fix links and minor rewording

Signed-off-by: rithin-pullela-aws <[email protected]>

Author: rithin-pullela-aws

_query-dsl/specialized/agentic.md

@@ -9,7 +9,7 @@ nav_order: 2
 **Introduced 3.2**
 {: .label .label-purple }
 
-Use the `agentic` query to ask questions in natural language and have OpenSearch automatically plan and execute the retrieval. The `agentic` query works in conjunction with a preconfigured agent that reads the question, plans the search, and returns relevant results. For more information about agentic search, see [Agentic search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/).
+Use the `agentic` query to ask questions in natural language and have OpenSearch automatically plan and execute the retrieval. The `agentic` query works in conjunction with a preconfigured agent that reads the question, plans the search, and returns relevant results. For more information about agentic search, see [Agentic search]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/).
 
 **Prerequisite**<br>
 Before using an `agentic` query, you must configure an agent with the [`QueryPlanningTool`]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/tools/query-planning-tool/) and create a search pipeline with an `agentic_query_translator` search request processor.

_search-plugins/search-pipelines/agentic-query-translator-processor.md

@@ -11,7 +11,7 @@ grand_parent: Search pipelines
 **Introduced 3.2**
 {: .label .label-purple }
 
-The `agentic_query_translator` search request processor enables natural language search by translating user queries into OpenSearch query domain-specific language (DSL) queries using machine learning (ML) agents. It works with [agentic search queries]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search) to provide conversational search capabilities:
+The `agentic_query_translator` search request processor enables natural language search by translating user queries into OpenSearch query domain-specific language (DSL) queries using machine learning (ML) agents. It works with [agentic search queries]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/) to provide conversational search capabilities:
 
 1. The processor sends the user's natural language query to the specified ML agent.
 2. The agent translates the query into OpenSearch DSL.

_vector-search/ai-search/agentic-search/agent-converse.md

@@ -13,7 +13,7 @@ Enable monitoring and conversation continuation using the `agentic_context` resp
 
 ## Prerequisites
 
-- An agent is registered and configured as in Agent customization
+- An agent is registered and configured as descrived in [Agent customization]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/agent-customization/#complete-agent-configuration)
 - The agent includes the QueryPlanningTool
 
 ## Setup: Create Search Pipeline
@@ -91,8 +91,8 @@ GET /_search?search_pipeline=agentic-pipeline
 
 ### Prerequisites: Register Models
 
-1. **Register a model for the Agent** - This model will be used by the conversational agent for reasoning and tool orchestration
-2. **Register a model for Query Planning Tool** - This model will be used specifically by the QueryPlanningTool to generate OpenSearch DSL queries
+1. **[Register a model for the Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-4-create-a-model-for-conversational-agent)** - This model will be used by the conversational agent for reasoning and tool orchestration
+2. **[Register a model for Query Planning Tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-3-create-a-model-for-query-planning-tool)** - This model will be used specifically by the QueryPlanningTool to generate OpenSearch DSL queries
 
 ### 1. Create product index
 

_vector-search/ai-search/agentic-search/agent-customization.md

@@ -130,7 +130,7 @@ You can add any tools from the [ML Commons Tools documentation]({{site.url}}{{si
 
 The conversational agent will automatically select and orchestrate the appropriate tools based on the query context.
 
-**Complete agent configuration:**
+**Complete agent configuration:** {#complete-agent-configuration}
 
 ```json
 POST /_plugins/_ml/agents/_register
@@ -141,8 +141,7 @@ POST /_plugins/_ml/agents/_register
     "llm": {
         "model_id": "your-conversational-model-id",
         "parameters": {
-            "max_iteration": 15,
-            "embedding_model_id": "your-embedding-model-id"
+            "max_iteration": 15
         }
     },
     "memory": {
@@ -198,6 +197,7 @@ GET /_search?search_pipeline=agentic-pipeline
 {% include copy-curl.html %}
 
 The agent will automatically discover product indices, analyze their structure, and generate appropriate queries.
+
 **Performance consideration**: When you don't specify an index in your search query, the search runs against all shards in the cluster, which can be expensive. For better performance, specify the target index when possible.
 
 ## Prompt engineering and customization

_vector-search/ai-search/agentic-search/flow-agent.md

@@ -34,7 +34,7 @@ Before using a flow agent, you need to:
 
 3. **Register a model for Query Planner Tool (QPT)**
 
-   See [Step 3: Create a model for Query Planning tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-3-create-a-model-for-query-planning-tool) for detailed instructions.
+   See [Step 3: Create a model for Query Planning tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-3-create-a-model-for-query-planning-tool) for detailed instructions.
 
 4. **Register a flow agent**
      ```json

_vector-search/ai-search/agentic-search/index.md

@@ -7,7 +7,7 @@ has_children: true
 ---
 
 # Agentic search
-Introduced 3.2
+**Introduced 3.2**
 {: .label .label-purple }
 
 Agentic search lets users ask questions in natural language and have OpenSearch plan and execute the retrieval automatically. A preconfigured **agent** reads the question, plans the search, and returns relevant results.

_vector-search/ai-search/agentic-search/neural-search.md

@@ -9,7 +9,7 @@ has_children: false
 
 # Neural Search
 
-Neural is a type of DSL query that requires a `model_id` to perform semantic search on KNN indices. You can provide the embedding model ID during the registration of the agent, and the Query Planner Tool reads this embedding model ID and produces a neural query when the question requires it. This example demonstrates how agentic search can intelligently generate neural DSL queries by providing the embedding model ID in multiple ways - during agent registration, or even directly in your question.
+`neural` is a type of DSL claude that requires a `model_id` to perform semantic search on KNN indices (when not model id is not directly associated with the index). To make Agentic Search generate neural queries with `model_id` field, you can provide the embedding model ID during the registration of the agent, and the Query Planner Tool reads this embedding model ID and produces a neural query when the question requires it. This example demonstrates how agentic search can intelligently generate neural DSL queries by providing the embedding model ID in multiple ways - during agent registration, or even directly in your `query_text`.
 
 ## Complete Example: Neural DSL Generation with Agentic Search
 
@@ -141,8 +141,8 @@ POST /_bulk
 
 Register two models as described in the [Agentic Search setup]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/):
 
-1. **[Register a model for the Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-4-create-a-model-for-conversational-agent)** - Step 4
-2. **[Register a model for Query Planning Tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-3-create-a-model-for-query-planning-tool)** - Step 3
+1. **[Register a model for the Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-4-create-a-model-for-conversational-agent)**
+2. **[Register a model for Query Planning Tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-3-create-a-model-for-query-planning-tool)**
 
 ### Step 6: Create Agent with Embedding Model ID
 

_vector-search/ai-search/agentic-search/search-templates.md

@@ -174,10 +174,10 @@ POST /_scripts/store_sum_skus
 
 Refer to these to register query planner model and the agent model:
 
-- [Create a model for Query Planning tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-3-create-a-model-for-query-planning-tool)
-- [Create a Model for Conversational Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-4-create-a-model-for-conversational-agent)
-- [Create an Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-5-create-an-agent)
-- [Create a search pipeline]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-6-create-a-search-pipeline)
+- [Create a model for Query Planning tool]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-3-create-a-model-for-query-planning-tool)
+- [Create a Model for Conversational Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-4-create-a-model-for-conversational-agent)
+- [Create an Agent]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-5-create-an-agent)
+- [Create a search pipeline]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-6-create-a-search-pipeline)
 
 ```json
 {
@@ -225,7 +225,7 @@ Refer to these to register query planner model and the agent model:
 
 Register the search pipeline before performing the query:
 
-- [Create a search pipeline]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/#step-6-create-a-search-pipeline)
+- [Create a search pipeline]({{site.url}}{{site.baseurl}}/vector-search/ai-search/agentic-search/index/#step-6-create-a-search-pipeline)
 
 **Agentic Search Query:** 
 ```json