## 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>
- **Description:**
This PR fixes the issue faces with duplicate input id in Clarifai
vectorstore class when ingesting documents into the vectorstore more
than the batch size.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Similar to https://github.com/langchain-ai/langchain/issues/5861, I've
experienced `KeyError`s resulting from unsafe lookups in the
`convert_dict_to_message` function in [this
file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/adapters/openai.py).
While that issue focused on `KeyError 'content'`, I've opened another
issue (#14764) about how the problem still exists in the same function
but with `KeyError 'role'`. The fix for #5861 only added a safe lookup
to the specific line that was giving them trouble.. This PR fixes the
unsafe lookup in the rest of the function but the problem still exists
across the repo.
## Issues
* #14764
* #5861
## Dependencies
* None
## Checklist
[x] make format
[x] make lint
[ ] make test - Results in `make: *** No rule to make target 'test'.
Stop.`
## Maintainers
* @hinthornw
---------
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:** Introducing an ability to work with the
[YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) embeddings
models.
---------
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
- **Description:** Modify community chat model vertexai to handle png
and other image types encoded in base64
- **Dependencies:** added `import re` but no new dependencies.
This addresses a problem where the vertexai method
_parse_chat_history_gemini() was only recognizing image uris in jpeg
format. I made a simple change to cover other extension types.
- **Description:** The Qianfan SDK offers multiple authentication
methods, but in the `QianfanEndpoint` of Langchain, it currently only
supports authentication through AK and SK. In order to accommodate users
who wish to use alternative authentication methods, this pull request
makes AK and SK optional. This change should not impact existing users,
while allowing users to configure other authentication methods as per
the Qianfan SDK documentation.
- **Issue:** /
- **Dependencies:** No
- **Tag maintainer:** No
- **Twitter handle:**
Added Entry ID as a return value inside get_summaries_as_docs
- **Description:** Added the Entry ID as a return, so it's easier to
track the IDs of the papers that are being returned.
With the addition return of the entry ID in functions like
ArxivRetriever, it will be easier to reference the ID of the paper
itself.
- **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
Very simple change in relation to the issue
https://github.com/langchain-ai/langchain/issues/14550
@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Added logic for re-calling the YandexGPT API in case of
an error
---------
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
Description: A new vector store Jaguar is being added. Class, test
scripts, and documentation is added.
Issue: None -- This is the first PR contributing to LangChain
Dependencies: This depends on "pip install -U jaguardb-http-client"
client http package
Tag maintainer: @baskaryan, @eyurtsev, @hwchase1
Twitter handle: @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Addded missed docstrings. Fixed inconsistency in docstrings.
**Note** CC @efriis
There were PR errors on
`langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py`
But, I didn't touch this file in this PR! Can it be some cache problems?
I fixed this error.
Fixing issue - https://github.com/langchain-ai/langchain/issues/14494 to
avoid Kendra query ValidationException
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Replace this entire comment with:
- **Description:** Update kendra.py to avoid Kendra query
ValidationException,
- **Issue:** the issue
#https://github.com/langchain-ai/langchain/issues/14494,
- **Dependencies:** None,
- **Tag maintainer:** ,
- **Twitter handle:**
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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).
Now that it's supported again for OAI chat models .
Shame this wouldn't include it in the `.invoke()` output though (it's
not included in the message itself). Would need to do a follow-up for
that to be the case
Fixed:
- `_agenerate` return value in the YandexGPT Chat Model
- duplicate line in the documentation
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
Builds out a developer documentation section in the docs
- Links it from contributing.md
- Adds an initial guide on how to contribute an integration
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds the option for `similarity_score_threshold` when using
`MongoDBAtlasVectorSearch` as a vector store retriever.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa_retriever = vector_search.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
"score_threshold": 0.5,
}
)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=qa_retriever,
)
docs = qa({"query": "..."})
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a
vector search index.
Replace this entire comment with:
- **Description:** added support for new Google GenerativeAI models
- **Twitter handle:** lkuligin
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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- **Issue:** the issue # it fixes (if applicable),
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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:
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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
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.
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Co-authored-by: fangkeke <3339698829@qq.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
h/t to @lkuligin
- **Description:** added new models on VertexAI
- **Twitter handle:** @lkuligin
---------
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds an example notebook for the Databricks Vector Search vector
store. It also adds an introduction to the Databricks Vector Search
product on the Databricks's provider page.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
When using local Chatglm2-6B by changing OPENAI_BASE_URL to localhost,
the token_usage in ChatOpenAI becomes None. This leads to an
AttributeError when trying to access token_usage.items().
This commit adds a check to ensure token_usage is not None before
accessing its items. This change prevents the AttributeError and allows
ChatOpenAI to work seamlessly with a local Chatglm2-6B model, aligning
with the way it operates with the OpenAI API.
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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
locally.
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`
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@baskaryan, @eyurtsev, @hwchase17.
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** This PR fixes `HuggingFaceHubEmbeddings` by making the
API token optional (as in the client beneath). Most models don't require
one. I also updated the notebook for TEI (text-embeddings-inference)
accordingly as requested here #14288. In addition, I fixed a mistake in
the POST call parameters.
**Tag maintainers:** @baskaryan
- **Description:** There is a bug in RedisNum filter that filter towards
value 0 will be parsed as "*". This is a fix to it.
- **Issue:** NA
- **Dependencies:** NA
- **Tag maintainer:** NA
- **Twitter handle:** NA
TIL `**` globstar doesn't work in make
Makefile changes fix that.
`__getattr__` changes allow import of all files, but raise error when
accessing anything from the module.
file deletions were corresponding libs change from #14559
