openai · jk-codertech · May 10, 2025 · May 10, 2025
diff --git a/articles/text_comparison_examples.md b/articles/text_comparison_examples.md
@@ -1,6 +1,6 @@
 # Text comparison examples
 
-The [OpenAI API embeddings endpoint](https://beta.openai.com/docs/guides/embeddings) can be used to measure relatedness or similarity between pieces of text.
+The [OpenAI API embeddings endpoint](https://platform.openai.com/docs/guides/embeddings) can be used to measure relatedness or similarity between pieces of text.
 
 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.
 

diff --git a/examples/Embedding_long_inputs.ipynb b/examples/Embedding_long_inputs.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "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",
     "\n",
-    "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"
+    "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"
    ]
   },
   {

diff --git a/examples/How_to_count_tokens_with_tiktoken.ipynb b/examples/How_to_count_tokens_with_tiktoken.ipynb
@@ -57,7 +57,7 @@
     "\n",
     "## How strings are typically tokenized\n",
     "\n",
-    "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."
+    "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."
    ]
   },
   {

diff --git a/examples/How_to_stream_completions.ipynb b/examples/How_to_stream_completions.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "## Downsides\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "## Example code\n",
     "\n",

diff --git a/examples/dalle/Image_generations_edits_and_variations_with_DALL-E.ipynb b/examples/dalle/Image_generations_edits_and_variations_with_DALL-E.ipynb
@@ -86,7 +86,7 @@
     "- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
     "- `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",
     "- `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",
-    "- `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)"
+    "- `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)"
    ]
   },
   {
@@ -166,7 +166,7 @@
     "- `n` (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.\n",
     "- `size` (str): The size of the generated images. Must be one of \"256x256\", \"512x512\", or \"1024x1024\". Smaller images are faster. Defaults to \"1024x1024\".\n",
     "- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
-    "- `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"
+    "- `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"
    ]
   },
   {
@@ -248,7 +248,7 @@
     "- `n` (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.\n",
     "- `size` (str): The size of the generated images. Must be one of \"256x256\", \"512x512\", or \"1024x1024\". Smaller images are faster. Defaults to \"1024x1024\".\n",
     "- `response_format` (str): The format in which the generated images are returned. Must be one of \"url\" or \"b64_json\". Defaults to \"url\".\n",
-    "- `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"
+    "- `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"
    ]
   },
   {

diff --git a/examples/fine-tuned_qa/olympics-2-create-qa.ipynb b/examples/fine-tuned_qa/olympics-2-create-qa.ipynb
@@ -12,7 +12,7 @@
    "metadata": {},
    "source": [
     "# 2. Creating a synthetic Q&A dataset\n",
-    "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",
+    "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",
     "\n",
     "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",
     "\n",
@@ -306,7 +306,7 @@
    "metadata": {},
    "source": [
     "## 2.5 Search file (DEPRECATED)\n",
-    "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",
+    "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",
     "\n",
     "<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"
    ]
@@ -333,7 +333,7 @@
    "source": [
     "## 2.6 Answer questions based on the context provided\n",
     "\n",
-    "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."
+    "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."
    ]
   },
   {
@@ -393,7 +393,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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.)"
+    "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.)"
    ]
   },
   {

diff --git a/examples/fine-tuned_qa/olympics-3-train-qa.ipynb b/examples/fine-tuned_qa/olympics-3-train-qa.ipynb
@@ -593,7 +593,7 @@
    "metadata": {},
    "source": [
     "## 3.4 Answering the question based on a knowledge base\n",
-    "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."
+    "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."
    ]
   },
   {

diff --git a/examples/vector_databases/PolarDB/Getting_started_with_PolarDB_and_OpenAI.ipynb b/examples/vector_databases/PolarDB/Getting_started_with_PolarDB_and_OpenAI.ipynb
@@ -41,7 +41,7 @@
     "\n",
     "1. PolarDB-PG cloud server instance.\n",
     "2. The 'psycopg2' library to interact with the vector database. Any other postgresql client library is ok.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys)."
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys)."
    ]
   },
   {
@@ -79,7 +79,7 @@
     "Prepare your OpenAI API key\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "If you don't have an OpenAI API key, you can get one from https://beta.openai.com/account/api-keys.\n",
+    "If you don't have an OpenAI API key, you can get one from https://platform.openai.com/account/api-keys.\n",
     "\n",
     "Once you get your key, please add it to your environment variables as OPENAI_API_KEY.\n",
     "\n",

diff --git a/examples/vector_databases/analyticdb/Getting_started_with_AnalyticDB_and_OpenAI.ipynb b/examples/vector_databases/analyticdb/Getting_started_with_AnalyticDB_and_OpenAI.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "1. AnalyticDB cloud server instance.\n",
     "2. The 'psycopg2' library to interact with the vector database. Any other postgresql client library is ok.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
     "\n"
    ]
   },
@@ -78,7 +78,7 @@
     "\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]

diff --git a/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb b/examples/vector_databases/chroma/hyde-with-chroma-and-openai.ipynb
@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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",
+    "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",
     "\n",
     "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. "
    ]

diff --git a/examples/vector_databases/hologres/Getting_started_with_Hologres_and_OpenAI.ipynb b/examples/vector_databases/hologres/Getting_started_with_Hologres_and_OpenAI.ipynb
@@ -38,7 +38,7 @@
     "\n",
     "1. Hologres cloud server instance.\n",
     "2. The 'psycopg2-binary' library to interact with the vector database. Any other postgresql client library is ok.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
     "\n"
    ]
   },
@@ -83,7 +83,7 @@
     "\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]

diff --git a/examples/vector_databases/myscale/Getting_started_with_MyScale_and_OpenAI.ipynb b/examples/vector_databases/myscale/Getting_started_with_MyScale_and_OpenAI.ipynb
@@ -33,7 +33,7 @@
     "\n",
     "1. A MyScale cluster deployed by following the [quickstart guide](https://docs.myscale.com/en/quickstart/).\n",
     "2. The 'clickhouse-connect' library to interact with MyScale.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys) for vectorization of queries."
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys) for vectorization of queries."
    ]
   },
   {

diff --git a/examples/vector_databases/qdrant/Getting_started_with_Qdrant_and_OpenAI.ipynb b/examples/vector_databases/qdrant/Getting_started_with_Qdrant_and_OpenAI.ipynb
@@ -132,7 +132,7 @@
     "\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY` by running following command:"
    ]

diff --git a/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb b/examples/vector_databases/qdrant/QA_with_Langchain_Qdrant_and_OpenAI.ipynb
@@ -29,7 +29,7 @@
     "1. Qdrant server instance. In our case a local Docker container.\n",
     "2. The [qdrant-client](https://github.com/qdrant/qdrant_client) library to interact with the vector database.\n",
     "3. [Langchain](https://github.com/hwchase17/langchain) as a framework.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
     "\n",
     "### Start Qdrant server\n",
     "\n",
@@ -120,7 +120,7 @@
     "\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY` by running following command:"
    ]

diff --git a/examples/vector_databases/redis/getting-started-with-redis-and-openai.ipynb b/examples/vector_databases/redis/getting-started-with-redis-and-openai.ipynb
@@ -43,7 +43,7 @@
     "* start a Redis database with RediSearch (redis-stack)\n",
     "* install libraries\n",
     "    * [Redis-py](https://github.com/redis/redis-py)\n",
-    "* get your [OpenAI API key](https://beta.openai.com/account/api-keys)\n",
+    "* get your [OpenAI API key](https://platform.openai.com/account/api-keys)\n",
     "\n",
     "===========================================================\n",
     "\n",
@@ -92,7 +92,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of query data.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY` by using following command:"
    ]

diff --git a/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb b/examples/vector_databases/redis/redis-hybrid-query-examples.ipynb
@@ -24,7 +24,7 @@
     "* start a Redis database with RediSearch (redis-stack)\n",
     "* install libraries\n",
     "    * [Redis-py](https://github.com/redis/redis-py)\n",
-    "* get your [OpenAI API key](https://beta.openai.com/account/api-keys)\n",
+    "* get your [OpenAI API key](https://platform.openai.com/account/api-keys)\n",
     "\n",
     "===========================================================\n",
     "\n",
@@ -100,7 +100,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of query data.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY` by using following command:"
    ]

diff --git a/examples/vector_databases/tair/Getting_started_with_Tair_and_OpenAI.ipynb b/examples/vector_databases/tair/Getting_started_with_Tair_and_OpenAI.ipynb
@@ -38,7 +38,7 @@
     "\n",
     "1. Tair cloud server instance.\n",
     "2. The 'tair' library to interact with the tair database.\n",
-    "3. An [OpenAI API key](https://beta.openai.com/account/api-keys).\n",
+    "3. An [OpenAI API key](https://platform.openai.com/account/api-keys).\n",
     "\n"
    ]
   },
@@ -109,7 +109,7 @@
     "\n",
     "The OpenAI API key is used for vectorization of the documents and queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it by getpass."
    ]

diff --git a/examples/vector_databases/weaviate/generative-search-with-weaviate-and-openai.ipynb b/examples/vector_databases/weaviate/generative-search-with-weaviate-and-openai.ipynb
@@ -30,7 +30,7 @@
     "* completed [Getting Started cookbook](./getting-started-with-weaviate-and-openai.ipynb),\n",
     "* crated a `Weaviate` instance,\n",
     "* imported data into your `Weaviate` instance,\n",
-    "* you have an [OpenAI API key](https://beta.openai.com/account/api-keys)"
+    "* you have an [OpenAI API key](https://platform.openai.com/account/api-keys)"
    ]
   },
   {
@@ -43,7 +43,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of your data at import, and for running queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]

diff --git a/examples/vector_databases/weaviate/getting-started-with-weaviate-and-openai.ipynb b/examples/vector_databases/weaviate/getting-started-with-weaviate-and-openai.ipynb
@@ -95,7 +95,7 @@
     "    * `weaviate-client`\n",
     "    * `datasets`\n",
     "    * `apache-beam`\n",
-    "* get your [OpenAI API key](https://beta.openai.com/account/api-keys)\n",
+    "* get your [OpenAI API key](https://platform.openai.com/account/api-keys)\n",
     "\n",
     "===========================================================\n",
     "### Create a Weaviate instance\n",
@@ -172,7 +172,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of your data at import, and for running queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]

diff --git a/examples/vector_databases/weaviate/hybrid-search-with-weaviate-and-openai.ipynb b/examples/vector_databases/weaviate/hybrid-search-with-weaviate-and-openai.ipynb
@@ -95,7 +95,7 @@
     "    * `weaviate-client`\n",
     "    * `datasets`\n",
     "    * `apache-beam`\n",
-    "* get your [OpenAI API key](https://beta.openai.com/account/api-keys)\n",
+    "* get your [OpenAI API key](https://platform.openai.com/account/api-keys)\n",
     "\n",
     "===========================================================\n",
     "### Create a Weaviate instance\n",
@@ -172,7 +172,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of your data at import, and for running queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]

diff --git a/examples/vector_databases/weaviate/question-answering-with-weaviate-and-openai.ipynb b/examples/vector_databases/weaviate/question-answering-with-weaviate-and-openai.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "This notebook is prepared for a scenario where:\n",
     "* Your data is not vectorized\n",
-    "* You want to run Q&A ([learn more](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/qna-openai)) on your data based on the [OpenAI completions](https://beta.openai.com/docs/api-reference/completions) endpoint.\n",
+    "* You want to run Q&A ([learn more](https://weaviate.io/developers/weaviate/modules/reader-generator-modules/qna-openai)) on your data based on the [OpenAI completions](https://platform.openai.com/docs/api-reference/completions) endpoint.\n",
     "* You want to use Weaviate with the OpenAI module ([text2vec-openai](https://weaviate.io/developers/weaviate/modules/retriever-vectorizer-modules/text2vec-openai)), to generate vector embeddings for you.\n",
     "\n",
     "This notebook takes you through a simple flow to set up a Weaviate instance, connect to it (with OpenAI API key), configure data schema, import data (which will automatically generate vector embeddings for your data), and run question answering.\n",
@@ -94,7 +94,7 @@
     "    * `weaviate-client`\n",
     "    * `datasets`\n",
     "    * `apache-beam`\n",
-    "* get your [OpenAI API key](https://beta.openai.com/account/api-keys)\n",
+    "* get your [OpenAI API key](https://platform.openai.com/account/api-keys)\n",
     "\n",
     "===========================================================\n",
     "### Create a Weaviate instance\n",
@@ -171,7 +171,7 @@
     "\n",
     "The `OpenAI API key` is used for vectorization of your data at import, and for queries.\n",
     "\n",
-    "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",
+    "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",
     "\n",
     "Once you get your key, please add it to your environment variables as `OPENAI_API_KEY`."
    ]