Issue: When the third-party package is not installed, whenever we need
to `pip install <package>` the ImportError is raised.
But sometimes, the `ValueError` or `ModuleNotFoundError` is raised. It
is bad for consistency.
Change: replaced the `ValueError` or `ModuleNotFoundError` with
`ImportError` when we raise an error with the `pip install <package>`
message.
Note: Ideally, we replace all `try: import... except... raise ... `with
helper functions like `import_aim` or just use the existing
[langchain_core.utils.utils.guard_import](https://api.python.langchain.com/en/latest/utils/langchain_core.utils.utils.guard_import.html#langchain_core.utils.utils.guard_import)
But it would be much bigger refactoring. @baskaryan Please, advice on
this.
This introduces `store_kwargs` which behaves similarly to `graph_kwargs`
on the `RdfGraph` object, which will enable users to pass `headers` and
other arguments to the underlying `SPARQLStore` object. I have also made
a [PR in `rdflib` to support passing
`default_graph`](https://github.com/RDFLib/rdflib/pull/2761).
Example usage:
```python
from langchain_community.graphs import RdfGraph
graph = RdfGraph(
query_endpoint="http://localhost/sparql",
standard="rdf",
store_kwargs=dict(
default_graph="http://example.com/mygraph"
)
)
```
<!--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>
Description: The PebbloSafeLoader should first check for owner,
full_path and size in metadata before implementing its own logic.
Dependencies: None
Documentation: NA.
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Issue: #20514
The current implementation of `construct_instance` expects a `texts:
List[str]` that will call the embedding function. This might not be
needed when we already have a client with collection and `path, you
don't want to add any text.
This PR adds a class method that returns a qdrant instance with an
existing client.
Here everytime
cb6e5e56c2/libs/community/langchain_community/vectorstores/qdrant.py (L1592)
`construct_instance` is called, this line sends some text for embedding
generation.
---------
Co-authored-by: Anush <anushshetty90@gmail.com>
**Description:** AzureSearch vector store has no tests. This PR adds
initial tests to validate the code can be imported and used.
**Issue:** N/A
**Dependencies:** azure-search-documents and azure-identity are added as
optional dependencies for testing
---------
Co-authored-by: Matt Gotteiner <[email protected]>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**:
_PebbloSafeLoader_: Add support for pebblo server and client version
**Documentation:** NA
**Unit test:** NA
**Issue:** NA
**Dependencies:** None
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- [ ] **Kinetica Document Loader**: "community: a class to load
Documents from Kinetica"
- [ ] **Kinetica Document Loader**:
- **Description:** implemented KineticaLoader in `kinetica_loader.py`
- **Dependencies:** install the Kinetica API using `pip install
gpudb==7.2.0.1 `
- **Description**:
- **add support for more data types**: by default `IpexLLM` will load
the model in int4 format. This PR adds more data types support such as
`sym_in5`, `sym_int8`, etc. Data formats like NF3, NF4, FP4 and FP8 are
only supported on GPU and will be added in future PR.
- Fix a small issue in saving/loading, update api docs
- **Dependencies**: `ipex-llm` library
- **Document**: In `docs/docs/integrations/llms/ipex_llm.ipynb`, added
instructions for saving/loading low-bit model.
- **Tests**: added new test cases to
`libs/community/tests/integration_tests/llms/test_ipex_llm.py`, added
config params.
- **Contribution maintainer**: @shane-huang
Description: Add support for Semantic topics and entities.
Classification done by pebblo-server is not used to enhance metadata of
Documents loaded by document loaders.
Dependencies: None
Documentation: Updated.
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**
- [x] **PR message**:
- **Description:** Deprecate persist method in Chroma no longer exists
in Chroma 0.4.x
- **Issue:** #20851
- **Dependencies:** None
- **Twitter handle:** AndresAlgaba1
- [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/
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:**
The RecursiveUrlLoader loader offers a link_regex parameter that can
filter out URLs. However, this filtering capability is limited, and if
the internal links of the website change, unexpected resources may be
loaded. These resources, such as font files, can cause problems in
subsequent embedding processing.
>
https://blog.langchain.dev/assets/fonts/source-sans-pro-v21-latin-ext_latin-regular.woff2?v=0312715cbf
We can add the Content-Type in the HTTP response headers to the document
metadata so developers can choose which resources to use. This allows
developers to make their own choices.
For example, the following may be a good choice for text knowledge.
- text/plain - simple text file
- text/html - HTML web page
- text/xml - XML format file
- text/json - JSON format data
- application/pdf - PDF file
- application/msword - Word document
and ignore the following
- text/css - CSS stylesheet
- text/javascript - JavaScript script
- application/octet-stream - binary data
- image/jpeg - JPEG image
- image/png - PNG image
- image/gif - GIF image
- image/svg+xml - SVG image
- audio/mpeg - MPEG audio files
- video/mp4 - MP4 video file
- application/font-woff - WOFF font file
- application/font-ttf - TTF font file
- application/zip - ZIP compressed file
- application/octet-stream - binary data
**Twitter handle:** @coolbeevip
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adapt JinaEmbeddings to run with the new Jina AI
Rerank API
- **Twitter handle:** https://twitter.com/JinaAI_
- [ ] **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.
- [ ] **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/
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add the remove_unwanted_classnames method to the
BeautifulSoupTransformer class, which can filter more effectively.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description :
- added functionalities - delete, index creation, using existing
connection object etc.
- updated usage
- Added LaceDB cloud OSS support
make lint_diff , make test checks done
Implemented the ability to enable full-text search within the
SingleStore vector store, offering users a versatile range of search
strategies. This enhancement allows users to seamlessly combine
full-text search with vector search, enabling the following search
strategies:
* Search solely by vector similarity.
* Conduct searches exclusively based on text similarity, utilizing
Lucene internally.
* Filter search results by text similarity score, with the option to
specify a threshold, followed by a search based on vector similarity.
* Filter results by vector similarity score before conducting a search
based on text similarity.
* Perform searches using a weighted sum of vector and text similarity
scores.
Additionally, integration tests have been added to comprehensively cover
all scenarios.
Updated notebook with examples.
CC: @baskaryan, @hwchase17
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- added guard on the `pyTigerGraph` import
- added a missed example page in the `docs/integrations/graphs/`
- formatted the `docs/integrations/providers/` page to the consistent
format. Added links.
- **Description:**
This PR adds support for advanced filtering to the integration of HANA
Vector Engine.
The newly supported filtering operators are: $eq, $ne, $gt, $gte, $lt,
$lte, $between, $in, $nin, $like, $and, $or
- **Issue:** N/A
- **Dependencies:** no new dependencies added
Added integration tests to:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Description of the new capabilities in notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Thank you for contributing to LangChain!
community:perplexity[patch]: standardize init args
updated pplx_api_key and request_timeout so that aliased to api_key, and
timeout respectively. Added test that both continue to set the same
underlying attributes.
Related to
[20085](https://github.com/langchain-ai/langchain/issues/20085)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:**
This PR fixes an issue in message formatting function for Anthropic
models on Amazon Bedrock.
Currently, LangChain BedrockChat model will crash if it uses Anthropic
models and the model return a message in the following type:
- `AIMessageChunk`
Moreover, when use BedrockChat with for building Agent, the following
message types will trigger the same issue too:
- `HumanMessageChunk`
- `FunctionMessage`
**Issue:**
https://github.com/langchain-ai/langchain/issues/18831
**Dependencies:**
No.
**Testing:**
Manually tested. The following code was failing before the patch and
works after.
```
@tool
def square_root(x: str):
"Useful when you need to calculate the square root of a number"
return math.sqrt(int(x))
llm = ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={ "temperature": 0.0 },
)
prompt = ChatPromptTemplate.from_messages(
[
("system", FUNCTION_CALL_PROMPT),
("human", "Question: {user_input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
tools = [square_root]
tools_string = format_tool_to_anthropic_function(square_root)
agent = (
RunnablePassthrough.assign(
user_input=lambda x: x['user_input'],
agent_scratchpad=lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
)
)
| prompt
| llm
| AnthropicFunctionsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
output = agent_executor.invoke({
"user_input": "What is the square root of 2?",
"tools_string": tools_string,
})
```
List of messages returned from Bedrock:
```
<SystemMessage> content='You are a helpful assistant.'
<HumanMessage> content='Question: What is the square root of 2?'
<AIMessageChunk> content="Okay, let's calculate the square root of 2.<scratchpad>\nTo calculate the square root of a number, I can use the square_root tool:\n\n<function_calls>\n <invoke>\n <tool_name>square_root</tool_name>\n <parameters>\n <__arg1>2</__arg1>\n </parameters>\n </invoke>\n</function_calls>\n</scratchpad>\n\n<function_results>\n<search_result>\nThe square root of 2 is approximately 1.414213562373095\n</search_result>\n</function_results>\n\n<answer>\nThe square root of 2 is approximately 1.414213562373095\n</answer>" id='run-92363df7-eff6-4849-bbba-fa16a1b2988c'"
<FunctionMessage> content='1.4142135623730951' name='square_root'
```
Hi! My name is Alex, I'm an SDK engineer from
[Comet](https://www.comet.com/site/)
This PR updates the `CometTracer` class.
Fixed an issue when `CometTracer` failed while logging the data to Comet
because this data is not JSON-encodable.
The problem was in some of the `Run` attributes that could contain
non-default types inside, now these attributes are taken not from the
run instance, but from the `run.dict()` return value.
This PR moves the implementations for chat history to core. So it's
easier to determine which dependencies need to be broken / add
deprecation warnings
Vector indexes in ClickHouse are experimental at the moment and can
sometimes break/change behaviour. So this PR makes it possible to say
that you don't want to specify an index type.
Any queries against the embedding column will be brute force/linear
scan, but that gives reasonable performance for small-medium dataset
sizes.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** implemented GraphStore class for Apache Age graph db
**Dependencies:** depends on psycopg2
Unit and integration tests included. Formatting and linting have been
run.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: you don't need to pass a version for Replicate official
models. That was broken on LangChain until now!
You can now run:
```
llm = Replicate(
model="meta/meta-llama-3-8b-instruct",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
```
I've updated the replicate.ipynb to reflect that.
twitter: @charliebholtz
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
ZhipuAI API only accepts `temperature` parameter between `(0, 1)` open
interval, and if `0` is passed, it responds with status code `400`.
However, 0 and 1 is often accepted by other APIs, for example, OpenAI
allows `[0, 2]` for temperature closed range.
This PR truncates temperature parameter passed to `[0.01, 0.99]` to
improve the compatibility between langchain's ecosystem's and ZhipuAI
(e.g., ragas `evaluate` often generates temperature 0, which results in
a lot of 400 invalid responses). The PR also truncates `top_p` parameter
since it has the same restriction.
Reference: [glm-4 doc](https://open.bigmodel.cn/dev/api#glm-4) (which
unfortunately is in Chinese though).
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
faster-whisper is a reimplementation of OpenAI's Whisper model using
CTranslate2, which is up to 4 times faster than enai/whisper for the
same accuracy while using less memory. The efficiency can be further
improved with 8-bit quantization on both CPU and GPU.
It can automatically detect the following 14 languages and transcribe
the text into their respective languages: en, zh, fr, de, ja, ko, ru,
es, th, it, pt, vi, ar, tr.
The gitbub repository for faster-whisper is :
https://github.com/SYSTRAN/faster-whisper
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
…gFaceTextGenInference)
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for [HuggingFaceTextGenInference]
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in [HuggingFaceTextGenInference]
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
fix timeout issue
fix zhipuai usecase notebookbook
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:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **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.
- [ ] **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.
@rgupta2508 I believe this change is necessary following
https://github.com/langchain-ai/langchain/pull/20318 because of how
Milvus handles defaults:
59bf5e811a/pymilvus/client/prepare.py (L82-L85)
```python
num_shards = kwargs[next(iter(same_key))]
if not isinstance(num_shards, int):
msg = f"invalid num_shards type, got {type(num_shards)}, expected int"
raise ParamError(message=msg)
req.shards_num = num_shards
```
this way lets Milvus control the default value (instead of maintaining a
separate default in Langchain).
Let me know if I've got this wrong or you feel it's unnecessary. Thanks.
To support number of the shards for the collection to create in milvus
vvectorstores.
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:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **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.
- [ ] **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.
- **Description:** added the headless parameter as optional argument to
the langchain_community.document_loaders AsyncChromiumLoader class
- **Dependencies:** None
- **Twitter handle:** @perinim_98
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>
**Description:** currently, the `DirectoryLoader` progress-bar maximum value is based on an incorrect number of files to process
In langchain_community/document_loaders/directory.py:127:
```python
paths = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
items = [
path
for path in paths
if not (self.exclude and any(path.match(glob) for glob in self.exclude))
]
```
`paths` returns both files and directories. `items` is later used to determine the maximum value of the progress-bar which gives an incorrect progress indication.
- Add functions (_stream, _astream)
- Connect to _generate and _agenerate
Thank you for contributing to LangChain!
- [x] **PR title**: "community: Add streaming logic in ChatHuggingFace"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Addition functions (_stream, _astream) and connection
to _generate and _agenerate
- **Issue:** #18782
- **Dependencies:** none
- **Twitter handle:** @lunara_x
**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: Add support for authorized identities in PebbloSafeLoader.
Now with this change, PebbloSafeLoader will extract
authorized_identities from metadata and send it to pebblo server
Dependencies: None
Documentation: None
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
From `langchain_community 0.0.30`, there's a bug that cannot send a
file-like object via `file` parameter instead of `file path` due to
casting the `file_path` to str type even if `file_path` is None.
which means that when I call the `partition_via_api()`, exactly one of
`filename` and `file` must be specified by the following error message.
however, from `langchain_community 0.0.30`, `file_path` is casted into
`str` type even `file_path` is None in `get_elements_from_api()` and got
an error at `exactly_one(filename=filename, file=file)`.
here's an error message
```
---> 51 exactly_one(filename=filename, file=file)
53 if metadata_filename and file_filename:
54 raise ValueError(
55 "Only one of metadata_filename and file_filename is specified. "
56 "metadata_filename is preferred. file_filename is marked for deprecation.",
57 )
File /opt/homebrew/lib/python3.11/site-packages/unstructured/partition/common.py:441, in exactly_one(**kwargs)
439 else:
440 message = f"{names[0]} must be specified."
--> 441 raise ValueError(message)
ValueError: Exactly one of filename and file must be specified.
```
So, I simply made a change that casting to str type when `file_path` is
not None.
I use `UnstructuredAPIFileLoader` like below.
```
from langchain_community.document_loaders.unstructured import UnstructuredAPIFileLoader
documents: list = UnstructuredAPIFileLoader(
file_path=None,
file=file, # file-like object, io.BytesIO type
mode='elements',
url='http://127.0.0.1:8000/general/v0/general',
content_type='application/pdf',
metadata_filename='asdf.pdf',
).load_and_split()
```
## Description:
The PR introduces 3 changes:
1. added `recursive` property to `O365BaseLoader`. (To keep the behavior
unchanged, by default is set to `False`). When `recursive=True`,
`_load_from_folder()` also recursively loads all nested folders.
2. added `folder_id` to SharePointLoader.(similar to (this
PR)[https://github.com/langchain-ai/langchain/pull/10780] ) This
provides an alternative to `folder_path` that doesn't seem to reliably
work.
3. when none of `document_ids`, `folder_id`, `folder_path` is provided,
the loader fetches documets from root folder. Combined with
`recursive=True` this provides an easy way of loading all compatible
documents from SharePoint.
The PR contains the same logic as [this stale
PR](https://github.com/langchain-ai/langchain/pull/10780) by
@WaleedAlfaris. I'd like to ask his blessing for moving forward with
this one.
## Issue:
- As described in https://github.com/langchain-ai/langchain/issues/19938
and https://github.com/langchain-ai/langchain/pull/10780 the sharepoint
loader often does not seem to work with folder_path.
- Recursive loading of subfolders is a missing functionality
## Dependecies: None
Twitter handle:
@martintriska1 @WRhetoric
This is my first PR here, please be gentle :-)
Please review @baskaryan
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR updates OctoAIEndpoint LLM to subclass BaseOpenAI as OctoAI is
an OpenAI-compatible service. The documentation and tests have also been
updated.
**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:** Make ChatDatabricks model supports stream
**Issue:** N/A
**Dependencies:** MLflow nightly build version (we will release next
MLflow version soon)
**Twitter handle:** N/A
Manually test:
(Before testing, please install `pip install
git+https://github.com/mlflow/mlflow.git`)
```python
# Test Databricks Foundation LLM model
from langchain.chat_models import ChatDatabricks
chat_model = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat",
max_tokens=500
)
from langchain_core.messages import AIMessageChunk
for chunk in chat_model.stream("What is mlflow?"):
print(chunk.content, end="|")
```
- [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.
---------
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.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>
**Description**: Support filter by OR and AND for deprecated PGVector
version
**Issue**: #20445
**Dependencies**: N/A
**Twitter** handle: @martinferenaz
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for [DeepInfra]
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in [DeepInfra]
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
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>
**Description:**
`_ListSQLDatabaseToolInput` raise error if model returns `{}`.
For example, gpt-4-turbo returns `{}` with SQL Agent initialized by
`create_sql_agent`.
So, I set default value `""` for `_ListSQLDatabaseToolInput` tool_input.
This is actually a gpt-4-turbo issue, not a LangChain issue, but I
thought it would be helpful to set a default value `""`.
This problem is discussed in detail in the following Issue.
**Issue:** https://github.com/langchain-ai/langchain/issues/20405
**Dependencies:** none
Sorry, I did not add or change the test code, as tests for this
components was not exist .
However, I have tested the following code based on the [SQL Agent
Document](https://python.langchain.com/docs/use_cases/sql/agents/), to
make sure it works.
```
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)
result = agent_executor.invoke("List the total sales per country. Which country's customers spent the most?")
print(result["output"])
```
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for Llamafile
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in community llamafile.py
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for HuggingFaceEndpoint
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in community HuggingFaceEndpoint
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@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>
Description: When multithreading is set to True and using the
DirectoryLoader, there was a bug that caused the return type to be a
double nested list. This resulted in other places upstream not being
able to utilize the from_documents method as it was no longer a
`List[Documents]` it was a `List[List[Documents]]`. The change made was
to just loop through the `future.result()` and yield every item.
Issue: #20093
Dependencies: N/A
Twitter handle: N/A
- **Description:** Bug fix. Removed extra line in `GCSDirectoryLoader`
to allow catching Exceptions. Now also logs the file path if Exception
is raised for easier debugging.
- **Issue:** #20198 Bug since langchain-community==0.0.31
- **Dependencies:** No change
- **Twitter handle:** timothywong731
---------
Co-authored-by: Bagatur <baskaryan@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:** Adds async variants of afrom_texts and
afrom_embeddings into `OpenSearchVectorSearch`, which allows for
`afrom_documents` to be called.
- **Issue:** I implemented this because my use case involves an async
scraper generating documents as and when they're ready to be ingested by
Embedding/OpenSearch
- **Dependencies:** None that I'm aware
Co-authored-by: Ben Mitchell <b.mitchell@reply.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
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: deprecating integrations moved to
langchain_google_community"
- [ ] **PR message**: deprecating integrations moved to
langchain_google_community
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Removes required usage of `requests` from `langchain-core`, all of which
has been deprecated.
- removes Tracer V1 implementations
- removes old `try_load_from_hub` github-based hub implementations
Removal done in a way where imports will still succeed, and usage will
fail with a `RuntimeError`.
- **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>
**Description:** adds integration with [Layerup
Security](https://uselayerup.com). Docs can be found
[here](https://docs.uselayerup.com). Integrates directly with our Python
SDK.
**Dependencies:**
[LayerupSecurity](https://pypi.org/project/LayerupSecurity/)
**Note**: all methods for our product require a paid API key, so I only
included 1 test which checks for an invalid API key response. I have
tested extensively locally.
**Twitter handle**: [@layerup_](https://twitter.com/layerup_)
---------
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>
- **Description:** Add attribution_token within
GoogleVertexAISearchRetriever so user can provide this information to
Google support team or product team during debug session.
Reference:
https://cloud.google.com/generative-ai-app-builder/docs/view-analytics#user-events
Attribution tokens. Attribution tokens are unique IDs generated by
Vertex AI Search and returned with each search request. Make sure to
include that attribution token as UserEvent.attributionToken with any
user events resulting from a search. This is needed to identify if a
search is served by the API. Only user events with a Google-generated
attribution token are used to compute metrics.
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** abehsu1992626
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Support reranking based on cross encoder models
available from HuggingFace.
- Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and
`SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality
to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it.
Please let me know if anything else needs to be done to make it visible
on the table-of-contents navigation bar on the left, or on the card list
on [retrievers documentation
page](https://python.langchain.com/docs/integrations/retrievers).
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
---------
Co-authored-by: Kenny Choe <kchoe@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
This implementation adds functionality from the AlphaVantage API,
renowned for its comprehensive financial data. The class encapsulates
various methods, each dedicated to fetching specific types of financial
information from the API.
### Implemented Functions
- **`search_symbols`**:
- Searches the AlphaVantage API for financial symbols using the provided
keywords.
- **`_get_market_news_sentiment`**:
- Retrieves market news sentiment for a specified stock symbol from the
AlphaVantage API.
- **`_get_time_series_daily`**:
- Fetches daily time series data for a specific symbol from the
AlphaVantage API.
- **`_get_quote_endpoint`**:
- Obtains the latest price and volume information for a given symbol
from the AlphaVantage API.
- **`_get_time_series_weekly`**:
- Gathers weekly time series data for a particular symbol from the
AlphaVantage API.
- **`_get_top_gainers_losers`**:
- Provides details on top gainers, losers, and most actively traded
tickers in the US market from the AlphaVantage API.
### Issue:
- #11994
### Dependencies:
- 'requests' library for HTTP requests. (import requests)
- 'pytest' library for testing. (import pytest)
---------
Co-authored-by: Adam Badar <94140103+adam-badar@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [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`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
