- [ *] **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.
langchain -> langchain langchain-huggingface
Updated the installation command from:
%pip install --upgrade --quiet langchain sentence_transformers to: %pip
install --upgrade --quiet langchain-huggingface sentence_transformers
This resolves an import error in the notebook when using from
langchain_huggingface.embeddings import HuggingFaceEmbeddings.
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
The tokens I get are:
```
['', '\n\n', 'The', ' sun', ' was', ' setting', ' over', ' the', ' horizon', ',', ' casting', '']
```
so possibly an extra empty token is included in the output.
lmk @efriis if we should look into this further.
- [ ] **PR title**:[langchain_community.llms.xinference]: Rewrite
_stream() method and support stream() method in xinference.py
- [ ] **PR message**: Rewrite the _stream method so that the
chain.stream() can be used to return data streams.
chain = prompt | llm
chain.stream(input=user_input)
- [ ] **tests**:
from langchain_community.llms import Xinference
from langchain.prompts import PromptTemplate
llm = Xinference(
server_url="http://0.0.0.0:9997", # replace your xinference server url
model_uid={model_uid} # replace model_uid with the model UID return from
launching the model
stream = True
)
prompt = PromptTemplate(input=['country'], template="Q: where can we
visit in the capital of {country}? A:")
chain = prompt | llm
chain.stream(input={'country': 'France'})
Add tools to interact with Dappier APIs with an example notebook.
For `DappierRealTimeSearchTool`, the tool can be invoked with:
```python
from langchain_dappier import DappierRealTimeSearchTool
tool = DappierRealTimeSearchTool()
tool.invoke({"query": "What happened at the last wimbledon"})
```
```
At the last Wimbledon in 2024, Carlos Alcaraz won the title by defeating Novak Djokovic. This victory marked Alcaraz's fourth Grand Slam title at just 21 years old! 🎉🏆🎾
```
For DappierAIRecommendationTool the tool can be invoked with:
```python
from langchain_dappier import DappierAIRecommendationTool
tool = DappierAIRecommendationTool(
data_model_id="dm_01j0pb465keqmatq9k83dthx34",
similarity_top_k=3,
ref="sportsnaut.com",
num_articles_ref=2,
search_algorithm="most_recent",
)
```
```
[{"author": "Matt Weaver", "image_url": "https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34...", "pubdate": "Fri, 17 Jan 2025 08:04:03 +0000", "source_url": "https://sportsnaut.com/chili-bowl-thursday-bell-column/", "summary": "The article highlights the thrilling unpredictability... ", "title": "Thursday proves why every lap of Chili Bowl..."},
{"author": "Matt Higgins", "image_url": "https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34...", "pubdate": "Fri, 17 Jan 2025 02:48:42 +0000", "source_url": "https://sportsnaut.com/new-york-mets-news-pete-alonso...", "summary": "The New York Mets are likely parting ways with star...", "title": "MLB insiders reveal New York Mets’ last-ditch..."},
{"author": "Jim Cerny", "image_url": "https://images.dappier.com/dm_01j0pb465keqmatq9k83dthx34...", "pubdate": "Fri, 17 Jan 2025 05:10:39 +0000", "source_url": "https://www.foreverblueshirts.com/new-york-rangers-news...", "summary": "The New York Rangers achieved a thrilling 5-3 comeback... ", "title": "Rangers score 3 times in 3rd period for stirring 5-3..."}]
```
The integration package can be found over here -
https://github.com/DappierAI/langchain-dappier
Expanded the Amazon Neptune documentation with new sections detailing
usage of chat message history with the
`create_neptune_opencypher_qa_chain` and
`create_neptune_sparql_qa_chain` functions.
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.
### Description
- Since there is no cost per 1k input tokens for a fine-tuned cached
version of `gpt-4o-mini-2024-07-18` is not available when using the
`OpenAICallbackHandler`, it raises an error when trying to make calls
with such model.
- To add the price in the `MODEL_COST_PER_1K_TOKENS` dictionary
cc. @efriis
# Description
## Summary
This PR adds support for handling multi-labeled page numbers in the
**PyPDFLoader**. Some PDFs use complex page numbering systems where the
actual content may begin after multiple introductory pages. The
page_label field helps accurately reflect the document’s page structure,
making it easier to handle such cases during document parsing.
## Motivation
This feature improves document parsing accuracy by allowing users to
access the actual page labels instead of relying only on the physical
page numbers. This is particularly useful for documents where the first
few pages have roman numerals or other non-standard page labels.
## Use Case
This feature is especially useful for **Retrieval-Augmented Generation**
(RAG) systems where users may reference page numbers when asking
questions. Some PDFs have both labeled page numbers (like roman numerals
for introductory sections) and index-based page numbers.
For example, a user might ask:
"What is mentioned on page 5?"
The system can now check both:
• **Index-based page number** (page)
• **Labeled page number** (page_label)
This dual-check helps improve retrieval accuracy. Additionally, the
results can be validated with an **agent or tool** to ensure the
retrieved pages match the user’s query contextually.
## Code Changes
- Added a page_label field to the metadata of the Document class in
**PyPDFLoader**.
- Implemented support for retrieving page_label from the
pdf_reader.page_labels.
- Created a test case (test_pypdf_loader_with_multi_label_page_numbers)
with a sample PDF containing multi-labeled pages
(geotopo-komprimiert.pdf) [[Source of
pdf](https://github.com/py-pdf/sample-files/blob/main/009-pdflatex-geotopo/GeoTopo-komprimiert.pdf)].
- Updated existing tests to ensure compatibility and verify page_label
extraction.
## Tests Added
- Added a new test case for a PDF with multi-labeled pages.
- Verified both page and page_label metadata fields are correctly
extracted.
## Screenshots
<img width="549" alt="image"
src="https://github.com/user-attachments/assets/65db9f5c-032e-4592-926f-824777c28f33"
/>
Title: community: add Financial Modeling Prep (FMP) API integration
Description: Adding LangChain integration for Financial Modeling Prep
(FMP) API to enable semantic search and structured tool creation for
financial data endpoints. This integration provides semantic endpoint
search using vector stores and automatic tool creation with proper
typing and error handling. Users can discover relevant financial
endpoints using natural language queries and get properly typed
LangChain tools for discovered endpoints.
Issue: N/A
Dependencies:
fmp-data>=0.3.1
langchain-core>=0.1.0
faiss-cpu
tiktoken
Twitter handle: @mehdizarem
Unit tests and example notebook have been added:
Tests are in tests/integration_tests/est_tools.py and
tests/unit_tests/test_tools.py
Example notebook is in docs/tools.ipynb
All format, lint and test checks pass:
pytest
mypy .
Dependencies are imported within functions and not added to
pyproject.toml. The changes are backwards compatible and only affect the
community package.
---------
Co-authored-by: mehdizare <mehdizare@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [ ] **PR title**: [langchain_community.llms.xinference]: fix error in
xinference.py
- [ ] **PR message**:
- The old code raised an ValidationError:
pydantic_core._pydantic_core.ValidationError: 1 validation error for
Xinference when import Xinference from xinference.py. This issue has
been resolved by adjusting it's type and default value.
File "/media/vdc/python/lib/python3.10/site-packages/pydantic/main.py",
line 212, in __init__
validated_self = self.__pydantic_validator__.validate_python(data,
self_instance=self)
pydantic_core._pydantic_core.ValidationError: 1 validation error for
Xinference
client
Field required [type=missing, input_value={'server_url':
'http://10...t4', 'model_kwargs': {}}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.9/v/missing
- [ ] **tests**:
from langchain_community.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", # replace your xinference server url
model_uid={model_uid} # replace model_uid with the model UID return from
launching the model
)
- [x] **PR title**: "docs: Fix typo in documentation"
- [x] **PR message**:
- **Description:** Fixed a typo in the documentation, changing "An
vectorstore" to "A vector store" for grammatical accuracy.
- **Issue:** N/A (no issue filed for this typo fix)
- **Dependencies:** None
- **Twitter handle:** N/A
- [x] **Add tests and docs**: This is a minor documentation fix that
doesn't require additional tests or example notebooks.
- [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/
- [langchain_community.utilities.SQLDatabase] **[fix] Convert table
names to list for compatibility in SQLDatabase**:
- The issue #29227 is being fixed here
- The "package" modified is community
- The issue lied in this block of code:
44b41b699c/libs/community/langchain_community/utilities/sql_database.py (L72-L77)
- [langchain_community.utilities.SQLDatabase] **[fix] Convert table
names to list for compatibility in SQLDatabase**:
- **Description:** When the SQLDatabase is initialized, it runs a code
`self._inspector.get_table_names(schema=schema)` which expects an output
of list. However, with some connectors (such as snowflake) the data type
returned could be another iterable. This results in a type error when
concatenating the table_names to view_names. I have added explicit type
casting to prevent this.
- **Issue:** The issue #29227 is being fixed here
- **Dependencies:** None
- **Twitter handle:** @BaqarAbbas2001
## Additional Information
When the following method is called for a Snowflake database:
44b41b699c/libs/community/langchain_community/utilities/sql_database.py (L75)
Snowflake under the hood calls:
```python
from snowflake.sqlalchemy.snowdialect import SnowflakeDialect
SnowflakeDialect.get_table_names
```
This method returns a `dict_keys()` object which is incompatible to
concatenate with a list and results in a `TypeError`
### Relevant Library Versions
- **snowflake-sqlalchemy**: 1.7.2
- **snowflake-connector-python**: 3.12.4
- **sqlalchemy**: 2.0.20
- **langchain_community**: 0.3.14
