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fix(broken-link): changed link from beta.openai.com to platform.openai.com openai#1833 
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‎articles/text_comparison_examples.md

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# Text comparison examples
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The [OpenAI API embeddings endpoint](https://beta.openai.com/docs/guides/embeddings) can be used to measure relatedness or similarity between pieces of text.
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The [OpenAI API embeddings endpoint](https://platform.openai.com/docs/guides/embeddings) can be used to measure relatedness or similarity between pieces of text.
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By leveraging GPT-3's understanding of text, these embeddings [achieved state-of-the-art results](https://arxiv.org/abs/2201.10005) on benchmarks in unsupervised learning and transfer learning settings.
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‎examples/Embedding_long_inputs.ipynb

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"\n",
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"OpenAI's embedding models cannot embed text that exceeds a maximum length. The maximum length varies by model, and is measured by _tokens_, not string length. If you are unfamiliar with tokenization, check out [How to count tokens with tiktoken](How_to_count_tokens_with_tiktoken.ipynb).\n",
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"\n",
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"This notebook shows how to handle texts that are longer than a model's maximum context length. We'll demonstrate using embeddings from `text-embedding-3-small`, but the same ideas can be applied to other models and tasks. To learn more about embeddings, check out the [OpenAI Embeddings Guide](https://beta.openai.com/docs/guides/embeddings).\n"
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"This notebook shows how to handle texts that are longer than a model's maximum context length. We'll demonstrate using embeddings from `text-embedding-3-small`, but the same ideas can be applied to other models and tasks. To learn more about embeddings, check out the [OpenAI Embeddings Guide](https://platform.openai.com/docs/guides/embeddings).\n"
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‎examples/How_to_count_tokens_with_tiktoken.ipynb

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"\n",
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"## How strings are typically tokenized\n",
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"\n",
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"In English, tokens commonly range in length from one character to one word (e.g., `\"t\"` or `\" great\"`), though in some languages tokens can be shorter than one character or longer than one word. Spaces are usually grouped with the starts of words (e.g., `\" is\"` instead of `\"is \"` or `\" \"`+`\"is\"`). You can quickly check how a string is tokenized at the [OpenAI Tokenizer](https://beta.openai.com/tokenizer), or the third-party [Tiktokenizer](https://tiktokenizer.vercel.app/) webapp."
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"In English, tokens commonly range in length from one character to one word (e.g., `\"t\"` or `\" great\"`), though in some languages tokens can be shorter than one character or longer than one word. Spaces are usually grouped with the starts of words (e.g., `\" is\"` instead of `\"is \"` or `\" \"`+`\"is\"`). You can quickly check how a string is tokenized at the [OpenAI Tokenizer](https://platform.openai.com/tokenizer), or the third-party [Tiktokenizer](https://tiktokenizer.vercel.app/) webapp."
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‎examples/How_to_stream_completions.ipynb

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"\n",
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"## Downsides\n",
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"\n",
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"Note that using `stream=True` in a production application makes it more difficult to moderate the content of the completions, as partial completions may be more difficult to evaluate. This may have implications for [approved usage](https://beta.openai.com/docs/usage-guidelines).\n",
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"Note that using `stream=True` in a production application makes it more difficult to moderate the content of the completions, as partial completions may be more difficult to evaluate. This may have implications for [approved usage](https://platform.openai.com/docs/usage-guidelines).\n",
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"## Example code\n",
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"\n",

‎examples/dalle/Image_generations_edits_and_variations_with_DALL-E.ipynb

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"- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
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"- `size` (str): The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models. Defaults to \"1024x1024\".\n",
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"- `style`(str | null): The style of the generated images. Must be one of vivid or natural. Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images. This param is only supported for dall-e-3.\n",
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://beta.openai.com/docs/usage-policies/end-user-ids)"
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://platform.openai.com/docs/usage-policies/end-user-ids)"
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"- `n` (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.\n",
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"- `size` (str): The size of the generated images. Must be one of \"256x256\", \"512x512\", or \"1024x1024\". Smaller images are faster. Defaults to \"1024x1024\".\n",
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"- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://beta.openai.com/docs/usage-policies/end-user-ids)\n"
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://platform.openai.com/docs/usage-policies/end-user-ids)\n"
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"- `n` (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.\n",
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"- `size` (str): The size of the generated images. Must be one of \"256x256\", \"512x512\", or \"1024x1024\". Smaller images are faster. Defaults to \"1024x1024\".\n",
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"- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://beta.openai.com/docs/usage-policies/end-user-ids)\n"
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"- `user` (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. [Learn more.](https://platform.openai.com/docs/usage-policies/end-user-ids)\n"
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‎examples/fine-tuned_qa/olympics-2-create-qa.ipynb

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"# 2. Creating a synthetic Q&A dataset\n",
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"We use [`davinci-instruct-beta-v3`](https://beta.openai.com/docs/engines/instruct-series-beta), a model specialized in following instructions, to create questions based on the given context. Then we also use [`davinci-instruct-beta-v3`](https://beta.openai.com/docs/engines/instruct-series-beta) to answer those questions, given the same context. \n",
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"We use [`davinci-instruct-beta-v3`](https://platform.openai.com/docs/engines/instruct-series-beta), a model specialized in following instructions, to create questions based on the given context. Then we also use [`davinci-instruct-beta-v3`](https://platform.openai.com/docs/engines/instruct-series-beta) to answer those questions, given the same context. \n",
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"This is expensive, and will also take a long time, as we call the davinci engine for each section. You can simply download the final dataset instead.\n",
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"## 2.5 Search file (DEPRECATED)\n",
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"We create a search file ([API reference](https://beta.openai.com/docs/api-reference/files/list)), which can be used to retrieve the relevant context when a question is asked.\n",
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"We create a search file ([API reference](https://platform.openai.com/docs/api-reference/files/list)), which can be used to retrieve the relevant context when a question is asked.\n",
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"<span style=\"color:orange; font-weight:bold\">DEPRECATED: The /search endpoint is deprecated in favour of using embeddings. Embeddings are cheaper, faster and can support a better search experience. See <a href=\"https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb\">Question Answering Guide</a> for a search implementation using the embeddings</span>\n"
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"## 2.6 Answer questions based on the context provided\n",
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"\n",
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"We will use a simple implementation of the answers endpoint. This works by simply using the [/search endpoint](https://beta.openai.com/docs/api-reference/searches), which searches over an indexed file to obtain the relevant sections which can be included in the context, following by a question and answering prompt given a specified model."
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"We will use a simple implementation of the answers endpoint. This works by simply using the [/search endpoint](https://platform.openai.com/docs/api-reference/searches), which searches over an indexed file to obtain the relevant sections which can be included in the context, following by a question and answering prompt given a specified model."
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"After we fine-tune the model for Q&A we'll be able to use it instead of [`davinci-instruct-beta-v3`](https://beta.openai.com/docs/engines/instruct-series-beta), to obtain better answers when the question can't be answered based on the context. We see a downside of [`davinci-instruct-beta-v3`](https://beta.openai.com/docs/engines/instruct-series-beta), which always attempts to answer the question, regardless of the relevant context being present or not. (Note the second question is asking about a future event, set in 2024.)"
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"After we fine-tune the model for Q&A we'll be able to use it instead of [`davinci-instruct-beta-v3`](https://platform.openai.com/docs/engines/instruct-series-beta), to obtain better answers when the question can't be answered based on the context. We see a downside of [`davinci-instruct-beta-v3`](https://platform.openai.com/docs/engines/instruct-series-beta), which always attempts to answer the question, regardless of the relevant context being present or not. (Note the second question is asking about a future event, set in 2024.)"
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‎examples/fine-tuned_qa/olympics-3-train-qa.ipynb

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"## 3.4 Answering the question based on a knowledge base\n",
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"Finally we can use a logic similar to the [/answers](https://beta.openai.com/docs/api-reference/answers) endpoint, where we first search for the relevant context, and then ask a Q&A model to answer the question given that context. If you'd like to see the implementation details, check out the [`answers_with_ft.py`](answers_with_ft.py) file."
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"Finally we can use a logic similar to the [/answers](https://platform.openai.com/docs/api-reference/answers) endpoint, where we first search for the relevant context, and then ask a Q&A model to answer the question given that context. If you'd like to see the implementation details, check out the [`answers_with_ft.py`](answers_with_ft.py) file."
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‎examples/vector_databases/PolarDB/Getting_started_with_PolarDB_and_OpenAI.ipynb

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"1. PolarDB-PG cloud server instance.\n",
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"2. The 'psycopg2' library to interact with the vector database. Any other postgresql client library is ok.\n",
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"3. An [OpenAI API key](https://beta.openai.com/account/api-keys)."
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"3. An [OpenAI API key](https://platform.openai.com/account/api-keys)."
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"Prepare your OpenAI API key\n",
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"If you don't have an OpenAI API key, you can get one from https://platform.openai.com/account/api-keys.\n",
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"Once you get your key, please add it to your environment variables as OPENAI_API_KEY.\n",
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‎examples/vector_databases/analyticdb/Getting_started_with_AnalyticDB_and_OpenAI.ipynb

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"1. AnalyticDB cloud server instance.\n",
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"2. The 'psycopg2' library to interact with the vector database. Any other postgresql client library is ok.\n",
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"3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
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"3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
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"If you don't have an OpenAI API key, you can get one from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys).\n",
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‎examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb

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"We use OpenAI's API's throughout this notebook. You can get an API key from [https://beta.openai.com/account/api-keys](https://beta.openai.com/account/api-keys)\n",
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"We use OpenAI's API's throughout this notebook. You can get an API key from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys)\n",
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"You can add your API key as an environment variable by executing the command `export OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx` in a terminal. Note that you will need to reload the notebook if the environment variable wasn't set yet. Alternatively, you can set it in the notebook, see below. "
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‎examples/vector_databases/hologres/Getting_started_with_Hologres_and_OpenAI.ipynb

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"3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
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"3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
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"If you don't have an OpenAI API key, you can get one from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys).\n",
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‎examples/vector_databases/myscale/Getting_started_with_MyScale_and_OpenAI.ipynb

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"1. A MyScale cluster deployed by following the [quickstart guide](https://docs.myscale.com/en/quickstart/).\n",
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"3. An [OpenAI API key](https://beta.openai.com/account/api-keys) for vectorization of queries."
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"3. An [OpenAI API key](https://platform.openai.com/account/api-keys) for vectorization of queries."
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‎examples/vector_databases/qdrant/Getting_started_with_Qdrant_and_OpenAI.ipynb

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"If you don't have an OpenAI API key, you can get one from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys).\n",
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"Once you get your key, please add it to your environment variables as `OPENAI_API_KEY` by running following command:"
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‎examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb

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‎examples/vector_databases/redis/getting-started-with-redis-and-openai.ipynb

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‎examples/vector_databases/redis/redis-hybrid-query-examples.ipynb

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‎examples/vector_databases/tair/Getting_started_with_Tair_and_OpenAI.ipynb

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‎examples/vector_databases/weaviate/generative-search-with-weaviate-and-openai.ipynb

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"\n",
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"If you don't have an OpenAI API key, you can get one from [https://beta.openai.com/account/api-keys](https://beta.openai.com/account/api-keys).\n",
46+
"If you don't have an OpenAI API key, you can get one from [https://platform.openai.com/account/api-keys](https://platform.openai.com/account/api-keys).\n",
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"\n",
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"Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
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]

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