Update app.py

app.py

@@ -210,11 +210,14 @@ def search(prompt: str):
         model="mixtral-8x22b-finetuned",
         generation_kwargs = {"max_tokens": 512}
     )
+    metadata_extractor = QueryMetadataExtractor()
+
     querying = Pipeline()
     querying.add_component("sparse_text_embedder", FastembedSparseTextEmbedder(model="Qdrant/bm42-all-minilm-l6-v2-attentions"))
     querying.add_component("dense_text_embedder", FastembedTextEmbedder(
      model="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", prefix="Represent this sentence for searching relevant passages: ")
      )
+    querying.add_component(instance=metadata_extractor, name="metadata_extractor")
     querying.add_component("retriever", QdrantHybridRetriever(document_store=document_store))
     querying.add_component("document_joiner", DocumentJoiner())
     querying.add_component("ranker", TransformersSimilarityRanker(model="BAAI/bge-m3"))    
@@ -223,17 +226,20 @@ def search(prompt: str):
     
     querying.connect("sparse_text_embedder.sparse_embedding", "retriever.query_sparse_embedding")
     querying.connect("dense_text_embedder.embedding", "retriever.query_embedding")
+    querying.connect("metadata_extractor.filters", "retriever.filters")
     querying.connect("retriever", "document_joiner")
     querying.connect("document_joiner", "ranker")
     querying.connect("ranker.documents", "prompt_builder.documents")
     querying.connect("prompt_builder", "llm")
     querying.debug=True
-    
+    metadata_fields =  {"publish_date", "publisher", "document_type"}
     results = querying.run(
-        {"dense_text_embedder": {"text": prompt},
-         "sparse_text_embedder": {"text": prompt},
-         "ranker": {"query": prompt},
-         "prompt_builder": {"question": prompt}
+        {
+            "dense_text_embedder": {"text": prompt},
+            "sparse_text_embedder": {"text": prompt},
+            "metadata_extractor": {"query": prompt, "metadata_fields": metadata_fields},
+            "ranker": {"query": prompt},
+            "prompt_builder": {"question": prompt}
         }
     )