**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>
**Description:** Callback handler to integrate fiddler with langchain.
This PR adds the following -
1. `FiddlerCallbackHandler` implementation into langchain/community
2. Example notebook `fiddler.ipynb` for usage documentation
[Internal Tracker : FDL-14305]
**Issue:**
NA
**Dependencies:**
- Installation of langchain-community is unaffected.
- Usage of FiddlerCallbackHandler requires installation of latest
fiddler-client (2.5+)
**Twitter handle:** @fiddlerlabs @behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
**Description:** Initial pull request for Kinetica LLM wrapper
**Issue:** N/A
**Dependencies:** No new dependencies for unit tests. Integration tests
require gpudb, typeguard, and faker
**Twitter handle:** @chad_juliano
Note: There is another pull request for Kinetica vectorstore. Ultimately
we would like to make a partner package but we are starting with a
community contribution.
- **Description:** In order to override the bool value of
"fetch_schema_from_transport" in the GraphQLAPIWrapper, a
"fetch_schema_from_transport" value needed to be added to the
"_EXTRA_OPTIONAL_TOOLS" dictionary in load_tools in the "graphql" key.
The parameter "fetch_schema_from_transport" must also be passed in to
the GraphQLAPIWrapper to allow reading of the value when creating the
client. Passing as an optional parameter is probably best to avoid
breaking changes. This change is necessary to support GraphQL instances
that do not support fetching schema, such as TigerGraph. More info here:
[TigerGraph GraphQL Schema
Docs](https://docs.tigergraph.com/graphql/current/schema)
- **Threads handle:** @zacharytoliver
---------
Co-authored-by: Zachary Toliver <zt10191991@hotmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
In this PR, I am adding a PolygonTickerNews Tool, which can be used to
get the latest news for a given ticker / stock.
Twitter handle: [@virattt](https://twitter.com/virattt)
**Description**: CogniSwitch focusses on making GenAI usage more
reliable. It abstracts out the complexity & decision making required for
tuning processing, storage & retrieval. Using simple APIs documents /
URLs can be processed into a Knowledge Graph that can then be used to
answer questions.
**Dependencies**: No dependencies. Just network calls & API key required
**Tag maintainer**: @hwchase17
**Twitter handle**: https://github.com/CogniSwitch
**Documentation**: Please check
`docs/docs/integrations/toolkits/cogniswitch.ipynb`
**Tests**: The usual tool & toolkits tests using `test_imports.py`
PR has passed linting and testing before this submission.
---------
Co-authored-by: Saicharan Sridhara <145636106+saiCogniswitch@users.noreply.github.com>
- **Description:** adds an `exclude` parameter to the DirectoryLoader
class, based on similar behavior in GenericLoader
- **Issue:** discussed in
https://github.com/langchain-ai/langchain/discussions/9059 and I think
in some other issues that I cannot find at the moment 🙇
- **Dependencies:** None
- **Twitter handle:** don't have one sorry! Just https://github/nejch
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR is adding support for NVIDIA NeMo embeddings issue #16095.
---------
Co-authored-by: Praveen Nakshatrala <pnakshatrala@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. integrate with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. update `langchain.llms`
3. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. integrate chat models with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
Yuan2.0 is a new generation Fundamental Large Language Model developed
by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan
2.0-51B, and Yuan 2.0-2B.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
I am submitting this for a school project as part of a team of 5. Other
team members are @LeilaChr, @maazh10, @Megabear137, @jelalalamy. This PR
also has contributions from community members @Harrolee and @Mario928.
Initial context is in the issue we opened (#11229).
This pull request adds:
- Generic framework for expanding the languages that `LanguageParser`
can handle, using the
[tree-sitter](https://github.com/tree-sitter/py-tree-sitter#py-tree-sitter)
parsing library and existing language-specific parsers written for it
- Support for the following additional languages in `LanguageParser`:
- C
- C++
- C#
- Go
- Java (contributed by @Mario928
https://github.com/ThatsJustCheesy/langchain/pull/2)
- Kotlin
- Lua
- Perl
- Ruby
- Rust
- Scala
- TypeScript (contributed by @Harrolee
https://github.com/ThatsJustCheesy/langchain/pull/1)
Here is the [design
document](https://docs.google.com/document/d/17dB14cKCWAaiTeSeBtxHpoVPGKrsPye8W0o_WClz2kk)
if curious, but no need to read it.
## Issues
- Closes#11229
- Closes#10996
- Closes#8405
## Dependencies
`tree_sitter` and `tree_sitter_languages` on PyPI. We have tried to add
these as optional dependencies.
## Documentation
We have updated the list of supported languages, and also added a
section to `source_code.ipynb` detailing how to add support for
additional languages using our framework.
## Maintainer
- @hwchase17 (previously reviewed
https://github.com/langchain-ai/langchain/pull/6486)
Thanks!!
## Git commits
We will gladly squash any/all of our commits (esp merge commits) if
necessary. Let us know if this is desirable, or if you will be
squash-merging anyway.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
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Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
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2. an example notebook showing its use. It lives in `docs/extras`
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Maaz Hashmi <mhashmi373@gmail.com>
Co-authored-by: LeilaChr <87657694+LeilaChr@users.noreply.github.com>
Co-authored-by: Jeremy La <jeremylai511@gmail.com>
Co-authored-by: Megabear137 <zubair.alnoor27@gmail.com>
Co-authored-by: Lee Harrold <lhharrold@sep.com>
Co-authored-by: Mario928 <88029051+Mario928@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Pebblo opensource project enables developers to
safely load data to their Gen AI apps. It identifies semantic topics and
entities found in the loaded data and summarizes them in a
developer-friendly report.
- **Dependencies:** none
- **Twitter handle:** srics
@hwchase17
**Description**: This PR adds a chain for Amazon Neptune graph database
RDF format. It complements the existing Neptune Cypher chain. The PR
also includes a Neptune RDF graph class to connect to, introspect, and
query a Neptune RDF graph database from the chain. A sample notebook is
provided under docs that demonstrates the overall effect: invoking the
chain to make natural language queries against Neptune using an LLM.
**Issue**: This is a new feature
**Dependencies**: The RDF graph class depends on the AWS boto3 library
if using IAM authentication to connect to the Neptune database.
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Improve test cases for `SQLDatabase` adapter
component, see
[suggestion](https://github.com/langchain-ai/langchain/pull/16655#pullrequestreview-1846749474).
- **Depends on:** GH-16655
- **Addressed to:** @baskaryan, @cbornet, @eyurtsev
_Remark: This PR is stacked upon GH-16655, so that one will need to go
in first._
Edit: Thank you for bringing in GH-17191, @eyurtsev. This is a little
aftermath, improving/streamlining the corresponding test cases.
- **Description:** This adds a delete method so that rocksetdb can be
used with `RecordManager`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
---------
Co-authored-by: Rockset API Bot <admin@rockset.io>
**Description:** changed filtering so that failed filter doesn't add
document to results. Currently filtering is entirely broken and all
documents are returned whether or not they pass the filter.
fixes issue introduced in
https://github.com/langchain-ai/langchain/pull/16190
- **Description:** Adds the document loader for [AWS
Athena](https://aws.amazon.com/athena/), a serverless and interactive
analytics service.
- **Dependencies:** Added boto3 as a dependency
- **Description:** This PR adds support for `search_types="mmr"` and
`search_type="similarity_score_threshold"` to retrievers using
`DatabricksVectorSearch`,
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:**
1. Modify LLMs/Anyscale to work with OAI v1
2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale
3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name
convention (reverted)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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, @eyurtsev, @hwchase17.
-->
- **Description: changes to you.com files**
- general cleanup
- adds community/utilities/you.py, moving bulk of code from retriever ->
utility
- removes `snippet` as endpoint
- adds `news` as endpoint
- adds more tests
<s>**Description: update community MAKE file**
- adds `integration_tests`
- adds `coverage`</s>
- **Issue:** the issue # it fixes if applicable,
- [For New Contributors: Update Integration
Documentation](https://github.com/langchain-ai/langchain/issues/15664#issuecomment-1920099868)
- **Dependencies:** n/a
- **Twitter handle:** @scottnath
- **Mastodon handle:** scottnath@mastodon.social
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Databricks LLM does not support SerDe the
transform_input_fn and transform_output_fn. After saving and loading,
the LLM will be broken. This PR serialize these functions into a hex
string using pickle, and saving the hex string in the yaml file. Using
pickle to serialize a function can be flaky, but this is a simple
workaround that unblocks many use cases. If more sophisticated SerDe is
needed, we can improve it later.
Test:
Added a simple unit test.
I did manual test on Databricks and it works well.
The saved yaml looks like:
```
llm:
_type: databricks
cluster_driver_port: null
cluster_id: null
databricks_uri: databricks
endpoint_name: databricks-mixtral-8x7b-instruct
extra_params: {}
host: e2-dogfood.staging.cloud.databricks.com
max_tokens: null
model_kwargs: null
n: 1
stop: null
task: null
temperature: 0.0
transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e
transform_output_fn: null
```
@baskaryan
```python
from langchain_community.embeddings import DatabricksEmbeddings
from langchain_community.llms import Databricks
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import mlflow
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
def transform_input(**request):
request["messages"] = [
{
"role": "user",
"content": request["prompt"]
}
]
del request["prompt"]
return request
llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input)
persist_dir = "faiss_databricks_embedding"
# Create the vector db, persist the db to a local fs folder
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
db = FAISS.from_documents(docs, embeddings)
db.save_local(persist_dir)
def load_retriever(persist_directory):
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
retriever = load_retriever(persist_dir)
retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever)
with mlflow.start_run() as run:
logged_model = mlflow.langchain.log_model(
retrievalQA,
artifact_path="retrieval_qa",
loader_fn=load_retriever,
persist_dir=persist_dir,
)
# Load the retrievalQA chain
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}]))
```
**Description:**
Implemented unique ID validation in the FAISS component to ensure all
document IDs are distinct. This update resolves issues related to
non-unique IDs, such as inconsistent behavior during deletion processes.
- **Description:**
Actually the test named `test_openai_apredict` isn't testing the
apredict method from ChatOpenAI.
- **Twitter handle:**
https://twitter.com/OAlmofadas
### Description
support load any github file content based on file extension.
Why not use [git
loader](https://python.langchain.com/docs/integrations/document_loaders/git#load-existing-repository-from-disk)
?
git loader clones the whole repo even only interested part of files,
that's too heavy. This GithubFileLoader only downloads that you are
interested files.
### Twitter handle
my twitter: @shufanhaotop
---------
Co-authored-by: Hao Fan <h_fan@apple.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Please tag this issue with `nvidia_genai`**
- **Description:** Added new Runnables for integration NVIDIA Riva into
LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech
(TTS).
- **Issue:** N/A
- **Dependencies:** To use these runnables, the NVIDIA Riva client
libraries are required. It they are not installed, an error will be
raised instructing how to install them. The Runnables can be safely
imported without the riva client libraries.
- **Twitter handle:** N/A
All of the Riva Runnables are inside a single folder in the Utilities
module. In this folder are four files:
- common.py - Contains all code that is common to both TTS and ASR
- stream.py - Contains a class representing an audio stream that allows
the end user to put data into the stream like a queue.
- asr.py - Contains the RivaASR runnable
- tts.py - Contains the RivaTTS runnable
The following Python function is an example of creating a chain that
makes use of both of these Runnables:
```python
def create(
config: Configuration,
audio_encoding: RivaAudioEncoding,
sample_rate: int,
audio_channels: int = 1,
) -> Runnable[ASRInputType, TTSOutputType]:
"""Create a new instance of the chain."""
_LOGGER.info("Instantiating the chain.")
# create the riva asr client
riva_asr = RivaASR(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
encoding=audio_encoding,
audio_channel_count=audio_channels,
sample_rate_hertz=sample_rate,
profanity_filter=config.riva_asr.profanity_filter,
enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation,
language_code=config.riva_asr.language_code,
)
# create the prompt template
prompt = PromptTemplate.from_template("{user_input}")
# model = ChatOpenAI()
model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore
# create the riva tts client
riva_tts = RivaTTS(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
output_directory=config.riva_tts.output_directory,
language_code=config.riva_tts.language_code,
voice_name=config.riva_tts.voice_name,
)
# construct and return the chain
return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore
```
The following code is an example of creating a new audio stream for
Riva:
```python
input_stream = AudioStream(maxsize=1000)
# Send bytes into the stream
for chunk in audio_chunks:
await input_stream.aput(chunk)
input_stream.close()
```
The following code is an example of how to execute the chain with
RivaASR and RivaTTS
```python
output_stream = asyncio.Queue()
while not input_stream.complete:
async for chunk in chain.astream(input_stream):
output_stream.put(chunk)
```
Everything should be async safe and thread safe. Audio data can be put
into the input stream while the chain is running without interruptions.
---------
Co-authored-by: Hayden Wolff <hwolff@nvidia.com>
Co-authored-by: Hayden Wolff <hwolff@Haydens-Laptop.local>
Co-authored-by: Hayden Wolff <haydenwolff99@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Previously, if this did not find a mypy cache then it wouldnt run
this makes it always run
adding mypy ignore comments with existing uncaught issues to unblock other prs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Adds:
* methods `aload()` and `alazy_load()` to interface `BaseLoader`
* implementation for class `MergedDataLoader `
* support for class `BaseLoader` in async function `aindex()` with unit
tests
Note: this is compatible with existing `aload()` methods that some
loaders already had.
**Twitter handle:** @cbornet_
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description:**
Filtering in a FAISS vectorstores is very inflexible and doesn't allow
that many use case. I think supporting callable like this enables a lot:
regular expressions, condition on multiple keys etc. **Note** I had to
manually alter a test. I don't understand if it was falty to begin with
or if there is something funky going on.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
Signed-off-by: thiswillbeyourgithub <26625900+thiswillbeyourgithub@users.noreply.github.com>
## Summary
This PR implements the "Connery Action Tool" and "Connery Toolkit".
Using them, you can integrate Connery actions into your LangChain agents
and chains.
Connery is an open-source plugin infrastructure for AI.
With Connery, you can easily create a custom plugin with a set of
actions and seamlessly integrate them into your LangChain agents and
chains. Connery will handle the rest: runtime, authorization, secret
management, access management, audit logs, and other vital features.
Additionally, Connery and our community offer a wide range of
ready-to-use open-source plugins for your convenience.
Learn more about Connery:
- GitHub: https://github.com/connery-io/connery-platform
- Documentation: https://docs.connery.io
- Twitter: https://twitter.com/connery_io
## TODOs
- [x] API wrapper
- [x] Integration tests
- [x] Connery Action Tool
- [x] Docs
- [x] Example
- [x] Integration tests
- [x] Connery Toolkit
- [x] Docs
- [x] Example
- [x] Formatting (`make format`)
- [x] Linting (`make lint`)
- [x] Testing (`make test`)
We can't use `json.dumps` by default as many types returned by the
cassandra driver are not serializable. It's safer to use `str` and let
users define their own custom `page_content_mapper` if needed.
- **Description:** Adding Baichuan Text Embedding Model and Baichuan Inc
introduction.
Baichuan Text Embedding ranks #1 in C-MTEB leaderboard:
https://huggingface.co/spaces/mteb/leaderboard
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
- **Description:** This PR adds [EdenAI](https://edenai.co/) for the
chat model (already available in LLM & Embeddings). It supports all
[ChatModel] functionality: generate, async generate, stream, astream and
batch. A detailed notebook was added.
- **Dependencies**: No dependencies are added as we call a rest API.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
community:
- **Description:**
- Add new ChatLiteLLMRouter class that allows a client to use a LiteLLM
Router as a LangChain chat model.
- Note: The existing ChatLiteLLM integration did not cover the LiteLLM
Router class.
- Add tests and Jupyter notebook.
- **Issue:** None
- **Dependencies:** Relies on existing ChatLiteLLM integration
- **Twitter handle:** @bburgin_0
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** Adding Oracle Cloud Infrastructure Generative AI
integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully
managed service that provides a set of state-of-the-art, customizable
large language models (LLMs) that cover a wide range of use cases, and
which is available through a single API. Using the OCI Generative AI
service you can access ready-to-use pretrained models, or create and
host your own fine-tuned custom models based on your own data on
dedicated AI clusters.
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
- **Issue:** None,
- **Dependencies:** OCI Python SDK,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
Passed
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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.
we provide unit tests. However, we cannot provide integration tests due
to Oracle policies that prohibit public sharing of api keys.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
This PR adds a VectorStore integration for SAP HANA Cloud Vector Engine,
which is an upcoming feature in the SAP HANA Cloud database
(https://blogs.sap.com/2023/11/02/sap-hana-clouds-vector-engine-announcement/).
- **Issue:** N/A
- **Dependencies:** [SAP HANA Python
Client](https://pypi.org/project/hdbcli/)
- **Twitter handle:** @sapopensource
Implementation of the integration:
`libs/community/langchain_community/vectorstores/hanavector.py`
Unit tests:
`libs/community/tests/unit_tests/vectorstores/test_hanavector.py`
Integration tests:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Example notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Access credentials for execution of the integration tests can be
provided to the maintainers.
---------
Co-authored-by: sascha <sascha.stoll@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This PR enables LangChain to access the iFlyTek's
Spark LLM via the chat_models wrapper.
- **Dependencies:** websocket-client ^1.6.1
- **Tag maintainer:** @baskaryan
### SparkLLM chat model usage
Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API
Console](https://console.xfyun.cn/services/bm3) (for more info, see
[iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set
environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY`
and `IFLYTEK_SPARK_API_SECRET` or pass parameters when using it like the
demo below:
```python3
from langchain.chat_models.sparkllm import ChatSparkLLM
client = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>"
)
```
This PR introduces update to Konko Integration with LangChain.
1. **New Endpoint Addition**: Integration of a new endpoint to utilize
completion models hosted on Konko.
2. **Chat Model Updates for Backward Compatibility**: We have updated
the chat models to ensure backward compatibility with previous OpenAI
versions.
4. **Updated Documentation**: Comprehensive documentation has been
updated to reflect these new changes, providing clear guidance on
utilizing the new features and ensuring seamless integration.
Thank you to the LangChain team for their exceptional work and for
considering this PR. Please let me know if any additional information is
needed.
---------
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MacBook-Pro.local>
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MBP.lan>
- **Description:** Baichuan Chat (with both Baichuan-Turbo and
Baichuan-Turbo-192K models) has updated their APIs. There are breaking
changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API
but is still required in Langchain. Baichuan's Langchain integration
needs to be updated to the latest version.
- **Issue:** #15206
- **Dependencies:** None,
- **Twitter handle:** None
@hwchase17.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
**Description:**
- Implement `SQLStrStore` and `SQLDocStore` classes that inherits from
`BaseStore` to allow to persist data remotely on a SQL server.
- SQL is widely used and sometimes we do not want to install a caching
solution like Redis.
- Multiple issues/comments complain that there is no easy remote and
persistent solution that are not in memory (users want to replace
InMemoryStore), e.g.,
https://github.com/langchain-ai/langchain/issues/14267,
https://github.com/langchain-ai/langchain/issues/15633,
https://github.com/langchain-ai/langchain/issues/14643,
https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain
- This is particularly painful when wanting to use
`ParentDocumentRetriever `
- This implementation is particularly useful when:
* it's expensive to construct an InMemoryDocstore/dict
* you want to retrieve documents from remote sources
* you just want to reuse existing objects
- This implementation integrates well with PGVector, indeed, when using
PGVector, you already have a SQL instance running. `SQLDocStore` is a
convenient way of using this instance to store documents associated to
vectors. An integration example with ParentDocumentRetriever and
PGVector is provided in docs/docs/integrations/stores/sql.ipynb or
[here](https://github.com/gcheron/langchain/blob/sql-store/docs/docs/integrations/stores/sql.ipynb).
- It persists `str` and `Document` objects but can be easily extended.
**Issue:**
Provide an easy SQL alternative to `InMemoryStore`.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description** : New documents loader for visio files (with extension
.vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with
extension .vsdx) is associated with Microsoft Visio, a diagram creation
software. It stores information about the structure, layout, and
graphical elements of a diagram. This format facilitates the creation
and sharing of visualizations in areas such as business, engineering,
and computer science.
A Visio file can contain multiple pages. Some of them may serve as the
background for others, and this can occur across multiple layers. This
loader extracts the textual content from each page and its associated
pages, enabling the extraction of all visible text from each page,
similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package
Description: Added support for asynchronous streaming in the Bedrock
class and corresponding tests.
Primarily:
async def aprepare_output_stream
async def _aprepare_input_and_invoke_stream
async def _astream
async def _acall
I've ensured that the code adheres to the project's linting and
formatting standards by running make format, make lint, and make test.
Issue: #12054, #11589
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @dominic_lovric
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
**Description:** Add support for querying TigerGraph databases through
the InquiryAI service.
**Issue**: N/A
**Dependencies:** N/A
**Twitter handle:** @TigerGraphDB
- **Description:** add support for kwargs in`MlflowEmbeddings`
`embed_document()` and `embed_query()` so that all the arguments
required by Cohere API (and others?) can be passed down to the server.
- **Issue:** #15234
- **Dependencies:** MLflow with MLflow Deployments (`pip install
mlflow[genai]`)
**Tests**
Now this code [adapted from the
docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example)
for the Cohere API works locally.
```python
"""
Setup
-----
export COHERE_API_KEY=...
mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml
Run
---
python /path/to/this/file.py
"""
embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
```
Output
```
[0.060455322, 0.028793335, -0.025848389]
[0.031707764, 0.021057129, -0.009361267]
```
Titan Express model was not supported as a chat model because LangChain
messages were not "translated" to a text prompt.
Co-authored-by: Guillem Orellana Trullols <guillem.orellana_trullols@siemens.com>
Implement similarity function selector for ElasticsearchStore. The
scores coming back from Elasticsearch are already similarities (not
distances) and they are already normalized (see
[docs](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)).
Hence we leave the scores untouched and just forward them.
This fixes#11539.
However, in hybrid mode (when keyword search and vector search are
involved) Elasticsearch currently returns no scores. This PR adds an
error message around this fact. We need to think a bit more to come up
with a solution for this case.
This PR also corrects a small error in the Elasticsearch integration
test.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
In this PR, I am adding a `PolygonLastQuote` Tool, which can be used to
get the latest price quote for a given ticker / stock.
Additionally, I've added a Polygon Toolkit, which we can use to
encapsulate future tools that we build for Polygon.
**Twitter handle:** [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The timeout function comes in handy when you want to kill longrunning
queries.
The value sanitization removes all lists that are larger than 128
elements. The idea here is to remove embedding properties from results.
- **Description:** As Shell tool is very versatile, while integrating it
into applications as openai functions, developers have no clue about
what command is being executed using the ShellTool. All one can see is:
Summarising my feature request:
1. There's no visibility about what command was executed.
2. There's no mechanism to prevent a command to be executed using
ShellTool, like a y/n human input which can be accepted from user to
proceed with executing the command.,
- **Issue:** the issue #15931 it fixes if applicable,
- **Dependencies:** There isn't any dependancy,
- **Twitter handle:** @krishnashed
**Description:** This new feature enhances the flexibility of pipeline
integration, particularly when working with RESTful APIs.
``JsonRequestsWrapper`` allows for the decoding of JSON output, instead
of the only option for text output.
---------
Co-authored-by: Zhichao HAN <hanzhichao2000@hotmail.com>
## Feature
- Follow parameter structure as per official documentation
- top level parameters (e.g. model, system, template) will be passed as
top level parameters
- other parameters will be sent in options unless options is provided
## Tests
- Test if top level parameters handled properly
- Test if parameters that are not top level parameters are handled as
options
- Test if options is provided, it will be passed as is
Support [Lantern](https://github.com/lanterndata/lantern) as a new
VectorStore type.
- Added Lantern as VectorStore.
It will support 3 distance functions `l2 squared`, `cosine` and
`hamming` and will use `HNSW` index.
- Added tests
- Added example notebook
## 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
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
Major changes:
- Rename `wasm_chat.py` to `llama_edge.py`
- Rename the `WasmChatService` class to `ChatService`
- Implement the `stream` interface for `ChatService`
- Add `test_chat_wasm_service_streaming` in the integration test
- Update `llama_edge.ipynb`
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
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- **Issue:** the issue # it fixes if applicable,
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
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submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
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tests, lint, etc: https://python.langchain.com/docs/contributing/
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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.
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@baskaryan, @eyurtsev, @hwchase17.
-->
See preview :
https://langchain-git-fork-cbornet-astra-loader-doc-langchain.vercel.app/docs/integrations/document_loaders/astradb
Todo
- [x] copy over integration tests
- [x] update docs with new instructions in #15513
- [x] add linear ticket to bump core -> community, community->langchain,
and core->openai deps
- [ ] (optional): add `pip install langchain-openai` command to each
notebook using it
- [x] Update docstrings to not need `openai` install
- [x] Add serialization
- [x] deprecate old models
Contributor steps:
- [x] Add secret names to manual integrations workflow in
.github/workflows/_integration_test.yml
- [x] Add secrets to release workflow (for pre-release testing) in
.github/workflows/_release.yml
Maintainer steps (Contributors should not do these):
- [x] set up pypi and test pypi projects
- [x] add credential secrets to Github Actions
- [ ] add package to conda-forge
Functional changes to existing classes:
- now relies on openai client v1 (1.6.1) via concrete dep in
langchain-openai package
Codebase organization
- some function calling stuff moved to
`langchain_core.utils.function_calling` in order to be used in both
community and langchain-openai
<!-- Thank you for contributing to LangChain!
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras`
directory.
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@baskaryan, @eyurtsev, @hwchase17.
-->
Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
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Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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2. an example notebook showing its use. It lives in `docs/extras`
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-->
- **Description:**
- This PR introduces a significant enhancement to the LangChain project
by integrating a new chat model powered by the third-generation base
large model, ChatGLM3, via the zhipuai API.
- This advanced model supports functionalities like function calls, code
interpretation, and intelligent Agent capabilities.
- The additions include the chat model itself, comprehensive
documentation in the form of Python notebook docs, and thorough testing
with both unit and integrated tests.
- **Dependencies:** This update relies on the ZhipuAI package as a key
dependency.
- **Twitter handle:** If this PR receives spotlight attention, we would
be honored to receive a mention for our integration of the advanced
ChatGLM3 model via the ZhipuAI API. Kindly tag us at @kaiwu.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
TO DO: Continue refining and enhancing both the unit tests and
integrated tests.
---------
Co-authored-by: jing <jingguo92@gmail.com>
Co-authored-by: hyy1987 <779003812@qq.com>
Co-authored-by: jianchuanqi <qijianchuan@hotmail.com>
Co-authored-by: lirq <whuclarence@gmail.com>
Co-authored-by: whucalrence <81530213+whucalrence@users.noreply.github.com>
Co-authored-by: Jing Guo <48378126+JaneCrystall@users.noreply.github.com>
Description: Volcano Ark is an enterprise-grade large-model service
platform for developers, providing a full range of functions and
services such as model training, inference, evaluation, fine-tuning. You
can visit its homepage at https://www.volcengine.com/docs/82379/1099455
for details. This change could help developers use the platform for
embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns
---------
Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi
Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream
generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should
be properly handled when set by environment variables or initialization
parameters.
- Changed the `dashscope` response to incremental output by setting the
parameter `incremental_output`, which eliminates the need for the
prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`,
`_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and
`Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke
the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** `tools.gmail.send_message` implements a
`SendMessageSchema` that is not used anywhere. `GmailSendMessage` also
does not have an `args_schema` attribute (this led to issues when
invoking the tool with an OpenAI functions agent, at least for me). Here
we add the missing attribute and a minimal test for the tool.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
---------
Co-authored-by: Chester Curme <chestercurme@microsoft.com>
- **Description:** This PR fixes test failures on Windows caused by path
handling differences and unescaped special characters in regex. The
failing tests are:
```
FAILED tests/unit_tests/storage/test_filesystem.py::test_yield_keys - AssertionError: assert ['key1', 'subdir\\key2'] == ['key1', 'subdir/key2']
FAILED tests/unit_tests/test_imports.py::test_importable_all - ModuleNotFoundError: No module named 'langchain_community.langchain_community\\adapters'
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_absolute - re.error: incomplete escape \U at position 53
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_parent_dir - re.error: incomplete escape \U at position 69
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_for_symlink_outside_root - re.error: incomplete escape \U at position 64
```
- **Issue:** fixes
https://github.com/langchain-ai/langchain/issues/11775 (partially)
- **Dependencies:** none
Builds on #14040 with community refactor merged and notebook updated.
Note that with this refactor, models will be imported from
`langchain_community.chat_models.huggingface` rather than the main
`langchain` repo.
---------
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Signed-off-by: ugm2 <unaigaraymaestre@gmail.com>
Signed-off-by: Yuchen Liang <yuchenl3@andrew.cmu.edu>
Co-authored-by: Andrew Reed <andrew.reed.r@gmail.com>
Co-authored-by: Andrew Reed <areed1242@gmail.com>
Co-authored-by: A-Roucher <aymeric.roucher@gmail.com>
Co-authored-by: Aymeric Roucher <69208727+A-Roucher@users.noreply.github.com>
Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
## Description
This PR intends to add support for Qdrant's new [sparse vector
retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing
a new retriever class, `QdrantSparseVectorRetriever`.
Necessary usage docs and integration tests have been added for the
retriever.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds support for PygmalionAI's [Aphrodite
Engine](https://github.com/PygmalionAI/aphrodite-engine), based on
vLLM's attention mechanism. At the moment, this PR does not include
support for the API servers, but they will be added in a later PR.
The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the
version to prevent breakage due to changes in the aphrodite-engine
library.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Going forward, we have a own API `pip install
gradientai`. Therefore gradually removing the self-build packages in
llamaindex, haystack and langchain.
- **Issue:** None.
- **Dependencies:** `pip install gradientai`
- **Tag maintainer:** @michaelfeil
This is technically a breaking change because it'll switch out default
models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI
is shutting off those endpoints on 1/4 anyways.
Feels less disruptive to switch out the default instead.
- **Description:** VertexAIEmbeddings performance improvements
- **Twitter handle:** @vladkol
## Improvements
- Dynamic batch size, starting from 250, lowering down to 5. Batch size
varies across regions.
Some regions support larger batches, and it significantly improves
performance.
When running large batches of texts in `us-central1`, performance gain
can be up to 3.5x.
The dynamic batching also makes sure every batch is below 20K token
limit.
- New model parameter `embeddings_type` that translates to `task_type`
parameter of the API. Newer model versions support [different embeddings
task
types](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#api_changes_to_models_released_on_or_after_august_2023).
