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docs: Remove template pages because templates already include explanatory notes within Langflow (#9235)

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* finish template description

* remove travel planning and simple agent pages

* remove sequential agent and memory chatbot

* remove 2 template, finish redirects, sidebar

* remove vector store rag page

* remove basic prompting page

* add bold to template name

* fix link
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docs/docs/Components/components-vector-stores.mdx
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@ -50,14 +50,14 @@ This component implements an [Astra DB Serverless vector store](https://docs.dat
| Name | Display Name | Info |
|------|--------------|------|
| token | Astra DB Application Token | The authentication token for accessing Astra DB. |
| token | Astra DB Application Token | An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections. |
| environment | Environment | The environment for the Astra DB API Endpoint. For example, `dev` or `prod`. |
| database_name | Database | The database name for the Astra DB instance. |
| database_name | Database | The name of the database that you want this component to connect to, or select **New Database** to create a new database. To create a new database, you must provide the database details, and then wait for the database to initialize. |
| api_endpoint | Astra DB API Endpoint | The API endpoint for the Astra DB instance. This supersedes the database selection. |
| collection_name | Collection | The name of the collection within Astra DB where the vectors are stored. |
| collection_name | Collection | The name of the collection that you want to use with this flow, or click **New Collection** to create a new collection. |
| keyspace | Keyspace | An optional keyspace within Astra DB to use for the collection. |
| embedding_choice | Embedding Model or Astra Vectorize | Choose an embedding model or use Astra vectorize. |
| embedding_model | Embedding Model | Specify the embedding model. Not required for Astra vectorize collections. |
| embedding_choice | Embedding Model or Astra Vectorize | Choose an embedding model or use Astra vectorize. If the collection has a vectorize integration, **Astra Vectorize** can be selected automatically. |
| embedding_model | Embedding Model | Specify the embedding model. Not required if the embedding choice is **Astra Vectorize** because the component automatically uses the integrated model. |
| number_of_results | Number of Search Results | The number of search results to return. Default:`4`. |
| search_type | Search Type | The search type to use. The options are `Similarity`, `Similarity with score threshold`, and `MMR (Max Marginal Relevance)`. |
| search_score_threshold | Search Score Threshold | The minimum similarity score threshold for search results when using the `Similarity with score threshold` option. |
@ -90,7 +90,7 @@ For more information, see the [Astra DB Serverless documentation](https://docs.d
With vectorize, the embedding model you choose when you create a collection cannot be changed later.
:::
For an example of using the **Astra DB** component with an embedding model, see the [**Vector Store RAG** template](/vector-store-rag).
For an example of using the **Astra DB** component with an embedding model, see the **Vector Store RAG** template.
### Hybrid search