Description: I added an example of how to reference the OpenAI API
Organization ID, because I couldn't find it before. In the example, it
is mentioned how to achieve this using environment variables as well as
parameters for the OpenAI()-class
Issue: -
Dependencies: -
Twitter @schop-rob
This PR improves upon the Clarifai LangChain integration with improved docs, errors, args and the addition of embedding model support in LancChain for Clarifai's embedding models and an overview of the various ways you can integrate with Clarifai added to the docs.
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Co-authored-by: Matthew Zeiler <zeiler@clarifai.com>
- Description: This is a chat model equivalent of HumanInputLLM. An
example notebook is also added.
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
Description: `flan-t5-xl` hangs, updated to `flan-t5-xxl`. Tested all
stabilityai LLMs- all hang so removed from tutorial. Temperature > 0 to
prevent unintended determinism.
Issue: #3275
Tag maintainer: @baskaryan
This PR adds a new LLM class for the Amazon API Gateway hosted LLM. The
PR also includes example notebooks for using the LLM class in an Agent
chain.
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Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Changes
This PR adds [Clarifai](https://www.clarifai.com/) integration to
Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user
the ability to use many types of LLM (OpenAI, cohere, ect and other open
source models). As well, a clarifai app can be treated as a vector
database to upload and retrieve data. The integrations includes:
- Clarifai LLM integration: Clarifai supports many types of language
model that users can utilize for their application
- Clarifai VectorDB: A Clarifai application can hold data and
embeddings. You can run semantic search with the embeddings
#### Before submitting
- [x] Added integration test for LLM
- [x] Added integration test for VectorDB
- [x] Added notebook for LLM
- [x] Added notebook for VectorDB
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
### Description
We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation
Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.
### Dependencies
No added dependencies were required for the change.
### Tests
Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.
### Notebook
A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.
### Twitters
[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)
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Co-authored-by: Matthew DeGuzman <91019033+matthewdeguzman@users.noreply.github.com>
Co-authored-by: prakharg-msft <75808410+prakharg-msft@users.noreply.github.com>
Added the functionality to leverage 3 new Codey models from Vertex AI:
- code-bison - Code generation using the existing LLM integration
- code-gecko - Code completion using the existing LLM integration
- codechat-bison - Code chat using the existing chat_model integration
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Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
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Fixes a link typo from `/-/route` to `/-/routes`.
and change endpoint format
from `f"{self.anyscale_service_url}/{self.anyscale_service_route}"` to
`f"{self.anyscale_service_url}{self.anyscale_service_route}"`
Also adding documentation about the format of the endpoint
#### Before submitting
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
