Reverts langchain-ai/langchain#21174
Hey team - going to revert this because it doesn't seem necessary for
testing. We should only be adding optional + extended_testing
dependencies for deps that have extended tests.
otherwise it just increases probability of dependency conflicts in the
community lockfile.
Thank you for contributing to LangChain!
community:baichuan[patch]: standardize init args
updated `baichuan_api_key` so that aliased to `api_key`. Added test that
it continues to set the same underlying attribute. Test checks for
`SecretStr`
updated `temperature` with Pydantic Field, added unit test.
Related to https://github.com/langchain-ai/langchain/issues/20085
If Session and/or keyspace are not provided, they are resolved from
cassio's context. So they are not required.
This change is fully backward compatible.
Issue: the `langkit` package is not presented in the `pyproject.toml`
but it is a requirement for the `WhyLabsCallbackHandler`
Change: added `langkit`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- Oracle AI Vector Search
Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
- Oracle AI Vector Search is designed for Artificial Intelligence (AI)
workloads that allows you to query data based on semantics, rather than
keywords. One of the biggest benefit of Oracle AI Vector Search is that
semantic search on unstructured data can be combined with relational
search on business data in one single system. This is not only powerful
but also significantly more effective because you don't need to add a
specialized vector database, eliminating the pain of data fragmentation
between multiple systems.
This Pull Requests Adds the following functionalities
Oracle AI Vector Search : Vector Store
Oracle AI Vector Search : Document Loader
Oracle AI Vector Search : Document Splitter
Oracle AI Vector Search : Summary
Oracle AI Vector Search : Oracle Embeddings
- We have added unit tests and have our own local unit test suite which
verifies all the code is correct. We have made sure to add guides for
each of the components and one end to end guide that shows how the
entire thing runs.
- We have made sure that make format and make lint run clean.
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: skmishraoracle <shailendra.mishra@oracle.com>
Co-authored-by: hroyofc <harichandan.roy@oracle.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Issue: we have several helper functions to import third-party libraries
like tools.gmail.utils.import_google in
[community.tools](https://api.python.langchain.com/en/latest/community_api_reference.html#id37).
And we have core.utils.utils.guard_import that works exactly for this
purpose.
The import_<package> functions work inconsistently and rather be private
functions.
Change: replaced these functions with the guard_import function.
Related to #21133
* Introduce individual `fetch_` methods for easier typing.
* Rework some docstrings to google style
* Move some logic to the tool
* Merge the 2 cassandra utility files
**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer
- [ ] **PR message**:
- **Description:** Refactored the lazy_load method to use asynchronous
execution for improved performance. The method now initiates scraping of
all URLs simultaneously using asyncio.gather, enhancing data fetching
efficiency. Each Document object is yielded immediately once its content
becomes available, streamlining the entire process.
- **Issue:** N/A
- **Dependencies:** Requires the asyncio library for handling
asynchronous tasks, which should already be part of standard Python
libraries in Python 3.7 and above.
- **Email:** [r73327118@gmail.com](mailto:r73327118@gmail.com)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
This pull request updates the Bagel Network package name from
"betabageldb" to "bagelML" to align with the latest changes made by the
Bagel Network team.
The following modifications have been made:
- Updated all references to the old package name ("betabageldb") with
the new package name ("bagelML") throughout the codebase.
- Modified the documentation, and any relevant scripts to reflect the
package name change.
- Tested the changes to ensure that the functionality remains intact and
no breaking changes were introduced.
By merging this pull request, our project will stay up to date with the
latest Bagel Network package naming convention, ensuring compatibility
and smooth integration with their updated library.
Please review the changes and provide any feedback or suggestions. Thank
you!
**Issue:**
Currently `AzureSearch` vector store does not implement `delete` method.
This PR implements it. This also makes it compatible with LangChain
indexer.
**Dependencies:**
None
**Twitter handle:**
@martintriska1
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
As shown in #13749 , `RecursiveUrlLoader` has encoding issue. This PR is
to solve this.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
When attempting to download PDF files from arXiv, an unexpected 404
error frequently occurs. This error halts the operation, regardless of
whether there are additional documents to process. As a solution, I
suggest implementing a mechanism to ignore and communicate this error
and continue processing the next document from the list.
Proposed Solution: To address the issue of unexpected 404 errors during
PDF downloads from arXiv, I propose implementing the following solution:
- Error Handling: Implement error handling mechanisms to catch and
handle 404 errors gracefully.
- Communication: Inform the user or logging system about the occurrence
of the 404 error.
- Continued Processing: After encountering a 404 error, continue
processing the remaining documents from the list without interruption.
This solution ensures that the application can handle unexpected errors
without terminating the entire operation. It promotes resilience and
robustness in the face of intermittent issues encountered during PDF
downloads from arXiv.
### Issue:
#20909
### Dependencies:
none
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Summary
I ran `ruff check --extend-select RUF100 -n` to identify `# noqa`
comments that weren't having any effect in Ruff, and then `ruff check
--extend-select RUF100 -n --fix` on select files to remove all of the
unnecessary `# noqa: F401` violations. It's possible that these were
needed at some point in the past, but they're not necessary in Ruff
v0.1.15 (used by LangChain) or in the latest release.
Co-authored-by: Erick Friis <erick@langchain.dev>
…/17690
Thank you for contributing to LangChain!
- [x] **Fix Google Lens knowledge graph issue**: "langchain: community"
- Fix for [No "knowledge_graph" property in Google Lens API call from
SerpAPI](https://github.com/langchain-ai/langchain/issues/17690)
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** handled the existence of keys in the json response of
Google Lens
- **Issue:** [No "knowledge_graph" property in Google Lens API call from
SerpAPI](https://github.com/langchain-ai/langchain/issues/17690)
- [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/
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>
## Description
Adding `UpstashVectorStore` to utilize [Upstash
Vector](https://upstash.com/docs/vector/overall/getstarted)!
#17012 was opened to add Upstash Vector to langchain but was closed to
wait for filtering. Now filtering is added to Upstash vector and we open
a new PR. Additionally, [embedding
feature](https://upstash.com/docs/vector/features/embeddingmodels) was
added and we add this to our vectorstore aswell.
## Dependencies
[upstash-vector](https://pypi.org/project/upstash-vector/) should be
installed to use `UpstashVectorStore`. Didn't update dependencies
because of [this comment in the previous
PR](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1876522450).
## Tests
Tests are added and they pass. Tests are naturally network bound since
Upstash Vector is offered through an API.
There was [a discussion in the previous PR about mocking the
unittests](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1891820567).
We didn't make changes to this end yet. We can update the tests if you
can explain how the tests should be mocked.
---------
Co-authored-by: ytkimirti <yusuftaha9@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Fixed the error that the model name is never actually put into GigaChat
request payload, always defaulting to `GigaChat-Lite`.
With this fix, model selection through
```python
import os
from langchain.chat_models.gigachat import GigaChat
chat = GigaChat(
name="GigaChat-Pro", # <- HERE!!!!!
...
)
```
should actually work, as intended in
[here](804390ba4b/libs/community/langchain_community/llms/gigachat.py (L36)).
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description**: ToolKit and Tools for accessing data in a Cassandra
Database primarily for Agent integration. Initially, this includes the
following tools:
- `cassandra_db_schema` Gathers all schema information for the connected
database or a specific schema. Critical for the agent when determining
actions.
- `cassandra_db_select_table_data` Selects data from a specific keyspace
and table. The agent can pass paramaters for a predicate and limits on
the number of returned records.
- `cassandra_db_query` Expiriemental alternative to
`cassandra_db_select_table_data` which takes a query string completely
formed by the agent instead of parameters. May be removed in future
versions.
Includes unit test and two notebooks to demonstrate usage.
**Dependencies**: cassio
**Twitter handle**: @PatrickMcFadin
---------
Co-authored-by: Phil Miesle <phil.miesle@datastax.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This pull request introduces a new feature to community
tools, enhancing its search capabilities by integrating the Mojeek
search engine
**Dependencies:** None
---------
Co-authored-by: Igor Brai <igor@mojeek.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
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>
