Update app.py

app.py

@@ -3,9 +3,9 @@ import streamlit as st
 import requests
 import zipfile
 from haystack.document_stores.in_memory import InMemoryDocumentStore
-from haystack.nodes import EmbeddingRetriever
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 from haystack.pipelines.standard_pipelines import TextIndexingPipeline
-from haystack.nodes import FARMReader
+from haystack.components.readers import ExtractiveReader
 from haystack.pipelines import ExtractiveQAPipeline
 from pydantic import BaseModel
 
@@ -70,7 +70,7 @@ def initializing_Document_Store():
 
 # Function to initialize the Retriever
 def initializing_Retriever(document_store):
-    retriever = EmbeddingRetriever(
+    retriever = InMemoryEmbeddingRetriever(
         document_store=document_store,
         embedding_model="sentence-transformers/all-MiniLM-L6-v2"
     )
@@ -79,14 +79,18 @@ def initializing_Retriever(document_store):
 
 # Function to initialize the Reader
 def initializing_Reader():
-    reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
+    reader = ExtractiveReader(model="deepset/roberta-base-squad2")
     return reader
 
 
 # Initializing components
 document_store, retriever = initializing_Document_Store()
 reader = initializing_Reader()
-pipe = ExtractiveQAPipeline(reader, retriever)
+reader.warm_up()
+pipe = Pipeline()
+pipe.add_component(instance=retriever, name="retriever")
+pipe.add_component(instance=reader, name="reader")
+pipe.connect("retriever.documents", "reader.documents")
 
 # User interaction via Streamlit
 st.title("Ask about Game of Thrones!")
@@ -101,7 +105,12 @@ if user_query:
     with st.spinner("Searching for an answer..."):
         try:
             # Use the pipeline to find the answer
-            answer = pipe.run(query=user_query, params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}})
+            answer = pipeline.run(
+                data={
+                "retriever": {"query": question, "top_k": 10},
+                "reader": {"query": question, "top_k": top_k},
+            }
+    )
 
             # Display the answers
             for idx, ans in enumerate(answer["answers"]):