fix(docs): Functions terminology

pages/apis.mdx

@@ -46,17 +46,27 @@ import { GuideBox } from "../components/feature-guide-tabs";
 <Row>
     <GuideBox
         content={{
-            title: "DELTA-V Chat API",
+            title: "DELTA-V Chat APIs",
             description: "Start working with DeltaV chat APIs.",
             path: "/apis/ai-engine/chat"
         }}
     />
 
       <GuideBox
         content={{
-            title: "DeltaV Functions API",
+            title: "DeltaV Functions APIs",
             description: "Start working with Functions APIs.",
-            path: "/apis/ai-engine/services"
+            path: "/apis/ai-engine/functions"
+        }}
+    />
+
+</Row>
+<Row>
+    <GuideBox
+        content={{
+            title: "DELTA-V Function Groups APIs",
+            description: "Explore DeltaV Function Groups APIs.",
+            path: "/apis/ai-engine/function_groups"
         }}
     />
 

pages/apis/ai-engine/function_groups.mdx

@@ -6,7 +6,8 @@ import {
 # DeltaV Function Groups API
 
 <div className="nx-text-fetch-content">
-  Most of the endpoints depends on various instances and are considered parameters for the actual endpoint, but these are not meant to be passed directly by the user.
+
+Most of the endpoints depends on various instances and are considered parameters for the actual endpoint, but these are not meant to be passed directly by the user.
 
 Instead, these instance-dependent elements are integrated and managed directly by the backend system. We refer to them as `*name_parameter`, where the asterisk (\*) is used to denote a specific instance-dependent parameter that the route relies on.
 

pages/concepts/introducing-fetchai.mdx

@@ -24,7 +24,7 @@ The **Agentverse** is a Software as a Service (SaaS) platform built primarily fo
 
 The Agentverse provides multiple use cases ranging from simple "Hello World" to AI-driven recommendation systems. These use cases are only a click away from being edited, tweaked, and deployed for yourself.
 
-You can post your agent(s) as a function on Agentverse so that the AI Engine can quickly find them and promote them to other users or agents via DeltaV.
+You can post your agent(s) and their functions on Agentverse so that the AI Engine can quickly find them and promote them to other users or agents via DeltaV.
 
 ### DeltaV and the AI Engine: enabling user interactions
 
@@ -44,7 +44,7 @@ The **Fetch Name Service** works as a decentralized naming system which simplifi
 
 The backbone of Fetch.ai decentralized infrastructure is represented by the **Fetch ledger**. It acts as a secure and immutable record-keeping system and provides a transparent record of transactions, fostering trust and reliability across the network. The Fetch ledger supports the decentralized economy, ensuring secure data sharing and transactional integrity.
 
-The overall system we depicted runs on a specific fuel: **FET tokens**. These tokens hold intrinsic value and utility, drive transactions, promote active participation, and empower various decentralized services within the Fetch network. FET serves as a medium of exchange and facilitates interactions among users, Agents, and services within the Fetch ecosystem.
+The overall system we depicted runs on a specific fuel: **FET tokens**. These tokens hold intrinsic value and utility, drive transactions, promote active participation, and empower various decentralized services within the Fetch network. FET serves as a medium of exchange and facilitates interactions among users, Agents, and Functions within the Fetch ecosystem.
 
 ### Next steps
 

pages/guides/apis/agent-function-creation-apis.mdx

@@ -143,22 +143,22 @@ This script interacts with the Agentverse API to achieve the Agent and Function
     })
     model = response_model.json()['models']
 
-    time.sleep(10) # Waiting before storing details to create services
+    time.sleep(10) # Waiting before storing details to create functions
     ```
 
 6. Now, save all the required details to create the Agent Function. Then, create the Agent Function on the basis of the details received:
 
     ```python
     # Taking inputs from user for details required to create a function
-    name_service = input("Please give function name: ")
+    name_function = input("Please give function name: ")
     description = input("Please enter function description: ")
     field_name = input("Please enter field name: ")
     field_description = input("Please enter field description: ")
     tasktype = input("Please tell primary or secondary function: ").upper()
 
     # Logging details provided by user
     print(
-        f"Service name: {name_service} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
+        f"Function name: {name_function} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
     )
 
     # Storing model digest and name to be used for function creation
@@ -170,7 +170,7 @@ This script interacts with the Agentverse API to achieve the Agent and Function
     # Creating payload for function creation
     data = {
         "agent": address,
-        "name": name_service,
+        "name": name_function,
         "description": description,
         "protocolDigest": protocol_digest,
         "modelDigest": model_digest,
@@ -242,18 +242,18 @@ time.sleep(10) # Waiting before getting model_digest
 # Request to get agent's model details
 response_model = requests.get(f"https://agentverse.ai/v1/almanac/manifests/protocols/{protocol_digest}", headers={"Authorization": token})
 model = response_model.json()['models']
-time.sleep(10) # Waiting before storing details to create services
+time.sleep(10) # Waiting before storing details to create functions
 
 # Taking inputs from user for details required to create a function
-name_service = input("Please give function name: ")
+name_function = input("Please give function name: ")
 description = input("Please enter function description: ")
 field_name = input("Please enter field name: ")
 field_description = input("Please enter field description: ")
 tasktype = input("Please tell primary or secondary function: ").upper()
 
 # Logging details provided by user
 print(
-    f"Service name: {name_service} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
+    f"Function name: {name_function} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
 )
 
 # Storing model digest and name to be used for function creation
@@ -265,7 +265,7 @@ print(f"Model Name : {model_name}")
 # Creating payload for function creation
 data = {
     "agent": address,
-    "name": name_service,
+    "name": name_function,
     "description": description,
     "protocolDigest": protocol_digest,
     "modelDigest": model_digest,
@@ -299,31 +299,31 @@ print(f"Function Created with name: {name}")
 3. Head over to the [Agentverse ↗️](https://agentverse.ai/profile/api-keys) and [generate API keys ↗️](#how-to-get-agentverse-api-tokens).
 4. Open script in editor and replace `token` field.
 5. Run command `python agent_create.py` and enter the required details.
-6. Provide Agent and Function Details as asked, then, check agent and function on Agentverse.
+6. Provide Agent and Function details as asked, then, check agent and function on Agentverse.
 
 ## Expected Output
 
 - Provide all details asked in the script:
 
-```
-abc@xyz-MacBook-Pro agents % python3 agents_create.py
-Please give name of your agent? my first API agent
-Agent Address : agent1q06l8hekn859e5rtwufewmyhwghe6j9y00g0wc8u7gcx05cjfk98jyf6lte
-Protocol Digest : c7a6f160fd8d8b7cb357dad9b5be420510ce466dbb67051c07caf2b860216b01
-Please give function name: location finder
-Please enter function description: this function helps to find nearest city using coordinates
-Please enter field name: location
-Please enter field description: this describes the coordinates of the location in string format longitude latitude
-Please tell primary or secondary function: primary
-Service name: location finder 
-Function Description: this function helps to find nearest city using coordinates 
-Field Name: location
-Field Description: this describes the coordinates of the location in string format longitude latitude
-Task Type: PRIMARY
-Model Digest : 10a2f843c4c92955688d5e7f22fabe79623869eabfcd97d97da83527b436d3e2
-Model Name : Coordinates
-Function Created with name: location finder
-```
+    ```
+    abc@xyz-MacBook-Pro agents % python3 agents_create.py
+    Please give name of your agent? my first API agent
+    Agent Address : agent1q06l8hekn859e5rtwufewmyhwghe6j9y00g0wc8u7gcx05cjfk98jyf6lte
+    Protocol Digest : c7a6f160fd8d8b7cb357dad9b5be420510ce466dbb67051c07caf2b860216b01
+    Please give function name: location finder
+    Please enter function description: this function helps to find nearest city using coordinates
+    Please enter field name: location
+    Please enter field description: this describes the coordinates of the location in string format longitude latitude
+    Please tell primary or secondary function: primary
+    Function name: location finder
+    Function Description: this function helps to find nearest city using coordinates
+    Field Name: location
+    Field Description: this describes the coordinates of the location in string format longitude latitude
+    Task Type: PRIMARY
+    Model Digest : 10a2f843c4c92955688d5e7f22fabe79623869eabfcd97d97da83527b436d3e2
+    Model Name : Coordinates
+    Function Created with name: location finder
+    ```
 
 - Agent created on Agentverse: