fix logger

app/rag.py

@@ -26,20 +26,21 @@ class ChatPDF:
     doc_ids = []
     nodes = []
     hyde_query_engine = None
+    logger = None
 
     def __init__(self):
         logging.basicConfig(level=logging.INFO)
-        logger = logging.getLogger(__name__)
+        self.logger = logging.getLogger(__name__)
 
         text_parser = SentenceSplitter(chunk_size=512, chunk_overlap=100)
 
-        logger.info("initializing the vector store related objects")
+        self.logger.info("initializing the vector store related objects")
         client = QdrantClient(url=QDRANT_API_URL, api_key=QDRANT_API_KEY)
         vector_store = QdrantVectorStore(client=client, collection_name="rag_documents")
 
-        logger.info("initializing the OllamaEmbedding")
+        self.logger.info("initializing the OllamaEmbedding")
         embed_model = OllamaEmbedding(model_name='mxbai-embed-large')
-        logger.info("initializing the global settings")
+        self.logger.info("initializing the global settings")
         Settings.embed_model = embed_model
         Settings.llm = Ollama(model="qwen:1.8b", request_timeout=1000000)
         Settings.transformations = [text_parser]
@@ -47,44 +48,44 @@ class ChatPDF:
     def ingest(self, dir_path: str):
         docs = SimpleDirectoryReader(input_dir=dir_path).load_data()
 
-        logger.info("enumerating docs")
+        self.logger.info("enumerating docs")
         for doc_idx, doc in enumerate(docs):
             curr_text_chunks = text_parser.split_text(doc.text)
             text_chunks.extend(curr_text_chunks)
             doc_ids.extend([doc_idx] * len(curr_text_chunks))
 
-        logger.info("enumerating text_chunks")
+        self.logger.info("enumerating text_chunks")
         for idx, text_chunk in enumerate(text_chunks):
             node = TextNode(text=text_chunk)
             src_doc = docs[doc_ids[idx]]
             node.metadata = src_doc.metadata
             nodes.append(node)
 
-        logger.info("enumerating nodes")
+        self.logger.info("enumerating nodes")
         for node in nodes:
             node_embedding = embed_model.get_text_embedding(
                 node.get_content(metadata_mode=MetadataMode.ALL)
             )
             node.embedding = node_embedding
 
-        logger.info("initializing the storage context")
+        self.logger.info("initializing the storage context")
         storage_context = StorageContext.from_defaults(vector_store=vector_store)
-        logger.info("indexing the nodes in VectorStoreIndex")
+        self.logger.info("indexing the nodes in VectorStoreIndex")
         index = VectorStoreIndex(
             nodes=nodes,
             storage_context=storage_context,
             transformations=Settings.transformations,
         )
 
-        logger.info("initializing the VectorIndexRetriever with top_k as 5")
+        self.logger.info("initializing the VectorIndexRetriever with top_k as 5")
         vector_retriever = VectorIndexRetriever(index=index, similarity_top_k=5)
         response_synthesizer = get_response_synthesizer()
-        logger.info("creating the RetrieverQueryEngine instance")
+        self.logger.info("creating the RetrieverQueryEngine instance")
         vector_query_engine = RetrieverQueryEngine(
             retriever=vector_retriever,
             response_synthesizer=response_synthesizer,
         )
-        logger.info("creating the HyDEQueryTransform instance")
+        self.logger.info("creating the HyDEQueryTransform instance")
         hyde = HyDEQueryTransform(include_original=True)
         self.hyde_query_engine = TransformQueryEngine(vector_query_engine, hyde)
 
@@ -92,9 +93,9 @@ class ChatPDF:
         if not self.hyde_query_engine:
             return "Please, add a PDF document first."
 
-        logger.info("retrieving the response to the query")
+        self.logger.info("retrieving the response to the query")
         response = self.hyde_query_engine.query(str_or_query_bundle=query)
-        print(response)
+        self.logger.info(response)
         return response
 
     def clear(self):