**Description:** Add support for Writer chat models
**Issue:** N/A
**Dependencies:** Add `writer-sdk` to optional dependencies.
**Twitter handle:** Please tag `@samjulien` and `@Get_Writer`
**Tests and docs**
- [x] Unit test
- [x] Example notebook in `docs/docs/integrations` directory.
**Lint and test**
- [x] Run `make format`
- [x] Run `make lint`
- [x] Run `make test`
---------
Co-authored-by: Johannes <tolstoy.work@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
- Fix bug in Replicate LLM class, where it was looking for parameter
names in a place where they no longer exist in pydantic 2, resulting in
the "Field required" validation error described in the issue.
- Fix Replicate LLM integration tests to:
- Use active models on Replicate.
- Use the correct model parameter `max_new_tokens` as shown in the
[Replicate
docs](https://replicate.com/docs/guides/language-models/how-to-use#minimum-and-maximum-new-tokens).
- Use callbacks instead of deprecated callback_manager.
**Issue:** #26937
**Dependencies:** n/a
**Twitter handle:** n/a
---------
Signed-off-by: Fayvor Love <fayvor@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Reopened as a personal repo outside the organization.
## Description
- Naver HyperCLOVA X community package
- Add chat model & embeddings
- Add unit test & integration test
- Add chat model & embeddings docs
- I changed partner
package(https://github.com/langchain-ai/langchain/pull/24252) to
community package on this PR
- Could this
embeddings(https://github.com/langchain-ai/langchain/pull/21890) be
deprecated? We are trying to replace it with embedding
model(**ClovaXEmbeddings**) in this PR.
Twitter handle: None. (if needed, contact with
joonha.jeon@navercorp.com)
---
you can check our previous discussion below:
> one question on namespaces - would it make sense to have these in
.clova namespaces instead of .naver?
I would like to keep it as is, unless it is essential to unify the
package name.
(ClovaX is a branding for the model, and I plan to add other models and
components. They need to be managed as separate classes.)
> also, could you clarify the difference between ClovaEmbeddings and
ClovaXEmbeddings?
There are 3 models that are being serviced by embedding, and all are
supported in the current PR. In addition, all the functionality of CLOVA
Studio that serves actual models, such as distinguishing between test
apps and service apps, is supported. The existing PR does not support
this content because it is hard-coded.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
**Description:**
This PR updates `CassandraGraphVectorStore` to be based off
`CassandraVectorStore`, instead of using a custom CQL implementation.
This allows users using a `CassandraVectorStore` to upgrade to a
`GraphVectorStore` without having to change their database schema or
re-embed documents.
This PR also updates the documentation of the `GraphVectorStore` base
class and contains native async implementations for the standard graph
methods: `traversal_search` and `mmr_traversal_search` in
`CassandraVectorStore`.
**Issue:** No issue number.
**Dependencies:** https://github.com/langchain-ai/langchain/pull/27078
(already-merged)
**Lint and test**:
- Lint and tests all pass, including existing
`CassandraGraphVectorStore` tests.
- Also added numerous additional tests based of the tests in
`langchain-astradb` which cover many more scenarios than the existing
tests for `Cassandra` and `CassandraGraphVectorStore`
** BREAKING CHANGE**
Note that this is a breaking change for existing users of
`CassandraGraphVectorStore`. They will need to wipe their database table
and restart.
However:
- The interfaces have not changed. Just the underlying storage
mechanism.
- Any one using `langchain_community.vectorstores.Cassandra` can instead
use `langchain_community.graph_vectorstores.CassandraGraphVectorStore`
and they will gain Graph capabilities without having to re-embed their
existing documents. This is the primary goal of this PR.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR updates the integration with OCI data science model deployment
service.
- Update LLM to support streaming and async calls.
- Added chat model.
- Updated tests and docs.
- Updated `libs/community/scripts/check_pydantic.sh` since the use of
`@pre_init` is removed from existing integration.
- Updated `libs/community/extended_testing_deps.txt` as this integration
requires `langchain_openai`.
---------
Co-authored-by: MING KANG <ming.kang@oracle.com>
Co-authored-by: Dmitrii Cherkasov <dmitrii.cherkasov@oracle.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**:
this PR enable VectorStore TLS and authentication (digest, basic) with
HTTP/2 for Infinispan server.
Based on httpx.
Added docker-compose facilities for testing
Added documentation
**Dependencies:**
requires `pip install httpx[http2]` if HTTP2 is needed
**Twitter handle:**
https://twitter.com/infinispan
**Description:** this PR adds a set of methods to deal with metadata
associated to the vector store entries. These, while essential to the
Graph-related extension of the `Cassandra` vector store, are also useful
in themselves. These are (all come in their sync+async versions):
- `[a]delete_by_metadata_filter`
- `[a]replace_metadata`
- `[a]get_by_document_id`
- `[a]metadata_search`
Additionally, a `[a]similarity_search_with_embedding_id_by_vector`
method is introduced to better serve the store's internal working (esp.
related to reranking logic).
**Issue:** no issue number, but now all Document's returned bear their
`.id` consistently (as a consequence of a slight refactoring in how the
raw entries read from DB are made back into `Document` instances).
**Dependencies:** (no new deps: packaging comes through langchain-core
already; `cassio` is now required to be version 0.1.10+)
**Add tests and docs**
Added integration tests for the relevant newly-introduced methods.
(Docs will be updated in a separate PR).
**Lint and test** Lint and (updated) test all pass.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Given the current erroring behavior, every time we've moved a kwarg from
model_kwargs and made it its own field that was a breaking change.
Updating this behavior to support the old instantiations /
serializations.
Assuming build_extra_kwargs was not something that itself is being used
externally and needs to be kept backwards compatible
These allow converting linked documents (such as those used with
GraphVectorStore) to networkx for rendering and/or in-memory graph
algorithms such as community detection.
**Description**:
Adds a vector store integration with
[sqlite-vec](https://alexgarcia.xyz/sqlite-vec/), the successor to
sqlite-vss that is a single C file with no external dependencies.
Pretty straightforward, just copy-pasted the sqlite-vss integration and
made a few tweaks and added integration tests. Only question is whether
all documentation should be directed away from sqlite-vss if it is
defacto deprecated (cc @asg017).
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: philippe-oger <philippe.oger@adevinta.com>
- **Description:** Added PebbloTextLoader for loading text in
PebbloSafeLoader.
- Since PebbloSafeLoader wraps document loaders, this new loader enables
direct loading of text into Documents using PebbloSafeLoader.
- **Issue:** NA
- **Dependencies:** NA
- [x] **Tests**: Added/Updated tests
# Description
[Vector store base
class](4cdaca67dc/libs/core/langchain_core/vectorstores/base.py (L65))
currently expects `ids` to be passed in and that is what it passes along
to the AzureSearch vector store when attempting to `add_texts()`.
However AzureSearch expects `keys` to be passed in. When they are not
present, AzureSearch `add_embeddings()` makes up new uuids. This is a
problem when trying to run indexing. [Indexing code
expects](b297af5482/libs/core/langchain_core/indexing/api.py (L371))
the documents to be uploaded using provided ids. Currently AzureSearch
ignores `ids` passed from `indexing` and makes up new ones. Later when
`indexer` attempts to delete removed file, it uses the `id` it had
stored when uploading the document, however it was uploaded under
different `id`.
**Twitter handle: @martintriska1**
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template 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>
### Description:
This pull request significantly enhances the MongodbLoader class in the
LangChain community package by adding robust metadata customization and
improved field extraction capabilities. The updated class now allows
users to specify additional metadata fields through the metadata_names
parameter, enabling the extraction of both top-level and deeply nested
document attributes as metadata. This flexibility is crucial for users
who need to include detailed contextual information without altering the
database schema.
Moreover, the include_db_collection_in_metadata flag offers optional
inclusion of database and collection names in the metadata, allowing for
even greater customization depending on the user's needs.
The loader's field extraction logic has been refined to handle missing
or nested fields more gracefully. It now employs a safe access mechanism
that avoids the KeyError previously encountered when a specified nested
field was absent in a document. This update ensures that the loader can
handle diverse and complex data structures without failure, making it
more resilient and user-friendly.
### Issue:
This pull request addresses a critical issue where the MongodbLoader
class in the LangChain community package could throw a KeyError when
attempting to access nested fields that may not exist in some documents.
The previous implementation did not handle the absence of specified
nested fields gracefully, leading to runtime errors and interruptions in
data processing workflows.
This enhancement ensures robust error handling by safely accessing
nested document fields, using default values for missing data, thus
preventing KeyError and ensuring smoother operation across various data
structures in MongoDB. This improvement is crucial for users working
with diverse and complex data sets, ensuring the loader can adapt to
documents with varying structures without failing.
### Dependencies:
Requires motor for asynchronous MongoDB interaction.
### Twitter handle:
N/A
### Add tests and docs
Tests: Unit tests have been added to verify that the metadata inclusion
toggle works as expected and that the field extraction correctly handles
nested fields.
Docs: An example notebook demonstrating the use of the enhanced
MongodbLoader is included in the docs/docs/integrations directory. This
notebook includes setup instructions, example usage, and outputs.
(Here is the notebook link : [colab
link](https://colab.research.google.com/drive/1tp7nyUnzZa3dxEFF4Kc3KS7ACuNF6jzH?usp=sharing))
Lint and test
Before submitting, I ran make format, make lint, and make test as per
the contribution guidelines. All tests pass, and the code style adheres
to the LangChain standards.
```python
import unittest
from unittest.mock import patch, MagicMock
import asyncio
from langchain_community.document_loaders.mongodb import MongodbLoader
class TestMongodbLoader(unittest.TestCase):
def setUp(self):
"""Setup the MongodbLoader test environment by mocking the motor client
and database collection interactions."""
# Mocking the AsyncIOMotorClient
self.mock_client = MagicMock()
self.mock_db = MagicMock()
self.mock_collection = MagicMock()
self.mock_client.get_database.return_value = self.mock_db
self.mock_db.get_collection.return_value = self.mock_collection
# Initialize the MongodbLoader with test data
self.loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="testdb",
collection_name="testcol"
)
@patch('langchain_community.document_loaders.mongodb.AsyncIOMotorClient', return_value=MagicMock())
def test_constructor(self, mock_motor_client):
"""Test if the constructor properly initializes with the correct database and collection names."""
loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="testdb",
collection_name="testcol"
)
self.assertEqual(loader.db_name, "testdb")
self.assertEqual(loader.collection_name, "testcol")
def test_aload(self):
"""Test the aload method to ensure it correctly queries and processes documents."""
# Setup mock data and responses for the database operations
self.mock_collection.count_documents.return_value = asyncio.Future()
self.mock_collection.count_documents.return_value.set_result(1)
self.mock_collection.find.return_value = [
{"_id": "1", "content": "Test document content"}
]
# Run the aload method and check responses
loop = asyncio.get_event_loop()
results = loop.run_until_complete(self.loader.aload())
self.assertEqual(len(results), 1)
self.assertEqual(results[0].page_content, "Test document content")
def test_construct_projection(self):
"""Verify that the projection dictionary is constructed correctly based on field names."""
self.loader.field_names = ['content', 'author']
self.loader.metadata_names = ['timestamp']
expected_projection = {'content': 1, 'author': 1, 'timestamp': 1}
projection = self.loader._construct_projection()
self.assertEqual(projection, expected_projection)
if __name__ == '__main__':
unittest.main()
```
### Additional Example for Documentation
Sample Data:
```json
[
{
"_id": "1",
"title": "Artificial Intelligence in Medicine",
"content": "AI is transforming the medical industry by providing personalized medicine solutions.",
"author": {
"name": "John Doe",
"email": "john.doe@example.com"
},
"tags": ["AI", "Healthcare", "Innovation"]
},
{
"_id": "2",
"title": "Data Science in Sports",
"content": "Data science provides insights into player performance and strategic planning in sports.",
"author": {
"name": "Jane Smith",
"email": "jane.smith@example.com"
},
"tags": ["Data Science", "Sports", "Analytics"]
}
]
```
Example Code:
```python
loader = MongodbLoader(
connection_string="mongodb://localhost:27017",
db_name="example_db",
collection_name="articles",
filter_criteria={"tags": "AI"},
field_names=["title", "content"],
metadata_names=["author.name", "author.email"],
include_db_collection_in_metadata=True
)
documents = loader.load()
for doc in documents:
print("Page Content:", doc.page_content)
print("Metadata:", doc.metadata)
```
Expected Output:
```
Page Content: Artificial Intelligence in Medicine AI is transforming the medical industry by providing personalized medicine solutions.
Metadata: {'author_name': 'John Doe', 'author_email': 'john.doe@example.com', 'database': 'example_db', 'collection': 'articles'}
```
Thank you.
---
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: ccurme <chester.curme@gmail.com>
- **PR title**: "community: add Jina Search tool"
- **Description:** Added the Jina Search tool for querying the Jina
search API. This includes the implementation of the JinaSearchAPIWrapper
and the JinaSearch tool, along with a Jupyter notebook example
demonstrating its usage.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** [Twitter
handle](https://x.com/yashp3020?t=7wM0gQ7XjGciFoh9xaBtqA&s=09)
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. 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/
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Adding a new option to the CSVLoader that allows us to implicitly
specify the columns that are used for generating the Document content.
Currently these are implicitly set as "all fields not part of the
metadata_columns".
In some cases however it is useful to have a field both as a metadata
and as part of the document content.
Description:
- Add system templates and user templates in integration testing
- initialize the response id field value to request_id
- Adjust the default model to hunyuan-pro
- Remove the default values of Temperature and TopP
- Add SystemMessage
all the integration tests have passed.
1、Execute integration tests for the first time
<img width="1359" alt="71ca77a2-e9be-4af6-acdc-4d665002bd9b"
src="https://github.com/user-attachments/assets/9298dc3a-aa26-4bfa-968b-c011a4e699c9">
2、Run the integration test a second time
<img width="1501" alt="image"
src="https://github.com/user-attachments/assets/61335416-4a67-4840-bb89-090ba668e237">
Issue: None
Dependencies: None
Twitter handle: None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** [IPEX-LLM](https://github.com/intel-analytics/ipex-llm)
is a PyTorch library for running LLM on Intel CPU and GPU (e.g., local
PC with iGPU, discrete GPU such as Arc, Flex and Max) with very low
latency. This PR adds Intel GPU support to `ipex-llm` llm integration.
**Dependencies:** `ipex-llm`
**Contribution maintainer**: @ivy-lv11 @Oscilloscope98
**tests and docs**:
- Add: langchain/docs/docs/integrations/llms/ipex_llm_gpu.ipynb
- Update: langchain/docs/docs/integrations/llms/ipex_llm_gpu.ipynb
- Update: langchain/libs/community/tests/llms/test_ipex_llm.py
---------
Co-authored-by: ivy-lv11 <zhicunlv@gmail.com>
- [x] **PR title - community: add neo4j query constructor for self
query**
- [x] **PR message**
- **Description:** adding a Neo4jTranslator so that the Neo4j vector
database can use SelfQueryRetriever
- **Issue:** this issue had been raised before in #19748
- **Dependencies:** none.
- **Twitter handle:** @moyi_dang
- p.s. I have not added the query constructor in BUILTIN_TRANSLATORS in
this PR, I want to make changes to only one package at a time.
- [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: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [ ] **PR title**: community: add tests for ChatOctoAI
- [ ] **PR message**:
Description: Added unit tests for the ChatOctoAI class in the community
package to ensure proper validation and default values. These tests
verify the correct initialization of fields, the handling of missing
required parameters, and the proper setting of aliases.
Issue: N/A
Dependencies: None
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Thank you for contributing to LangChain!
community:premai[patch]: standardize init args
- updated `temperature` with Pydantic Field, updated the unit test.
- updated `max_tokens` with Pydantic Field, updated the unit test.
- updated `max_retries` with Pydantic Field, updated the unit test.
Related to #20085
---------
Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
it fixes two issues:
### YGPTs are broken #25575
```
File ....conda/lib/python3.11/site-packages/langchain_community/embeddings/yandex.py:211, in _make_request(self, texts, **kwargs)
..
--> 211 res = stub.TextEmbedding(request, metadata=self._grpc_metadata) # type: ignore[attr-defined]
AttributeError: 'YandexGPTEmbeddings' object has no attribute '_grpc_metadata'
```
My gut feeling that #23841 is the cause.
I have to drop leading underscore from `_grpc_metadata` for quickfix,
but I just don't know how to do it _pydantic_ enough.
### minor issue:
if we use `api_key`, which is not the best practice the code fails with
```
File ~/git/...../python3.11/site-packages/langchain_community/embeddings/yandex.py:119, in YandexGPTEmbeddings.validate_environment(cls, values)
...
AttributeError: 'tuple' object has no attribute 'append'
```
- Added new integration test. But it requires YGPT env available and
active account. I don't know how int tests dis\enabled in CI.
- added small unit tests with mocks. Should be fine.
---------
Co-authored-by: mikhail-khludnev <mikhail_khludnev@rntgroup.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
Support passing extra params when executing UC functions:
The params should be a dictionary with key EXECUTE_FUNCTION_ARG_NAME,
the assumption is that the function itself doesn't use such variable
name (starting and ending with double underscores), and if it does we
raise Exception.
If invalid params passing to the execute_statement, we raise Exception
as well.
- [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.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
adds an init method to ChatDeepInfra to set the model_name attribute
accordings to the argument
### Issue
currently, the model_name specified by the user during initialization of
the ChatDeepInfra class is never set. Therefore, it always chooses the
default model (meta-llama/Llama-2-70b-chat-hf, however probably since
this is deprecated it always uses meta-llama/Llama-3-70b-Instruct). We
stumbled across this issue and fixed it as proposed in this pull
request. Feel free to change the fix according to your coding guidelines
and style, this is just a proposal and we want to draw attention to this
problem.
### Dependencies
no additional dependencies required
Feel free to contact me or @timo282 and @finitearth if you have any
questions.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Added Azure Search Access Token Authentication instead of API KEY auth.
Fixes Issue: https://github.com/langchain-ai/langchain/issues/24263
Dependencies: None
Twitter: @levalencia
@baskaryan
Could you please review? First time creating a PR that fixes some code.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR introduces adjustments to ensure compatibility with the recently
released preview version of [TiDB Serverless Vector
Search](https://tidb.cloud/ai), aiming to prevent user confusion.
- TiDB Vector now supports vector indexing with cosine and l2 distance
strategies, although inner_product remains unsupported.
- Changing the distance strategy is currently not supported, so the test
cased should be adjusted.
updated stop and request_timeout so they aliased to stop_sequences, and
timeout respectively. Added test that both continue to set the same
underlying attributes.
Related to
[20085](https://github.com/langchain-ai/langchain/issues/20085)
Co-authored-by: ccurme <chester.curme@gmail.com>
Description: Send both the query and query_embedding to the Databricks
index for hybrid search.
Issue: When using hybrid search with non-Databricks managed embedding we
currently don't pass both the embedding and query_text to the index.
Hybrid search requires both of these. This change fixes this issue for
both `similarity_search` and `similarity_search_by_vector`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Refactor PebbloSafeLoader**
- Created `APIWrapper` and moved API logic into it.
- Moved helper functions to the utility file.
- Created smaller functions and methods for better readability.
- Properly read environment variables.
- Removed unused code.
**Issue:** NA
**Dependencies:** NA
**tests**: Updated
- [x] NatbotChain: move to community, deprecate langchain version.
Update to use `prompt | llm | output_parser` instead of LLMChain.
- [x] LLMMathChain: deprecate + add langgraph replacement example to API
ref
- [x] HypotheticalDocumentEmbedder (retriever): update to use `prompt |
llm | output_parser` instead of LLMChain
- [x] FlareChain: update to use `prompt | llm | output_parser` instead
of LLMChain
- [x] ConstitutionalChain: deprecate + add langgraph replacement example
to API ref
- [x] LLMChainExtractor (document compressor): update to use `prompt |
llm | output_parser` instead of LLMChain
- [x] LLMChainFilter (document compressor): update to use `prompt | llm
| output_parser` instead of LLMChain
- [x] RePhraseQueryRetriever (retriever): update to use `prompt | llm |
output_parser` instead of LLMChain
- **Description:** Standardize SparkLLM, include:
- docs, the issue #24803
- to support stream
- update api url
- model init arg names, the issue #20085
- In the in ` embedding-3 ` and later models of Zhipu AI, it is
supported to specify the dimensions parameter of Embedding. Ref:
https://bigmodel.cn/dev/api#text_embedding-3 .
- Add test case for `embedding-3` model by assigning dimensions.
This PR gets rid `root_validators(allow_reuse=True)` logic used in
EdenAI Tool in preparation for pydantic 2 upgrade.
- add another test to secret_from_env_factory
Change all usages of __fields__ with get_fields adapter merged into
langchain_core.
Code mod generated using the following grit pattern:
```
engine marzano(0.1)
language python
`$X.__fields__` => `get_fields($X)` where {
add_import(source="langchain_core.utils.pydantic", name="get_fields")
}
```
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
- **Description:** Instantiating `GPT4AllEmbeddings` with no
`gpt4all_kwargs` argument raised a `ValidationError`. Root cause: #21238
added the capability to pass `gpt4all_kwargs` through to the `GPT4All`
instance via `Embed4All`, but broke code that did not specify a
`gpt4all_kwargs` argument.
- **Issue:** #25119
- **Dependencies:** None
- **Twitter handle:** [`@metadaddy`](https://twitter.com/metadaddy)
**Description:**
In this PR, I am adding three stock market tools from
financialdatasets.ai (my API!):
- get balance sheets
- get cash flow statements
- get income statements
Twitter handle: [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- description: I remove the limitation of mandatory existence of
`QIANFAN_AK` and default model name which langchain uses cause there is
already a default model nama underlying `qianfan` SDK powering langchain
component.
---------
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,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- **Description:**
Support ChatMlflow.bind_tools method
Tested in Databricks:
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/fa28ef50-0110-4698-8eda-4faf6f0b9ef8">
- [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.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
This PR adds annotations in comunity package.
Annotations are only strictly needed in subclasses of BaseModel for
pydantic 2 compatibility.
This PR adds some unnecessary annotations, but they're not bad to have
regardless for documentation pages.
- **Title:** [PebbloSafeLoader] Implement content-size-based batching in
the classification flow(loader/doc API)
- **Description:**
- Implemented content-size-based batching in the loader/doc API, set to
100KB with no external configuration option, intentionally hard-coded to
prevent timeouts.
- Remove unused field(pb_id) from doc_metadata
- **Issue:** NA
- **Dependencies:** NA
- **Add tests and docs:** Updated
Description: The old method will be discontinued; use the official SDK
for more model options.
Issue: None
Dependencies: None
Twitter handle: None
Co-authored-by: trumanyan <trumanyan@tencent.com>
## Description
This PR:
- Fixes the validation error in `FastEmbedEmbeddings`.
- Adds support for `batch_size`, `parallel` params.
- Removes support for very old FastEmbed versions.
- Updates the FastEmbed doc with the new params.
Associated Issues:
- Resolves#24039
- Resolves #https://github.com/qdrant/fastembed/issues/296
Thank you for contributing to LangChain!
- [x] **PR title**: "community:add Yi LLM", "docs:add Yi Documentation"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** This PR adds support for the Yi model to LangChain.
- **Dependencies:**
[langchain_core,requests,contextlib,typing,logging,json,langchain_community]
- **Twitter handle:** 01.AI
- [x] **Add tests and docs**: I've added the corresponding documentation
to the relevant paths
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Description:
- This PR adds a self query retriever implementation for SAP HANA Cloud
Vector Engine. The retriever supports all operators except for contains.
- Issue: N/A
- Dependencies: no new dependencies added
**Add tests and docs:**
Added integration tests to:
libs/community/tests/unit_tests/query_constructors/test_hanavector.py
**Documentation for self query retriever:**
/docs/integrations/retrievers/self_query/hanavector_self_query.ipynb
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Expanded the chat model functionality to support tools
in the 'baichuan.py' file. Updated module imports and added tool object
handling in message conversions. Additional changes include the
implementation of tool binding and related unit tests. The alterations
offer enhanced model capabilities by enabling interaction with tool-like
objects.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
- [x] **PR title**:
community: Add OCI Generative AI tool and structured output support
- [x] **PR message**:
- **Description:** adding tool calling and structured output support for
chat models offered by OCI Generative AI services. This is an update to
our last PR 22880 with changes in
/langchain_community/chat_models/oci_generative_ai.py
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
- [x] **Add tests and docs**:
1. we have updated our unit tests
2. we have updated our documentation under
/docs/docs/integrations/chat/oci_generative_ai.ipynb
- [x] **Lint and test**: `make format`, `make lint` and `make test` we
run successfully
---------
Co-authored-by: RHARPAZ <RHARPAZ@RHARPAZ-5750.us.oracle.com>
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
**Description:**
- This PR exposes some functions in VDMS vectorstore, updates VDMS
related notebooks, updates tests, and upgrade version of VDMS (>=0.0.20)
**Issue:** N/A
**Dependencies:**
- Update vdms>=0.0.20
Fixes for Eden AI Custom tools and ChatEdenAI:
- add missing import in __init__ of chat_models
- add `args_schema` to custom tools. otherwise '__arg1' would sometimes
be passed to the `run` method
- fix IndexError when no human msg is added in ChatEdenAI
Thank you for contributing to LangChain!
**Description:**
This PR allows users of `langchain_community.llms.ollama.Ollama` to
specify the `auth` parameter, which is then forwarded to all internal
calls of `requests.request`. This works in the same way as the existing
`headers` parameters. The auth parameter enables the usage of the given
class with Ollama instances, which are secured by more complex
authentication mechanisms, that do not only rely on static headers. An
example are AWS API Gateways secured by the IAM authorizer, which
expects signatures dynamically calculated on the specific HTTP request.
**Issue:**
Integrating a remote LLM running through Ollama using
`langchain_community.llms.ollama.Ollama` only allows setting static HTTP
headers with the parameter `headers`. This does not work, if the given
instance of Ollama is secured with an authentication mechanism that
makes use of dynamically created HTTP headers which for example may
depend on the content of a given request.
**Dependencies:**
None
**Twitter handle:**
None
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Added [ScrapingAnt](https://scrapingant.com/) Web Loader integration.
ScrapingAnt is a web scraping API that allows extracting web page data
into accessible and well-formatted markdown.
Description: Added ScrapingAnt web loader for retrieving web page data
as markdown
Dependencies: scrapingant-client
Twitter: @WeRunTheWorld3
---------
Co-authored-by: Oleg Kulyk <oleg@scrapingant.com>
This PR is under WIP and adds the following functionalities:
- [X] Supports tool calling across the langchain ecosystem. (However
streaming is not supported)
- [X] Update documentation
- [ ] **Community**: "Retrievers: Product Quantization"
- [X] This PR adds Product Quantization feature to the retrievers to the
Langchain Community. PQ is one of the fastest retrieval methods if the
embeddings are rich enough in context due to the concepts of
quantization and representation through centroids
- **Description:** Adding PQ as one of the retrievers
- **Dependencies:** using the package nanopq for this PR
- **Twitter handle:** vishnunkumar_
- [X] **Add tests and docs**: If you're adding a new integration, please
include
- [X] Added unit tests for the same in the retrievers.
- [] Will add an example notebook subsequently
- [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/ -
done the same
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- This PR adds vector search filtering for Azure Cosmos DB Mongo vCore
and NoSQL.
- [ ] **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
This pull request added new document loaders to load documents of
various formats using [Dedoc](https://github.com/ispras/dedoc):
- `DedocFileLoader` (determine file types automatically and parse)
- `DedocPDFLoader` (for `PDF` and images parsing)
- `DedocAPIFileLoader` (determine file types automatically and parse
using Dedoc API without library installation)
[Dedoc](https://dedoc.readthedocs.io) is an open-source library/service
that extracts texts, tables, attached files and document structure
(e.g., titles, list items, etc.) from files of various formats. The
library is actively developed and maintained by a group of developers.
`Dedoc` supports `DOCX`, `XLSX`, `PPTX`, `EML`, `HTML`, `PDF`, images
and more.
Full list of supported formats can be found
[here](https://dedoc.readthedocs.io/en/latest/#id1).
For `PDF` documents, `Dedoc` allows to determine textual layer
correctness and split the document into paragraphs.
### Issue
This pull request extends variety of document loaders supported by
`langchain_community` allowing users to choose the most suitable option
for raw documents parsing.
### Dependencies
The PR added a new (optional) dependency `dedoc>=2.2.5` ([library
documentation](https://dedoc.readthedocs.io)) to the
`extended_testing_deps.txt`
### Twitter handle
None
### Add tests and docs
1. Test for the integration:
`libs/community/tests/integration_tests/document_loaders/test_dedoc.py`
2. Example notebook:
`docs/docs/integrations/document_loaders/dedoc.ipynb`
3. Information about the library:
`docs/docs/integrations/providers/dedoc.mdx`
### Lint and test
Done locally:
- `make format`
- `make lint`
- `make integration_tests`
- `make docs_build` (from the project root)
---------
Co-authored-by: Nasty <bogatenkova.anastasiya@mail.ru>
- **Description:** Add a DocumentTransformer for executing one or more
`LinkExtractor`s and adding the extracted links to each document.
- **Issue:** n/a
- **Depedencies:** none
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
