**Description:** We improve the performance of the InMemoryVectorStore.
**Isue:** Originally, similarity was computed document by document:
```
for doc in self.store.values():
vector = doc["vector"]
similarity = float(cosine_similarity([embedding], [vector]).item(0))
```
This is inefficient and does not make use of numpy vectorization.
This PR computes the similarity in one vectorized go:
```
docs = list(self.store.values())
similarity = cosine_similarity([embedding], [doc["vector"] for doc in docs])
```
**Dependencies:** None
**Twitter handle:** @b12_consulting, @Vincent_Min
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:** Returns the document id along with the Vector Search
results
**Issue:** Fixes https://github.com/langchain-ai/langchain/issues/26860
for CouchbaseVectorStore
- [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.
Co-authored-by: Erick Friis <erick@langchain.dev>
Reopened as a personal repo outside the organization.
## Description
- Naver HyperCLOVA X community package
- Add chat model & embeddings
- Add unit test & integration test
- Add chat model & embeddings docs
- I changed partner
package(https://github.com/langchain-ai/langchain/pull/24252) to
community package on this PR
- Could this
embeddings(https://github.com/langchain-ai/langchain/pull/21890) be
deprecated? We are trying to replace it with embedding
model(**ClovaXEmbeddings**) in this PR.
Twitter handle: None. (if needed, contact with
joonha.jeon@navercorp.com)
---
you can check our previous discussion below:
> one question on namespaces - would it make sense to have these in
.clova namespaces instead of .naver?
I would like to keep it as is, unless it is essential to unify the
package name.
(ClovaX is a branding for the model, and I plan to add other models and
components. They need to be managed as separate classes.)
> also, could you clarify the difference between ClovaEmbeddings and
ClovaXEmbeddings?
There are 3 models that are being serviced by embedding, and all are
supported in the current PR. In addition, all the functionality of CLOVA
Studio that serves actual models, such as distinguishing between test
apps and service apps, is supported. The existing PR does not support
this content because it is hard-coded.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Vadym Barda <vadym@langchain.dev>
## Description
I encountered an error while using the` gemma-2-2b-it model` with the
`HuggingFacePipeline` class and have implemented a fix to resolve this
issue.
### What is Problem
```python
model_id="google/gemma-2-2b-it"
gemma_2_model = AutoModelForCausalLM.from_pretrained(model_id)
gemma_2_tokenizer = AutoTokenizer.from_pretrained(model_id)
gen = pipeline(
task='text-generation',
model=gemma_2_model,
tokenizer=gemma_2_tokenizer,
max_new_tokens=1024,
device=0 if torch.cuda.is_available() else -1,
temperature=.5,
top_p=0.7,
repetition_penalty=1.1,
do_sample=True,
)
llm = HuggingFacePipeline(pipeline=gen)
for chunk in llm.stream("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World."):
print(chunk, end="", flush=True)
```
This code outputs the following error message:
```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
Exception in thread Thread-19 (generate):
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1874, in generate
self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 1266, in _validate_generated_length
raise ValueError(
ValueError: Input length of input_ids is 31, but `max_length` is set to 20. This can lead to unexpected behavior. You should consider increasing `max_length` or, better yet, setting `max_new_tokens`.
```
In addition, the following error occurs when the number of tokens is
reduced.
```python
for chunk in llm.stream("Hello World"):
print(chunk, end="", flush=True)
```
```
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1258: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1885: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.
warnings.warn(
Exception in thread Thread-20 (generate):
Traceback (most recent call last):
File "/usr/lib/python3.10/threading.py", line 1016, in _bootstrap_inner
self.run()
File "/usr/lib/python3.10/threading.py", line 953, in run
self._target(*self._args, **self._kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2024, in generate
result = self._sample(
File "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py", line 2982, in _sample
outputs = self(**model_inputs, return_dict=True)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 994, in forward
outputs = self.model(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/transformers/models/gemma2/modeling_gemma2.py", line 803, in forward
inputs_embeds = self.embed_tokens(input_ids)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/sparse.py", line 164, in forward
return F.embedding(
File "/usr/local/lib/python3.10/dist-packages/torch/nn/functional.py", line 2267, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)
```
On the other hand, in the case of invoke, the output is normal:
```
llm.invoke("Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.")
```
```
'Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World. Hello World.\n\nThis is a simple program that prints the phrase "Hello World" to the console. \n\n**Here\'s how it works:**\n\n* **`print("Hello World")`**: This line of code uses the `print()` function, which is a built-in function in most programming languages (like Python). The `print()` function takes whatever you put inside its parentheses and displays it on the screen.\n* **`"Hello World"`**: The text within the double quotes (`"`) is called a string. It represents the message we want to print.\n\n\nLet me know if you\'d like to explore other programming concepts or see more examples! \n'
```
### Problem Analysis
- Apparently, I put kwargs in while generating pipelines and it applied
to `invoke()`, but it's not applied in the `stream()`.
- When using the stream, `inputs = self.pipeline.tokenizer (prompt,
return_tensors = "pt")` enters cpu.
- This can crash when the model is in gpu.
### Solution
Just use `self.pipeline` instead of `self.pipeline.model.generate`.
- **Original Code**
```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
inputs = self.pipeline.tokenizer(prompt, return_tensors="pt")
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
inputs,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline.model.generate, kwargs=generation_kwargs)
t1.start()
```
- **Updated Code**
```python
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
text_inputs= prompt,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline, kwargs=generation_kwargs)
t1.start()
```
By using the `pipeline` directly, the `kwargs` of the pipeline are
applied, and there is no need to consider the `device` of the `tensor`
made with the `tokenizer`.
> According to the change to use `pipeline`, it was modified to put
`text_inputs=prompts` directly into `generation_kwargs`.
## Issue
None
## Dependencies
None
## Twitter handle
None
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
**Issue:** : https://github.com/langchain-ai/langchain/issues/22961
**Description:**
Previously, the documentation for `DuckDuckGoSearchResults` said that it
returns a JSON string, however the code returns a regular string that
can't be parsed as is.
for example running
```python
from langchain_community.tools import DuckDuckGoSearchResults
# Create a DuckDuckGo search instance
search = DuckDuckGoSearchResults()
# Invoke the search
result = search.invoke("Obama")
# Print the result
print(result)
# Print the type of the result
print("Result Type:", type(result))
```
will return
```
snippet: Harris will hold a campaign event with former President Barack Obama in Georgia next Thursday, the first time the pair has campaigned side by side, a senior campaign official said. A week from ..., title: Obamas to hit the campaign trail in first joint appearances with Harris, link: https://www.nbcnews.com/politics/2024-election/obamas-hit-campaign-trail-first-joint-appearances-harris-rcna176034, snippet: Item 1 of 3 Former U.S. first lady Michelle Obama and her husband, former U.S. President Barack Obama, stand on stage during Day 2 of the Democratic National Convention (DNC) in Chicago, Illinois ..., title: Obamas set to hit campaign trail with Kamala Harris for first time, link: https://www.reuters.com/world/us/obamas-set-hit-campaign-trail-with-kamala-harris-first-time-2024-10-18/, snippet: Barack and Michelle Obama will make their first campaign appearances alongside Kamala Harris at rallies in Georgia and Michigan. By Reid J. Epstein Reporting from Ashwaubenon, Wis. Here come the ..., title: Harris Will Join Michelle Obama and Barack Obama on Campaign Trail, link: https://www.nytimes.com/2024/10/18/us/politics/kamala-harris-michelle-obama-barack-obama.html, snippet: Obama's leaving office was "a turning point," Mirsky said. "That was the last time anybody felt normal." A few feet over, a 64-year-old physics professor named Eric Swanson who had grown ..., title: Obama's reemergence on the campaign trail for Harris comes as he ..., link: https://www.cnn.com/2024/10/13/politics/obama-campaign-trail-harris-biden/index.html
Result Type: <class 'str'>
```
After the change in this PR, `DuckDuckGoSearchResults` takes an
additional `output_format = "list" | "json" | "string"` ("string" =
current behavior, default). For example, invoking
`DuckDuckGoSearchResults(output_format="list")` return a list of
dictionaries in the format
```
[{'snippet': '...', 'title': '...', 'link': '...'}, ...]
```
e.g.
```
[{'snippet': "Obama has in a sense been wrestling with Trump's impact since the real estate magnate broke onto the political stage in 2015. Trump's victory the next year, defeating Obama's secretary of ...", 'title': "Obama's fears about Trump drive his stepped-up campaigning", 'link': 'https://www.washingtonpost.com/politics/2024/10/18/obama-trump-anxiety-harris-campaign/'}, {'snippet': 'Harris will hold a campaign event with former President Barack Obama in Georgia next Thursday, the first time the pair has campaigned side by side, a senior campaign official said. A week from ...', 'title': 'Obamas to hit the campaign trail in first joint appearances with Harris', 'link': 'https://www.nbcnews.com/politics/2024-election/obamas-hit-campaign-trail-first-joint-appearances-harris-rcna176034'}, {'snippet': 'Item 1 of 3 Former U.S. first lady Michelle Obama and her husband, former U.S. President Barack Obama, stand on stage during Day 2 of the Democratic National Convention (DNC) in Chicago, Illinois ...', 'title': 'Obamas set to hit campaign trail with Kamala Harris for first time', 'link': 'https://www.reuters.com/world/us/obamas-set-hit-campaign-trail-with-kamala-harris-first-time-2024-10-18/'}, {'snippet': 'Barack and Michelle Obama will make their first campaign appearances alongside Kamala Harris at rallies in Georgia and Michigan. By Reid J. Epstein Reporting from Ashwaubenon, Wis. Here come the ...', 'title': 'Harris Will Join Michelle Obama and Barack Obama on Campaign Trail', 'link': 'https://www.nytimes.com/2024/10/18/us/politics/kamala-harris-michelle-obama-barack-obama.html'}]
Result Type: <class 'list'>
```
---------
Co-authored-by: vbarda <vadym@langchain.dev>
- [ ] **Description:**
- pass the device_map into model_kwargs
- removing the unused device_map variable in the hf_pipeline function
call
- [ ] **Issue:** issue #13128
When using the from_model_id function to load a Hugging Face model for
text generation across multiple GPUs, the model defaults to loading on
the CPU despite multiple GPUs being available using the expected format
``` python
llm = HuggingFacePipeline.from_model_id(
model_id="model-id",
task="text-generation",
device_map="auto",
)
```
Currently, to enable multiple GPU , we have to pass in variable in this
format instead
``` python
llm = HuggingFacePipeline.from_model_id(
model_id="model-id",
task="text-generation",
device=None,
model_kwargs={
"device_map": "auto",
}
)
```
This issue arises due to improper handling of the device and device_map
parameters.
- [ ] **Explanation:**
1. In from_model_id, the model is created using model_kwargs and passed
as the model variable of the pipeline function. So at this moment, to
load the model with multiple GPUs, "device_map" needs to be set to
"auto" within model_kwargs. Otherwise, the model defaults to loading on
the CPU.
2. The device_map variable in from_model_id is not utilized correctly.
In the pipeline function's source code of tnansformer:
- The device_map variable is stored in the model_kwargs dictionary
(lines 867-878 of transformers/src/transformers/pipelines/\__init__.py).
```python
if device_map is not None:
......
model_kwargs["device_map"] = device_map
```
- The model is constructed with model_kwargs containing the device_map
value ONLY IF it is a string (lines 893-903 of
transformers/src/transformers/pipelines/\__init__.py).
```python
if isinstance(model, str) or framework is None:
model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
framework, model = infer_framework_load_model( ... , **model_kwargs, )
```
- Consequently, since a model object is already passed to the pipeline
function, the device_map variable from from_model_id is never used.
3. The device_map variable in from_model_id not only appears unused but
also causes errors. Without explicitly setting device=None, attempting
to load the model on multiple GPUs may result in the following error:
```
Device has 2 GPUs available. Provide device={deviceId} to
`from_model_id` to use available GPUs for execution. deviceId is -1
(default) for CPU and can be a positive integer associated with CUDA
device id.
Traceback (most recent call last):
File "foo.py", line 15, in <module>
llm = HuggingFacePipeline.from_model_id(
File
"foo\site-packages\langchain_huggingface\llms\huggingface_pipeline.py",
line 217, in from_model_id
pipeline = hf_pipeline(
File "foo\lib\site-packages\transformers\pipelines\__init__.py", line
1108, in pipeline
return pipeline_class(model=model, framework=framework, task=task,
**kwargs)
File "foo\lib\site-packages\transformers\pipelines\text_generation.py",
line 96, in __init__
super().__init__(*args, **kwargs)
File "foo\lib\site-packages\transformers\pipelines\base.py", line 835,
in __init__
raise ValueError(
ValueError: The model has been loaded with `accelerate` and therefore
cannot be moved to a specific device. Please discard the `device`
argument when creating your pipeline object.
```
This error occurs because, in from_model_id, the default values in from_model_id for device and device_map are -1 and None, respectively. It would passes the statement (`device_map is not None and device < 0`) and keep the device as -1 so the pipeline function later raises an error when trying to move a GPU-loaded model back to the CPU.
19eb82e68b/libs/community/langchain_community/llms/huggingface_pipeline.py (L204-L213)
If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: vbarda <vadym@langchain.dev>
This PR introduces a new `azure_ad_async_token_provider` attribute to
the `AzureOpenAI` and `AzureChatOpenAI` classes in `partners/openai` and
`community` packages, given it's currently supported on `openai` package
as
[AsyncAzureADTokenProvider](https://github.com/openai/openai-python/blob/main/src/openai/lib/azure.py#L33)
type.
The reason for creating a new attribute is to avoid breaking changes.
Let's say you have an existing code that uses a `AzureOpenAI` or
`AzureChatOpenAI` instance to perform both sync and async operations.
The `azure_ad_token_provider` will work exactly as it is today, while
`azure_ad_async_token_provider` will override it for async requests.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
**Description:**
This PR updates `CassandraGraphVectorStore` to be based off
`CassandraVectorStore`, instead of using a custom CQL implementation.
This allows users using a `CassandraVectorStore` to upgrade to a
`GraphVectorStore` without having to change their database schema or
re-embed documents.
This PR also updates the documentation of the `GraphVectorStore` base
class and contains native async implementations for the standard graph
methods: `traversal_search` and `mmr_traversal_search` in
`CassandraVectorStore`.
**Issue:** No issue number.
**Dependencies:** https://github.com/langchain-ai/langchain/pull/27078
(already-merged)
**Lint and test**:
- Lint and tests all pass, including existing
`CassandraGraphVectorStore` tests.
- Also added numerous additional tests based of the tests in
`langchain-astradb` which cover many more scenarios than the existing
tests for `Cassandra` and `CassandraGraphVectorStore`
** BREAKING CHANGE**
Note that this is a breaking change for existing users of
`CassandraGraphVectorStore`. They will need to wipe their database table
and restart.
However:
- The interfaces have not changed. Just the underlying storage
mechanism.
- Any one using `langchain_community.vectorstores.Cassandra` can instead
use `langchain_community.graph_vectorstores.CassandraGraphVectorStore`
and they will gain Graph capabilities without having to re-embed their
existing documents. This is the primary goal of this PR.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Fixes#27411
**Description:** Adds `template_format` to the `ImagePromptTemplate`
class and updates passing in the `template_format` parameter from
ChatPromptTemplate instead of the hardcoded "f-string".
Also updated docs and typing related to `template_format` to be more
up-to-date and specific.
**Dependencies:** None
**Add tests and docs**: Added unit tests to validate fix. Needed to
update `test_chat` snapshot due to adding new attribute
`template_format` in `ImagePromptTemplate`.
---------
Co-authored-by: Vadym Barda <vadym@langchain.dev>
Thank you for contributing to LangChain!
- [ X] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [ X]
- **Issue:** issue #26941
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, ccurme, vbarda, hwchase17.
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**
This PR introduces the proxies parameter to the RecursiveUrlLoader
class, allowing the user to specify proxy servers for requests. This
update enables crawling through proxy servers, providing enhanced
flexibility for network configurations.
The key changes include:
1.Added an optional proxies parameter to the constructor (__init__).
2.Updated the documentation to explain the proxies parameter usage with
an example.
3.Modified the _get_child_links_recursive method to pass the proxies
parameter to the requests.get function.
**Sample Usage**
```python
from bs4 import BeautifulSoup as Soup
from langchain_community.document_loaders.recursive_url_loader import RecursiveUrlLoader
proxies = {
"http": "http://localhost:1080",
"https": "http://localhost:1080",
}
url = "https://python.langchain.com/docs/concepts/#langchain-expression-language-lcel"
loader = RecursiveUrlLoader(
url=url, max_depth=1, extractor=lambda x: Soup(x, "html.parser").text,proxies=proxies
)
docs = loader.load()
```
---------
Co-authored-by: root <root@thb>
We have released the
[langchain-databricks](https://github.com/langchain-ai/langchain-databricks)
package for Databricks integration. This PR deprecates the legacy
classes within `langchain-community`.
---------
Signed-off-by: B-Step62 <yuki.watanabe@databricks.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**:
This PR add support of clob/blob data type for oracle document loader,
clob/blob can only be read by oracledb package when connection is open,
so reformat code to process data before connection closes.
**Dependencies**:
oracledb package same as before. pip install oracledb
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
- This pull request addresses a bug in Langchain's VLLM integration,
where the use_beam_search parameter was erroneously passed to
SamplingParams. The SamplingParams class in vLLM does not support the
use_beam_search argument, which caused a TypeError.
- This PR introduces logic to filter out unsupported parameters,
ensuring that only valid parameters are passed to SamplingParams. As a
result, the integration now functions as expected without errors.
- The bug was reproduced by running the code sample from Langchain’s
documentation, which triggered the error due to the invalid parameter.
This fix resolves that error by implementing proper parameter filtering.
**VLLM Sampling Params Class:**
https://github.com/vllm-project/vllm/blob/main/vllm/sampling_params.py
**Issue:**
I could not found an Issue that belongs to this. Fixes "TypeError:
Unexpected keyword argument 'use_beam_search'" error when using VLLM
from Langchain.
**Dependencies:**
None.
**Tests and Documentation**:
Tests:
No new functionality was added, but I tested the changes by running
multiple prompts through the VLLM integration with various parameter
configurations. All tests passed successfully without breaking
compatibility.
Docs
No documentation changes were necessary as this is a bug fix.
**Reproducing the Error:**
https://python.langchain.com/docs/integrations/llms/vllm/
The code sample from the original documentation can be used to reproduce
the error I got.
from langchain_community.llms import VLLM
llm = VLLM(
model="mosaicml/mpt-7b",
trust_remote_code=True, # mandatory for hf models
max_new_tokens=128,
top_k=10,
top_p=0.95,
temperature=0.8,
)
print(llm.invoke("What is the capital of France ?"))
This PR resolves the issue by ensuring that only valid parameters are
passed to SamplingParams.
**Description**: PR fixes some formatting errors in deprecation message
in the `langchain_community.vectorstores.pgvector` module, where it was
missing spaces between a few words, and one word was misspelled.
**Issue**: n/a
**Dependencies**: n/a
Signed-off-by: mpeveler@timescale.com
Co-authored-by: Erick Friis <erick@langchain.dev>
PR message:
Description:
This PR refactors the Arxiv API wrapper by extracting the Arxiv search
logic into a helper function (_fetch_results) to reduce code duplication
and improve maintainability. The helper function is used in methods like
get_summaries_as_docs, run, and lazy_load, streamlining the code and
making it easier to maintain in the future.
Issue:
This is a minor refactor, so no specific issue is being fixed.
Dependencies:
No new dependencies are introduced with this change.
Add tests and docs:
No new integrations were added, so no additional tests or docs are
necessary for this PR.
Lint and test:
I have run make format, make lint, and make test to ensure all checks
pass successfully.
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Co-authored-by: Erick Friis <erick@langchain.dev>
This PR updates the integration with OCI data science model deployment
service.
- Update LLM to support streaming and async calls.
- Added chat model.
- Updated tests and docs.
- Updated `libs/community/scripts/check_pydantic.sh` since the use of
`@pre_init` is removed from existing integration.
- Updated `libs/community/extended_testing_deps.txt` as this integration
requires `langchain_openai`.
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Co-authored-by: MING KANG <ming.kang@oracle.com>
Co-authored-by: Dmitrii Cherkasov <dmitrii.cherkasov@oracle.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
