**Community: Unify Titan Takeoff Integrations and Adding Embedding
Support**
**Description:**
Titan Takeoff no longer reflects this either of the integrations in the
community folder. The two integrations (TitanTakeoffPro and
TitanTakeoff) where causing confusion with clients, so have moved code
into one place and created an alias for backwards compatibility. Added
Takeoff Client python package to do the bulk of the work with the
requests, this is because this package is actively updated with new
versions of Takeoff. So this integration will be far more robust and
will not degrade as badly over time.
**Issue:**
Fixes bugs in the old Titan integrations and unified the code with added
unit test converge to avoid future problems.
**Dependencies:**
Added optional dependency takeoff-client, all imports still work without
dependency including the Titan Takeoff classes but just will fail on
initialisation if not pip installed takeoff-client
**Twitter**
@MeryemArik9
Thanks all :)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Adds ThirdAI NeuralDB retriever integration. NeuralDB
is a CPU-friendly and fine-tunable text retrieval engine. We previously
added a vector store integration but we think that it will be easier for
our customers if they can also find us under under
langchain-community/retrievers.
---------
Co-authored-by: kartikTAI <129414343+kartikTAI@users.noreply.github.com>
Co-authored-by: Kartik Sarangmath <kartik@thirdai.com>
- **Description:**
This PR adds a callback handler for UpTrain. It performs evaluations in
the RAG pipeline to check the quality of retrieved documents, generated
queries and responses.
- **Dependencies:**
- The UpTrainCallbackHandler requires the uptrain package
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Fix of YandexGPT embeddings.
The current version uses a single `model_name` for queries and
documents, essentially making the `embed_documents` and `embed_query`
methods the same. Yandex has a different endpoint (`model_uri`) for
encoding documents, see
[this](https://yandex.cloud/en/docs/yandexgpt/concepts/embeddings). The
bug may impact retrievers built with `YandexGPTEmbeddings` (for instance
FAISS database as retriever) since they use both `embed_documents` and
`embed_query`.
A simple snippet to test the behaviour:
```python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings()
q_emb = embeddings.embed_query('hello world')
doc_emb = embeddings.embed_documents(['hello world', 'hello world'])
q_emb == doc_emb[0]
```
The response is `True` with the current version and `False` with the
changes I made.
Twitter: @egor_krash
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added the [FireCrawl](https://firecrawl.dev) document loader. Firecrawl
crawls and convert any website into LLM-ready data. It crawls all
accessible subpages and give you clean markdown for each.
- **Description:** Adds FireCrawl data loader
- **Dependencies:** firecrawl-py
- **Twitter handle:** @mendableai
ccing contributors: (@ericciarla @nickscamara)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR should make it easier for linters to do type checking and for IDEs to jump to definition of code.
See #20050 as a template for this PR.
- As a byproduct: Added 3 missed `test_imports`.
- Added missed `SolarChat` in to __init___.py Added it into test_import
ut.
- Added `# type: ignore` to fix linting. It is not clear, why linting
errors appear after ^ changes.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- make Tencent Cloud VectorDB support metadata filtering.
- implement delete function for Tencent Cloud VectorDB.
- support both Langchain Embedding model and Tencent Cloud VDB embedding
model.
- Tencent Cloud VectorDB support filter search keyword, compatible with
langchain filtering syntax.
- add Tencent Cloud VectorDB TranslationVisitor, now work with self
query retriever.
- more documentations.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue `langchain_community.cross_encoders` didn't have flattening
namespace code in the __init__.py file.
Changes:
- added code to flattening namespaces (used #20050 as a template)
- added ut for a change
- added missed `test_imports` for `chat_loaders` and
`chat_message_histories` modules
- [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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Langchain-Predibase integration was failing, because
it was not current with the Predibase SDK; in addition, Predibase
integration tests were instantiating the Langchain Community `Predibase`
class with one required argument (`model`) missing. This change updates
the Predibase SDK usage and fixes the integration tests.
- **Twitter handle:** `@alexsherstinsky`
- [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, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Last year Microsoft [changed the
name](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
of Azure Cognitive Search to Azure AI Search. This PR updates the
Langchain Azure Retriever API and it's associated docs to reflect this
change. It may be confusing for users to see the name Cognitive here and
AI in the Microsoft documentation which is why this is needed. I've also
added a more detailed example to the Azure retriever doc page.
There are more places that need a similar update but I'm breaking it up
so the PRs are not too big 😄 Fixing my errors from the previous PR.
Twitter: @marlene_zw
Two new tests added to test backward compatibility in
`libs/community/tests/integration_tests/retrievers/test_azure_cognitive_search.py`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** In order to use index and aindex in
libs/langchain/langchain/indexes/_api.py, I implemented delete method
and all async methods in opensearch_vector_search
- **Dependencies:** No changes
- **Description:** add `remove_comments` option (default: True): do not
extract html _comments_,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** @nfcampos ,
- **Twitter handle:** peter_v
I ran `make format`, `make lint` and `make test`.
Discussion: I my use case, I prefer to not have the comments in the
extracted text:
* e.g. from a Google tag that is added in the html as comment
* e.g. content that the authors have temporarily hidden to make it non
visible to the regular reader
Removing the comments makes the extracted text more alike the intended
text to be seen by the reader.
**Choice to make:** do we prefer to make the default for this
`remove_comments` option to be True or False?
I have changed it to True in a second commit, since that is how I would
prefer to use it by default. Have the
cleaned text (without technical Google tags etc.) and also closer to the
actually visible and intended content.
I am not sure what is best aligned with the conventions of langchain in
general ...
INITIAL VERSION (new version above):
~**Choice to make:** do we prefer to make the default for this
`ignore_comments` option to be True or False?
I have set it to False now to be backwards compatible. On the other
hand, I would use it mostly with True.
I am not sure what is best aligned with the conventions of langchain in
general ...~
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
[Dria](https://dria.co/) is a hub of public RAG models for developers to
both contribute and utilize a shared embedding lake. This PR adds a
retriever that can retrieve documents from Dria.
Description: Update `ChatZhipuAI` to support the latest `glm-4` model.
Issue: N/A
Dependencies: httpx, httpx-sse, PyJWT
The previous `ChatZhipuAI` implementation requires the `zhipuai`
package, and cannot call the latest GLM model. This is because
- The old version `zhipuai==1.*` doesn't support the latest model.
- `zhipuai==2.*` requires `pydantic V2`, which is incompatible with
'langchain-community'.
This re-implementation invokes the GLM model by sending HTTP requests to
[open.bigmodel.cn](https://open.bigmodel.cn/dev/api) via the `httpx`
package, and uses the `httpx-sse` package to handle stream events.
---------
Co-authored-by: zR <2448370773@qq.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: added support for llmsherpa library"
- [x] **Add tests and docs**:
1. Integration test:
'docs/docs/integrations/document_loaders/test_llmsherpa.py'.
2. an example notebook:
`docs/docs/integrations/document_loaders/llmsherpa.ipynb`.
- [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, hwchase17.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Feature
- Set additional headers in constructor
- Headers will be sent in post request
This feature is useful if deploying Ollama on a cloud service such as
hugging face, which requires authentication tokens to be passed in the
request header.
## Tests
- Test if header is passed
- Test if header is not passed
Similar to https://github.com/langchain-ai/langchain/pull/15881
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: I implemented a tool to use Hugging Face text-to-speech
inference API.
Issue: n/a
Dependencies: n/a
Twitter handle: No Twitter, but do have
[LinkedIn](https://www.linkedin.com/in/robby-horvath/) lol.
---------
Co-authored-by: Robby <h0rv@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: Implement DirectoryLoader lazy_load
function"
- [x] **Description**: The `lazy_load` function of the `DirectoryLoader`
yields each document separately. If the given `loader_cls` of the
`DirectoryLoader` also implemented `lazy_load`, it will be used to yield
subdocuments of the file.
- [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:
`libs/community/tests/unit_tests/document_loaders/test_directory_loader.py`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory:
`docs/docs/integrations/document_loaders/directory.ipynb`
- [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, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- **Description:** added unit tests for NotebookLoader. Linked PR:
https://github.com/langchain-ai/langchain/pull/17614
- **Issue:**
[#17614](https://github.com/langchain-ai/langchain/pull/17614)
- **Twitter handle:** @paulodoestech
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [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.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: lachiewalker <lachiewalker1@hotmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Created a Langchain Tool for OpenAI DALLE Image
Generation.
**Issue:**
[#15901](https://github.com/langchain-ai/langchain/issues/15901)
**Dependencies:** n/a
**Twitter handle:** @paulodoestech
- [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, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Added missing `from_documents` method to `KNNRetriever`,
providing the ability to supply metadata to LangChain `Document`s, and
to give it parity to the other retrievers, which do have
`from_documents`.
- Issue: None
- Dependencies: None
- Twitter handle: None
Co-authored-by: Victor Adan <vadan@netroadshow.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This is a follow up to #18371. These are the changes:
- New **Azure AI Services** toolkit and tools to replace those of
**Azure Cognitive Services**.
- Updated documentation for Microsoft platform.
- The image analysis tool has been rewritten to use the new package
`azure-ai-vision-imageanalysis`, doing a proper replacement of
`azure-ai-vision`.
These changes:
- Update outdated naming from "Azure Cognitive Services" to "Azure AI
Services".
- Update documentation to use non-deprecated methods to create and use
agents.
- Removes need to depend on yanked python package (`azure-ai-vision`)
There is one new dependency that is needed as a replacement to
`azure-ai-vision`:
- `azure-ai-vision-imageanalysis`. This is optional and declared within
a function.
There is a new `azure_ai_services.ipynb` notebook showing usage; Changes
have been linted and formatted.
I am leaving the actions of adding deprecation notices and future
removal of Azure Cognitive Services up to the LangChain team, as I am
not sure what the current practice around this is.
---
If this PR makes it, my handle is @galo@mastodon.social
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description**: `bigdl-llm` library has been renamed to
[`ipex-llm`](https://github.com/intel-analytics/ipex-llm). This PR
migrates the `bigdl-llm` integration to `ipex-llm` .
- **Issue**: N/A. The original PR of `bigdl-llm` is
https://github.com/langchain-ai/langchain/pull/17953
- **Dependencies**: `ipex-llm` library
- **Contribution maintainer**: @shane-huang
Updated doc: docs/docs/integrations/llms/ipex_llm.ipynb
Updated test:
libs/community/tests/integration_tests/llms/test_ipex_llm.py
- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Introduction
[Intel® Extension for
Transformers](https://github.com/intel/intel-extension-for-transformers)
is an innovative toolkit designed to accelerate GenAI/LLM everywhere
with the optimal performance of Transformer-based models on various
Intel platforms
Description
adding ITREX runtime embeddings using intel-extension-for-transformers.
added mdx documentation and example notebooks
added embedding import testing.
---------
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update Azure Document Intelligence implementation by
Microsoft team and RAG cookbook with Azure AI Search
---------
Co-authored-by: Lu Zhang (AI) <luzhan@microsoft.com>
Co-authored-by: Yateng Hong <yatengh@microsoft.com>
Co-authored-by: teethache <hongyateng2006@126.com>
Co-authored-by: Lu Zhang <44625949+luzhang06@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
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:** Adding oracle autonomous database document loader
integration. This will allow users to connect to oracle autonomous
database through connection string or TNS configuration.
https://www.oracle.com/autonomous-database/
- **Issue:** None
- **Dependencies:** oracledb python package
https://pypi.org/project/oracledb/
- **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.
Unit test and doc are added.
- [ ] **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, hwchase17.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Issue
Recently, the new `allow_dangerous_deserialization` flag was introduced
for preventing unsafe model deserialization that relies on pickle
without user's notice (#18696). Since then some LLMs like Databricks
requires passing in this flag with true to instantiate the model.
However, this breaks existing functionality to loading such LLMs within
a chain using `load_chain` method, because the underlying loader
function
[load_llm_from_config](f96dd57501/libs/langchain/langchain/chains/loading.py (L40))
(and load_llm) ignores keyword arguments passed in.
### Solution
This PR fixes this issue by propagating the
`allow_dangerous_deserialization` argument to the class loader iff the
LLM class has that field.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
### Prem SDK integration in LangChain
This PR adds the integration with [PremAI's](https://www.premai.io/)
prem-sdk with langchain. User can now access to deployed models
(llms/embeddings) and use it with langchain's ecosystem. This PR adds
the following:
### This PR adds the following:
- [x] Add chat support
- [X] Adding embedding support
- [X] writing integration tests
- [X] writing tests for chat
- [X] writing tests for embedding
- [X] writing unit tests
- [X] writing tests for chat
- [X] writing tests for embedding
- [X] Adding documentation
- [X] writing documentation for chat
- [X] writing documentation for embedding
- [X] run `make test`
- [X] run `make lint`, `make lint_diff`
- [X] Final checks (spell check, lint, format and overall testing)
---------
Co-authored-by: Anindyadeep Sannigrahi <anindyadeepsannigrahi@Anindyadeeps-MacBook-Pro.local>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:**
This PR adds [Dappier](https://dappier.com/) for the chat model. It
supports generate, async generate, and batch functionalities. We added
unit and integration tests as well as a notebook with more details about
our chat model.
**Dependencies:**
No extra dependencies are needed.
DuckDB has a cosine similarity function along list and array data types,
which can be used as a vector store.
- **Description:** The latest version of DuckDB features a cosine
similarity function, which can be used with its support for list or
array column types. This PR surfaces this functionality to langchain.
- **Dependencies:** duckdb 0.10.0
- **Twitter handle:** @igocrite
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This is a basic VectorStore implementation using an in-memory dict to
store the documents.
It doesn't need any extra/optional dependency as it uses numpy which is
already a dependency of langchain.
This is useful for quick testing, demos, examples.
Also it allows to write vendor-neutral tutorials, guides, etc...
Add `keep_alive` parameter to control how long the model will stay
loaded into memory with Ollama。
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Classes are missed in __all__ and in different places of __init__.py
- BaichuanLLM
- ChatDatabricks
- ChatMlflow
- Llamafile
- Mlflow
- Together
Added classes to __all__. I also sorted __all__ list.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
## Add Passio Nutrition AI Food Search Tool to Community Package
### Description
We propose adding a new tool to the `community` package, enabling
integration with Passio Nutrition AI for food search functionality. This
tool will provide a simple interface for retrieving nutrition facts
through the Passio Nutrition AI API, simplifying user access to
nutrition data based on food search queries.
### Implementation Details
- **Class Structure:** Implement `NutritionAI`, extending `BaseTool`. It
includes an `_run` method that accepts a query string and, optionally, a
`CallbackManagerForToolRun`.
- **API Integration:** Use `NutritionAIAPI` for the API wrapper,
encapsulating all interactions with the Passio Nutrition AI and
providing a clean API interface.
- **Error Handling:** Implement comprehensive error handling for API
request failures.
### Expected Outcome
- **User Benefits:** Enable easy querying of nutrition facts from Passio
Nutrition AI, enhancing the utility of the `langchain_community` package
for nutrition-related projects.
- **Functionality:** Provide a straightforward method for integrating
nutrition information retrieval into users' applications.
### Dependencies
- `langchain_core` for base tooling support
- `pydantic` for data validation and settings management
- Consider `requests` or another HTTP client library if not covered by
`NutritionAIAPI`.
### Tests and Documentation
- **Unit Tests:** Include tests that mock network interactions to ensure
tool reliability without external API dependency.
- **Documentation:** Create an example notebook in
`docs/docs/integrations/tools/passio_nutrition_ai.ipynb` showing usage,
setup, and example queries.
### Contribution Guidelines Compliance
- Adhere to the project's linting and formatting standards (`make
format`, `make lint`, `make test`).
- Ensure compliance with LangChain's contribution guidelines,
particularly around dependency management and package modifications.
### Additional Notes
- Aim for the tool to be a lightweight, focused addition, not
introducing significant new dependencies or complexity.
- Potential future enhancements could include caching for common queries
to improve performance.
### Twitter Handle
- Here is our Passio AI [twitter handle](https://twitter.com/@passio_ai)
where we announce our products.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
For some DBs with lots of tables, reflection of all the tables can take
very long. So this change will make the tables be reflected lazily when
get_table_info() is called and `lazy_table_reflection` is True.
## Description
- Add [Friendli](https://friendli.ai/) integration for `Friendli` LLM
and `ChatFriendli` chat model.
- Unit tests and integration tests corresponding to this change are
added.
- Documentations corresponding to this change are added.
## Dependencies
- Optional dependency
[`friendli-client`](https://pypi.org/project/friendli-client/) package
is added only for those who use `Frienldi` or `ChatFriendli` model.
## Twitter handle
- https://twitter.com/friendliai
This pull request introduces initial support for the TiDB vector store.
The current version is basic, laying the foundation for the vector store
integration. While this implementation provides the essential features,
we plan to expand and improve the TiDB vector store support with
additional enhancements in future updates.
Upcoming Enhancements:
* Support for Vector Index Creation: To enhance the efficiency and
performance of the vector store.
* Support for max marginal relevance search.
* Customized Table Structure Support: Recognizing the need for
flexibility, we plan for more tailored and efficient data store
solutions.
Simple use case exmaple
```python
from typing import List, Tuple
from langchain.docstore.document import Document
from langchain_community.vectorstores import TiDBVectorStore
from langchain_openai import OpenAIEmbeddings
db = TiDBVectorStore.from_texts(
embedding=embeddings,
texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'],
table_name="tidb_vector_langchain",
connection_string=tidb_connection_url,
distance_strategy="cosine",
)
query = "Can you tell me about Alexandra?"
docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
```
**Description:**
This integrates Infinispan as a vectorstore.
Infinispan is an open-source key-value data grid, it can work as single
node as well as distributed.
Vector search is supported since release 15.x
For more: [Infinispan Home](https://infinispan.org)
Integration tests are provided as well as a demo notebook
This is a PR that adds a dangerous load parameter to force users to opt in to use pickle.
This is a PR that's meant to raise user awareness that the pickling module is involved.
This is a patch for `CVE-2024-2057`:
https://www.cve.org/CVERecord?id=CVE-2024-2057
This affects users that:
* Use the `TFIDFRetriever`
* Attempt to de-serialize it from an untrusted source that contains a
malicious payload
- **Description:** Databricks SerDe uses cloudpickle instead of pickle
when serializing a user-defined function transform_input_fn since pickle
does not support functions defined in `__main__`, and cloudpickle
supports this.
- **Dependencies:** cloudpickle>=2.0.0
Added a unit test.
### Description
Changed the value specified for `content_key` in JSONLoader from a
single key to a value based on jq schema.
I created [similar
PR](https://github.com/langchain-ai/langchain/pull/11255) before, but it
has several conflicts because of the architectural change associated
stable version release, so I re-create this PR to fit new architecture.
### Why
For json data like the following, specify `.data[].attributes.message`
for page_content and `.data[].attributes.id` or
`.data[].attributes.attributes. tags`, etc., the `content_key` must also
parse the json structure.
<details>
<summary>sample json data</summary>
```json
{
"data": [
{
"attributes": {
"message": "message1",
"tags": [
"tag1"
]
},
"id": "1"
},
{
"attributes": {
"message": "message2",
"tags": [
"tag2"
]
},
"id": "2"
}
]
}
```
</details>
<details>
<summary>sample code</summary>
```python
def metadata_func(record: dict, metadata: dict) -> dict:
metadata["source"] = None
metadata["id"] = record.get("id")
metadata["tags"] = record["attributes"].get("tags")
return metadata
sample_file = "sample1.json"
loader = JSONLoader(
file_path=sample_file,
jq_schema=".data[]",
content_key=".attributes.message", ## content_key is parsable into jq schema
is_content_key_jq_parsable=True, ## this is added parameter
metadata_func=metadata_func
)
data = loader.load()
data
```
</details>
### Dependencies
none
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
Deprecates the old langchain-hub repository. Does *not* deprecate the
new https://smith.langchain.com/hub
@PinkDraconian has correctly raised that in the event someone is loading
unsanitized user input into the `try_load_from_hub` function, they have
the ability to load files from other locations in github than the
hwchase17/langchain-hub repository.
This PR adds some more path checking to that function and deprecates the
functionality in favor of the hub built into LangSmith.
- **Description:** finishes adding the you.com functionality including:
- add async functions to utility and retriever
- add the You.com Tool
- add async testing for utility, retriever, and tool
- add a tool integration notebook page
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** @scottnath
* **Description:** adds `LlamafileEmbeddings` class implementation for
generating embeddings using
[llamafile](https://github.com/Mozilla-Ocho/llamafile)-based models.
Includes related unit tests and notebook showing example usage.
* **Issue:** N/A
* **Dependencies:** N/A
Description-
- Changed the GitHub endpoint as existing was not working and giving 404
not found error
- Also the existing function was failing if file_filter is not passed as
the tree api return all paths including directory as well, and when
get_file_content was iterating over these path, the function was failing
for directory as the api was returning list of files inside the
directory, so added a condition to ignore the paths if it a directory
- Fixes this issue -
https://github.com/langchain-ai/langchain/issues/17453
Co-authored-by: Radhika Bansal <Radhika.Bansal@veritas.com>
**Description:**
In this PR, I am adding a `PolygonFinancials` tool, which can be used to
get financials data for a given ticker. The financials data is the
fundamental data that is found in income statements, balance sheets, and
cash flow statements of public US companies.
**Twitter**:
[@virattt](https://twitter.com/virattt)
- **Description:** A generic document loader adapter for SQLAlchemy on
top of LangChain's `SQLDatabaseLoader`.
- **Needed by:** https://github.com/crate-workbench/langchain/pull/1
- **Depends on:** GH-16655
- **Addressed to:** @baskaryan, @cbornet, @eyurtsev
Hi from CrateDB again,
in the same spirit like GH-16243 and GH-16244, this patch breaks out
another commit from https://github.com/crate-workbench/langchain/pull/1,
in order to reduce the size of this patch before submitting it, and to
separate concerns.
To accompany the SQLAlchemy adapter implementation, the patch includes
integration tests for both SQLite and PostgreSQL. Let me know if
corresponding utility resources should be added at different spots.
With kind regards,
Andreas.
### Software Tests
```console
docker compose --file libs/community/tests/integration_tests/document_loaders/docker-compose/postgresql.yml up
```
```console
cd libs/community
pip install psycopg2-binary
pytest -vvv tests/integration_tests -k sqldatabase
```
```
14 passed
```
---------
Co-authored-by: Andreas Motl <andreas.motl@crate.io>
**Description**: This PR adds support for using the [LLMLingua project
](https://github.com/microsoft/LLMLingua) especially the LongLLMLingua
(Enhancing Large Language Model Inference via Prompt Compression) as a
document compressor / transformer.
The LLMLingua project is an interesting project that can greatly improve
RAG system by compressing prompts and contexts while keeping their
semantic relevance.
**Issue**: https://github.com/microsoft/LLMLingua/issues/31
**Dependencies**: [llmlingua](https://pypi.org/project/llmlingua/)
@baskaryan
---------
Co-authored-by: Ayodeji Ayibiowu <ayodeji.ayibiowu@getinge.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
