Merge branch 'google:main' into main

Author: kyolee415

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -5,7 +5,7 @@ body:
   - type: markdown
     attributes:
       value: >
-        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please post on the [Google Developers Community](https://discord.gg/google-dev-community) Discord server's #palm-api channel instead. This is a great place to interact with developers, and to learn, share, and support each other.
+        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please visit the [Discourse forum](https://discuss.ai.google.dev/). This is a great place to interact with developers, and to learn, share, and support each other.
   - type: textarea
     id: description
     attributes:

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -5,7 +5,7 @@ body:
   - type: markdown
     attributes:
       value: >
-        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please post on the [Google Developers Community](https://discord.gg/google-dev-community) Discord server's #palm-api channel instead. This is a great place to interact with developers, and to learn, share, and support each other.
+        **Note:** If this is a support question (e.g. _How do I do XYZ?_), please visit the [Discourse forum](https://discuss.ai.google.dev/). This is a great place to interact with developers, and to learn, share, and support each other.
   - type: textarea
     id: description
     attributes:

examples/gemini/python/vectordb_with_chroma/vectordb_with_chroma.ipynb

@@ -800,7 +800,7 @@
       "source": [
         "## Next steps\n",
         "\n",
-        "To learn more about how you can use the embeddings, check out the [examples](https://ai.google.dev/examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](../../../../site/en/tutorials/python_quickstart.ipynb)."
+        "To learn more about how you can use the embeddings, check out the [examples](https://ai.google.dev/examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python)."
       ]
     }
   ],

site/en/gemini-api/docs/prompting_with_media.ipynb

@@ -48,12 +48,12 @@
       "source": [
         "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://ai.google.dev/tutorials/prompting_with_media\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
+        "    <a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/prompting_with_media\"><img src=\"https://ai.google.dev/static/site-assets/images/docs/notebook-site-button.png\" height=\"32\" width=\"32\" />View on ai.google.dev</a>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
         "  </td>\n",
         "  <td>\n",
-        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/tutorials/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
+        "    <a target=\"_blank\" href=\"https://github.com/google/generative-ai-docs/blob/main/site/en/gemini-api/docs/prompting_with_media.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "</table>"
       ]

site/en/gemini-api/docs/semantic_retrieval.ipynb

@@ -72,7 +72,7 @@
         "\n",
         "This notebook walks you through a workflow to improve an LLM's response by augmenting its knowledge with external text corpora and performing semantic information retrieval to answer questions using the Semantic Retriever and the Attributed Question & Answering (AQA) APIs of the [Generative Language API](../api/python/google/ai/generativelanguage).\n",
         "\n",
-        "Note: This API is currently in [beta](/docs/api_versions) and is [only available in certain regions](../available_regions).\n"
+        "Note: This API is currently in [beta](https://ai.google.dev/gemini-api/docs/api-versions) and is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions).\n"
       ]
     },
     {
@@ -719,7 +719,7 @@
       "source": [
         "## Attributed Question-Answering\n",
         "\n",
-        "Use the [`GenerateAnswer`](../api/python/google/ai/generativelanguage/GenerateAnswerRequest) method to perform Attributed Question-Answering over your document, corpus, or a set of passages.\n",
+        "Use the [`GenerateAnswer`](https://ai.google.dev/api/python/google/ai/generativelanguage/GenerateAnswerRequest) method to perform Attributed Question-Answering over your document, corpus, or a set of passages.\n",
         "\n",
         "Attributed Question-Answering (AQA) refers to answering questions grounded to a given context and providing attributions(s), while minimizing hallucination.\n",
         "\n",
@@ -769,7 +769,7 @@
       "source": [
         "### AQA Helpful Tips\n",
         "\n",
-        "For full API specifications, refer to the [`GenerateAnswerRequest` API Reference](../api/python/google/ai/generativelanguage/GenerateAnswerRequest).\n",
+        "For full API specifications, refer to the [`GenerateAnswerRequest` API Reference](https://ai.google.dev/api/python/google/ai/generativelanguage/GenerateAnswerRequest).\n",
         "\n",
         "* *Passage length*: Up to 300 tokens per passage are recommended.\n",
         "* *Passage sorting*:\n",
@@ -870,7 +870,7 @@
       "source": [
         "## Share the corpus\n",
         "\n",
-        "You can choose to share the corpus with others using the [`CreatePermissionRequest`](../api/python/google/ai/generativelanguage/CreatePermissionRequest) API.\n",
+        "You can choose to share the corpus with others using the [`CreatePermissionRequest`](https://ai.google.dev/api/python/google/ai/generativelanguage/CreatePermissionRequest) API.\n",
         "\n",
         "Constraints:\n",
         "\n",
@@ -912,7 +912,7 @@
       "source": [
         "## Delete the corpus\n",
         "\n",
-        "Use [`DeleteCorpusRequest`](../api/python/google/ai/generativelanguage/DeleteCorpusRequest) to delete a user corpus and all associated `Document`s & `Chunk`s.\n",
+        "Use [`DeleteCorpusRequest`](https://ai.google.dev/api/python/google/ai/generativelanguage/DeleteCorpusRequest) to delete a user corpus and all associated `Document`s & `Chunk`s.\n",
         "\n",
         "Note that non-empty corpora will throw an error without specifying an `force=True` flag. If you set `force=True`, any `Chunk`s and objects related to this `Document` will also be deleted.\n",
         "\n",

site/en/gemini-api/tutorials/anomaly_detection.ipynb

@@ -68,7 +68,7 @@
         "\n",
         "This tutorial demonstrates how to use the embeddings from the Gemini API to detect potential outliers in your dataset. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and detect outliers outside a particular radius of the central point of each categorical cluster.\n",
         "\n",
-        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](../tutorials/python_quickstart.ipynb#use_embeddings).\n",
+        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart#use_embeddings).\n",
         "\n",
         "## Prerequisites\n",
         "\n",
@@ -162,7 +162,7 @@
       "source": [
         "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
         "\n",
-        "**Note**: At this time, the Gemini API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/available_regions)."
       ]
     },
     {
@@ -2934,7 +2934,7 @@
         "\n",
         "You've now created an anomaly detector using embeddings! Try using your own textual data to visualize them as embeddings, and choose some bound such that you can detect outliers. You can perform dimensionality reduction in order to complete the visualization step. Note that t-SNE is good at clustering inputs, but can take a longer time to converge or might get stuck at local minima. If you run into this issue, another technique you could consider are [principal components analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis){:.external}.\n",
         "\n",
-        "To learn how to use other services in the Gemini API, visit the [Python quickstart](../tutorials/python_quickstart.ipynb).\n",
+        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/tutorials/python_quickstart).\n",
         "\n",
         "To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to create them from scratch, see TensorFlow's [Word Embeddings](https://www.tensorflow.org/text/guide/word_embeddings){:.external} tutorial."
       ]

site/en/gemini-api/tutorials/clustering_with_embeddings.ipynb

@@ -69,7 +69,7 @@
         "\n",
         "This tutorial demonstrates how to visualize and perform clustering with the embeddings from the Gemini API. You will visualize a subset of the 20 Newsgroup dataset using [t-SNE](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html){:.external} and cluster that subset using the KMeans algorithm.\n",
         "\n",
-        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](../tutorials/python_quickstart.ipynb#use_embeddings).\n",
+        "For more information on getting started with embeddings generated from the Gemini API, check out the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python#use_embeddings).\n",
         "\n",
         "## Prerequisites\n",
         "\n",
@@ -165,7 +165,7 @@
       "source": [
         "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
         "\n",
-        "**Note**: At this time, the Gemini API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions)."
       ]
     },
     {
@@ -2857,7 +2857,7 @@
         "\n",
         "There are other clustering algorithms outside of KMeans as well, such as [density-based spatial clustering (DBSCAN)](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html){:.external}.\n",
         "\n",
-        "To learn how to use other services in the Gemini API, visit the [Python quickstart](../tutorials/python_quickstart.ipynb). To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to create them from scratch, see TensorFlow's [Word Embeddings](https://www.tensorflow.org/text/guide/word_embeddings){:.external} tutorial."
+        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python). To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to create them from scratch, see TensorFlow's [Word Embeddings](https://www.tensorflow.org/text/guide/word_embeddings){:.external} tutorial."
       ]
     }
   ],

site/en/gemini-api/tutorials/document_search.ipynb

@@ -159,7 +159,7 @@
       "source": [
         "Key Point: Next, you will choose a model. Any embedding model will work for this tutorial, but for real applications it's important to choose a specific model and stick with it. The outputs of different models are not compatible with each other.\n",
         "\n",
-        "**Note**: At this time, the Gemini API is [only available in certain regions](https://developers.generativeai.google/available_regions)."
+        "**Note**: At this time, the Gemini API is [only available in certain regions](https://ai.google.dev/gemini-api/docs/available-regions)."
       ]
     },
     {
@@ -1191,7 +1191,7 @@
       },
       "outputs": [],
       "source": [
-        "model = genai.GenerativeModel('gemini-ultra')\n",
+        "model = genai.GenerativeModel('gemini-1.5-pro-latest')\n",
         "answer = model.generate_content(prompt)"
       ]
     },
@@ -1228,7 +1228,7 @@
       "source": [
         "## Next steps\n",
         "\n",
-        "To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](../tutorials/python_quickstart.ipynb)."
+        "To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python)."
       ]
     }
   ],

site/en/gemini-api/tutorials/text_classifier_embeddings.ipynb

@@ -1537,7 +1537,7 @@
       "source": [
         "## Next steps\n",
         "\n",
-        "To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](../tutorials/python_quickstart.ipynb)."
+        "To learn more about how you can use the embeddings, check out the [examples](../examples?keywords=embed) available. To learn how to use other services in the Gemini API, visit the [Python quickstart](https://ai.google.dev/gemini-api/docs/get-started/python)."
       ]
     }
   ],