Create app.py

app.py

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+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
+from datasets import load_dataset
+
+# Load the dataset - Here we use the wiki_dpr dataset for retrieval
+dataset = load_dataset('wiki_dpr', 'psgs_w100.nq.exact')
+
+# Initialize the RAG tokenizer (use the T5 tokenizer for RAG)
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
+
+# Initialize the RAG Retriever with the correct index name for wiki_dpr dataset
+retriever = RagRetriever.from_pretrained("facebook/rag-token-nq", index_name="compressed", use_dummy_dataset=True)
+
+# Initialize the RAG Sequence Model (T5-based)
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-token-nq")
+
+# Tokenize a sample from the dataset (using wiki_dpr for retrieval)
+sample = dataset["train"][0]  # or dataset["validation"][0]
+input_text = sample["query"]
+context_text = sample["passage"]
+
+# Tokenize the input question
+inputs = tokenizer(input_text, return_tensors="pt")
+
+# Generate the answer using the RAG model
+outputs = model.generate(input_ids=inputs['input_ids'],
+                         decoder_start_token_id=model.config.pad_token_id,
+                         num_beams=3,
+                         num_return_sequences=1,
+                         do_sample=False)
+
+# Decode the generated output
+generated_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(f"Question: {input_text}")
+print(f"Answer: {generated_answer}")