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Merge pull request #13 from groq/release-please--branches--main--changes--next
release: 0.4.1
2 parents 9638e4f + f9edc0a commit d9bdc57

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‎.devcontainer/Dockerfile‎

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USER vscode
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RUN curl -sSf https://rye-up.com/get | RYE_VERSION="0.15.2" RYE_INSTALL_OPTION="--yes" bash
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RUN curl -sSf https://rye-up.com/get | RYE_VERSION="0.24.0" RYE_INSTALL_OPTION="--yes" bash
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ENV PATH=/home/vscode/.rye/shims:$PATH
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RUN echo "[[ -d .venv ]] && source .venv/bin/activate" >> /home/vscode/.bashrc

‎.github/workflows/ci.yml‎

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curl -sSf https://rye-up.com/get | bash
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echo "$HOME/.rye/shims" >> $GITHUB_PATH
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env:
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RYE_VERSION: 0.15.2
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RYE_VERSION: 0.24.0
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RYE_INSTALL_OPTION: "--yes"
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- name: Install dependencies

‎.github/workflows/publish-pypi.yml‎

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curl -sSf https://rye-up.com/get | bash
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echo "$HOME/.rye/shims" >> $GITHUB_PATH
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env:
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RYE_VERSION: 0.15.2
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RYE_VERSION: 0.24.0
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RYE_INSTALL_OPTION: "--yes"
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- name: Publish to PyPI

‎.release-please-manifest.json‎

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{
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".": "0.4.0"
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".": "0.4.1"
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}

‎CHANGELOG.md‎

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# Changelog
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## 0.4.0 (2024-02-15)
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## 0.4.1 (2024-02-21)
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Full Changelog: [v0.1.0...v0.4.0](https://github.com/groq/groq-python/compare/v0.1.0...v0.4.0)
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Full Changelog: [v0.1.0...v0.4.1](https://github.com/groq/groq-python/compare/v0.1.0...v0.4.1)
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### Features
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* Add initial Stainless SDK ([4de80db](https://github.com/groq/groq-python/commit/4de80dbe4c27d6ecbcc2d2b6192d27ade5da3866))
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* Add initial Stainless SDK ([d5a8512](https://github.com/groq/groq-python/commit/d5a851262e04e625dde130367ed91d8f95683599))
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* Add initial Stainless SDK ([316de2c](https://github.com/groq/groq-python/commit/316de2ccfeb76e36fe34bb8656ea90a8d42a7d00))
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* create default branch ([7e00266](https://github.com/groq/groq-python/commit/7e00266e3c691d92d508e753e2c14c03297c09f9))
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* update via SDK Studio ([#10](https://github.com/groq/groq-python/issues/10)) ([0c0d204](https://github.com/groq/groq-python/commit/0c0d20405a96167f060a03a2b8a58a49d9a1c7c8))
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* update via SDK Studio ([#14](https://github.com/groq/groq-python/issues/14)) ([153744e](https://github.com/groq/groq-python/commit/153744e8ca331900adb0bd88a688afedf9a4bf00))
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* update via SDK Studio ([#3](https://github.com/groq/groq-python/issues/3)) ([8d92c08](https://github.com/groq/groq-python/commit/8d92c086e320c2715e02bc79807ff872e84c0b0f))
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### Chores
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* go live ([#2](https://github.com/groq/groq-python/issues/2)) ([ba81c42](https://github.com/groq/groq-python/commit/ba81c42d6d0fd6d47819e0d58962235cb70ca4f1))
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* go live ([#5](https://github.com/groq/groq-python/issues/5)) ([75ea081](https://github.com/groq/groq-python/commit/75ea081a84bbcf15702dcb53fd6411c8de497c83))
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* go live ([#5](https://github.com/groq/groq-python/issues/5)) ([af9a838](https://github.com/groq/groq-python/commit/af9a838e240bb0f7385bc33fb18ce246427ca2f7))
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* update branch ([#12](https://github.com/groq/groq-python/issues/12)) ([28f34e1](https://github.com/groq/groq-python/commit/28f34e1cc08b974d31744fd94bf31eafc9e6e867))
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* update branch ([#8](https://github.com/groq/groq-python/issues/8)) ([b9b55b4](https://github.com/groq/groq-python/commit/b9b55b41cb158efd155f9cda829808c877493afd))
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## 0.1.0 (2024-02-10)
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‎examples/chat_completion.py‎

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# Set an optional system message. This sets the behavior of the
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# assistant and can be used to provide specific instructions for
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# how it should behave throughout the conversation.
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{
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"role": "system",
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"content": "you are a helpful assistant."
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},
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{"role": "system", "content": "you are a helpful assistant."},
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# Set a user message for the assistant to respond to.
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{
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"role": "user",
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"content": "Explain the importance of low latency LLMs",
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},
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],
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# The language model which will generate the completion.
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model="mixtral-8x7b-32768",
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#
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# Optional parameters
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# Controls randomness: lowering results in less random completions.
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# As the temperature approaches zero, the model will become deterministic
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temperature=0.5,
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# The maximum number of tokens to generate. Requests can use up to
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# 2048 tokens shared between prompt and completion.
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max_tokens=1024,
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# Controls diversity via nucleus sampling: 0.5 means half of all
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# likelihood-weighted options are considered.
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top_p=1,
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# A stop sequence is a predefined or user-specified text string that
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# signals an AI to stop generating content, ensuring its responses
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# remain focused and concise. Examples include punctuation marks and
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stop=None,
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# If set, partial message deltas will be sent.
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stream=False,
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)

‎examples/chat_completion_async.py‎

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# Set an optional system message. This sets the behavior of the
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# assistant and can be used to provide specific instructions for
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# how it should behave throughout the conversation.
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{
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"role": "system",
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"content": "you are a helpful assistant."
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},
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{"role": "system", "content": "you are a helpful assistant."},
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# Set a user message for the assistant to respond to.
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"content": "Explain the importance of low latency LLMs",
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},
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# The language model which will generate the completion.
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model="mixtral-8x7b-32768",
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# Controls randomness: lowering results in less random completions.
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temperature=0.5,
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# The maximum number of tokens to generate. Requests can use up to
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# 2048 tokens shared between prompt and completion.
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max_tokens=1024,
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# Controls diversity via nucleus sampling: 0.5 means half of all
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# likelihood-weighted options are considered.
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top_p=1,
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# A stop sequence is a predefined or user-specified text string that
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# signals an AI to stop generating content, ensuring its responses
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# remain focused and concise. Examples include punctuation marks and
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‎examples/chat_completion_async_streaming.py‎

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# Set an optional system message. This sets the behavior of the
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# assistant and can be used to provide specific instructions for
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# how it should behave throughout the conversation.
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{
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"role": "system",
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"content": "you are a helpful assistant."
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},
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{"role": "system", "content": "you are a helpful assistant."},
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# Set a user message for the assistant to respond to.
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# The language model which will generate the completion.
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max_tokens=1024,
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stop=None,
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# Controls diversity via nucleus sampling: 0.5 means half of all
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stream=True,

‎examples/chat_completion_stop.py‎

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# Set an optional system message. This sets the behavior of the
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# how it should behave throughout the conversation.
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"role": "system",
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"content": "you are a helpful assistant."
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{"role": "system", "content": "you are a helpful assistant."},
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# Set a user message for the assistant to respond to.
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"content": "Count to 10. Your response must begin with \"1, \". example: 1, 2, 3, ...",
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"content": 'Count to 10. Your response must begin with "1, ". example: 1, 2, 3, ...',
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},
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max_tokens=1024,
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‎examples/chat_completion_streaming.py‎

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# Set an optional system message. This sets the behavior of the
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# how it should behave throughout the conversation.
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{
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"role": "system",
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"content": "you are a helpful assistant."
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{"role": "system", "content": "you are a helpful assistant."},
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# Set a user message for the assistant to respond to.
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{
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"role": "user",
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"content": "Explain the importance of low latency LLMs",
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model="mixtral-8x7b-32768",
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# Controls randomness: lowering results in less random completions.
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temperature=0.5,
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# The maximum number of tokens to generate. Requests can use up to
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# 2048 tokens shared between prompt and completion.
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max_tokens=1024,
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# Controls diversity via nucleus sampling: 0.5 means half of all
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# likelihood-weighted options are considered.
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top_p=1,
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# A stop sequence is a predefined or user-specified text string that
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# signals an AI to stop generating content, ensuring its responses
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# remain focused and concise. Examples include punctuation marks and
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# markers like "[end]".
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stop=None,
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# If set, partial message deltas will be sent.
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stream=True,
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)

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