AI Thought Process and Iterative Analysis

This section showcases the iterative process of how AI models, such as DeepSeek and other foundational models, are used to understand user inputs, refine intent recognition, and optimize decision-making. The sample code demonstrates how AI continuously refines its responses through chain-of-thought processes and knowledge base lookups.

Support for multiple foundational models, including DeepSeek, ChatGPT, Ollama, Gemini, and QWen, through various methods such as embedded thought processes and multi-agent collaboration. These models are used for intent recognition, knowledge understanding and generation, and action planning to create advanced AI-driven interactions.

def deepseek_iterative_analysis(user_input, max_iterations=4):
    # Step 1: Call DeepSeek R1 to generate an initial analysis result and thought process
    initial_result = deepseek_r1_api(user_input, mode="chain-of-thought")
    # Example of initial_result format:
    # {"result": "Initial analysis result", "chain_of_thought": "Initial thought process"}
    
    combined_input = initial_result["chain_of_thought"]
    for i in range(max_iterations):
        # Query the knowledge base to find background data based on the current thought process
        kb_info = query_knowledge_base(combined_input)
        
        # Combine knowledge base info with current thought content to form refined input
        input_for_refinement = f"{combined_input}\nAdditional Info: {kb_info}"
        
        # Iterative refinement: Call DeepSeek R1 for further analysis
        refined_result = deepseek_r1_api(input_for_refinement, mode="iterative-refinement")
        
        # Update the thought process
        combined_input = refined_result.get("chain_of_thought", combined_input)
        
        # Exit iteration early if convergence criteria are met
        if has_converged(combined_input):
            break
    
    final_result = {
        "final_intent": refined_result.get("result", initial_result["result"]),
        "final_chain_of_thought": combined_input
    }
    return final_result

# Auxiliary functions (pseudo-code implementation)

This code demonstrates how an AI system processes user inputs iteratively. It starts with an initial analysis using a "chain-of-thought" model and refines it through knowledge base queries and iterative optimization. This process ensures that the AI can converge on accurate conclusions while incorporating dynamic real-world knowledge.