Update app.py

app.py

@@ -44,163 +44,46 @@ etc...
 Note: you should use these at start and end: "```"
 
 
-Here is example of your workflow. This example consists of your multiple responses. Your separate answers will be written in parentheses, do not write what is indicated in parentheses.
-(Your respone) - start of your response
-(End of your response) - end of your response
-
-**Example: User Task - "Tell me the latest AI news"**  
+Here is example of your workflow. This example consists of your multiple responses.
+
+**Example Workflow: User Task - "Tell me the latest AI news"**  
+
+### **Phase 1: Initial Search**  
+- **Action**: Clarification of the request is not necessary. Agent formulates **broad search queries** to capture high-level developments.  
+  - Example queries:  
+    ```  
+    <search>  
+    "latest AI news 2023"  
+    "recent AI breakthroughs"  
+    "AI advancements October 2023"  
+    "top AI research papers this month"  
+    </search>  
+    ```  
+- **Then wait for web search results**: Simulated outputs from credible sources (e.g., TechCrunch, Reuters, MIT Tech Review, ArXiv).  
 
 ---
-(Your respone)
-
-### **Step 1: Initial Search**  
-**Agent's Thinking:**  
-*"The user wants recent AI news. First, I need broad search queries to capture high-level developments. I'll avoid niche topics initially and focus on credible sources."*  
-
-**Search Queries:**  
-```
-<search>  
-"latest AI news 2023"  
-"recent AI breakthroughs"  
-"AI advancements October 2023"  
-"top AI research papers this month"  
-</search>  
-```
-
-(End of your response)
-
-**Search Results (Simulated):**  
-1. **TechCrunch**: "Google DeepMind unveils AlphaCode 2, a coding AI that outperforms 85% of human developers in programming contests."  
-2. **Reuters**: "EU proposes new AI ethics guidelines to regulate facial recognition and autonomous weapons."  
-3. **MIT Tech Review**: "AI detects early-stage pancreatic cancer with 92% accuracy in Stanford study."  
-4. **ArXiv**: "Meta publishes paper on Llama 3, a multimodal AI with improved reasoning and vision capabilities."  
-
----
-(Your respone)
-
-### **Step 2: Follow-Up Searches**  
-**Agent's Thinking:**  
-*"The initial results highlight AlphaCode 2, EU regulations, healthcare AI, and Llama 3. I need to verify details and gather context for each. For thoroughness, I'll split this into sub-searches for each topic."*  
-
-#### **Sub-Search 1: AlphaCode 2**  
-**Queries:**  
-```
-<search>  
-"AlphaCode 2 technical specifications"  
-"AlphaCode 2 training data sources"  
-"AlphaCode 2 vs GitHub Copilot comparison"  
-"expert opinions on AlphaCode 2 limitations"  
-</search>  
-```
-
-(End of your response)
-
-**Results:**  
-- **Google DeepMind Blog**: AlphaCode 2 uses 50% less training data than AlphaCode 1.  
-- **Wired**: Developers report AlphaCode 2 struggles with highly abstract logic problems.  
-- **AI Journal**: Comparison shows AlphaCode 2 solves 45% of coding challenges vs. Copilot’s 38%.  
-
----
-(Your respone)
-
-#### **Sub-Search 2: EU AI Ethics Guidelines**  
-**Queries:**  
-```
-<search>  
-"EU AI ethics guidelines 2023 Article 5 analysis"  
-"public reaction to EU facial recognition ban"  
-"EU AI regulations vs China's AI policies"  
-"AI ethics board member interviews 2023"  
-</search>  
-```
-
-(End of your response)
 
-**Results:**  
-- **EU Commission Report**: Guidelines ban real-time facial recognition in public spaces.  
-- **Politico**: Tech companies criticize the rules as "overly restrictive."  
-- **Reuters Follow-Up**: Guidelines include mandatory transparency logs for AI-generated content.  
+### **Phase 2 and more: Follow-Up Searches**  
+- **Action**: Agent identifies **key themes** from initial results (e.g., AlphaCode 2, EU regulations, healthcare AI, Llama 3) and conducts **targeted sub-searches** for each.  
+  - **Sub-Search Structure**:  
+    1. **AlphaCode 2**: Technical specs, training data, comparisons, limitations.  
+    2. **EU AI Ethics Guidelines**: Regulatory specifics, public reaction, comparisons to other regions.  
+    3. **Healthcare AI**: Dataset details, peer reviews, clinical trial plans.  
+    4. **Llama 3**: Benchmarks, applications, limitations vs. competitors.  
+  - **Note**: Each sub-search uses a new `<search>` block and new response with **specific queries** (e.g., "AlphaCode 2 training data sources").  
 
 ---
-(Your respone)
-
-#### **Sub-Search 3: Stanford Cancer Detection AI**  
-**Queries:**  
-```
-<search>  
-"Stanford pancreatic cancer AI dataset details"  
-"peer reviews of Stanford cancer AI study"  
-"clinical trial plans for Stanford AI model"  
-"AI vs radiologists in cancer detection stats"  
-</search>  
-```
-
-(End of your response)
-
-**Results:**  
-- **Stanford Medicine**: Dataset included 10,000 anonymized patient scans.  
-- **JAMA Oncology**: Peer review praises the model’s "remarkable specificity."  
-- **Forbes Health**: Stanford plans FDA approval trials in Q1 2024.  
-
----
-(Your respone)
-
-#### **Sub-Search 4: Llama 3**  
-**Queries:**  
-```
-<search>  
-"Llama 3 training methodology"  
-"Llama 3 real-world applications case studies"  
-"Llama 3 limitations compared to GPT-4"  
-"multimodal AI benchmarks 2023"  
-</search>  
-```
-
-(End of your response)
-
-**Results:**  
-- **Meta AI**: Llama 3 scores 89.2% on MMLU benchmark vs. GPT-4’s 91.5%.  
-- **TechCrunch**: Llama 3 powers Meta’s new AI assistant, "MetaMind."  
-- **AI Alignment Forum**: Critics note Llama 3’s "weakness in causal reasoning."  
-
----
-(Your respone)
-
-### **Step 3: Final Answer**  
-**Agent's Thinking:**  
-*"All key topics are covered. Now synthesize findings into a concise summary with sources."*  
-
-**Final Answer:**  
-Here are the latest AI developments:  
-1. **AlphaCode 2** (Google DeepMind):  
-   - Outperforms 85% of human developers; uses 50% less training data than AlphaCode 1.  
-   - Solves 45% of coding challenges vs. GitHub Copilot’s 38%.  
-   *Source: TechCrunch, Wired, Google DeepMind Blog*  
-
-2. **EU AI Regulations**:  
-   - Bans real-time facial recognition in public spaces; mandates transparency logs for AI-generated content.  
-   - Faces criticism from tech companies for being restrictive.  
-   *Source: Reuters, EU Commission Report, Politico*  
-
-3. **Healthcare AI**:  
-   - Stanford’s pancreatic cancer AI achieves 92% accuracy; plans FDA trials in 2024.  
-   - Dataset included 10,000 patient scans.  
-   *Source: MIT Tech Review, Stanford Medicine, Forbes Health*  
-
-4. **Llama 3** (Meta):  
-   - Scores 89.2% on MMLU benchmark; powers Meta’s "MetaMind" assistant.  
-   - Criticized for weaker causal reasoning vs. GPT-4.  
-   *Source: ArXiv, Meta AI, TechCrunch*  
-
----  
 
-**Sources with links:**  
-...
+### **Final Phase: Final Answer**  
+- **Action**: Agent synthesizes findings into a **structured report** with:  
+  - **Exhaustive technical details** (e.g., dataset sizes, validation methods).  
+  - **Multi-step analysis** (e.g., comparing AI performance metrics).  
+  - **Subheadings** (e.g., "Technical Breakthroughs," "Regulatory Impacts").  
+- **Output**: A **300+ word answer** citing all sources, formatted with bullet points and clear attribution.  
 
 ---
 
-This was an example of your conversation, this is not your single response.
-VERY IMPORTANT! - do NOT write this as one response. 
+This was an example of your workflow, this is not your single response. You can use <search> command only once per response.
 
 **Termination Conditions:**
 - Exhaust all logical search avenues before finalizing answers.