IBM USER MAP

 

    

UNLOCK UNIFIED UNDERSTANDING

User Prompt (REQUEST) → LLM (Query Formation) → KGNN (Real-Time Answer) → Mixture of Agents (Refinement) → User (NLP Response)

1. User Prompt: (REQUEST) The user inputs a question or request. This could be in natural language, text, voice, or other forms of interaction.

2.  LLM Forms the Question: An LLM (Large Language Model) processes the input, interpreting and converting it into a structured query. This model understands the context, intent, and nuances of the user's prompt.

3.  KGNN Formulates Real-Time Answer: (ADDING REAL-TIME DATA) The structured query is then sent to the KGNN (Knowledge Graph Neural Network). The KGNN uses its advanced data structuring and semantic mapping capabilities to pull relevant information from various sources, integrate it, and generate a real-time, coherent answer.

4.  Mixture of Agents Runs Algorithms: (BEE AGENTIC FRAMEWORK) This answer is further processed by a set of specialized agents (software modules), which run algorithms to refine the response, ensure accuracy, and add any additional context or details. These agents might include ML models, data processing units, and other AI components working in concert.

5.  User Receives Answer in NLP:(TEXT or SPEECH) The final refined response is delivered back to the user in natural language , making it easy to understand and interact with.

Equitus AI's Knowledge Graph Neural Network (KGNN) could interact with the components you mentioned in the following way:

1. User Prompt: The user inputs a query or prompt into the system17

2. LLM Forms the Question: A Large Language Model processes the user's input, understanding the context and intent. It then formulates a structured question that can be used to query the knowledge graph25.

3. KGNN Formulates Real-time Answer:

   • KGNN, leveraging its vast ontology and comprehensive default knowledge graph, processes the structured question

   • It uses its flexible ingest pipelines to autonomously handle data ingestion, mapping, and disambiguation.

   • The self-generating knowledge graph enables near real-time, multi-disciplinary complex queries, revealing hidden connections and insights4.

4. Mixture of Agents Runs Algorithms:

   • Various AI agents within the system could work in parallel, each specializing in different aspects of the query

   • These agents might include:

     • Information retrieval agents to fetch relevant data from the knowledge graph

     • Reasoning agents to make inferences based on the retrieved data

     • Natural language generation agents to formulate a coherent response

5. User Receives Answer in NLP:

   • The system combines the outputs from various agents and the KGNN.

   • An LLM could then be used to translate this information into natural language

   • The final answer is presented to the user in a coherent, contextually relevant format

This process would leverage KGNN's ability to provide immediate usability, autonomous data management, and real-time insights, while combining it with the power of LLMs for natural language understanding and generation/