured_output(Temperature)
```
```python
# 681 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
```python
# 6.98 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
Some o-series models will raise a 400 error for `"role": "system"`
(`o1-mini` and `o1-preview` will raise, `o1` and `o3-mini` will not).
Here we update `ChatOpenAI` to update the role to `"developer"` for all
model names matching `^o\d`.
We only make this change on the ChatOpenAI class (not BaseChatOpenAI).
For Context please check #29626
The Deepseek is using langchain_openai. The error happens that it show
`json decode error`.
I added a handler for this to give a more sensible error message which
is DeepSeek API returned empty/invalid json.
Reproducing the issue is a bit challenging as it is inconsistent,
sometimes DeepSeek returns valid data and in other times it returns
invalid data which triggers the JSON Decode Error.
This PR is an exception handling, but not an ultimate fix for the issue.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** As commented on the commit
[41b6a86](41b6a86bbe)
it introduced a bug for when we do an embedding request and the model
returns a non-nested list. Typically it's the case for model
**_nomic-embed-text_**.
- I added the unit test, and ran `make format`, `make lint` and `make
test` from the `community` package.
- No new dependency.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [x] **PR title**: docs: (community) update ChatLiteLLM
- [x] **PR message**:
- **Description:** updated description of model_kwargs parameter which
was wrongly describing for temperature.
- **Issue:** #29862
- **Dependencies:** N/A
- [x] **Add tests and docs**: N/A
- [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/
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
See https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
The interest compared to only mypy is that ruff is very fast at
detecting missing annotations.
ANN101 and ANN102 are deprecated so we ignore them
ANN401 (no Any type) ignored to be in sync with mypy config
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Which area of LangChain is being modified?
- This PR adds a new "Permit" integration to the `docs/integrations/`
folder.
- Introduces two new Tools (`LangchainJWTValidationTool` and
`LangchainPermissionsCheckTool`)
- Introduces two new Retrievers (`PermitSelfQueryRetriever` and
`PermitEnsembleRetriever`)
- Adds demo scripts in `examples/` showcasing usage.
## Description of Changes
- Created `langchain_permit/tools.py` for JWT validation and permission
checks with Permit.
- Created `langchain_permit/retrievers.py` for custom Permit-based
retrievers.
- Added documentation in `docs/integrations/providers/permit.ipynb` (or
`.mdx`) to explain setup, usage, and examples.
- Provided sample scripts in `examples/demo_scripts/` to illustrate
usage of these tools and retrievers.
- Ensured all code is linted and tested locally.
Thank you again for reviewing!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:**
Since mlx_lm 0.20, all calls to mlx crash due to deprecation of the way
parameters are passed to methods generate and generate_step.
Parameters top_p, temp, repetition_penalty and repetition_context_size
are not passed directly to those method anymore but wrapped into
"sampler" and "logit_processor".
- **Dependencies:** mlx_lm (optional)
- **Tests:**
I've had a new test to existing test file:
tests/integration_tests/llms/test_mlx_pipeline.py
---------
Co-authored-by: Jean-Philippe Dournel <jp@insightkeeper.io>
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.
