- **Description:** added support for chat_history for Google
GenerativeAI (to actually use the `chat` API) plus since Gemini
currently doesn't have a support for SystemMessage, added support for it
only if a user provides additional `convert_system_message_to_human`
flag during model initialization (in this case, SystemMessage would be
prepanded to the first HumanMessage)
- **Issue:** #14710
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** lkuligin
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
- updated `Tencent` provider page: added a chat model and document
loader references; company description
- updated Chat model and Document loader pages with descriptions, links
- renamed files to consistent formats; redirected file names
Note:
I was getting this linting error on code that **was not changed in my
PR**!
> Error:
docs/docs/guides/safety/hugging_face_prompt_injection.ipynb:1:1: I001
Import block is un-sorted or un-formatted
> make: *** [Makefile:47: lint_package] Error 1
I've fixed this error in the notebook
Replace this entire comment with:
- **Description:** OPENAI_PROXY is not working for openai==1.3.9, The
`proxies` argument is deprecated. The `http_client` argument should be
passed instead,
- **Issue:** OPENAI_PROXY is not working,
- **Dependencies:** None,
- **Tag maintainer:** @hwchase17 ,
- **Twitter handle:** timothy66666
- **Description:** This is addition to [my previous
PR](https://github.com/langchain-ai/langchain/pull/13930) with
improvements to flexibility allowing different models and notebook to
use ONNX runtime for faster speed. Since the last PR, [our
model](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection)
got more than 660k downloads, and with the [public
benchmark](https://huggingface.co/spaces/laiyer/prompt-injection-benchmark)
showed much fewer false-positives than the previous one from deepset.
Additionally, on the ONNX runtime, it can be running 3x faster on the
CPU, which might be handy for builders using Langchain.
**Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** N/A
- **Twitter handle:** `@laiyer_ai`
Fixing issue - https://github.com/langchain-ai/langchain/issues/14494 to
avoid Kendra query ValidationException
<!-- Thank you for contributing to LangChain!
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:**
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description**
The contributing docs lists a poetry command to install community for
dev work that includes a poetry group called `integration_tests`. This
is a mistake: the poetry group for integration tests is called
`test_integration`, not `integration_tests`. See here:
https://github.com/langchain-ai/langchain/blob/master/libs/community/pyproject.toml#L119
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** no!
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://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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** SignetCode!
- **Description:**
- Add a break case to `text_splitter.py::split_text_on_tokens()` to
avoid unwanted item at the end of result.
- Add a testcase to enforce the behavior.
- **Issue:**
- #14649
- #5897
- **Dependencies:** n/a,
---
**Quick illustration of change:**
```
text = "foo bar baz 123"
tokenizer = Tokenizer(
chunk_overlap=3,
tokens_per_chunk=7
)
output = split_text_on_tokens(text=text, tokenizer=tokenizer)
```
output before change: `["foo bar", "bar baz", "baz 123", "123"]`
output after change: `["foo bar", "bar baz", "baz 123"]`
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:** Modification of descriptions for marketing purposes
and transitioning towards `platforms` directory if possible.
- **Issue:** Some marketing opportunities, lodging PR and awaiting later
discussions.
-
This PR is intended to be merged when decisions settle/hopefully after
further considerations. Submitting as Draft for now. Nobody @'d yet.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Gpt-3.5 sometimes calls with empty string arguments instead of `{}`
I'd assume it's because the typescript representation on their backend
makes it a bit ambiguous.
- **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>
<!-- 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
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`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Bagatur <baskaryan@gmail.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.
… (#14723)
- **Description:** Minor updates per marketing requests. Namely, name
decisions (AI Foundation Models / AI Playground)
- **Tag maintainer:** @hinthornw
Do want to pass around the PR for a bit and ask a few more marketing
questions before merge, but just want to make sure I'm not working in a
vacuum. No major changes to code functionality intended; the PR should
be for documentation and only minor tweaks.
Note: QA model is a bit borked across staging/prod right now. Relevant
teams have been informed and are looking into it, and I'm placeholdered
the response to that of a working version in the notebook.
Co-authored-by: Vadim Kudlay <32310964+VKudlay@users.noreply.github.com>
