Thank you for contributing to LangChain!
- [x] **PR title**: langchain: add URL parameter to ChatDeepInfra class
- [x] **PR message**: add URL parameter to ChatDeepInfra class
- **Description:** This PR introduces a url parameter to the
ChatDeepInfra class in LangChain, allowing users to specify a custom
URL. Previously, the URL for the DeepInfra API was hardcoded to
"https://stage.api.deepinfra.com/v1/openai/chat/completions", which
caused issues when the staging endpoint was not functional. The _url
method was updated to return the value from the url parameter, enabling
greater flexibility and addressing the problem. out!
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Adds a helper that renders documents with the
GraphVectorStore metadata fields to Graphviz for visualization. This is
helpful for understanding and debugging.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
**PR title**: "community: fix PDF Filter Type Error"
- **Description:** fix PDF Filter Type Error"
- **Issue:** the issue #27153 it fixes,
- **Dependencies:** no
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [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: Erick Friis <erick@langchain.dev>
# Description
Implements the `atransform_documents` method for
`MarkdownifyTransformer` using the `asyncio` built-in library for
concurrency.
Note that this is mainly for API completeness when working with async
frameworks rather than for performance, since the `markdownify` function
is not I/O bound because it works with `Document` objects already in
memory.
# Issue
Fixes#27865
# Dependencies
No new dependencies added, but
[`markdownify`](https://github.com/matthewwithanm/python-markdownify) is
required since this PR updates the `markdownify` integration.
# Tests and docs
- Tests added
- I did not modify the docstrings since they already described the basic
functionality, and [the API docs also already included a
description](https://python.langchain.com/api_reference/community/document_transformers/langchain_community.document_transformers.markdownify.MarkdownifyTransformer.html#langchain_community.document_transformers.markdownify.MarkdownifyTransformer.atransform_documents).
If it would be helpful, I would be happy to update the docstrings and/or
the API docs.
# Lint and test
- [x] format
- [x] lint
- [x] test
I ran formatting with `make format`, linting with `make lint`, and
confirmed that tests pass using `make test`. Note that some unit tests
pass in CI but may fail when running `make_test`. Those unit tests are:
- `test_extract_html` (and `test_extract_html_async`)
- `test_strip_tags` (and `test_strip_tags_async`)
- `test_convert_tags` (and `test_convert_tags_async`)
The reason for the difference is that there are trailing spaces when the
tests are run in the CI checks, and no trailing spaces when run with
`make test`. I ensured that the tests pass in CI, but they may fail with
`make test` due to the addition of trailing spaces.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:** This PR introduces a `model` alias for the embedding
classes that contain the attribute `model_name`, to ensure consistency
across the codebase, as suggested by a moderator in a previous PR. The
change aligns the usage of attribute names across the project (see for
example
[here](65deeddd5d/libs/partners/groq/langchain_groq/chat_models.py (L304))).
**Issue:** This PR addresses the suggestion from the review of issue
#28269.
**Dependencies:** None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
community: add hybrid search in opensearch
# Langchain OpenSearch Hybrid Search Implementation
## Implementation of Hybrid Search:
I have taken LangChain's OpenSearch integration to the next level by
adding hybrid search capabilities. Building on the existing
OpenSearchVectorSearch class, I have implemented Hybrid Search
functionality (which combines the best of both keyword and semantic
search). This new functionality allows users to harness the power of
OpenSearch's advanced hybrid search features without leaving the
familiar LangChain ecosystem. By blending traditional text matching with
vector-based similarity, the enhanced class delivers more accurate and
contextually relevant results. It's designed to seamlessly fit into
existing LangChain workflows, making it easy for developers to upgrade
their search capabilities.
In implementing the hybrid search for OpenSearch within the LangChain
framework, I also incorporated filtering capabilities. It's important to
note that according to the OpenSearch hybrid search documentation, only
post-filtering is supported for hybrid queries. This means that the
filtering is applied after the hybrid search results are obtained,
rather than during the initial search process.
**Note:** For the implementation of hybrid search, I strictly followed
the official OpenSearch Hybrid search documentation and I took
inspiration from
https://github.com/AndreasThinks/langchain/tree/feature/opensearch_hybrid_search
Thanks Mate!
### Experiments
I conducted few experiments to verify that the hybrid search
implementation is accurate and capable of reproducing the results of
both plain keyword search and vector search.
Experiment - 1
Hybrid Search
Keyword_weight: 1, vector_weight: 0
I conducted an experiment to verify the accuracy of my hybrid search
implementation by comparing it to a plain keyword search. For this test,
I set the keyword_weight to 1 and the vector_weight to 0 in the hybrid
search, effectively giving full weightage to the keyword component. The
results from this hybrid search configuration matched those of a plain
keyword search, confirming that my implementation can accurately
reproduce keyword-only search results when needed. It's important to
note that while the results were the same, the scores differed between
the two methods. This difference is expected because the plain keyword
search in OpenSearch uses the BM25 algorithm for scoring, whereas the
hybrid search still performs both keyword and vector searches before
normalizing the scores, even when the vector component is given zero
weight. This experiment validates that my hybrid search solution
correctly handles the keyword search component and properly applies the
weighting system, demonstrating its accuracy and flexibility in
emulating different search scenarios.
Experiment - 2
Hybrid Search
keyword_weight = 0.0, vector_weight = 1.0
For experiment-2, I took the inverse approach to further validate my
hybrid search implementation. I set the keyword_weight to 0 and the
vector_weight to 1, effectively giving full weightage to the vector
search component (KNN search). I then compared these results with a pure
vector search. The outcome was consistent with my expectations: the
results from the hybrid search with these settings exactly matched those
from a standalone vector search. This confirms that my implementation
accurately reproduces vector search results when configured to do so. As
with the first experiment, I observed that while the results were
identical, the scores differed between the two methods. This difference
in scoring is expected and can be attributed to the normalization
process in hybrid search, which still considers both components even
when one is given zero weight. This experiment further validates the
accuracy and flexibility of my hybrid search solution, demonstrating its
ability to effectively emulate pure vector search when needed while
maintaining the underlying hybrid search structure.
Experiment - 3
Hybrid Search - balanced
keyword_weight = 0.5, vector_weight = 0.5
For experiment-3, I adopted a balanced approach to further evaluate the
effectiveness of my hybrid search implementation. In this test, I set
both the keyword_weight and vector_weight to 0.5, giving equal
importance to keyword-based and vector-based search components. This
configuration aims to leverage the strengths of both search methods
simultaneously. By setting both weights to 0.5, I intended to create a
scenario where the hybrid search would consider lexical matches and
semantic similarity equally. This balanced approach is often ideal for
many real-world applications, as it can capture both exact keyword
matches and contextually relevant results that might not contain the
exact search terms.
Kindly verify the notebook for the experiments conducted!
**Notebook:**
https://github.com/karthikbharadhwajKB/Langchain_OpenSearch_Hybrid_search/blob/main/Opensearch_Hybridsearch.ipynb
### Instructions to follow for Performing Hybrid Search:
**Step-1: Instantiating OpenSearchVectorSearch Class:**
```python
opensearch_vectorstore = OpenSearchVectorSearch(
index_name=os.getenv("INDEX_NAME"),
embedding_function=embedding_model,
opensearch_url=os.getenv("OPENSEARCH_URL"),
http_auth=(os.getenv("OPENSEARCH_USERNAME"),os.getenv("OPENSEARCH_PASSWORD")),
use_ssl=False,
verify_certs=False,
ssl_assert_hostname=False,
ssl_show_warn=False
)
```
**Parameters:**
1. **index_name:** The name of the OpenSearch index to use.
2. **embedding_function:** The function or model used to generate
embeddings for the documents. It's assumed that embedding_model is
defined elsewhere in the code.
3. **opensearch_url:** The URL of the OpenSearch instance.
4. **http_auth:** A tuple containing the username and password for
authentication.
5. **use_ssl:** Set to False, indicating that the connection to
OpenSearch is not using SSL/TLS encryption.
6. **verify_certs:** Set to False, which means the SSL certificates are
not being verified. This is often used in development environments but
is not recommended for production.
7. **ssl_assert_hostname:** Set to False, disabling hostname
verification in SSL certificates.
8. **ssl_show_warn:** Set to False, suppressing SSL-related warnings.
**Step-2: Configure Search Pipeline:**
To initiate hybrid search functionality, you need to configures a search
pipeline first.
**Implementation Details:**
This method configures a search pipeline in OpenSearch that:
1. Normalizes the scores from both keyword and vector searches using the
min-max technique.
2. Applies the specified weights to the normalized scores.
3. Calculates the final score using an arithmetic mean of the weighted,
normalized scores.
**Parameters:**
* **pipeline_name (str):** A unique identifier for the search pipeline.
It's recommended to use a descriptive name that indicates the weights
used for keyword and vector searches.
* **keyword_weight (float):** The weight assigned to the keyword search
component. This should be a float value between 0 and 1. In this
example, 0.3 gives 30% importance to traditional text matching.
* **vector_weight (float):** The weight assigned to the vector search
component. This should be a float value between 0 and 1. In this
example, 0.7 gives 70% importance to semantic similarity.
```python
opensearch_vectorstore.configure_search_pipelines(
pipeline_name="search_pipeline_keyword_0.3_vector_0.7",
keyword_weight=0.3,
vector_weight=0.7,
)
```
**Step-3: Performing Hybrid Search:**
After creating the search pipeline, you can perform a hybrid search
using the `similarity_search()` method (or) any methods that are
supported by `langchain`. This method combines both `keyword-based and
semantic similarity` searches on your OpenSearch index, leveraging the
strengths of both traditional information retrieval and vector embedding
techniques.
**parameters:**
* **query:** The search query string.
* **k:** The number of top results to return (in this case, 3).
* **search_type:** Set to `hybrid_search` to use both keyword and vector
search capabilities.
* **search_pipeline:** The name of the previously created search
pipeline.
```python
query = "what are the country named in our database?"
top_k = 3
pipeline_name = "search_pipeline_keyword_0.3_vector_0.7"
matched_docs = opensearch_vectorstore.similarity_search_with_score(
query=query,
k=top_k,
search_type="hybrid_search",
search_pipeline = pipeline_name
)
matched_docs
```
twitter handle: @iamkarthik98
---------
Co-authored-by: Karthik Kolluri <karthik.kolluri@eidosmedia.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
JSONparse, in _validate_metadata_func(), checks the consistency of the
_metadata_func() function. To do this, it invokes it and makes sure it
receives a dictionary in response. However, during the call, it does not
respect future calls, as shown on line 100. This generates errors if,
for example, the function is like this:
```python
def generate_metadata(json_node:Dict[str,Any],kwargs:Dict[str,Any]) -> Dict[str,Any]:
return {
"source": url,
"row": kwargs['seq_num'],
"question":json_node.get("question"),
}
loader = JSONLoader(
file_path=file_path,
content_key="answer",
jq_schema='.[]',
metadata_func=generate_metadata,
text_content=False)
```
To avoid this, the verification must comply with the specifications.
This patch does just that.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
The delete methods in the VectorStore and DocumentIndex interfaces
return a status indicating the result. Therefore, we can assume that
their implementations don't throw exceptions but instead return a result
indicating whether the delete operations have failed. The current
implementation doesn't check the returned value, so I modified it to
throw an exception when the operation fails.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
~Note that this PR is now Draft, so I didn't add change to `aindex`
function and didn't add test codes for my change.
After we have an agreement on the direction, I will add commits.~
`batch_size` is very difficult to decide because setting a large number
like >10000 will impact VectorDB and RecordManager, while setting a
small number will delete records unnecessarily, leading to redundant
work, as the `IMPORTANT` section says.
On the other hand, we can't use `full` because the loader returns just a
subset of the dataset in our use case.
I guess many people are in the same situation as us.
So, as one of the possible solutions for it, I would like to introduce a
new argument, `scoped_full_cleanup`.
This argument will be valid only when `claneup` is Full. If True, Full
cleanup deletes all documents that haven't been updated AND that are
associated with source ids that were seen during indexing. Default is
False.
This change keeps backward compatibility.
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
I reported the bug 2 weeks ago here:
https://github.com/langchain-ai/langchain/issues/28447
I believe this is a critical bug for the indexer, so I submitted a PR to
revert the change and added unit tests to prevent similar bugs from
being introduced in the future.
@eyurtsev Could you check this?
Thank you for contributing to LangChain!
- [x] **PR title**: community: add TablestoreVectorStore
- [x] **PR message**:
- **Description:** add TablestoreVectorStore
- **Dependencies:** none
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration: yes
2. an example notebook showing its use: yes
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## What are we doing in this PR
We're adding `modified_since` optional argument to `O365BaseLoader`.
When set, O365 loader will only load documents newer than
`modified_since` datetime.
## Why?
OneDrives / Sharepoints can contain large number of documents. Current
approach is to download and parse all files and let indexer to deal with
duplicates. This can be prohibitively time-consuming. Especially when
using OCR-based parser like
[zerox](fa06188834/libs/community/langchain_community/document_loaders/pdf.py (L948)).
This argument allows to skip documents that are older than known time of
indexing.
_Q: What if a file was modfied during last indexing process?
A: Users can set the `modified_since` conservatively and indexer will
still take care of duplicates._
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** The stack diagram illustration in the community
README fails to render due to an invalid branch reference. This PR
replaces the broken image link with a valid one referencing master
branch.
This PR fixes JSONLoader._get_text not converting objects to json string
correctly.
If an object is serializable and is not a dict, JSONLoader will use
python built-in str() method to convert it to string. This may cause
object converted to strings not following json standard. For example, a
list will be converted to string with single quotes, and if json.loads
try to load this string, it will cause error.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
### About
- **Description:** In the Gitlab utilities used for the Gitlab tool
there are no methods to create branches, list branches and files, as
this is already done for Github
- **Issue:** None
- **Dependencies:** None
This Pull request add the methods:
- create_branch
- list_branches_in_repo
- set_active_branch
- list_files_in_main_branch
- list_files_in_bot_branch
- list_files_from_directory
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Description:
snowflake.py
Add _stream and _stream_content methods to enable streaming
functionality
fix pydantic issues and added functionality with the overall langchain
version upgrade
added bind_tools method for agentic workflows support through langgraph
updated the _generate method to account for agentic workflows support
through langgraph
cosmetic changes to comments and if conditions
snowflake.ipynb
Added _stream example
cosmetic changes to comments
fixed lint errors
check_pydantic.sh
Decreased counter from 126 to 125 as suggested when formatting
---------
Co-authored-by: Prathamesh Nimkar <prathamesh.nimkar@snowflake.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Currently `_convert_TGI_message_to_LC_message` replaces `'` in the tool
arguments, so an argument like "It's" will be converted to `It"s` and
could cause a json parser to fail.
---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
This change modifies the token cost calculation logic to support
cross-region inference profile IDs for Anthropic Claude models. Instead
of explicitly listing all regional variants of new inference profile IDs
in the cost dictionaries, the code now extracts a base model ID from the
input model ID (or inference profile ID), making it more maintainable
and automatically supporting new regional variants.
These inference profile IDs follow the format:
`<region>.<vendor>.<model-name>` (e.g.,
`us.anthropic.claude-3-haiku-xxx`, `eu.anthropic.claude-3-sonnet-xxx`).
Cross-region inference profiles are system-defined identifiers that
enable distributing model inference requests across multiple AWS
regions. They help manage unplanned traffic bursts and enhance
resilience during peak demands without additional routing costs.
References for Amazon Bedrock's cross-region inference profiles:-
-
https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference.html
-
https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Description:
When using langchain.retrievers.parent_document_retriever.py with
vectorstore is OpenSearchVectorSearch, I found that the bulk_size param
I passed into OpenSearchVectorSearch class did not work on my
ParentDocumentRetriever.add_documents() function correctly, it will be
overwrite with int 500 the function which OpenSearchVectorSearch class
had (e.g., add_texts(), add_embeddings()...).
So I made this PR requset to fix this, thanks!
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR fixes a bug with the current implementation for Model2Vec
embeddings where `embed_documents` does not work as expected.
- **Description**: the current implementation uses `encode_as_sequence`
for encoding documents. This is incorrect, as `encode_as_sequence`
creates token embeddings and not mean embeddings. The normal `encode`
function handles both single and batched inputs and should be used
instead. The return type was also incorrect, as encode returns a NumPy
array. This PR converts the embedding to a list so that the output is
consistent with the Embeddings ABC.
- **Description:** The current version of the `delete` method assumes
that the id field will always be called `id`.
- **Issue:** n/a
- **Dependencies:** n/a
- **Twitter handle:** ugh, Twitter :D
---
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: Erick Friis <erick@langchain.dev>
- **Description:** `requests_kwargs` is not being passed to `_fetch`
which is fetching pages asynchronously. In this PR, making sure that we
are passing `requests_kwargs` to `_fetch` just like `_scrape`.
- **Issue:** #28634
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:**: In the event of a Rate Limit Error from the
MistralAI server, the response JSON raises a KeyError. To address this,
a simple retry mechanism has been implemented to handle cases where the
request limit is exceeded.
- **Issue:** #27790
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: The multimodal(tongyi) response format "message": {"role":
"assistant", "content": [{"text": "图像"}]}}]} is not compatible with
LangChain.
Dependencies: No
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**:
This PR modifies the doc_intelligence.py parser in the community package
to include all metadata returned by the Azure Doc Intelligence API in
the Document object. Previously, only the parsed content (markdown) was
retained, while other important metadata such as bounding boxes (bboxes)
for images and tables was discarded. These image bboxes are crucial for
supporting use cases like multi-modal RAG workflows when using Azure Doc
Intelligence.
The change ensures that all information returned by the Azure Doc
Intelligence API is preserved by setting the metadata attribute of the
Document object to the entire result returned by the API, rather than an
empty dictionary. This extends the parser's utility for complex use
cases without breaking existing functionality.
**Issue**:
This change does not address a specific issue number, but it resolves a
critical limitation in supporting multimodal workflows when using the
LangChain wrapper for the Azure API.
**Dependencies**:
No additional dependencies are required for this change.
---------
Co-authored-by: jmohren <johannes.mohren@aol.de>
**Description:**
- **Memgraph** no longer relies on `Neo4jGraphStore` but **implements
`GraphStore`**, just like other graph databases.
- **Memgraph** no longer relies on `GraphQAChain`, but implements
`MemgraphQAChain`, just like other graph databases.
- The refresh schema procedure has been updated to try using `SHOW
SCHEMA INFO`. The fallback uses Cypher queries (a combination of schema
and Cypher) → **LangChain integration no longer relies on MAGE
library**.
- The **schema structure** has been reformatted. Regardless of the
procedures used to get schema, schema structure is the same.
- The `add_graph_documents()` method has been implemented. It transforms
`GraphDocument` into Cypher queries and creates a graph in Memgraph. It
implements the ability to use `baseEntityLabel` to improve speed
(`baseEntityLabel` has an index on the `id` property). It also
implements the ability to include sources by creating a `MENTIONS`
relationship to the source document.
- Jupyter Notebook for Memgraph has been updated.
- **Issue:** /
- **Dependencies:** /
- **Twitter handle:** supe_katarina (DX Engineer @ Memgraph)
Closes#25606
**Description**
This PR updates the `as_retriever` method in the `AzureSearch` to ensure
that the `search_type` parameter defaults to 'similarity' when not
explicitly provided.
Previously, if the `search_type` was omitted, it did not default to any
specific value. So it was inherited from
`AzureSearchVectorStoreRetriever`, which defaults to 'hybrid'.
This change ensures that the intended default behavior aligns with the
expected usage.
**Issue**
No specific issue was found related to this change.
**Dependencies**
No new dependencies are introduced with this change.
---------
Co-authored-by: prrao87 <prrao87@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
- [x] **PR title**: "community: Kuzu - Add graph documents via
LLMGraphTransformer"
- This PR adds a new method `add_graph_documents` to use the
`GraphDocument`s extracted by `LLMGraphTransformer` and store in a Kùzu
graph backend.
- This allows users to transform unstructured text into a graph that
uses Kùzu as the graph store.
- [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: pookam90 <pookam@microsoft.com>
Co-authored-by: Pooja Kamath <60406274+Pookam90@users.noreply.github.com>
Co-authored-by: hsm207 <hsm207@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [ ] **PR title**: "core: google docstring parsing fix"
- [x] **PR message**:
- **Description:** Added a solution for invalid parsing of google
docstring such as:
Args:
net_annual_income (float): The user's net annual income (in current year
dollars).
- **Issue:** Previous code would return arg = "net_annual_income
(float)" which would cause exception in
_validate_docstring_args_against_annotations
- **Dependencies:** None
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
Co-authored-by: Erick Friis <erick@langchain.dev>
- [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"
- **Description:** I realized the invocation parameters were not being
passed into `_generate` so I added those in but then realized that the
parameters contained some old fields designed for an older openai client
which I removed. Parameters work fine now.
- **Issue:** Fixes#28229
- **Dependencies:** No new dependencies.
- **Twitter handle:** @arch_plane
- [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: Erick Friis <erick@langchain.dev>
## Description
First of all, thanks for the great framework that is LangChain!
At [Linkup](https://www.linkup.so/) we're working on an API to connect
LLMs and agents to the internet and our partner sources. We'd be super
excited to see our API integrated in LangChain! This essentially
consists in adding a LangChain retriever and tool, which is done in our
own [package](https://pypi.org/project/langchain-linkup/). Here we're
simply following the [integration
documentation](https://python.langchain.com/docs/contributing/how_to/integrations/)
and update the documentation of LangChain to mention the Linkup
integration.
We do have tests (both units & integration) in our [source
code](https://github.com/LinkupPlatform/langchain-linkup), and tried to
follow as close as possible the [integration
documentation](https://python.langchain.com/docs/contributing/how_to/integrations/)
which specifically requests to focus on documentation changes for an
integration PR, so I'm not adding tests here, even though the PR
checklist seems to suggest so. Feel free to correct me if I got this
wrong!
By the way, we would be thrilled by being mentioned in the list of
providers which have standalone packages
[here](https://langchain-git-fork-linkupplatform-cj-doc-langchain.vercel.app/docs/integrations/providers/),
is there something in particular for us to do for that? 🙂
## Twitter handle
Linkup_platform
<!--
## PR Checklist
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.
--!>
set open_browser to false to resolve "could not locate runnable browser"
error while default browser is None
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: Erick Friis <erick@langchain.dev>
# What problem are we fixing?
Currently documents loaded using `O365BaseLoader` fetch source from
`file.web_url` (where `file` is `<class 'O365.drive.File'>`). This works
well for `.pdf` documents. Unfortunately office documents (`.xlsx`,
`.docx` ...) pass their `web_url` in following format:
`https://sharepoint_address/sites/path/to/library/root/Doc.aspx?sourcedoc=%XXXXXXXX-1111-1111-XXXX-XXXXXXXXXX%7D&file=filename.xlsx&action=default&mobileredirect=true`
This obfuscates the path to the file. This PR utilizes the parrent
folder's path and file name to reconstruct the actual location of the
file. Knowing the file's location can be crucial for some RAG
applications (path to the file can carry information we don't want to
loose).
@vbarda Could you please look at this one? I'm @-mentioning you since
we've already closed some PRs together :-)
Co-authored-by: Erick Friis <erick@langchain.dev>
## **Description:**
Enable `ConfluenceLoader` to include labels with `include_labels` option
(`false` by default for backward compatibility). and the labels are set
to `metadata` in the `Document`. e.g. `{"labels": ["l1", "l2"]}`
## Notes
Confluence API supports to get labels by providing `metadata.labels` to
`expand` query parameter
All of the following functions support `expand` in the same way:
- confluence.get_page_by_id
- confluence.get_all_pages_by_label
- confluence.get_all_pages_from_space
- cql (internally using
[/api/content/search](https://developer.atlassian.com/cloud/confluence/rest/v1/api-group-content/#api-wiki-rest-api-content-search-get))
## **Issue:**
No issue related to this PR.
## **Dependencies:**
No changes.
## **Twitter handle:**
[@gymnstcs](https://x.com/gymnstcs)
- [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/
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Support for new Pinecone class PineconeVectorStore in
PebbloRetrievalQA.
- **Issue:** NA
- **Dependencies:** NA
- **Tests:** -
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Streaming response from Mistral model using Vertex AI
raises KeyError when trying to access `choices` key, that the last chunk
doesn't have. The fix is to access the key safely using `get()`.
- **Issue:** https://github.com/langchain-ai/langchain/issues/27886
- **Dependencies:**
- **Twitter handle:**
- **Description:** `kwargs` are not being passed to `run` of the
`BaseTool` which has been fixed
- **Issue:** #28114
---------
Co-authored-by: Stevan Kapicic <kapicic.ste1@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
As seen in #23188, turned on Google-style docstrings by enabling
`pydocstyle` linting in the `text-splitters` package. Each resulting
linting error was addressed differently: ignored, resolved, suppressed,
and missing docstrings were added.
Fixes one of the checklist items from #25154, similar to #25939 in
`core` package. Ran `make format`, `make lint` and `make test` from the
root of the package `text-splitters` to ensure no issues were found.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** update MODEL_COST_PER_1K_TOKENS for new gpt-4o-11-20.
- **Issue:** with latest gpt-4o-11-20, openai callback return
token_cost=0.0
- **Dependencies:** None (just simple dict fix.)
- **Twitter handle:** I Don't Use Twitter.
- (However..., I have a YouTube channel. Could you upload this there, by
any chance?
https://www.youtube.com/@%EA%B2%9C%EC%B0%BD%EB%B6%80%EA%B3%A0%EB%AC%B8AI%EC%9E%90%EB%AC%B8%EC%84%BC%EC%84%B8)
- **Description:**
- Trim functions were incorrectly deleting nodes with more than 1
outgoing/incoming edge, so an extra condition was added to check for
this directly. A unit test "test_trim_multi_edge" was written to test
this test case specifically.
- **Issue:**
- Fixes#28411
- Fixes https://github.com/langchain-ai/langgraph/issues/1676
- **Dependencies:**
- No changes were made to the dependencies
- [x] Unit tests were added to verify the changes.
- [x] Updated documentation where necessary.
- [x] Ran make format, make lint, and make test to ensure compliance
with project standards.
---------
Co-authored-by: Tasif Hussain <tasif006@gmail.com>
Hi Langchain team!
I'm the co-founder and mantainer at
[ScrapeGraphAI](https://scrapegraphai.com/).
By following the integration
[guide](https://python.langchain.com/docs/contributing/how_to/integrations/publish/)
on your site, I have created a new lib called
[langchain-scrapegraph](https://github.com/ScrapeGraphAI/langchain-scrapegraph).
With this PR I would like to integrate Scrapegraph as provider in
Langchain, adding the required documentation files.
Let me know if there are some changes to be made to be properly
integrated both in the lib and in the documentation.
Thank you 🕷️🦜
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Invalid `tool_choice` is given to `ChatLiteLLM` to
`bind_tools` due to it's parent's class default value being pass through
`with_structured_output`.
- **Issue:** #28176
**Issue:** Added support for creating indexes in the SAP HANA Vector
engine.
**Changes**:
1. Introduced a new function `create_hnsw_index` in `hanavector.py` that
enables the creation of indexes for SAP HANA Vector.
2. Added integration tests for the index creation function to ensure
functionality.
3. Updated the documentation to reflect the new index creation feature,
including examples and output from the notebook.
4. Fix the operator issue in ` _process_filter_object` function and
change the array argument to a placeholder in the similarity search SQL
statement.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- Description: Azure AI takes an issue with the safe_mode parameter
being set to False instead of None. Therefore, this PR changes the
default value of safe_mode from False to None. This results in it being
filtered out before the request is sent - avoind the extra-parameter
issue described below.
- Issue: #26029
- Dependencies: /
---------
Co-authored-by: blaufink <sebastian.brueckner@outlook.de>
Co-authored-by: Erick Friis <erick@langchain.dev>
