debug

app.py

@@ -367,30 +367,30 @@ try:
         logger.info("#### getRagData() entered.")
         ###############################################################################
         # Initial the the sentence transformer and encode the query prompt.
-        logger.debug(f"#### Encode text query prompt to create vectors. {promptText}")
+        logger.info(f"#### Encode text query prompt to create vectors. {promptText}")
         model = SentenceTransformer('/app/multi-qa-MiniLM-L6-cos-v1')
 
         vector = model.encode(promptText)
         vectorList = []
 
-        logger.debug("#### Print vectors.")
+        logger.info("#### Print vectors.")
         for vec in vector:
             vectorList.append(vec)
-        logger.debug(f"vectorList: {vectorList[2]}")
+        logger.info(f"vectorList: {vectorList[2]}")
 
         # Fetch chunks and print chunks.
-        logger.debug("#### Retrieve semchunks from db using vectors from prompt.")
+        logger.info("#### Retrieve semchunks from db using vectors from prompt.")
         wpChunksCollection = st.session_state.wpChunksCollection
         semChunks = wpChunksCollection.query.near_vector(
             near_vector=vectorList,
             distance=0.7,
             limit=3
         )
-        logger.debug(f"### semChunks[0]: {semChunks}")
+        logger.info(f"### semChunks[0]: {semChunks}")
 
         # Print chunks, corresponding document and document title.
         ragData = ""
-        logger.debug("#### Print individual retrieved chunks.")
+        logger.info("#### Print individual retrieved chunks.")
         wpCollection = st.session_state.wpCollection
         for chunk in enumerate(semChunks.objects):
             logger.info(f"#### chunk: {chunk}")