sort search results by match score

app.py

@@ -8,6 +8,8 @@ PINECONE_API_KEY = st.secrets["PINECONE_API_KEY"]
 OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
 INDEX_NAME = 'realvest-data-v2'
 EMBEDDING_MODEL = "text-embedding-ada-002"  # OpenAI's best embeddings as of Apr 2023
+MAX_LENGTH_DESC = 200
+MATCH_SCORE_THR = 0.0
 
 def test_pinecone(sleep_time: int=1):
     MAX_TRIALS = 5
@@ -43,6 +45,13 @@ def query_pinecone(xq, top_k: int=3, include_metadata: bool=True, sleep_time: in
     
     raise Exception(f'max trials {MAX_TRIALS} Exceeded!')
 
+def sort_dict_by_value(d: dict, ascending: bool=True):
+    """
+    Sort dictionary {k1: v1, k2: v2} by its value. The output is
+    a sorted list of tuples [(k1, v1), (k2, v2)]
+    """
+    return sorted(d.items(), key=lambda x: x[1], reverse=not ascending)
+
 # initialize connection to pinecone (get API key at app.pinecone.io)
 pinecone.init(
     api_key=PINECONE_API_KEY,
@@ -53,6 +62,14 @@ stats = test_pinecone()
 print(f"Pinecone DB stats: {stats}")
 
 ### Main
+# st.set_page_config(layout="centered")
+css='''
+<style>
+    section.main > div {max-width:70rem}
+</style>
+'''
+st.markdown(css, unsafe_allow_html=True)
+
 # Create a text input field
 query = st.text_input("What are you looking for?")
 
@@ -62,23 +79,20 @@ if st.button('Submit'):
     # ### call OpenAI text-embedding
     res = openai.Embedding.create(model=EMBEDDING_MODEL, input=[query], api_key=OPENAI_API_KEY)
     xq = res['data'][0]['embedding']
-    # out = index.query(xq, top_k=3, include_metadata=True)
-    out = query_pinecone(xq, top_k=3, include_metadata=True)
-
-    ### display
-    # print(f"{'*'*30}results #3: {out}")
-    # st.write("Matched results")
-    # for match in out['matches']:
-    #     st.write( match['id'] )
+    out = query_pinecone(xq, top_k=20, include_metadata=True)
 
     ### candidates
-    pids = [
-        match['metadata']['product_id']
-        for match in out['matches']
+    metadata = {match['metadata']['product_id']: match['metadata'] for match in out['matches']}
+    match_score = {match['metadata']['product_id']: match['score'] for match in out['matches']}
+    above_thr_sorted = [
+        item 
+        for item in sort_dict_by_value(match_score, ascending=False) 
+        if item[1] > MATCH_SCORE_THR
     ]
+    pids = metadata.keys()
 
     ### query pids
-    pids_str = [f"'{pid}'"for pid in pids]
+    pids_str = [f"'{pid}'" for pid, _ in above_thr_sorted]
     query = f"""
     SELECT productid, name, category, alternatename, url, logo, description
     FROM main_products
@@ -86,17 +100,37 @@ if st.button('Submit'):
     """
 
     results = query_postgresql_realvest(query)
-    print(results)
+    results = {
+        result['productid']: result
+        for result in results
+    }
+
+    st.header("Results")
+    st.divider()
+
+    # display matched results
+    for pid, match_score in above_thr_sorted:
+        
+        if pid not in results:
+            continue
 
-    for result in results:
-        col_icon, col_info = st.columns([1, 5])
+        result = results[pid]
+        col_icon, col_info = st.columns([1, 3])
 
         with col_icon:
             st.image(result["logo"])
 
         with col_info:
-            st.subheader(result['name'])
-            st.write(result["description"])
+            st.markdown(f"""match score: { round(100 * match_score, 2) }
+            <br>
+            **{result['name']}**
+            <br>
+            _Asking Price:_ {metadata[pid].get('asking_price', 'N/A')}
+            <br>
+            _Category:_ {metadata[pid].get('category', 'N/A')}
+            <br>
+            _Location:_ {metadata[pid].get('location', 'N/A')}
+            """, unsafe_allow_html=True)
 
-        # st.write("---")
-        # st.json(result)
+            st.markdown(f"""**_Description:_** {result['description'][:MAX_LENGTH_DESC]}...[more]({result['url']})
+            """)