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Refactor DocumentationTool for improved logging and search request handling

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This commit is contained in:
Andrea Terzani
2025-06-28 10:22:58 +02:00
parent 49d935d07d
commit a389eca598
2 changed files with 92 additions and 43 deletions
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16
src/main/java/com/olympus/hermione/services/ScenarioExecutionService.java
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@@ -441,18 +441,12 @@ public class ScenarioExecutionService {
AzureOpenAiChatOptions.Builder optionsBuilder = AzureOpenAiChatOptions.builder()
.deploymentName(aiModel.getModel());
// Aggiungi maxTokens solo se non è nullo
if (aiModel.getMaxTokens() != null) {
if (aiModel.isLegacy() ) {
logger.info("Using legacy Azure OpenAI model: " + aiModel.getModel());
optionsBuilder.temperature(aiModel.getTemperature().doubleValue());
optionsBuilder.maxTokens(aiModel.getMaxTokens());
logger.info("Setting maxTokens: " + aiModel.getMaxTokens());
}
// Aggiungi temperature solo se non è nullo
if (aiModel.getTemperature() != null) {
optionsBuilder.temperature(Double.valueOf(aiModel.getTemperature()));
logger.info("Setting temperature: " + aiModel.getTemperature());
}
}
AzureOpenAiChatOptions openAIChatOptions = optionsBuilder.build();

119
src/main/java/com/olympus/hermione/tools/DocumentationTool.java
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@@ -13,52 +13,107 @@ import org.springframework.ai.vectorstore.VectorStore;
public class DocumentationTool {
Logger logger = Logger.getLogger(DocumentationTool.class.getName());
private String application;
private String project;
private VectorStore vectorStore;
private static final Logger logger = Logger.getLogger(DocumentationTool.class.getName());
private static final int DEFAULT_TOP_K = 5;
private static final double DEFAULT_SIMILARITY_THRESHOLD = 0.7;
private final String application;
private final String project;
private final VectorStore vectorStore;
/**
* Costruttore per DocumentationTool.
*
* @param application Nome dell'applicazione
* @param project Nome del progetto
* @param vectorStore Vector store contenente i documenti
*/
public DocumentationTool(String application, String project, VectorStore vectorStore) {
this.application = application;
this.project = project;
this.vectorStore = vectorStore;
}
@Tool(description = "Find any functional information about the application. Can be used to answeer any questron with a specific focus on the application")
/**
* @param query The user's question or information request regarding the application.
* @return A formatted response containing the most relevant documentation excerpts.
*
* <p>
* This method is intended for integration with conversational AI systems (such as ChatGPT),
* enabling them to provide accurate and context-aware answers about the application's functionality.
* </p>
*/
@Tool(description = "Retrieves functional information about the application based on the provided query.\r\n" + //
" * It performs a semantic search over the application's documentation and returns the most relevant results.")
public String getDocumentation(String query) {
logRequestDetails(query);
String filterExpression = buildFilterExpression();
logger.info("[TOOL]LLM DocumentationTool Getting documentation for filterExpression: " + filterExpression);
SearchRequest request = buildSearchRequest(query, filterExpression);
List<Document> docs = this.vectorStore.similaritySearch(request);
logger.info("Number of VDB retrieved documents: " + docs.size());
return formatDocumentsResponse(docs);
}
/**
* Registra i dettagli della richiesta nei log.
*
* @param query La query di ricerca
*/
private void logRequestDetails(String query) {
logger.info("[TOOL]LLM DocumentationTool Getting documentation for query: " + query);
logger.info("[TOOL]LLM DocumentationTool Getting documentation for project: " + this.project);
logger.info("[TOOL]LLM DocumentationTool Getting documentation for vectorStore: " + this.vectorStore);
String filterExpression = "'KsProjectName' == '"+ this.project +"' AND 'KsDoctype' == 'functional'";
logger.info("[TOOL]LLM DocumentationTool Getting documentation for filterExpression: " + filterExpression);
SearchRequest request = SearchRequest.builder()
.query(query)
.topK(3)
.similarityThreshold(0.7)
.filterExpression( filterExpression).build();
List<Document> docs = this.vectorStore.similaritySearch(request);
logger.info("Number of VDB retrieved documents: " + docs.size());
String docToolResponse = "";
for (Document doc : docs) {
docToolResponse += "-------USE THIS AS PART OF YOUR CONTEXT----------------------------------------- \n";
docToolResponse += doc.getText() + "------------------------------------------------------------------------------------------ \n";
}
/**
* Costruisce l'espressione di filtro per la ricerca.
*
* @return L'espressione di filtro formattata
*/
private String buildFilterExpression() {
return "'KsProjectName' == '"+ this.project +"' AND 'KsDoctype' == 'functional'";
}
/**
* Costruisce la richiesta di ricerca.
*
* @param query La query di ricerca
* @param filterExpression L'espressione di filtro
* @return La richiesta di ricerca configurata
*/
private SearchRequest buildSearchRequest(String query, String filterExpression) {
return SearchRequest.builder()
.query(query)
.topK(DEFAULT_TOP_K)
.similarityThreshold(DEFAULT_SIMILARITY_THRESHOLD)
.filterExpression(filterExpression)
.build();
}
/**
* Formatta i documenti recuperati in una risposta leggibile.
*
* @param docs Lista di documenti recuperati
* @return Risposta formattata contenente i testi dei documenti
*/
private String formatDocumentsResponse(List<Document> docs) {
if (docs.isEmpty()) {
return "Nessun documento trovato per la query.";
}
return docToolResponse;
StringBuilder response = new StringBuilder();
for (Document doc : docs) {
response.append("-------USE THIS AS PART OF YOUR CONTEXT-----------------------------------------\n")
.append(doc.getText())
.append("------------------------------------------------------------------------------------------\n");
}
return response.toString();
}