**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
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'})
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
)
- [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
## Description
This PR modifies the is_public_page function in ConfluenceLoader to
prevent exceptions caused by deleted pages during the execution of
ConfluenceLoader.process_pages().
**Example scenario:**
Consider the following usage of ConfluenceLoader:
```python
import os
from langchain_community.document_loaders import ConfluenceLoader
loader = ConfluenceLoader(
url=os.getenv("BASE_URL"),
token=os.getenv("TOKEN"),
max_pages=1000,
cql=f'type=page and lastmodified >= "2020-01-01 00:00"',
include_restricted_content=False,
)
# Raised Exception : HTTPError: Outdated version/old_draft/trashed? Cannot find content Please provide valid ContentId.
documents = loader.load()
```
If a deleted page exists within the query result, the is_public_page
function would previously raise an exception when calling
get_all_restrictions_for_content, causing the loader.load() process to
fail for all pages.
By adding a pre-check for the page's "current" status, unnecessary API
calls to get_all_restrictions_for_content for non-current pages are
avoided.
This fix ensures that such pages are skipped without affecting the rest
of the loading process.
## Issue
N/A (No specific issue number)
## Dependencies
No new dependencies are introduced with this change.
## Twitter handle
[@zenoengine](https://x.com/zenoengine)
**Description:** Added Additional parameters that could be useful for
usage of OpenAIAssistantV2Runnable.
This change is thought to allow langchain users to set parameters that
cannot be set using assistants UI
(max_completion_tokens,max_prompt_tokens,parallel_tool_calls) and
parameters that could be useful for experimenting like top_p and
temperature.
This PR originated from the need of using parallel_tool_calls in
langchain, this parameter is very important in openAI assistants because
without this parameter set to False strict mode is not respected by
OpenAI Assistants
(https://platform.openai.com/docs/guides/function-calling#parallel-function-calling).
> Note: Currently, if the model calls multiple functions in one turn
then strict mode will be disabled for those calls.
**Issue:** None
**Dependencies:** openai
Related: https://github.com/langchain-ai/langchain-aws/pull/322
The legacy `NeptuneOpenCypherQAChain` and `NeptuneSparqlQAChain` classes
are being replaced by the new LCEL format chains
`create_neptune_opencypher_qa_chain` and
`create_neptune_sparql_qa_chain`, respectively, in the `langchain_aws`
package.
This PR adds deprecation warnings to all Neptune classes and functions
that have been migrated to `langchain_aws`. All relevant documentation
has also been updated to replace `langchain_community` usage with the
new `langchain_aws` implementations.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Makes it possible to instantiate
`OpenAIModerationChain` with an `openai_api_key` argument only and no
`OPENAI_API_KEY` environment variable defined.
**Issue:** https://github.com/langchain-ai/langchain/issues/25176
**Dependencies:** `openai`
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
### Description
This PR adds docs for the
[langchain-hyperbrowser](https://pypi.org/project/langchain-hyperbrowser/)
package. It includes a document loader that uses Hyperbrowser to scrape
or crawl any urls and return formatted markdown or html content as well
as relevant metadata.
[Hyperbrowser](https://hyperbrowser.ai) is a platform for running and
scaling headless browsers. It lets you launch and manage browser
sessions at scale and provides easy to use solutions for any webscraping
needs, such as scraping a single page or crawling an entire site.
### Issue
None
### Dependencies
None
### Twitter Handle
`@hyperbrowser`
# **PR title**: "community: Fix rank-llm import paths for new 0.20.3
version"
- The "community" package is being modified to handle updated import
paths for the new `rank-llm` version.
---
## Description
This PR updates the import paths for the `rank-llm` package to account
for changes introduced in version `0.20.3`. The changes ensure
compatibility with both pre- and post-revamp versions of `rank-llm`,
specifically version `0.12.8`. Conditional imports are introduced based
on the detected version of `rank-llm` to handle different path
structures for `VicunaReranker`, `ZephyrReranker`, and `SafeOpenai`.
## Issue
RankLLMRerank usage throws an error when used GPT (not only) when
rank-llm version is > 0.12.8 - #29156
## Dependencies
This change relies on the `packaging` and `pkg_resources` libraries to
handle version checks.
## Twitter handle
@tymzar
- **Description**: In the functions `_create_run` and `_acreate_run`,
the parameters passed to the creation of
`openai.resources.beta.threads.runs` were limited.
Source:
```
def _create_run(self, input: dict) -> Any:
params = {
k: v
for k, v in input.items()
if k in ("instructions", "model", "tools", "run_metadata")
}
return self.client.beta.threads.runs.create(
input["thread_id"],
assistant_id=self.assistant_id,
**params,
)
```
- OpenAI Documentation
([createRun](https://platform.openai.com/docs/api-reference/runs/createRun))
- Full list of parameters `openai.resources.beta.threads.runs` ([source
code](https://github.com/openai/openai-python/blob/main/src/openai/resources/beta/threads/runs/runs.py#L91))
- **Issue:** Fix#17574
- [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: ccurme <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**
- **Description:** Add a missing format specifier in an an error log in
`langchain_community.document_loaders.CubeSemanticLoader`
- **Issue:** raises `TypeError: not all arguments converted during
string formatting`
- [ ] **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.
- **partner**: "Update Aiohttp for resolving vulnerability issue"
- **Description:** I have updated the upper limit of aiohttp from `3.10`
to `3.10.5` in the pyproject.toml file of langchain-pinecone. Hopefully
this will resolve#28771 . Please review this as I'm quite unsure.
---------
Co-authored-by: = <=>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Goal
Solve the following problems with `langchain-openai`:
- Structured output with `o1` [breaks out of the
box](https://langchain.slack.com/archives/C050X0VTN56/p1735232400232099).
- `with_structured_output` by default does not use OpenAI’s [structured
output
feature](https://platform.openai.com/docs/guides/structured-outputs).
- We override API defaults for temperature and other parameters.
## Breaking changes:
- Default method for structured output is changing to OpenAI’s dedicated
[structured output
feature](https://platform.openai.com/docs/guides/structured-outputs).
For schemas specified via TypedDict or JSON schema, strict schema
validation is disabled by default but can be enabled by specifying
`strict=True`.
- To recover previous default, pass `method="function_calling"` into
`with_structured_output`.
- Models that don’t support `method="json_schema"` (e.g., `gpt-4` and
`gpt-3.5-turbo`, currently the default model for ChatOpenAI) will raise
an error unless `method` is explicitly specified.
- To recover previous default, pass `method="function_calling"` into
`with_structured_output`.
- Schemas specified via Pydantic `BaseModel` that have fields with
non-null defaults or metadata (like min/max constraints) will raise an
error.
- To recover previous default, pass `method="function_calling"` into
`with_structured_output`.
- `strict` now defaults to False for `method="json_schema"` when schemas
are specified via TypedDict or JSON schema.
- To recover previous behavior, use `with_structured_output(schema,
strict=True)`
- Schemas specified via Pydantic V1 will raise a warning (and use
`method="function_calling"`) unless `method` is explicitly specified.
- To remove the warning, pass `method="function_calling"` into
`with_structured_output`.
- Streaming with default structured output method / Pydantic schema no
longer generates intermediate streamed chunks.
- To recover previous behavior, pass `method="function_calling"` into
`with_structured_output`.
- We no longer override default temperature (was 0.7 in LangChain, now
will follow OpenAI, currently 1.0).
- To recover previous behavior, initialize `ChatOpenAI` or
`AzureChatOpenAI` with `temperature=0.7`.
- Note: conceptually there is a difference between forcing a tool call
and forcing a response format. Tool calls may have more concise
arguments vs. generating content adhering to a schema. Prompts may need
to be adjusted to recover desired behavior.
---------
Co-authored-by: Jacob Lee <jacoblee93@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adds backticks to generate_schema function in the
arango graph client
- **Issue:** We experienced an issue with the generate schema function
when talking to our arango database where these backticks were missing
- **Dependencies:** none
- **Twitter handle:** @anangelofgrace
## Description
Add `__init__` for `UnstructuredHTMLLoader` to restrict the input type
to `str` or `Path`, and transfer the `self.file_path` to `str` just like
`UnstructuredXMLLoader` does.
## Issue
Fix#29090
## Dependencies
No changes.
## Description
This PR enables label inclusion for documents loaded via CQL in the
confluence-loader.
- Updated _lazy_load to pass the include_labels parameter instead of
False in process_pages calls for documents loaded via CQL.
- Ensured that labels can now be fetched and added to the metadata for
documents queried with cql.
## Related Modification History
This PR builds on the previous functionality introduced in
[#28259](https://github.com/langchain-ai/langchain/pull/28259), which
added support for including labels with the include_labels option.
However, this functionality did not work as expected for CQL queries,
and this PR fixes that issue.
If the False handling was intentional due to another issue, please let
me know. I have verified with our Confluence instance that this change
allows labels to be correctly fetched for documents loaded via CQL.
## Issue
Fixes#29088
## Dependencies
No changes.
## Twitter Handle
[@zenoengine](https://x.com/zenoengine)
This PR updates model names in the upstage library to reflect the latest
naming conventions and removes deprecated models.
Changes:
Renamed Models:
- `solar-1-mini-chat` -> `solar-mini`
- `solar-1-mini-embedding-query` -> `embedding-query`
Removed Deprecated Models:
- `layout-analysis` (replaced to `document-parse`)
Reference:
- https://console.upstage.ai/docs/getting-started/overview
-
https://github.com/langchain-ai/langchain-upstage/releases/tag/libs%2Fupstage%2Fv0.5.0
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 Kùzu
### Description
This PR adds docs for the `langchain-kuzu` package [on
PyPI](https://pypi.org/project/langchain-kuzu/) that was recently
published, allowing Kùzu users to more easily use and work with
LangChain QA chains. The package will also make it easier for the Kùzu
team to continue supporting and updating the integration over future
releases.
### Twitter Handle
Please tag [@kuzudb](https://x.com/kuzudb) on Twitter once this PR is
merged, so LangChain users can be notified!
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
Title: langchain-pinecone: improve test structure and async handling
Description: This PR improves the test infrastructure for the
langchain-pinecone package by:
1. Implementing LangChain's standard test patterns for embeddings
2. Adding comprehensive configuration testing
3. Improving async test coverage
4. Fixing integration test issues with namespaces and async markers
The changes make the tests more robust, maintainable, and aligned with
LangChain's testing standards while ensuring proper async behavior in
the embeddings implementation.
Key improvements:
- Added standard EmbeddingsTests implementation
- Split custom configuration tests into a separate test class
- Added proper async test coverage with pytest-asyncio
- Fixed namespace handling in vector store integration tests
- Improved test organization and documentation
Dependencies: None (uses existing test dependencies)
Tests and Documentation:
- ✅ Added standard test implementation following LangChain's patterns
- ✅ Added comprehensive unit tests for configuration and async behavior
- ✅ All tests passing locally
- No documentation changes needed (internal test improvements only)
Twitter handle: N/A
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Refactoring PDF loaders step 1**: "community: Refactoring PDF
loaders to standardize approaches"
- **Description:** Declare CloudBlobLoader in __init__.py. file_path is
Union[str, PurePath] anywhere
- **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).
@eyurtsev it's the start of a PR series.
Fixes#29010
This PR updates the example for FewShotChatMessagePromptTemplate by
modifying the human input prompt to include a more descriptive and
user-friendly question format ('What is {input}?') instead of just
'{input}'. This change enhances clarity and usability in the
documentation example.
Co-authored-by: Erick Friis <erick@langchain.dev>
- [x] **PR title**: "docs: add langchain-pull-md Markdown loader"
- [x] **PR message**:
- **Description:** This PR introduces the `langchain-pull-md` package to
the LangChain community. It includes a new document loader that utilizes
the pull.md service to convert URLs into Markdown format, particularly
useful for handling web pages rendered with JavaScript frameworks like
React, Angular, or Vue.js. This loader helps in efficient and reliable
Markdown conversion directly from URLs without local rendering, reducing
server load.
- **Issue:** NA
- **Dependencies:** requests >=2.25.1
- **Twitter handle:** https://x.com/eugeneevstafev?s=21
- [x] **Add tests and docs**:
1. Added unit tests to verify URL checking and conversion
functionalities.
2. Created a comprehensive example notebook detailing the usage of the
new loader.
- [x] **Lint and test**:
- Completed local testing using `make format`, `make lint`, and `make
test` commands as per the LangChain contribution guidelines.
**Related Links:**
- [Package Repository](https://github.com/chigwell/langchain-pull-md)
- [PyPI Package](https://pypi.org/project/langchain-pull-md/)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Add option to return content and artifacts, to also be able to access
the full info of the retrieved documents.
They are returned as a list of dicts in the `artifacts` property if
parameter `response_format` is set to `"content_and_artifact"`.
Defaults to `"content"` to keep current behavior.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
(Inspired by https://github.com/langchain-ai/langchain/issues/26918)
We rely on some deprecated public functions in the hot path for tool
binding (`convert_pydantic_to_openai_function`,
`convert_python_function_to_openai_function`, and
`format_tool_to_openai_function`). My understanding is that what is
deprecated is not the functionality they implement, but use of them in
the public API -- we expect to continue to rely on them.
Here we update these functions to be private and not deprecated. We keep
the public, deprecated functions as simple wrappers that can be safely
deleted.
The `@deprecated` wrapper adds considerable latency due to its use of
the `inspect` module. This update speeds up `bind_tools` by a factor of
~100x:
Before:
After:
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "docs: fix typo"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a minor fix of typo
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
- [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/
Add a retriever to interact with Dappier APIs with an example notebook.
The retriever can be invoked with:
```python
from langchain_dappier import DappierRetriever
retriever = DappierRetriever(
data_model_id="dm_01jagy9nqaeer9hxx8z1sk1jx6",
k=5
)
retriever.invoke("latest tech news")
```
To retrieve 5 documents related to latest news in the tech sector. The
included notebook also includes deeper details about controlling filters
such as selecting a data model, number of documents to return, site
domain reference, minimum articles from the reference domain, and search
algorithm, as well as including the retriever in a chain.
The integration package can be found over here -
https://github.com/DappierAI/langchain-dappier
This commit updates the documentation and package registry for the
FalkorDB Chat Message History integration.
**Changes:**
- Added a comprehensive example notebook
falkordb_chat_message_history.ipynb demonstrating how to use FalkorDB
for session-based chat message storage.
- Added a provider notebook for FalkorDB
- Updated libs/packages.yml to register FalkorDB as an integration
package, following LangChain's new guidelines for community
integrations.
**Notes:**
- This update aligns with LangChain's process for registering new
integrations via documentation updates and package registry
modifications.
- No functional or core package changes were made in this commit.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- In this PR, I have updated the AzureML Endpoint with the latest
endpoint.
- **Description:** I have changed the existing `/chat/completions` to
`/models/chat/completions` in
libs/community/langchain_community/llms/azureml_endpoint.py
- **Issue:** #25702
---------
Co-authored-by: = <=>
**Description:**
This PR updates the codebase to reflect the deprecation of the AgentType
feature. It includes the following changes:
Documentation Update:
Added a deprecation notice to the AgentType class comment.
Provided a reference to the official LangChain migration guide for
transitioning to LangGraph agents.
Reference Link: https://python.langchain.com/docs/how_to/migrate_agent/
**Twitter handle:** @hrrrriiiishhhhh
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
To integrate ModelScope inference API endpoints for both Embeddings,
LLMs and ChatModels, install the package
`langchain-modelscope-integration` (as discussed in issue #28928 ). This
is necessary because the package name `langchain-modelscope` was already
registered by another party.
ModelScope is a premier platform designed to connect model checkpoints
with model applications. It provides the necessary infrastructure to
share open models and promote model-centric development. For more
information, visit GitHub page:
[ModelScope](https://github.com/modelscope).
- **Description:**
This PR addresses an issue with the `stop_sequences` field in the
`ChatGroq` class. Currently, the field is defined as:
```python
stop: Optional[Union[List[str], str]] = Field(None, alias="stop_sequences")
```
This causes the language server (LSP) to raise an error indicating that
the `stop_sequences` parameter must be implemented. The issue occurs
because `Field(None, alias="stop_sequences")` is different compared to
`Field(default=None, alias="stop_sequences")`.
To resolve the issue, the field is updated to:
```python
stop: Optional[Union[List[str], str]] = Field(default=None, alias="stop_sequences")
```
While this issue does not affect runtime behavior, it ensures
compatibility with LSPs and improves the development experience.
- **Issue:** N/A
- **Dependencies:** None
In the previous commit, the cached model key for this model was omitted.
When using the "gpt-4o-2024-11-20" model, the token count in the
callback appeared as 0, and the cost was recorded as 0.
We add model and cost information so that the token count and cost can
be displayed for the respective model.
- The message before modification is as follows.
```
Tokens Used: 0
Prompt Tokens: 0
Prompt Tokens Cached: 0
Completion Tokens: 0
Reasoning Tokens: 0
Successful Requests: 0
Total Cost (USD): $0.0
```
- The message after modification is as follows.
```
Tokens Used: 3783
Prompt Tokens: 3625
Prompt Tokens Cached: 2560
Completion Tokens: 158
Reasoning Tokens: 0
Successful Requests: 1
Total Cost (USD): $0.010642500000000001
```
Hi Erick. Coming back from a previous attempt, we now made a separate
package for the CrateDB adapter, called `langchain-cratedb`, as advised.
Other than registering the package within `libs/packages.yml`, this
patch includes a minimal amount of documentation to accompany the advent
of this new package. Let us know about any mistakes we made, or changes
you would like to see. Thanks, Andreas.
## About
- **Description:** Register a new database adapter package,
`langchain-cratedb`, providing traditional vector store, document
loader, and chat message history features for a start.
- **Addressed to:** @efriis, @eyurtsev
- **References:** GH-27710
- **Preview:** [Providers » More »
CrateDB](https://langchain-git-fork-crate-workbench-register-la-4bf945-langchain.vercel.app/docs/integrations/providers/cratedb/)
## Status
- **PyPI:** https://pypi.org/project/langchain-cratedb/
- **GitHub:** https://github.com/crate/langchain-cratedb
- **Documentation (CrateDB):**
https://cratedb.com/docs/guide/integrate/langchain/
- **Documentation (LangChain):** _This PR._
## Backlog?
Is this applicable for this kind of patch?
> - [ ] **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.
## Q&A
1. Notebooks that use the LangChain CrateDB adapter are currently at
[CrateDB LangChain
Examples](https://github.com/crate/cratedb-examples/tree/main/topic/machine-learning/llm-langchain),
and the documentation refers to them. Because they are derived from very
old blueprints coming from LangChain 0.0.x times, we guess they need a
refresh before adding them to `docs/docs/integrations`. Is it applicable
to merge this minimal package registration + documentation patch, which
already includes valid code snippets in `cratedb.mdx`, and add
corresponding notebooks on behalf of a subsequent patch later?
2. How would it work getting into the tabular list of _Integration
Packages_ enumerated on the [documentation entrypoint page about
Providers](https://python.langchain.com/docs/integrations/providers/)?
/cc Please also review, @ckurze, @wierdvanderhaar, @kneth,
@simonprickett, if you can find the time. Thanks!
- **Description:** `embed_documents` and `embed_query` was throwing off
the error as stated in the issue. The issue was that `Llama` client is
returning the embeddings in a nested list which is not being accounted
for in the current implementation and therefore the stated error is
being raised.
- **Issue:** #28813
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
community: optimize DataFrame document loader
**Description:**
Simplify the `lazy_load` method in the DataFrame document loader by
combining text extraction and metadata cleanup into a single operation.
This makes the code more concise while maintaining the same
functionality.
**Issue:** N/A
**Dependencies:** None
**Twitter handle:** N/A
- **Description:** The aload function, contrary to its name, is not an
asynchronous function, so it cannot work concurrently with other
asynchronous functions.
- **Issue:** #28336
- **Test: **: Done
- **Docs: **
[here](e0a95e5646/docs/docs/integrations/document_loaders/web_base.ipynb (L201))
- **Lint: ** All checks passed
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>
