update summarize

src/main/java/com/olympus/hermione/stepSolvers/ExternalCodeGenieSolver.java

@@ -44,7 +44,7 @@ public class ExternalCodeGenieSolver extends StepSolver {
     @Autowired
     private ScenarioExecutionRepository scenarioExecutionRepo;
 
-    Logger logger = (Logger) LoggerFactory.getLogger(BasicQueryRagSolver.class);
+    Logger logger = (Logger) LoggerFactory.getLogger(ExternalCodeGenieSolver.class);
 
     private void loadParameters() {
         logger.info("Loading parameters");

src/main/java/com/olympus/hermione/stepSolvers/SummarizeDocSolver.java

@@ -46,6 +46,10 @@ public class SummarizeDocSolver extends StepSolver {
     private String qai_custom_memory_id;
     private Integer max_output_token;
     private String qai_system_prompt_template_minimum;
+    private String qai_system_prompt_template_formatter;
+    private boolean isChunked = false;
+    private Double chunk_size_token_calc;
+    private Double perc = 0.2;
     // private boolean qai_load_graph_schema=false;
 
     Logger logger = (Logger) LoggerFactory.getLogger(SummarizeDocSolver.class);
@@ -75,6 +79,8 @@ public class SummarizeDocSolver extends StepSolver {
         this.qai_system_prompt_template_minimum = attributeParser
                 .parse((String) this.step.getAttributes().get("qai_system_prompt_template_minimum"));
 
+        this.qai_system_prompt_template_formatter = attributeParser
+                .parse((String) this.step.getAttributes().get("qai_system_prompt_template_formatter"));
     }
 
     @Override
@@ -99,13 +105,16 @@ public class SummarizeDocSolver extends StepSolver {
             // Conta i token
             // int tokenCount = encoding.get().encode(text).size();
             tokenCount = encoding.get().countTokens(text);
+            logger.info("token count input: " + tokenCount);
             int charCount = text.length();
 
             // Stima media caratteri per token
             // double charPerToken = (double) charCount / tokenCount;
 
-            //Double output_char = (double) charCount * ((double) this.percent_summarize / 100.0);
+           // Double output_charD = (double) charCount * ((double) this.percent_summarize / 100.0);
-            Double min_output_token = (double) tokenCount * ((double) this.percent_summarize / 100.0);
+           // Integer output_char = output_charD.intValue();
+            Double min_output_tokenD = (double) tokenCount * ((double) this.percent_summarize / 100.0);
+            Integer min_output_token = min_output_tokenD.intValue();
             String content = new String("");
             content = this.qai_system_prompt_template.replace("max_number_token",
                     max_output_token.toString());
@@ -116,15 +125,31 @@ public class SummarizeDocSolver extends StepSolver {
                         min_output_token.toString());
             }
             
+            chunk_size_token_calc = (Double)((double)tokenCount * perc);
+            if(chunk_size_token_calc>chunk_size_token){
+                chunk_size_token_calc = (double)chunk_size_token;
+
+            }
             // **Fase di Summarization**
             String summarizedText = summarize(text); // 🔹 Applica la funzione di riassunto
             // String template = this.qai_system_prompt_template+" The output length should
             // be of " + output_char + " characters";
-            logger.info("template: " + content);
+            
             // Creazione dei messaggi per il modello AI
             Message userMessage = new UserMessage(summarizedText);
-            Message systemMessage = new SystemMessage(content);
+            Message systemMessage = null;
-            logger.info("template: " + systemMessage.getContent().toString());
+
+            int tokenCountSummary = encoding.get().countTokens(summarizedText);
+
+            if(isChunked && tokenCountSummary < max_output_token){
+                systemMessage = new SystemMessage(this.qai_system_prompt_template_formatter);
+                logger.info("template formatter: " + this.qai_system_prompt_template_formatter);
+            }else{
+                //here
+                systemMessage = new SystemMessage(content);
+                logger.info("template: " + content);
+            }
+
             CallResponseSpec resp = chatClient.prompt()
                     .messages(userMessage, systemMessage)
                     .advisors(advisor -> advisor
@@ -144,6 +169,9 @@ public class SummarizeDocSolver extends StepSolver {
                 logger.info("Token usage information is not available.");
             }
 
+            tokenCount = encoding.get().countTokens(output);
+            logger.info("token count output: " + tokenCount);
+
             // Salvataggio dell'output nel contesto di esecuzione
             this.scenarioExecution.getExecSharedMap().put(this.qai_output_variable, output);
             this.scenarioExecution.setNextStepId(this.step.getNextStepId());
@@ -160,11 +188,13 @@ public class SummarizeDocSolver extends StepSolver {
         // Se il testo è già corto, non riassumere
         logger.info("length: " + text.length());
         Double chunk_size_text;
+
+        tokenCount = encoding.get().countTokens(text);
         int textLengthPlus = (int) (text.length() * 1.1);
         int tokenCountPlus = (int) (tokenCount * 1.1);
-        // chunk_size_token/(ratio+10%)
+
-        // Double ratio = Math.floor((textLengthPlus / charMax) + 1);
+        Double ratio = Math.floor((tokenCountPlus / chunk_size_token_calc) + 1);
-        Double ratio = Math.floor((tokenCountPlus / chunk_size_token) + 1);
+        //Double ratio = Math.floor((tokenCountPlus / chunk_size_token) + 1);
         if (ratio == 1) {
             return text;
         } else {
@@ -175,7 +205,8 @@ public class SummarizeDocSolver extends StepSolver {
         List<String> chunks = chunkText(text, chunk_size_text.intValue());
         List<String> summarizedChunks = new ArrayList<>();
         
-        Double maxTokenChunkD = Math.ceil(chunk_size_token / ratio);
+        //Double maxTokenChunkD = Math.ceil(chunk_size_token / ratio);
+        Double maxTokenChunkD = Math.ceil(chunk_size_token_calc / ratio);
         maxTokenChunk = maxTokenChunkD.intValue();
 
         // Riassumere ogni chunk singolarmente
@@ -184,12 +215,13 @@ public class SummarizeDocSolver extends StepSolver {
             summarizedChunks.add(summarizeChunk(chunk));
         }
 
+        isChunked = true;
         // Unire i riassunti
         String summarizedText = String.join(" ", summarizedChunks);
         int tokenCountSummarizedText = encoding.get().countTokens(summarizedText);
 
         // Se il riassunto è ancora troppo lungo, applicare ricorsione
-        if (tokenCountSummarizedText > chunk_size_token) {
+        if (tokenCountSummarizedText > max_output_token) {
             return summarize(summarizedText);
         } else {
             return summarizedText;
@@ -210,13 +242,16 @@ public class SummarizeDocSolver extends StepSolver {
     private String summarizeChunk(String chunk) {
         String content = new String("");
 
-        if (maxTokenChunk < max_output_token) {
+     /*    if (maxTokenChunk < max_output_token) {
             content = this.qai_system_prompt_template_chunk.replace("max_number_token",
             maxTokenChunk.toString());
         }else{
             content = this.qai_system_prompt_template_chunk.replace("max_number_token",
             max_output_token.toString());
-        }
+        }*/
+
+        content = this.qai_system_prompt_template_chunk.replace("max_number_token",
+            maxTokenChunk.toString());
 
         Message chunkMessage = new UserMessage(chunk);
         Message systemMessage = new SystemMessage(