… converters
One way to convert anything to an OAI function:
convert_to_openai_function
One way to convert anything to an OAI tool: convert_to_openai_tool
Corresponding bind functions on OAI models: bind_functions, bind_tools
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>
- **Description:**
The parameters for user and assistant in Anthropic should be 'ai ->
assistant,' but they are reversed to 'assistant -> ai.'
Below is error code.
```python
anthropic.BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'messages: Unexpected role "ai". Allowed roles are "user" or "assistant"'}}
```
[anthropic](7177f3a71f/src/anthropic/types/beta/message_param.py (L13))
- **Issue:** : #16561
- **Dependencies:** : None
- **Twitter handle:** : None
<!-- 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:
- checked that the doc chat/google_vertex_ai_palm is using new
functions: invoke, stream etc.
- added Gemini example
- fixed wrong output in Sanskrit example
Issue: https://github.com/langchain-ai/langchain/issues/15664
Dependencies: None
Twitter handle: None
Flushing out the `mypy` config in `langchain-google-vertexai` to show
error codes and other warnings
This PR also bumps `mypy` to above version 1's stable release
**Description:**
Handle unsupported languages in same way as when none is provided
**Issue:**
The following line will throw a KeyError if the language is not
supported.
```python
self.Segmenter = LANGUAGE_SEGMENTERS[language]
```
E.g. when using `Language.CPP` we would get `KeyError: <Language.CPP:
'cpp'>`
---------
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,
- **Dependencies:** any dependencies required for this change,
- **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.
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.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** added the conversational task to hugginFace endpoint
in order to use models designed for chatbot programming.
- **Dependencies:** None
---------
Co-authored-by: Alessio Serra (ext.) <alessio.serra@partner.bmw.de>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.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>"
)
```
- **Description:**
This PR aims to enhance the `langchain` library by enabling the support
for passing `custom_headers` in the `GraphQLAPIWrapper` usage within
`langchain/agents/load_tools.py`.
While the `GraphQLAPIWrapper` from the `langchain_community` module is
inherently capable of handling `custom_headers`, its current invocation
in `load_tools.py` does not facilitate this functionality.
This limitation restricts the use of the `graphql` tool with databases
or APIs that require token-based authentication.
The absence of support for `custom_headers` in this context also leads
to a lack of error messages when attempting to interact with secured
GraphQL endpoints, making debugging and troubleshooting more
challenging.
This update modifies the `load_tools` function to correctly handle
`custom_headers`, thereby allowing secure and authenticated access to
GraphQL services requiring tokens.
Example usage after the proposed change:
```python
tools = load_tools(
["graphql"],
graphql_endpoint="https://your-graphql-endpoint.com/graphql",
custom_headers={"Authorization": f"Token {api_token}"},
)
```
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
- **Description:** This addresses the issue tagged below where if you
try to pass your own client when creating an OpenAI assistant, a
pydantic error is raised:
Example code:
```python
import openai
from langchain.agents.openai_assistant import OpenAIAssistantRunnable
client = openai.OpenAI()
interpreter_assistant = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
tools=[{"type": "code_interpreter"}],
model="gpt-4-1106-preview",
client=client
)
```
Error:
`pydantic.v1.errors.ConfigError: field "client" not yet prepared, so the
type is still a ForwardRef. You might need to call
OpenAIAssistantRunnable.update_forward_refs()`
It additionally updates type hints and docstrings to indicate that an
AzureOpenAI client is permissible as well.
- **Issue:** https://github.com/langchain-ai/langchain/issues/15948
- **Dependencies:** N/A
