Add semamtic option

README.md

@@ -5,7 +5,7 @@ colorFrom: purple
 colorTo: blue
 sdk: gradio
 sdk_version: 5.34.2
-python_version: 3.13
+python_version: 3.12 #Does not work!!!!
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -15,7 +15,10 @@ import SPARQLWrapper
 SPARQL_ENDPOINT = os.getenv("SPARQL_ENDPOINT", "http://192.168.10.174:8890/sparql")
 
 prompt_valid = open("./templates/prompt_valid.txt").read()
+
 prompt_sparql = open("./templates/prompt_sparql.txt").read()
+prompt_sparql_semantic = open("./templates/prompt_sparql_semantic.txt").read()
+
 
 system =  open("./templates/system1.txt").read()
 
@@ -122,7 +125,17 @@ def check_valid_nlq(message, model_name, model_temperature, model_thinking):
     
     return valid
 
-def generate_sparql(message, model_name, model_temperature, model_thinking):
+def semantic_search(message):
+
+    gradio.Info("Performing semantic search...", duration=5)
+
+    results = extractor.extract(message, n_results=25, rerank=True)
+
+    print(results)
+
+    return results
+
+def generate_sparql(message, model_name, model_temperature, semantic_results, model_thinking):
 
     gradio.Info("Generating SPARQL query...", duration=5)
 
@@ -132,7 +145,13 @@ def generate_sparql(message, model_name, model_temperature, model_thinking):
 
     print("NLQ:", message)
 
-    prompt = prompt_sparql.replace("%nlq", message)
+    if semantic_results is not None:
+
+        prompt = prompt_sparql_semantic.replace("%nlq", message).replace("%semantic", semantic_results)
+
+    else:
+
+        prompt = prompt_sparql.replace("%nlq", message)
 
     print("Prompt:", prompt)
 
@@ -154,11 +173,17 @@ def generate_sparql(message, model_name, model_temperature, model_thinking):
     
     return gradio.Code(sparql, visible=True)
 
-def process_query(message, model_name, model_temperature, model_thinking, nlq_validate):
+def process_query(message, model_name, model_temperature, model_semantic, model_thinking, nlq_validate):
 
     if not nlq_validate or check_valid_nlq(message, model_name, model_temperature, model_thinking):
 
-        return generate_sparql(message, model_name, model_temperature, model_thinking)
+        semantic_results = None
+
+        if model_semantic:
+
+            semantic_results = semantic_search(message)
+
+        return generate_sparql(message, model_name, model_temperature, semantic_results, model_thinking)
     
     else:
         raise gradio.Error("Your request cannot be answered by generating a SPARQL query, try a different one that apply to the ERA Knowledge Graph.", duration=5)
@@ -170,9 +195,10 @@ def clear_query():
 with gradio.Blocks() as demo:
 
     # Options
-    model_selector = gradio.Dropdown([(item["name"], item["repo"]) for item in model_options], value="daniel-dona/sparql-model-era-lora-128-qwen3-0.6b", render=False, interactive=True, label="Model", info="Base model provided as reference, SFT model is trained on generated datasets, GRPO model is reinforced on to of SFT.")
+    model_selector = gradio.Dropdown([(item["name"], item["repo"]) for item in model_options], value="daniel-dona/sparql-model-era-lora-128-qwen3-4b", render=False, interactive=True, label="Model", info="Base model provided as reference, SFT model is trained on generated datasets, GRPO model is reinforced on to of SFT.")
     model_temperature = gradio.Slider(0, 1, render=False, step=0.1, value=0.6, label="Temperature", info="Ajust model variability, with a value of 0, the model use greedy decoding.")
     sparql_endpoint = gradio.Textbox(value=SPARQL_ENDPOINT, render=False, interactive=True, label="SPARQL endpoint", info="SPARQL endpoint to send the generate queries to fetch results.")
+    model_semantic = gradio.Checkbox(value=True, render=False, interactive=True, label="Enable semantic entity lookup", info="Use embeddings and reranking model to retrieve relevant objects.")
     model_thinking = gradio.Checkbox(value=False, render=False, interactive=True, label="Enable thinking", info="Use thinking mode in the Jinja chat template, mostly for GRPO experiments.")
     nlq_validate = gradio.Checkbox(value=True, render=False, interactive=True, label="Enable query prevalidation", info="Use the model first to determine if the user query is applicable to the ERA KG")
 
@@ -199,13 +225,14 @@ with gradio.Blocks() as demo:
         model_selector.render()
         model_temperature.render()
         sparql_endpoint.render()
+        model_semantic.render()
         model_thinking.render()
         nlq_validate.render()
 
 
         
          
-    query.submit(process_query,  inputs=[query, model_selector, model_temperature, model_thinking, nlq_validate], outputs=[sparql_block])
+    query.submit(process_query,  inputs=[query, model_selector, model_temperature, model_semantic, model_thinking, nlq_validate], outputs=[sparql_block])
     #query.change(clear_query, inputs=[], outputs=[sparql_block, sparql_results])
     sparql_block.change(execute_sparql, inputs=[sparql_block, sparql_endpoint], outputs=[sparql_results])
 

templates/prompt_sparql_semantic.txt

@@ -0,0 +1,13 @@
+Write a SPARQL query for the following natural language request:
+
+```
+%nlq
+```
+
+Consider the following entities if they match the user request:
+
+```
+%semantic
+```
+
+Provide the SPARQL query inside a markdown block.