- **Description:** Add `TrainableLLM` for those LLM support fine-tuning
- **Tag maintainer:** @hwchase17
This PR add training methods to `GradientLLM`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Hi there
This PR is aim to implement chat model for Alibaba Tongyi LLM model. It
contains work below:
1.Implement ChatTongyi chat model in langchain.chat_models.tongyi. Note
this is different with tongyi llm model to another PR
https://github.com/langchain-ai/langchain/pull/10878.
For detail it implements _generate() and _stream() function in
ChatTongyi.
2. Add some examples in chat/tongyi.ipynb.
3. Add integration test in chat_models/test_tongyi.py
Note async completion for the Text API is not yet supported.
Dependencies: dashscope. It will be installed manually cause it is not
need by everyone.
**Description**
This PR adds the `ElasticsearchChatMessageHistory` implementation that
stores chat message history in the configured
[Elasticsearch](https://www.elastic.co/elasticsearch/) deployment.
```python
from langchain.memory.chat_message_histories import ElasticsearchChatMessageHistory
history = ElasticsearchChatMessageHistory(
es_url="https://my-elasticsearch-deployment-url:9200", index="chat-history-index", session_id="123"
)
history.add_ai_message("This is me, the AI")
history.add_user_message("This is me, the human")
```
**Dependencies**
- [elasticsearch client](https://elasticsearch-py.readthedocs.io/)
required
Co-authored-by: Bagatur <baskaryan@gmail.com>
Instead of accessing `langchain.debug`, `langchain.verbose`, or
`langchain.llm_cache`, please use the new getter/setter functions in
`langchain.globals`:
- `langchain.globals.set_debug()` and `langchain.globals.get_debug()`
- `langchain.globals.set_verbose()` and
`langchain.globals.get_verbose()`
- `langchain.globals.set_llm_cache()` and
`langchain.globals.get_llm_cache()`
Using the old globals directly will now raise a warning.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
Add a document loader for the RSpace Electronic Lab Notebook
(www.researchspace.com), so that scientific documents and research notes
can be easily pulled into Langchain pipelines.
**Issue**
This is an new contribution, rather than an issue fix.
**Dependencies:**
There are no new required dependencies.
In order to use the loader, clients will need to install rspace_client
SDK using `pip install rspace_client`
---------
Co-authored-by: richarda23 <richard.c.adams@infinityworks.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update Indexing API docs to specify vectorstores that
are compatible with the Indexing API. I add a unit test to remind
developers to update the documentation whenever they add or change a
vectorstore in a way that affects compatibility. For the unit test I
repurposed existing code from
[here](https://github.com/langchain-ai/langchain/blob/v0.0.311/libs/langchain/langchain/indexes/_api.py#L245-L257).
This is my first PR to an open source project. This is a trivially
simple PR whose main purpose is to make me more comfortable submitting
Langchain PRs. If this PR goes through I plan to submit PRs with more
substantive changes in the near future.
**Issue:** Resolves
[10482](https://github.com/langchain-ai/langchain/discussions/10482).
**Dependencies:** No new dependencies.
**Twitter handle:** None.
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Allows MMR functionality only for the case where we have access to the
embedding function. Also allows for users to request for fields from
elasticsearch store. These are added to the document metadata.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Introducing an ability to load a transcription document of
audio file using [Yandex
SpeechKit](https://cloud.yandex.com/en-ru/services/speechkit)
Issue: None
Dependencies: yandex-speechkit
Tag maintainer: @rlancemartin, @eyurtsev
**Description**
This PR implements the usage of the correct tokenizer in Bedrock LLMs,
if using anthropic models.
**Issue:** #11560
**Dependencies:** optional dependency on `anthropic` python library.
**Twitter handle:** jtolgyesi
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Modify Anyscale integration to work with [Anyscale
Endpoint](https://docs.endpoints.anyscale.com/)
and it supports invoke, async invoke, stream and async invoke features
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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Should delegate to parse_result, not to aparse, as parse_result is a
method that some output parsers override
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**Description:** Avoid huggingfacepipeline to truncate the response if
user setup return_full_text as False within huggingface pipeline.
**Dependencies:** : None
**Tag maintainer:** Maybe @sam-h-bean ?
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** implements a retriever on top of DocAI Warehouse (to
interact with existing enterprise documents)
https://cloud.google.com/document-ai-warehouse?hl=en
- **Issue:** new functionality
@baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
No relevant documents may be found for a given question. In some use
cases, we could directly respond with a fixed message instead of doing
an LLM call with an empty context. This PR exposes this as an option:
response_if_no_docs_found.
---------
Co-authored-by: Sudharsan Rangarajan <sudranga@nile-global.com>
