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Alternatively, to set the API key through a config file, first determine your data directory path by running the following command in your terminal: From 958c21c5a0f8915d343dcab7986075b6ae83f338 Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Mon, 4 Dec 2023 09:16:12 -0800 Subject: [PATCH 10/12] Update docs/source/developers/index.md Co-authored-by: Jason Weill <93281816+JasonWeill@users.noreply.github.com> --- docs/source/developers/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/developers/index.md b/docs/source/developers/index.md index 17bb6a5b1..dd79d4c05 100644 --- a/docs/source/developers/index.md +++ b/docs/source/developers/index.md @@ -49,4 +49,4 @@ class MyProvider(BaseProvider, FakeListLLM): return super().get_prompt_template(format) ``` -Please note that this will only work with Jupyter AI magics (the `%ai` and `%%ai` magic commands), customization of prompt templates for chat is not available yet (see [issue #498](https://github.com/jupyterlab/jupyter-ai/issues/498)). +Please note that this will only work with Jupyter AI magics (the `%ai` and `%%ai` magic commands). Custom prompt templates are not used in the chat interface yet. From 739b53554bebaf6293ac7429bcb7f729b3403d11 Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Mon, 4 Dec 2023 09:31:03 -0800 Subject: [PATCH 11/12] move "Custom model providers" section to Dev page --- docs/source/developers/index.md | 70 +++++++++++++++++++++++++++++++++ docs/source/users/index.md | 70 --------------------------------- 2 files changed, 70 insertions(+), 70 deletions(-) diff --git a/docs/source/developers/index.md b/docs/source/developers/index.md index dd79d4c05..12e714048 100644 --- a/docs/source/developers/index.md +++ b/docs/source/developers/index.md @@ -17,6 +17,76 @@ For more details about using `langchain.pydantic_v1` in an environment with Pydantic v2 installed, see the [LangChain documentation on Pydantic compatibility](https://python.langchain.com/docs/guides/pydantic_compatibility). +## Custom model providers + +You can define new providers using the LangChain framework API. Custom providers +inherit from both `jupyter-ai`'s `BaseProvider` and `langchain`'s [`LLM`][LLM]. +You can either import a pre-defined model from [LangChain LLM list][langchain_llms], +or define a [custom LLM][custom_llm]. +In the example below, we define a provider with two models using +a dummy `FakeListLLM` model, which returns responses from the `responses` +keyword argument. + +```python +# my_package/my_provider.py +from jupyter_ai_magics import BaseProvider +from langchain.llms import FakeListLLM + + +class MyProvider(BaseProvider, FakeListLLM): + id = "my_provider" + name = "My Provider" + model_id_key = "model" + models = [ + "model_a", + "model_b" + ] + def __init__(self, **kwargs): + model = kwargs.get("model_id") + kwargs["responses"] = ( + ["This is a response from model 'a'"] + if model == "model_a" else + ["This is a response from model 'b'"] + ) + super().__init__(**kwargs) +``` + + +If the new provider inherits from [`BaseChatModel`][BaseChatModel], it will be available +both in the chat UI and with magic commands. Otherwise, users can only use the new provider +with magic commands. + +To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html): + +```toml +# my_package/pyproject.toml +[project] +name = "my_package" +version = "0.0.1" + +[project.entry-points."jupyter_ai.model_providers"] +my-provider = "my_provider:MyProvider" +``` + +To test that the above minimal provider package works, install it with: + +```sh +# from `my_package` directory +pip install -e . +``` + +Then, restart JupyterLab. You should now see an info message in the log that mentions +your new provider's `id`: + +``` +[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`. +``` + +[langchain_llms]: https://api.python.langchain.com/en/v0.0.339/api_reference.html#module-langchain.llms +[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm +[LLM]: https://api.python.langchain.com/en/v0.0.339/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM +[BaseChatModel]: https://api.python.langchain.com/en/v0.0.339/chat_models/langchain.chat_models.base.BaseChatModel.html + ## Prompt templates Each provider can define **prompt templates** for each supported format. A prompt diff --git a/docs/source/users/index.md b/docs/source/users/index.md index ac72e4fe8..8864050cf 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -938,73 +938,3 @@ data: runtime: /Users/3coins/Library/Jupyter/runtime ``` - -## Custom model providers - -You can define new providers using the LangChain framework API. Custom providers -inherit from both `jupyter-ai`'s `BaseProvider` and `langchain`'s [`LLM`][LLM]. -You can either import a pre-defined model from [LangChain LLM list][langchain_llms], -or define a [custom LLM][custom_llm]. -In the example below, we define a provider with two models using -a dummy `FakeListLLM` model, which returns responses from the `responses` -keyword argument. - -```python -# my_package/my_provider.py -from jupyter_ai_magics import BaseProvider -from langchain.llms import FakeListLLM - - -class MyProvider(BaseProvider, FakeListLLM): - id = "my_provider" - name = "My Provider" - model_id_key = "model" - models = [ - "model_a", - "model_b" - ] - def __init__(self, **kwargs): - model = kwargs.get("model_id") - kwargs["responses"] = ( - ["This is a response from model 'a'"] - if model == "model_a" else - ["This is a response from model 'b'"] - ) - super().__init__(**kwargs) -``` - - -If the new provider inherits from [`BaseChatModel`][BaseChatModel], it will be available -both in the chat UI and with magic commands. Otherwise, users can only use the new provider -with magic commands. - -To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html): - -```toml -# my_package/pyproject.toml -[project] -name = "my_package" -version = "0.0.1" - -[project.entry-points."jupyter_ai.model_providers"] -my-provider = "my_provider:MyProvider" -``` - -To test that the above minimal provider package works, install it with: - -```sh -# from `my_package` directory -pip install -e . -``` - -Then, restart JupyterLab. You should now see an info message in the log that mentions -your new provider's `id`: - -``` -[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`. -``` - -[langchain_llms]: https://api.python.langchain.com/en/v0.0.339/api_reference.html#module-langchain.llms -[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm -[LLM]: https://api.python.langchain.com/en/v0.0.339/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM -[BaseChatModel]: https://api.python.langchain.com/en/v0.0.339/chat_models/langchain.chat_models.base.BaseChatModel.html From a08f522c0c2e12a2f98cf2c30bd210f84e2fe88f Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Mon, 4 Dec 2023 10:50:50 -0800 Subject: [PATCH 12/12] Update lang model choice screen to show OpenAI models mentioned in text around the screenshot --- .../chat-settings-choose-language-model.png | Bin 50886 -> 68198 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git 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zf~?j?@utf26oIp`4E}I4<~hJikDC>fQfq??LlR zuayk~YrdrCXy^R>)BTL&(`)Q#D z^?Ju{n?!V9Tk1W%soW*?DLaBl{Ce~7N&>9@dPfY#&ND!dxoAGr5QrE=Gp zUXEJhC-)8*=f27gvzOQ!>3jiRHp=?L`R$gZdRwzqyjRbN?QZ&`BEZ}4vUHK2Jj+q- zzM#PEq5eu=fZINM%@(jI2)@~z(xT42X_D^rJs0N#58!??{p$B*P^Dswt^b5s9q@kA2N=R!CQB?pgY?$_nVJ4B)W(v>Pn8+*y}h zw{m?4)t4c_Z5;kNFC!v1u1k43FK+uC!4gE+Kce=rF+YO zC0M{+U= Date: Thu, 30 Nov 2023 12:52:28 -0800 Subject: [PATCH 04/12] =?UTF-8?q?Promote=20=E2=80=9CCustom=20model=20provi?= =?UTF-8?q?ders=E2=80=9D=20and=20=E2=80=9CCustomizing=20prompt=20templates?= =?UTF-8?q?=E2=80=9D=20to=20subsections,=20move=20them=20to=20the=20bottom?= =?UTF-8?q?=20of=20the=20users=20doc=20page?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/users/index.md | 180 ++++++++++++++++++------------------- 1 file changed, 90 insertions(+), 90 deletions(-) diff --git a/docs/source/users/index.md b/docs/source/users/index.md index 42bd15092..0c1efd2d7 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -188,96 +188,6 @@ responsible for all charges they incur when they make API requests. Review your provider's pricing information before submitting requests via Jupyter AI. ::: -### Custom model providers - -You can define new providers using the LangChain framework API. Custom providers -inherit from both `jupyter-ai`'s ``BaseProvider`` and `langchain`'s [``LLM``][LLM]. -You can either import a pre-defined model from [LangChain LLM list][langchain_llms], -or define a [custom LLM][custom_llm]. -In the example below, we define a provider with two models using -a dummy ``FakeListLLM`` model, which returns responses from the ``responses`` -keyword argument. - -```python -# my_package/my_provider.py -from jupyter_ai_magics import BaseProvider -from langchain.llms import FakeListLLM - - -class MyProvider(BaseProvider, FakeListLLM): - id = "my_provider" - name = "My Provider" - model_id_key = "model" - models = [ - "model_a", - "model_b" - ] - def __init__(self, **kwargs): - model = kwargs.get("model_id") - kwargs["responses"] = ( - ["This is a response from model 'a'"] - if model == "model_a" else - ["This is a response from model 'b'"] - ) - super().__init__(**kwargs) -``` - - -If the new provider inherits from [``BaseChatModel``][BaseChatModel], it will be available -both in the chat UI and with magic commands. Otherwise, users can only use the new provider -with magic commands. - -To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html): - -```toml -# my_package/pyproject.toml -[project] -name = "my_package" -version = "0.0.1" - -[project.entry-points."jupyter_ai.model_providers"] -my-provider = "my_provider:MyProvider" -``` - -To test that the above minimal provider package works, install it with: - -```sh -# from `my_package` directory -pip install -e . -``` - -Then, restart JupyterLab. You should now see an info message in the log that mentions -your new provider's `id`: - -``` -[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`. -``` - -[langchain_llms]: https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.llms -[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm -[LLM]: https://api.python.langchain.com/en/latest/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM -[BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html - - -### Customizing prompt templates - -To modify the prompt template for a given format, override the ``get_prompt_template`` method: - -```python -from langchain.prompts import PromptTemplate - - -class MyProvider(BaseProvider, FakeListLLM): - # (... properties as above ...) - def get_prompt_template(self, format) -> PromptTemplate: - if format === "code": - return PromptTemplate.from_template( - "{prompt}\n\nProduce output as source code only, " - "with no text or explanation before or after it." - ) - return super().get_prompt_template(format) -``` - ## The chat interface The easiest way to get started with Jupyter AI is to use the chat interface. @@ -1046,3 +956,93 @@ data: runtime: /Users/3coins/Library/Jupyter/runtime ``` + +## Custom model providers + +You can define new providers using the LangChain framework API. Custom providers +inherit from both `jupyter-ai`'s ``BaseProvider`` and `langchain`'s [``LLM``][LLM]. +You can either import a pre-defined model from [LangChain LLM list][langchain_llms], +or define a [custom LLM][custom_llm]. +In the example below, we define a provider with two models using +a dummy ``FakeListLLM`` model, which returns responses from the ``responses`` +keyword argument. + +```python +# my_package/my_provider.py +from jupyter_ai_magics import BaseProvider +from langchain.llms import FakeListLLM + + +class MyProvider(BaseProvider, FakeListLLM): + id = "my_provider" + name = "My Provider" + model_id_key = "model" + models = [ + "model_a", + "model_b" + ] + def __init__(self, **kwargs): + model = kwargs.get("model_id") + kwargs["responses"] = ( + ["This is a response from model 'a'"] + if model == "model_a" else + ["This is a response from model 'b'"] + ) + super().__init__(**kwargs) +``` + + +If the new provider inherits from [``BaseChatModel``][BaseChatModel], it will be available +both in the chat UI and with magic commands. Otherwise, users can only use the new provider +with magic commands. + +To make the new provider available, you need to declare it as an [entry point](https://setuptools.pypa.io/en/latest/userguide/entry_point.html): + +```toml +# my_package/pyproject.toml +[project] +name = "my_package" +version = "0.0.1" + +[project.entry-points."jupyter_ai.model_providers"] +my-provider = "my_provider:MyProvider" +``` + +To test that the above minimal provider package works, install it with: + +```sh +# from `my_package` directory +pip install -e . +``` + +Then, restart JupyterLab. You should now see an info message in the log that mentions +your new provider's `id`: + +``` +[I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`. +``` + +[langchain_llms]: https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.llms +[custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm +[LLM]: https://api.python.langchain.com/en/latest/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM +[BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html + + +## Customizing prompt templates + +To modify the prompt template for a given format, override the ``get_prompt_template`` method: + +```python +from langchain.prompts import PromptTemplate + + +class MyProvider(BaseProvider, FakeListLLM): + # (... properties as above ...) + def get_prompt_template(self, format) -> PromptTemplate: + if format === "code": + return PromptTemplate.from_template( + "{prompt}\n\nProduce output as source code only, " + "with no text or explanation before or after it." + ) + return super().get_prompt_template(format) +``` From 2be81c43621ac766da20fbc6e3ec32bed2aa4bcd Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Thu, 30 Nov 2023 13:35:11 -0800 Subject: [PATCH 05/12] Update docs/source/users/index.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: MichaÅ‚ Krassowski <5832902+krassowski@users.noreply.github.com> --- docs/source/users/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/users/index.md b/docs/source/users/index.md index 0c1efd2d7..f973d7296 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -1028,7 +1028,7 @@ your new provider's `id`: [BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html -## Customizing prompt templates +### Customizing prompt templates To modify the prompt template for a given format, override the ``get_prompt_template`` method: From 72a51e54bf21c4eb3e56db33550ad258a480e75b Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Thu, 30 Nov 2023 14:16:34 -0800 Subject: [PATCH 06/12] Update docs/source/users/index.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: MichaÅ‚ Krassowski <5832902+krassowski@users.noreply.github.com> --- docs/source/users/index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/users/index.md b/docs/source/users/index.md index f973d7296..21036c414 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -1022,10 +1022,10 @@ your new provider's `id`: [I 2023-10-29 13:56:16.915 AiExtension] Registered model provider `my_provider`. ``` -[langchain_llms]: https://api.python.langchain.com/en/latest/api_reference.html#module-langchain.llms +[langchain_llms]: https://api.python.langchain.com/en/v0.0.339/api_reference.html#module-langchain.llms [custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm -[LLM]: https://api.python.langchain.com/en/latest/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM -[BaseChatModel]: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.base.BaseChatModel.html +[LLM]: https://api.python.langchain.com/en/v0.0.339/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM +[BaseChatModel]: https://api.python.langchain.com/en/v0.0.339/chat_models/langchain.chat_models.base.BaseChatModel.html ### Customizing prompt templates From 2360f603651cc08e0e82bbfd08406b2dec3422c7 Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Thu, 30 Nov 2023 14:17:19 -0800 Subject: [PATCH 07/12] change double-backtick to single-backticks as they serve the same function --- docs/source/users/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/users/index.md b/docs/source/users/index.md index 21036c414..af35a57d7 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -960,11 +960,11 @@ runtime: ## Custom model providers You can define new providers using the LangChain framework API. Custom providers -inherit from both `jupyter-ai`'s ``BaseProvider`` and `langchain`'s [``LLM``][LLM]. +inherit from both `jupyter-ai`'s `BaseProvider` and `langchain`'s [`LLM`][LLM]. You can either import a pre-defined model from [LangChain LLM list][langchain_llms], or define a [custom LLM][custom_llm]. In the example below, we define a provider with two models using -a dummy ``FakeListLLM`` model, which returns responses from the ``responses`` +a dummy `FakeListLLM` model, which returns responses from the `responses` keyword argument. ```python @@ -992,7 +992,7 @@ class MyProvider(BaseProvider, FakeListLLM): ``` -If the new provider inherits from [``BaseChatModel``][BaseChatModel], it will be available +If the new provider inherits from [`BaseChatModel`][BaseChatModel], it will be available both in the chat UI and with magic commands. Otherwise, users can only use the new provider with magic commands. @@ -1030,7 +1030,7 @@ your new provider's `id`: ### Customizing prompt templates -To modify the prompt template for a given format, override the ``get_prompt_template`` method: +To modify the prompt template for a given format, override the `get_prompt_template` method: ```python from langchain.prompts import PromptTemplate From f1bda420695210a873439b380a99c29b6b31e94c Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Thu, 30 Nov 2023 23:04:40 -0800 Subject: [PATCH 08/12] move Prompt templates sections to Developers page --- docs/source/developers/index.md | 34 +++++++++++++++++++++++++++++++++ docs/source/users/index.md | 34 --------------------------------- 2 files changed, 34 insertions(+), 34 deletions(-) diff --git a/docs/source/developers/index.md b/docs/source/developers/index.md index 123315f7a..17bb6a5b1 100644 --- a/docs/source/developers/index.md +++ b/docs/source/developers/index.md @@ -16,3 +16,37 @@ Jupyter AI classes. For more details about using `langchain.pydantic_v1` in an environment with Pydantic v2 installed, see the [LangChain documentation on Pydantic compatibility](https://python.langchain.com/docs/guides/pydantic_compatibility). + +## Prompt templates + +Each provider can define **prompt templates** for each supported format. A prompt +template guides the language model to produce output in a particular +format. The default prompt templates are a +[Python dictionary mapping formats to templates](https://github.com/jupyterlab/jupyter-ai/blob/57a758fa5cdd5a87da5519987895aa688b3766a8/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py#L138-L166). +Developers who write subclasses of `BaseProvider` can override templates per +output format, per model, and based on the prompt being submitted, by +implementing their own +[`get_prompt_template` function](https://github.com/jupyterlab/jupyter-ai/blob/57a758fa5cdd5a87da5519987895aa688b3766a8/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py#L186-L195). +Each prompt template includes the string `{prompt}`, which is replaced with +the user-provided prompt when the user runs a magic command. + +### Customizing prompt templates + +To modify the prompt template for a given format, override the `get_prompt_template` method: + +```python +from langchain.prompts import PromptTemplate + + +class MyProvider(BaseProvider, FakeListLLM): + # (... properties as above ...) + def get_prompt_template(self, format) -> PromptTemplate: + if format === "code": + return PromptTemplate.from_template( + "{prompt}\n\nProduce output as source code only, " + "with no text or explanation before or after it." + ) + return super().get_prompt_template(format) +``` + +Please note that this will only work with Jupyter AI magics (the `%ai` and `%%ai` magic commands), customization of prompt templates for chat is not available yet (see [issue #498](https://github.com/jupyterlab/jupyter-ai/issues/498)). diff --git a/docs/source/users/index.md b/docs/source/users/index.md index af35a57d7..40d135f9a 100644 --- a/docs/source/users/index.md +++ b/docs/source/users/index.md @@ -620,20 +620,6 @@ A function that computes the lowest common multiples of two integers, and a function that runs 5 test cases of the lowest common multiple function ``` -### Prompt templates - -Each provider can define **prompt templates** for each supported format. A prompt -template guides the language model to produce output in a particular -format. The default prompt templates are a -[Python dictionary mapping formats to templates](https://github.com/jupyterlab/jupyter-ai/blob/57a758fa5cdd5a87da5519987895aa688b3766a8/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py#L138-L166). -Developers who write subclasses of `BaseProvider` can override templates per -output format, per model, and based on the prompt being submitted, by -implementing their own -[`get_prompt_template` function](https://github.com/jupyterlab/jupyter-ai/blob/57a758fa5cdd5a87da5519987895aa688b3766a8/packages/jupyter-ai-magics/jupyter_ai_magics/providers.py#L186-L195). -Each prompt template includes the string `{prompt}`, which is replaced with -the user-provided prompt when the user runs a magic command. - - ### Clearing the OpenAI chat history With the `openai-chat` provider *only*, you can run a cell magic command using the `-r` or @@ -1026,23 +1012,3 @@ your new provider's `id`: [custom_llm]: https://python.langchain.com/docs/modules/model_io/models/llms/custom_llm [LLM]: https://api.python.langchain.com/en/v0.0.339/llms/langchain.llms.base.LLM.html#langchain.llms.base.LLM [BaseChatModel]: https://api.python.langchain.com/en/v0.0.339/chat_models/langchain.chat_models.base.BaseChatModel.html - - -### Customizing prompt templates - -To modify the prompt template for a given format, override the `get_prompt_template` method: - -```python -from langchain.prompts import PromptTemplate - - -class MyProvider(BaseProvider, FakeListLLM): - # (... properties as above ...) - def get_prompt_template(self, format) -> PromptTemplate: - if format === "code": - return PromptTemplate.from_template( - "{prompt}\n\nProduce output as source code only, " - "with no text or explanation before or after it." - ) - return super().get_prompt_template(format) -``` From ea61df1104fb9b1755ceb8259e99fb893d711158 Mon Sep 17 00:00:00 2001 From: Andrii Ieroshenko Date: Thu, 30 Nov 2023 23:48:00 -0800 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