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docs: Fix various typos

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Merge https://github.com/google/adk-python/pull/4186

COPYBARA_INTEGRATE_REVIEW=https://github.com/google/adk-python/pull/4186 from didier-durand:main f8a52b58e133551bec1d98660fd552afd881a8b9
PiperOrigin-RevId: 859203551
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Didier Durand
2026-01-21 12:01:54 -08:00
committed by Copybara-Service
parent e3d542a5ba
commit 5d941460b6
5 changed files with 6 additions and 6 deletions
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contributing/samples/application_integration_agent/README.md
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@@ -7,7 +7,7 @@ This sample demonstrates how to use the `ApplicationIntegrationToolset` within a
## Prerequisites ## Prerequisites
1. **Set up Integration Connection:** 1. **Set up Integration Connection:**
* You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create an Jira connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection. * You need an existing [Integration connection](https://cloud.google.com/integration-connectors/docs/overview) configured to interact with your Jira instance. Follow the [documentation](https://google.github.io/adk-docs/tools/google-cloud-tools/#use-integration-connectors) to provision the Integration Connector in Google Cloud and then use this [documentation](https://cloud.google.com/integration-connectors/docs/connectors/jiracloud/configure) to create a Jira connection. Note the `Connection Name`, `Project ID`, and `Location` of your connection.
* *
2. **Configure Environment Variables:** 2. **Configure Environment Variables:**
contributing/samples/multi_agent_seq_config/README.md
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@@ -6,7 +6,7 @@ The whole process is:
1. An agent backed by a cheap and fast model to write initial version. 1. An agent backed by a cheap and fast model to write initial version.
2. An agent backed by a smarter and a little more expensive to review the code. 2. An agent backed by a smarter and a little more expensive to review the code.
3. An final agent backed by the smartest and slowest model to write the final revision. 3. A final agent backed by the smartest and slowest model to write the final revision.
Sample queries: Sample queries:
contributing/samples/spanner_rag_agent/README.md
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@@ -181,7 +181,7 @@ type.
## 💬 Sample prompts ## 💬 Sample prompts
* I'd like to buy a starter bike for my 3 year old child, can you show me the recommendation? * I'd like to buy a starter bike for my 3-year-old child, can you show me the recommendation?
![Spanner RAG Sample Agent](Spanner_RAG_Sample_Agent.png) ![Spanner RAG Sample Agent](Spanner_RAG_Sample_Agent.png)
src/google/adk/tools/pubsub/__init__.py
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@@ -14,7 +14,7 @@
"""Pub/Sub Tools (Experimental). """Pub/Sub Tools (Experimental).
Pub/Sub Tools under this module are hand crafted and customized while the tools Pub/Sub Tools under this module are handcrafted and customized while the tools
under google.adk.tools.google_api_tool are auto generated based on API under google.adk.tools.google_api_tool are auto generated based on API
definition. The rationales to have customized tool are: definition. The rationales to have customized tool are:
src/google/adk/tools/spanner/settings.py
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@@ -115,7 +115,7 @@ class VectorSearchIndexSettings(BaseModel):
""" """
num_branches: Optional[int] = None num_branches: Optional[int] = None
"""Optional. The number of branches to further parititon the vector data. """Optional. The number of branches to further partition the vector data.
You can only designate num_branches for trees with 3 levels. You can only designate num_branches for trees with 3 levels.
The number of branches must be fewer than the number of leaves The number of branches must be fewer than the number of leaves
@@ -165,7 +165,7 @@ class SpannerVectorStoreSettings(BaseModel):
"""Required. The vector store table columns to return in the vector similarity search result. """Required. The vector store table columns to return in the vector similarity search result.
By default, only the `content_column` value and the distance value are returned. By default, only the `content_column` value and the distance value are returned.
If sepecified, the list of selected columns and the distance value are returned. If specified, the list of selected columns and the distance value are returned.
For example, if `selected_columns` is ['col1', 'col2'], then the result will contain the values of 'col1' and 'col2' columns and the distance value. For example, if `selected_columns` is ['col1', 'col2'], then the result will contain the values of 'col1' and 'col2' columns and the distance value.
""" """