AI Agent in SynaptiQ Systems

The AI Agent is the core component of SynaptiQ Systems. Each agent is modular, autonomous, and capable of handling various tasks, integrating with decentralized systems, and collaborating with other agents.

Key Features

Multi-Modal Task Execution: Support for text, image, and audio processing.
Knowledge Management: Build, query, and visualize a knowledge graph.
Distributed Task Management: Use Redis-backed task queues to manage workloads.
Collaboration Framework: Enable inter-agent communication and task delegation.
Blockchain Integration: Interact with Solana and Ethereum for decentralized transactions and logging.
IPFS Integration: Store and retrieve files using decentralized storage.
Reinforcement Learning: Optimize task execution through self-learning.
Swarm Decision-Making: Participate in swarm-level consensus and voting.

How It Works

Each AI Agent is initialized with a unique agent_id and a specific role. Agents can interact with their environment, other agents, or decentralized systems to complete tasks effectively.

Key Methods

1. Multi-Modal Task Execution

execute_text_task(task_description): Processes text-based tasks.
execute_image_task(image_path, text_prompts): Handles image-related tasks.
execute_audio_task(audio_path): Processes audio inputs.

2. Knowledge Management

add_knowledge(concept, attributes): Adds a concept to the knowledge graph.
add_knowledge_relationship(concept1, concept2, relationship_type): Links concepts.
query_knowledge(concept): Queries the knowledge graph.
visualize_knowledge_graph(output_path): Visualizes the graph.

3. Distributed Task Queue

push_task_to_queue(task_description): Adds a task to the distributed queue.
pull_task_from_queue(): Pulls and processes a task from the queue.

4. Collaboration Framework

send_message(recipient_id, message): Sends a message to another agent.
receive_messages(): Retrieves messages for the agent.
delegate_task(recipient_id, task_description): Delegates a task to another agent.

5. Blockchain Integration

get_sol_balance(): Retrieves the agent’s Solana wallet balance.
send_sol(recipient_pubkey, amount): Sends SOL to a recipient.
get_eth_balance(address): Checks an Ethereum wallet’s balance.
send_eth(sender_key, recipient_address, amount_ether): Transfers ETH.

6. IPFS Integration

upload_to_ipfs(file_path): Uploads a file to IPFS.
download_from_ipfs(cid, output_path): Retrieves a file using its IPFS hash.

7. Self-Optimization (Reinforcement Learning)

optimize_task_execution(state): Optimizes task execution based on rewards.
execute_action(action): Executes a specific action and returns a reward.
get_environment_state(): Retrieves the agent’s current state.

8. Swarm Decision-Making

propose_task_to_swarm(task_description): Proposes a task for swarm consensus.
vote_on_task(proposal_id): Votes on a proposed task.
check_consensus(): Checks if consensus has been reached.

Example Code

Here’s an example of setting up and using an AI Agent in SynaptiQ Systems:

pythonCopy codefrom src.agents.ai_agent import AIAgent

# Initialize an AI Agent
agent = AIAgent(agent_id=1, role="data manager", provider="openai", base_url="https://api.openai.com")

# Add knowledge to the graph
agent.add_knowledge("Artificial Intelligence", {"field": "Computer Science"})
agent.add_knowledge_relationship("Artificial Intelligence", "Machine Learning", "includes")

# Propose a task to the swarm
agent.propose_task_to_swarm("Analyze market trends")

# Optimize task execution
state = agent.get_environment_state()
agent.optimize_task_execution(state)

# Send a message to another agent
agent.send_message(recipient_id=2, message="Please assist with data preprocessing.")

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Last updated 5 months ago

pythonCopy codefrom src.agents.ai_agent import AIAgent # Initialize an AI Agent agent = AIAgent(agent_id=1, role="data manager", provider="openai", base_url="https://api.openai.com") # Add knowledge to the graph agent.add_knowledge("Artificial Intelligence", {"field": "Computer Science"}) agent.add_knowledge_relationship("Artificial Intelligence", "Machine Learning", "includes") # Propose a task to the swarm agent.propose_task_to_swarm("Analyze market trends") # Optimize task execution state = agent.get_environment_state() agent.optimize_task_execution(state) # Send a message to another agent agent.send_message(recipient_id=2, message="Please assist with data preprocessing.")