fetchai · devjsc · Nov 21, 2024 · Nov 19, 2024 · Nov 19, 2024 · Nov 21, 2024
@@ -528,7 +528,7 @@ import {
   ]}
   pathParameters={{
     address: "agent1qvnppqyhk4hu0q64tnfkspux8hpd9zayyclhafvkz5340uqx3ax02txfll7",
-    
+
   }}
   responses={{
 

@@ -138,7 +138,6 @@ There are 4 types of messages that can be sent to the backend:
 - `user_json`: This is a JSON message sent by the user.
 - `stop`: This is the last message that should be sent to the backend. It indicates that the conversation has ended.
 
-
 IMPORTANT: you need to either send `start` or `execute_functions`. These two can't be combined.
 ### Sending start message
 
@@ -665,7 +664,6 @@ responseDescription="On a successful request, there is no response"
   responseDescription="Returns a list of new messages for the session, if specified then returns the messages since the last checked message. If there are no new messages, an empty list is returned. If the session is not found, a 404 error with a descriptive message is returned."
 />
 
-
 ## Provide feedback for a session
 
 <ApiEndpointRequestResponse

@@ -293,7 +293,6 @@ properties={[
   responseDescription="Returns a list of function summary objects associated with the user."
 />
 
-
 ## Fetch a function
 
 <ApiEndpointRequestResponse
@@ -578,7 +577,6 @@ properties={[
   responseDescription="Returns the function object (identified by function_id) that we have just started to test."
 />
 
-
 ## Submit a function for review
 
 <ApiEndpointRequestResponse
@@ -651,7 +649,6 @@ properties={[
   responseDescription="Returns the function object (identified by function_id) that we have just unpublished."
 />
 
-
 ## Add function to function group
 
 <ApiEndpointRequestResponse
@@ -714,7 +711,6 @@ properties={[
   responseDescription="Returns an empty JSON object."
 />
 
-
 ## Delete all user's functions
 <ApiEndpointRequestResponse
   apiUrl="https://agentverse.ai"

@@ -1701,7 +1701,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
                     The optional URL for the currency image.
 
-
             > Extra properties in:
             >
             > - **`NativeCurrency`**
@@ -1820,7 +1819,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
     <Col>
 
-
     </Col>
 
 </Row>
@@ -1844,7 +1842,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
     <Col>
 
-
     </Col>
 
 </Row>
@@ -1872,7 +1869,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
     <Col>
 
-
     </Col>
 
 </Row>
@@ -1891,7 +1887,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
     <Col>
 
-
     </Col>
 
 </Row>
@@ -1914,7 +1909,6 @@ The wallet emits certain window events that can be useful to update the data sho
 
     <Col>
 
-
     </Col>
 
 </Row>

@@ -8,7 +8,6 @@
 import GuidesMdx from "../components/guide-mdx"
 import { AgentsIcon, AgentverseIcon ,FetchNetwork, LedgerIcon} from "src/icons/main-page-icons";
 
-
 <GuidesMdx content={[
   {
     type: "Introduction",

@@ -15,7 +15,6 @@ The Agentverse My Agents tab enables all users to get quickly started and to dep
 
 With the world moving every increasingly towards an automated future where technology will play a greater central role in everyone's life, we will all require an agent to represent and help us in interacting and carry out operations with one other.
 
-
 <ImageByTheme
   darkSrc={DarkAnAgentForEveryone}
   lightSrc={AnAgentForEveryone}
@@ -36,7 +35,6 @@ The **My Agents** section manages all the important steps about making sure that
 
 The **My Agents** section is designed to scale automatically with the number of messages that your agent receives. The benefit for you is that you do not have to carry the hassle of continuously deploying your agents, given that we run them for you using cloud technology. This means that your agents will continue running and receiving messages without the need of your presence for them to be deployed. Thus, as a developer, you can focus on making the most awesome agent code!
 
-
 <ImageByTheme
   darkSrc={DarkScalingHosting}
   lightSrc={ScalingHosting}
@@ -63,4 +61,3 @@ Hosted agents on the Agentverse can be given a human name so that you can find t
   alt="hosting-name-service1"
 />
 
-
@@ -10,7 +10,6 @@ import DarkSystemDiagramAiEngine from 'src/images/concepts/ai-engine/dark_system
 
 The **AI Engine** is a system that combines [Agents ↗️](/concepts/agents/agents) with human-readable text input to create a scalable AI infrastructure that supports Large Language Models (LLMs). It is at the heart of [DeltaV ↗️](/concepts/ai-engine/deltav) and its functionalities. The goal of the AI Engine is to analyze, understand and link human input to agents by facilitating natural language interactions. The AI Engine reads user input, converts it into actionable tasks and selects the most appropriate AI agent registered in the Agentverse to perform the Objective Tasks provided by users.
 
-
 <ImageByTheme
   darkSrc={DarkSystemDiagramAiEngine}
   lightSrc={SystemDiagramAiEngine}

@@ -27,7 +27,6 @@ The following AI Engine personality types are available for users: **Talkative V
     - With version **v0.25** the Talkative personality (previously associated with AI Engine V1) started to refer to AI Engine V2 (previously available under the Next Generation personality). To preserve AI Engine V1, a new **Legacy personality** had been introduced, with a scheduled removal date of September 13, 2024. With this release the following features were introduced: _Functions Metadata_ (location settings can now be assigned to functions), _Permission System_ (users can now share private function groups with others via email), _Dialogue Execution Improvements_, _Permission Sharing in Python SDK_ (added functionality to the Python SDK) and _Next Generation Personality (i.e., This was a new iteration of the Next Generation personality, now called Talkative) supports location filtering on functions with location metadata based on objectives_.
     - Version **v0.26** introduced significant enhancements to the AI Engine; in particular, it introduced **GPT-4o-mini** for enhanced recommendation accuracy within the NextGeneration personality.
 
-
 ## Exploring the Feature Set of AI Engine Personalities
 
 Here's a breakdown of the key functionalities offered by each personality:

@@ -16,7 +16,6 @@ The AI Engine stands at the core of [DeltaV ↗️](https://deltav.agentverse.ai
   alt="ai-with-personal-data"
 />
 
-
 This system is equipped with personalized capabilities, supported by an internal agent that performs tasks efficiently. An internal agent is created by the AI Engine and made available for communication via the DeltaV user interface. The AI Engine interprets the human text input provided to the agent and starts working asynchronously on your behalf as soon as it receives your intent. This customized method uses Large Language Models (LLMs), which are essential for improving the AI Engine's understanding, coordination and problem-solving capabilities.
 
 ## Establishing connections

@@ -32,7 +32,6 @@ Agents register to Almanac so that they can be discovered in the network by othe
 
 **Agents** are programs designed to operate freely and communicate with whomever they're programmed to. Agents can connect, search, and transact in order to create dynamic markets, and they can be programmed with logic to interact both within their environment and with other agents in the network. Because they're siloed, and decentralized they can safely accomplish particular activities and objectives without requiring human participation. Our documentation gets your started on building an agent to be part [of the network ↗️](/guides/agents/getting-started/create-a-uagent)
 
-
 Agents can come together to become multi-agent workflows, single agents which call one another to complete a task.  This can be done natively, or with AI-Engine.
 
 Agents are built with the  **uAgents Framework**, a lightweight library designed to facilitate the development of decentralized **Agents**. At the uAgents core is an open sourced communication protocol for agents.

@@ -28,7 +28,6 @@ In this example, we demonstrate how to use agents to communicate and exchange st
 
     loop = asyncio.get_event_loop()
 
-
     agent = Agent(
         name="looper",
         seed="<YOUR_SEED>",
@@ -38,23 +37,19 @@ In this example, we demonstrate how to use agents to communicate and exchange st
         agents=[agent],
     )
 
-
     @agent.on_event("startup")
     async def startup(ctx: Context):
         ctx.logger.info(">>> Looper is starting up.")
 
-
     @agent.on_event("shutdown")
     async def shutdown(ctx: Context):
         ctx.logger.info(">>> Looper is shutting down.")
 
-
     async def coro():
         while True:
             print("Doing hard work...")
             await asyncio.sleep(1)
 
-
     if __name__ == "__main__":
         print("Attaching the agent or bureau to the external loop...")
         loop.create_task(coro())
@@ -83,7 +78,6 @@ In this example, we demonstrate how to use agents to communicate and exchange st
 
     loop = asyncio.get_event_loop()
 
-
     agent = Agent(
         name="looper",
         seed="<YOUR_SEED>",
@@ -95,23 +89,19 @@ In this example, we demonstrate how to use agents to communicate and exchange st
         loop=loop,
     )
 
-
     @agent.on_event("startup")
     async def startup(ctx: Context):
         ctx.logger.info(">>> Looper is starting up.")
 
-
     @agent.on_event("shutdown")
     async def shutdown(ctx: Context):
         ctx.logger.info(">>> Looper is shutting down.")
 
-
     async def coro():
         while True:
             print("Doing hard work...")
             await asyncio.sleep(1)
 
-
     if __name__ == "__main__":
         print("Starting the external loop from the agent or bureau...")
         loop.create_task(coro())

@@ -173,7 +173,6 @@ Engine ↗️](/concepts/ai-engine/ai-engine-intro) and create [DeltaV ↗️](h
         - `user_choice` - the choice of subtask user wants AI-Engine to use.
         - `token` - AI-Engine fauna token to connect DeltaV or AI-Engine.
 
-
 8. `send_user_message` function:
 
     ``` python

@@ -40,22 +40,18 @@ Each dialogue transition needs a separate message:
         """I initiate ChitChat dialogue request"""
         pass
 
-
     class AcceptChitChatDialogue(Model):
         """I accept ChitChat dialogue request"""
         pass
 
-
     class ChitChatDialogueMessage(DialogueMessage):
         """ChitChat dialogue message"""
         pass
 
-
     class ConcludeChitChatDialogue(Model):
         """I conclude ChitChat dialogue request"""
         pass
 
-
     class RejectChitChatDialogue(Model):
         """I reject ChitChat dialogue request"""
         pass
@@ -117,7 +113,6 @@ Setup the functions making API calls to get ticker symbol and stock price:
         # do something when the dialogue is initiated
         await ctx.send(sender, AcceptChitChatDialogue())
 
-
     @chitchat_dialogue.on_start_dialogue(AcceptChitChatDialogue)
     async def accepted_chitchat(
         ctx: Context,
@@ -128,7 +123,6 @@ Setup the functions making API calls to get ticker symbol and stock price:
             f"session with {sender} was accepted. This shouldn't be called as this agent is not the initiator."
         )
 
-
     @chitchat_dialogue.on_reject_session(RejectChitChatDialogue)
     async def reject_chitchat(
         ctx: Context,
@@ -138,7 +132,6 @@ Setup the functions making API calls to get ticker symbol and stock price:
         # do something when the dialogue is rejected and nothing has been sent yet
         ctx.logger.info(f"Received conclude message from: {sender}")
 
-
     @chitchat_dialogue.on_continue_dialogue(ChitChatDialogueMessage)
     async def continue_chitchat(
         ctx: Context,
@@ -161,7 +154,6 @@ Setup the functions making API calls to get ticker symbol and stock price:
         except EOFError:
             await ctx.send(sender, ConcludeChitChatDialogue())
 
-
     @chitchat_dialogue.on_end_session(ConcludeChitChatDialogue)
     async def conclude_chitchat(
         ctx: Context,
@@ -172,7 +164,6 @@ Setup the functions making API calls to get ticker symbol and stock price:
         ctx.logger.info(f"Received conclude message from: {sender}; accessing history:")
         ctx.logger.info(chitchat_dialogue.get_conversation(ctx.session))
 
-
     agent.include(chitchat_dialogue, publish_manifest=True)
 
     ```

@@ -25,7 +25,6 @@ This script sets up the `ChitChatDialogue` class, This system includes nodes rep
 - Open a terminal and create a directory using `mkdir Dialogues` and navigate into it with `cd Dialogues`.
 - Create a python file `chitchat.py` in `Dialogues` directory.
 
-
 <CodeGroup hasCopy isOSFile>
 <DocsCode mac={true}>
     ```py copy filename="mac"
@@ -208,7 +207,6 @@ The contents of this script are to be shared between the agents that want to use
 - if any of the agent sends `exit` message the dialogue is concluded.
 - Run `cd ..` to go out of `Dialogues` directory and create `agent1.py` Python script
 
-
 <CodeGroup hasCopy isOSFile>
 <DocsCode mac={true}>
     ```py copy filename="mac"
@@ -487,7 +485,6 @@ The contents of this script are to be shared between the agents that want to use
             ctx, CHIT_AGENT_ADDRESS, InitiateChitChatDialogue()
         )
 
-
     if __name__ == "__main__":
         print(f"Agent address: {agent.address}")
         agent.run()
@@ -512,7 +509,7 @@ The contents of this script are to be shared between the agents that want to use
     - This agent will automatically attempt to initiate a dialogue by sending an `InitiateChitChatDialogue` message to `agent1`.
 
 ## Expected output
-    
+
 - `agent1` logger/terminal output:
 
     ```

@@ -44,7 +44,7 @@ This example illustrates an automated dialogue scenario using a hardcoded dialog
     </DocsCode>
 
 </CodeGroup>
-  
+
 - Import required libraries.
 
     ```python
@@ -237,14 +237,14 @@ Include all the above mentioned script sections into a single script and save it
 ## Step 2: setting up the agents
 
 These two agents use the `hardcoded_chitchat` dialogue system to automate the interaction with minimal intervention. The dialogue is mostly automated, with `agent3` configured to respond to initiation and continue based on predefined rules, and `agent4` automatically initiating the conversation.
-    
+
 ### Setting up `agent1`
 
 `agent1` is designed to receive and respond to messages according to hardcoded rules within the ChitChatDialogue class.
 
     - Navigate to your working directory and create a Python script named `agent1.py`.
     - Copy the following script into `agent1.py`:
-    
+
 <CodeGroup hasCopy isLocalHostedFile>
     <DocsCode local={true}>
 
@@ -296,7 +296,6 @@ These two agents use the `hardcoded_chitchat` dialogue system to automate the in
             except EOFError:
                 await ctx.send(sender, ConcludeChitChatDialogue())
 
-
         agent.include(chitchat_dialogue)  # Including dialogue in agent
 
         if __name__ == "__main__":
@@ -335,7 +334,6 @@ These two agents use the `hardcoded_chitchat` dialogue system to automate the in
             endpoint="http://127.0.0.1:8002/submit",
         )
 
-
         # Instantiate the dialogues
         chitchat_dialogue = ChitChatDialogue(
             version="0.1",
@@ -351,7 +349,6 @@ These two agents use the `hardcoded_chitchat` dialogue system to automate the in
             ctx.logger.info(f"Returning: {msg.text}")
             await ctx.send(sender, ChitChatDialogueMessage(text=msg.text))
 
-
         # Initiate dialogue after 5 seconds
         @agent.on_event("startup")
         async def start_cycle(ctx: Context):
@@ -360,7 +357,6 @@ These two agents use the `hardcoded_chitchat` dialogue system to automate the in
                 ctx, CHIT_AGENT_ADDRESS, InitiateChitChatDialogue()
             )
 
-
         agent.include(chitchat_dialogue)
 
         if __name__ == "__main__":