Add relevance scores

app.py

@@ -91,17 +91,21 @@ def search_table(
         results = (
             table.search(query, query_type=query_type)
             .where(f"creation_date >= '{filter_year}'", prefilter=True)
-            .rerank(reranker=reranker)
             .limit(top_k * overfetch_factor)
-            .to_pydantic(Document)
+            .rerank(reranker=reranker)
+            .limit(top_k)
+            .to_list() # to get access to '_relevance_score'
+            #.to_pydantic(Document)
         )
     elif query_type == "hybrid":
         results = (
             table.search(query, query_type=query_type)
             .where(f"creation_date >= '{filter_year}'", prefilter=True)
+            .limit(top_k * overfetch_factor)
             .rerank(reranker=reranker)
             .limit(top_k)
-            .to_pydantic(Document)
+            .to_list() # to get access to '_relevance_score'
+            #.to_pydantic(Document)
         )
 
     return results[:top_k]
@@ -111,13 +115,19 @@ def search_table(
 # Generatton: query + context --> response
 #
 
-def create_bulleted_list(texts: list[str]) -> str:
+def create_bulleted_list(texts: list[str], scores: list[float]=None) -> str:
     """
     This function takes a list of strings and returns HTML with a bulleted list.
     """
     html_items = []
-    for item in texts:
-        html_items.append(f"<li>{item}</li>")
+
+    if scores is not None:
+        for text, score in zip(texts, scores):
+            html_items.append(f"<li>(Score={score:.2f})\t{text}</li>")
+    else:
+        for text in texts:
+            html_items.append(f"<li>{text}</li>")
+
     return "<ul>" + "".join(html_items) + "</ul>"
 
 
@@ -125,7 +135,8 @@ def generate_response(
         query: str,
         query_type: str,
         reranker_name: str,
-        filter_year: int
+        filter_year: int,
+        top_k: int
     ) -> list[str, str, str]:
     """Generate a response given query, search type and reranker.
 
@@ -142,14 +153,16 @@ def generate_response(
         query=query,
         query_type=query_type,
         reranker_name=reranker_name,
-        filter_year=filter_year
+        filter_year=filter_year,
+        top_k=top_k
     )
     
-    references = [result.file_name for result in results]
+    references = [result['file_name'] for result in results]
     references_html = "<h4>References</h4>\n" + create_bulleted_list(references)
 
-    snippets = [result.text for result in results]
-    snippets_html = "<h4>Snippets</h4>\n" + create_bulleted_list(snippets)
+    snippets = [result['text'] for result in results]
+    scores = [result['_relevance_score'] for result in results]
+    snippets_html = "<h4>Snippets</h4>\n" + create_bulleted_list(snippets, scores)
 
     # Generate the reponse from the LLM
     stream_reponse = llm_utils.generate_chat_response_streaming(
@@ -209,6 +222,10 @@ with gr.Blocks() as demo:
         minimum=2005, maximum=2020, value=2005, step=1,
         label='Creation date >=', render=False
     )
+    top_k = gr.Slider(
+        minimum=2, maximum=10, value=5, step=1,
+        label='Top k result', render=False
+    )
 
     with gr.Row():
         # Example questions given default provided PDF file
@@ -224,7 +241,7 @@ with gr.Blocks() as demo:
                     ['What are the "Ten Global Principles" for fighting tax crime?',],
                     ["What are some recent developments in the fight against offshore tax evasion?",],
                 ],
-                inputs=[question, query_type, reranker_name, filter_year],
+                inputs=[question, query_type, reranker_name, filter_year, top_k],
                 outputs=[response, references, snippets],
                 fn=generate_response,
                 cache_examples=False,
@@ -237,6 +254,7 @@ with gr.Blocks() as demo:
                 query_type.render()
                 reranker_name.render()
                 filter_year.render()
+                top_k.render()
     
     # Documentation
     with gr.Accordion("Documentation", open=False):
@@ -256,7 +274,7 @@ with gr.Blocks() as demo:
     # Click actions
     response_button.click(
         fn=generate_response,
-        inputs=[question, query_type, reranker_name, filter_year],
+        inputs=[question, query_type, reranker_name, filter_year, top_k],
         outputs=[response, references, snippets]
     )
     clear_button.click(