inspiration

IDEAS.MD

@@ -85,9 +85,130 @@ In the context of swarm LLMs, one could consider an **Omni-Vector Embedding Data
 
 
 
-# Inspiration
+# Technical Analysis Document: Particle Swarm of AI Agents using Ocean Database
-[🐪CAMEL🐪](https://twitter.com/hwchase17/status/1645834030519296000)
+
-[MultiAgent](https://github.com/rumpfmax/Multi-GPT/blob/master/multigpt/multi_agent_manager.py)
+## Overview
-[AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT)
+
-[SuperAGI]()
+The goal is to create a particle swarm of AI agents using the OpenAI API for the agents and the Ocean database as the communication space, where the embeddings act as particles. The swarm will work collectively to perform tasks and optimize their behavior based on the interaction with the Ocean database.
-[AgentForge](https://github.com/DataBassGit/AgentForge)
+
+## Algorithmic Overview
+
+1. Initialize the AI agents and the Ocean database.
+2. Assign tasks to the AI agents.
+3. AI agents use the OpenAI API to perform tasks and generate embeddings.
+4. AI agents store their embeddings in the Ocean database.
+5. AI agents query the Ocean database for relevant embeddings.
+6. AI agents update their positions based on the retrieved embeddings.
+7. Evaluate the performance of the swarm and update the agents' behavior accordingly.
+8. Repeat steps 3-7 until a stopping criterion is met.
+
+## Python Implementation Logic
+
+1. **Initialize the AI agents and the Ocean database.**
+
+```python
+import openai
+import oceandb
+from oceandb.utils.embedding_functions import ImageBindEmbeddingFunction
+
+# Initialize Ocean database
+client = oceandb.Client()
+text_embedding_function = ImageBindEmbeddingFunction(modality="text")
+collection = client.create_collection("all-my-documents", embedding_function=text_embedding_function)
+
+# Initialize AI agents
+agents = initialize_agents(...)
+```
+
+2. **Assign tasks to the AI agents.**
+
+```python
+tasks = assign_tasks_to_agents(agents, ...)
+```
+
+3. **AI agents use the OpenAI API to perform tasks and generate embeddings.**
+
+```python
+def agent_perform_task(agent, task):
+    # Perform the task using the OpenAI API
+    result = perform_task_with_openai_api(agent, task)
+    # Generate the embedding
+    embedding = generate_embedding(result)
+    return embedding
+
+embeddings = [agent_perform_task(agent, task) for agent, task in zip(agents, tasks)]
+```
+
+4. **AI agents store their embeddings in the Ocean database.**
+
+```python
+def store_embeddings_in_database(embeddings, collection):
+    for i, embedding in enumerate(embeddings):
+        document_id = f"agent_{i}"
+        collection.add(documents=[embedding], ids=[document_id])
+
+store_embeddings_in_database(embeddings, collection)
+```
+
+5. **AI agents query the Ocean database for relevant embeddings.**
+
+```python
+def query_database_for_embeddings(agent, collection, n_results=1):
+    query_result = collection.query(query_texts=[agent], n_results=n_results)
+    return query_result
+
+queried_embeddings = [query_database_for_embeddings(agent, collection) for agent in agents]
+```
+
+6. **AI agents update their positions based on the retrieved embeddings.**
+
+```python
+def update_agent_positions(agents, queried_embeddings):
+    for agent, embedding in zip(agents, queried_embeddings):
+        agent.update_position(embedding)
+
+update_agent_positions(agents, queried_embeddings)
+```
+
+7. **Evaluate the performance of the swarm and update the agents' behavior accordingly.**
+
+```python
+def evaluate_swarm_performance(agents, ...):
+    # Evaluate the performance of the swarm
+    performance = compute_performance_metric(agents, ...)
+    return performance
+
+def update_agent_behavior(agents, performance):
+    # Update agents' behavior based on swarm performance
+    for agent in agents:
+        agent.adjust_behavior(performance)
+
+performance = evaluate_swarm_performance(agents, ...)
+update_agent_behavior(agents, performance)
+```
+
+8. **Repeat steps 3-7 until a stopping criterion is met.**
+
+```python
+while not stopping_criterion_met():
+    # Perform tasks and generate embeddings
+    embeddings = [agent_perform_task(agent, task) for agent, task in zip(agents, tasks)]
+
+    # Store embeddings in the Ocean database
+    store_embeddings_in_database(embeddings, collection)
+
+    # Query the Ocean database for relevant embeddings
+    queried_embeddings = [query_database_for_embeddings(agent, collection) for agent in agents]
+
+    # Update AI agent positions based on the retrieved embeddings
+    update_agent_positions(agents, queried_embeddings)
+
+    # Evaluate the performance of the swarm and update the agents' behavior accordingly
+    performance = evaluate_swarm_performance(agents, ...)
+    update_agent_behavior(agents, performance)
+```
+
+This code demonstrates the complete loop to repeat steps 3-7 until a stopping criterion is met. You will need to define the `stopping_criterion_met()` function, which could be based on a predefined number of iterations, a target performance level, or any other condition that indicates that the swarm has reached a desired state.
+
+
+

README.md

@@ -193,3 +193,36 @@ Remember, these are potential improvements. It's important to revisit your prior
 3. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability.
 
 Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise.
+
+
+
+# Inspiration
+
+
+* [🐪CAMEL🐪](https://twitter.com/hwchase17/status/1645834030519296000)
+* [MultiAgent](https://github.com/rumpfmax/Multi-GPT/blob/master/multigpt/multi_agent_manager.py)
+* [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT)
+
+* [SuperAGI]()
+* [AgentForge](https://github.com/DataBassGit/AgentForge)
+* [Voyager](https://github.com/MineDojo/Voyager)
+
+
+* [Gorilla: Large Language Model Connected with Massive APIs](https://arxiv.org/abs/2305.15334)
+* [LLM powered agents](https://lilianweng.github.io/posts/2023-06-23-agent/)
+
+
+## Agent System Overview
+In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:
+
+* Planning Subgoal and decomposition: The agent breaks down large tasks into smaller, manageable subgoals, enabling efficient handling of complex tasks.
+Reflection and refinement: The agent can do self-criticism and self-reflection over past actions, learn from mistakes and refine them for future steps, thereby improving the quality of final results.
+
+* Memory Short-term memory: I would consider all the in-context learning (See Prompt Engineering) as utilizing short-term memory of the model to learn.
+Long-term memory: This provides the agent with the capability to retain and recall (infinite) information over extended periods, often by leveraging an external vector store and fast retrieval.
+
+* Tool use
+The agent learns to call external APIs for extra information that is missing from the model weights (often hard to change after pre-training), including current information, code execution capability, access to proprietary information sources and more.
+
+* Communication -> How reliable and fast is the communication between each indivual agent.
+