- **Description:** Adds the document loader for [AWS
Athena](https://aws.amazon.com/athena/), a serverless and interactive
analytics service.
- **Dependencies:** Added boto3 as a dependency
- **Description:** This PR adds support for `search_types="mmr"` and
`search_type="similarity_score_threshold"` to retrievers using
`DatabricksVectorSearch`,
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
Make some functions work with Milvus:
1. get_ids: Get primary keys by field in the metadata
2. delete: Delete one or more entities by ids
3. upsert: Update/Insert one or more entities
**Issue**
None
**Dependencies**
None
**Tag maintainer:**
@hwchase17
**Twitter handle:**
None
---------
Co-authored-by: HoaNQ9 <hoanq.1811@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
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https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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- **Description:**
1. Modify LLMs/Anyscale to work with OAI v1
2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale
3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name
convention (reverted)
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR enables changing the behaviour of huggingface pipeline between
different calls. For example, before this PR there's no way of changing
maximum generation length between different invocations of the chain.
This is desirable in cases, such as when we want to scale the maximum
output size depending on a dynamic prompt size.
Usage example:
```python
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
hf = HuggingFacePipeline(pipeline=pipe)
hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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-->
- **Description: changes to you.com files**
- general cleanup
- adds community/utilities/you.py, moving bulk of code from retriever ->
utility
- removes `snippet` as endpoint
- adds `news` as endpoint
- adds more tests
<s>**Description: update community MAKE file**
- adds `integration_tests`
- adds `coverage`</s>
- **Issue:** the issue # it fixes if applicable,
- [For New Contributors: Update Integration
Documentation](https://github.com/langchain-ai/langchain/issues/15664#issuecomment-1920099868)
- **Dependencies:** n/a
- **Twitter handle:** @scottnath
- **Mastodon handle:** scottnath@mastodon.social
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Databricks LLM does not support SerDe the
transform_input_fn and transform_output_fn. After saving and loading,
the LLM will be broken. This PR serialize these functions into a hex
string using pickle, and saving the hex string in the yaml file. Using
pickle to serialize a function can be flaky, but this is a simple
workaround that unblocks many use cases. If more sophisticated SerDe is
needed, we can improve it later.
Test:
Added a simple unit test.
I did manual test on Databricks and it works well.
The saved yaml looks like:
```
llm:
_type: databricks
cluster_driver_port: null
cluster_id: null
databricks_uri: databricks
endpoint_name: databricks-mixtral-8x7b-instruct
extra_params: {}
host: e2-dogfood.staging.cloud.databricks.com
max_tokens: null
model_kwargs: null
n: 1
stop: null
task: null
temperature: 0.0
transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e
transform_output_fn: null
```
@baskaryan
```python
from langchain_community.embeddings import DatabricksEmbeddings
from langchain_community.llms import Databricks
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import mlflow
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
def transform_input(**request):
request["messages"] = [
{
"role": "user",
"content": request["prompt"]
}
]
del request["prompt"]
return request
llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input)
persist_dir = "faiss_databricks_embedding"
# Create the vector db, persist the db to a local fs folder
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
db = FAISS.from_documents(docs, embeddings)
db.save_local(persist_dir)
def load_retriever(persist_directory):
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
retriever = load_retriever(persist_dir)
retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever)
with mlflow.start_run() as run:
logged_model = mlflow.langchain.log_model(
retrievalQA,
artifact_path="retrieval_qa",
loader_fn=load_retriever,
persist_dir=persist_dir,
)
# Load the retrievalQA chain
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}]))
```
- **Description:** Fixes in the Ontotext GraphDB Graph and QA Chain
related to the error handling in case of invalid SPARQL queries, for
which `prepareQuery` doesn't throw an exception, but the server returns
400 and the query is indeed invalid
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @OntotextGraphDB
**Description:**
Implemented unique ID validation in the FAISS component to ensure all
document IDs are distinct. This update resolves issues related to
non-unique IDs, such as inconsistent behavior during deletion processes.
- **Description:**
Actually the test named `test_openai_apredict` isn't testing the
apredict method from ChatOpenAI.
- **Twitter handle:**
https://twitter.com/OAlmofadas
**Please tag this issue with `nvidia_genai`**
- **Description:** Added new Runnables for integration NVIDIA Riva into
LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech
(TTS).
- **Issue:** N/A
- **Dependencies:** To use these runnables, the NVIDIA Riva client
libraries are required. It they are not installed, an error will be
raised instructing how to install them. The Runnables can be safely
imported without the riva client libraries.
- **Twitter handle:** N/A
All of the Riva Runnables are inside a single folder in the Utilities
module. In this folder are four files:
- common.py - Contains all code that is common to both TTS and ASR
- stream.py - Contains a class representing an audio stream that allows
the end user to put data into the stream like a queue.
- asr.py - Contains the RivaASR runnable
- tts.py - Contains the RivaTTS runnable
The following Python function is an example of creating a chain that
makes use of both of these Runnables:
```python
def create(
config: Configuration,
audio_encoding: RivaAudioEncoding,
sample_rate: int,
audio_channels: int = 1,
) -> Runnable[ASRInputType, TTSOutputType]:
"""Create a new instance of the chain."""
_LOGGER.info("Instantiating the chain.")
# create the riva asr client
riva_asr = RivaASR(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
encoding=audio_encoding,
audio_channel_count=audio_channels,
sample_rate_hertz=sample_rate,
profanity_filter=config.riva_asr.profanity_filter,
enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation,
language_code=config.riva_asr.language_code,
)
# create the prompt template
prompt = PromptTemplate.from_template("{user_input}")
# model = ChatOpenAI()
model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore
# create the riva tts client
riva_tts = RivaTTS(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
output_directory=config.riva_tts.output_directory,
language_code=config.riva_tts.language_code,
voice_name=config.riva_tts.voice_name,
)
# construct and return the chain
return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore
```
The following code is an example of creating a new audio stream for
Riva:
```python
input_stream = AudioStream(maxsize=1000)
# Send bytes into the stream
for chunk in audio_chunks:
await input_stream.aput(chunk)
input_stream.close()
```
The following code is an example of how to execute the chain with
RivaASR and RivaTTS
```python
output_stream = asyncio.Queue()
while not input_stream.complete:
async for chunk in chain.astream(input_stream):
output_stream.put(chunk)
```
Everything should be async safe and thread safe. Audio data can be put
into the input stream while the chain is running without interruptions.
---------
Co-authored-by: Hayden Wolff <hwolff@nvidia.com>
Co-authored-by: Hayden Wolff <hwolff@Haydens-Laptop.local>
Co-authored-by: Hayden Wolff <haydenwolff99@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Previously, if this did not find a mypy cache then it wouldnt run
this makes it always run
adding mypy ignore comments with existing uncaught issues to unblock other prs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Description
In #16608, the calling `collection_name` was wrong.
I made a fix for it.
Sorry for the inconvenience!
## Issue
https://github.com/langchain-ai/langchain/issues/16962
## Dependencies
N/A
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
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of the package you've modified to check this locally.
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tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Kumar Shivendu <kshivendu1@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Adds:
* methods `aload()` and `alazy_load()` to interface `BaseLoader`
* implementation for class `MergedDataLoader `
* support for class `BaseLoader` in async function `aindex()` with unit
tests
Note: this is compatible with existing `aload()` methods that some
loaders already had.
**Twitter handle:** @cbornet_
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description**: fully async versions are available for astrapy 0.7+.
For older astrapy versions or if the user provides a sync client without
an async one, the async methods will call the sync ones wrapped in
`run_in_executor`
- **Twitter handle:** cbornet_
Replace this entire comment with:
- **Description:** Add Baichuan LLM to integration/llm, also updated
related docs.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
- **Description:**
Filtering in a FAISS vectorstores is very inflexible and doesn't allow
that many use case. I think supporting callable like this enables a lot:
regular expressions, condition on multiple keys etc. **Note** I had to
manually alter a test. I don't understand if it was falty to begin with
or if there is something funky going on.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
Signed-off-by: thiswillbeyourgithub <26625900+thiswillbeyourgithub@users.noreply.github.com>
## Summary
This PR implements the "Connery Action Tool" and "Connery Toolkit".
Using them, you can integrate Connery actions into your LangChain agents
and chains.
Connery is an open-source plugin infrastructure for AI.
With Connery, you can easily create a custom plugin with a set of
actions and seamlessly integrate them into your LangChain agents and
chains. Connery will handle the rest: runtime, authorization, secret
management, access management, audit logs, and other vital features.
Additionally, Connery and our community offer a wide range of
ready-to-use open-source plugins for your convenience.
Learn more about Connery:
- GitHub: https://github.com/connery-io/connery-platform
- Documentation: https://docs.connery.io
- Twitter: https://twitter.com/connery_io
## TODOs
- [x] API wrapper
- [x] Integration tests
- [x] Connery Action Tool
- [x] Docs
- [x] Example
- [x] Integration tests
- [x] Connery Toolkit
- [x] Docs
- [x] Example
- [x] Formatting (`make format`)
- [x] Linting (`make lint`)
- [x] Testing (`make test`)
We can't use `json.dumps` by default as many types returned by the
cassandra driver are not serializable. It's safer to use `str` and let
users define their own custom `page_content_mapper` if needed.
- **Description:** This PR adds [EdenAI](https://edenai.co/) for the
chat model (already available in LLM & Embeddings). It supports all
[ChatModel] functionality: generate, async generate, stream, astream and
batch. A detailed notebook was added.
- **Dependencies**: No dependencies are added as we call a rest API.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
community:
- **Description:**
- Add new ChatLiteLLMRouter class that allows a client to use a LiteLLM
Router as a LangChain chat model.
- Note: The existing ChatLiteLLM integration did not cover the LiteLLM
Router class.
- Add tests and Jupyter notebook.
- **Issue:** None
- **Dependencies:** Relies on existing ChatLiteLLM integration
- **Twitter handle:** @bburgin_0
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** Adding Oracle Cloud Infrastructure Generative AI
integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully
managed service that provides a set of state-of-the-art, customizable
large language models (LLMs) that cover a wide range of use cases, and
which is available through a single API. Using the OCI Generative AI
service you can access ready-to-use pretrained models, or create and
host your own fine-tuned custom models based on your own data on
dedicated AI clusters.
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
- **Issue:** None,
- **Dependencies:** OCI Python SDK,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
Passed
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
we provide unit tests. However, we cannot provide integration tests due
to Oracle policies that prohibit public sharing of api keys.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added support for optionally supplying 'Guardrails for Amazon Bedrock'
on both types of model invocations (batch/regular and streaming) and for
all models supported by the Amazon Bedrock service.
@baskaryan @hwchase17
```python
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
guardrails={"id": " <guardrail_id>",
"version": "<guardrail_version>",
"trace": True}, callbacks=[BedrockAsyncCallbackHandler()])
class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
"""Async callback handler that can be used to handle callbacks from langchain."""
async def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> Any:
reason = kwargs.get("reason")
if reason == "GUARDRAIL_INTERVENED":
# kwargs contains additional trace information sent by 'Guardrails for Bedrock' service.
print(f"""Guardrails: {kwargs}""")
# streaming
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
streaming=True,
guardrails={"id": "<guardrail_id>",
"version": "<guardrail_version>"})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
This PR adds a VectorStore integration for SAP HANA Cloud Vector Engine,
which is an upcoming feature in the SAP HANA Cloud database
(https://blogs.sap.com/2023/11/02/sap-hana-clouds-vector-engine-announcement/).
- **Issue:** N/A
- **Dependencies:** [SAP HANA Python
Client](https://pypi.org/project/hdbcli/)
- **Twitter handle:** @sapopensource
Implementation of the integration:
`libs/community/langchain_community/vectorstores/hanavector.py`
Unit tests:
`libs/community/tests/unit_tests/vectorstores/test_hanavector.py`
Integration tests:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Example notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Access credentials for execution of the integration tests can be
provided to the maintainers.
---------
Co-authored-by: sascha <sascha.stoll@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Updated `_get_elements()` function of
`UnstructuredFileLoader `class to check if the argument self.file_path
is a file or list of files. If it is a list of files then it iterates
over the list of file paths, calls the partition function for each one,
and appends the results to the elements list. If self.file_path is not a
list, it calls the partition function as before.
- **Issue:** Fixed#15607,
- **Dependencies:** NA
- **Twitter handle:** NA
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
- **Description:** This PR enables LangChain to access the iFlyTek's
Spark LLM via the chat_models wrapper.
- **Dependencies:** websocket-client ^1.6.1
- **Tag maintainer:** @baskaryan
### SparkLLM chat model usage
Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API
Console](https://console.xfyun.cn/services/bm3) (for more info, see
[iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set
environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY`
and `IFLYTEK_SPARK_API_SECRET` or pass parameters when using it like the
demo below:
```python3
from langchain.chat_models.sparkllm import ChatSparkLLM
client = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>"
)
```
This PR introduces update to Konko Integration with LangChain.
1. **New Endpoint Addition**: Integration of a new endpoint to utilize
completion models hosted on Konko.
2. **Chat Model Updates for Backward Compatibility**: We have updated
the chat models to ensure backward compatibility with previous OpenAI
versions.
4. **Updated Documentation**: Comprehensive documentation has been
updated to reflect these new changes, providing clear guidance on
utilizing the new features and ensuring seamless integration.
Thank you to the LangChain team for their exceptional work and for
considering this PR. Please let me know if any additional information is
needed.
---------
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MacBook-Pro.local>
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MBP.lan>
- **Description:** Baichuan Chat (with both Baichuan-Turbo and
Baichuan-Turbo-192K models) has updated their APIs. There are breaking
changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API
but is still required in Langchain. Baichuan's Langchain integration
needs to be updated to the latest version.
- **Issue:** #15206
- **Dependencies:** None,
- **Twitter handle:** None
@hwchase17.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
**Description:**
- Implement `SQLStrStore` and `SQLDocStore` classes that inherits from
`BaseStore` to allow to persist data remotely on a SQL server.
- SQL is widely used and sometimes we do not want to install a caching
solution like Redis.
- Multiple issues/comments complain that there is no easy remote and
persistent solution that are not in memory (users want to replace
InMemoryStore), e.g.,
https://github.com/langchain-ai/langchain/issues/14267,
https://github.com/langchain-ai/langchain/issues/15633,
https://github.com/langchain-ai/langchain/issues/14643,
https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain
- This is particularly painful when wanting to use
`ParentDocumentRetriever `
- This implementation is particularly useful when:
* it's expensive to construct an InMemoryDocstore/dict
* you want to retrieve documents from remote sources
* you just want to reuse existing objects
- This implementation integrates well with PGVector, indeed, when using
PGVector, you already have a SQL instance running. `SQLDocStore` is a
convenient way of using this instance to store documents associated to
vectors. An integration example with ParentDocumentRetriever and
PGVector is provided in docs/docs/integrations/stores/sql.ipynb or
[here](https://github.com/gcheron/langchain/blob/sql-store/docs/docs/integrations/stores/sql.ipynb).
- It persists `str` and `Document` objects but can be easily extended.
**Issue:**
Provide an easy SQL alternative to `InMemoryStore`.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description** : New documents loader for visio files (with extension
.vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with
extension .vsdx) is associated with Microsoft Visio, a diagram creation
software. It stores information about the structure, layout, and
graphical elements of a diagram. This format facilitates the creation
and sharing of visualizations in areas such as business, engineering,
and computer science.
A Visio file can contain multiple pages. Some of them may serve as the
background for others, and this can occur across multiple layers. This
loader extracts the textual content from each page and its associated
pages, enabling the extraction of all visible text from each page,
similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package
Description: Added support for asynchronous streaming in the Bedrock
class and corresponding tests.
Primarily:
async def aprepare_output_stream
async def _aprepare_input_and_invoke_stream
async def _astream
async def _acall
I've ensured that the code adheres to the project's linting and
formatting standards by running make format, make lint, and make test.
Issue: #12054, #11589
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @dominic_lovric
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
**Description:** Add support for querying TigerGraph databases through
the InquiryAI service.
**Issue**: N/A
**Dependencies:** N/A
**Twitter handle:** @TigerGraphDB
