Create rag_pipeline.py

rag_pipeline.py

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+import faiss
+import pickle
+from transformers import pipeline
+
+# Load FAISS index
+with open('faiss_index.index', 'rb') as f:
+    faiss_index = pickle.load(f)
+
+# Load a pre-trained generative model (e.g., GPT-3 or T5)
+generator = pipeline("text-generation", model="gpt2")
+
+# Example query
+query = "What is the capital of France?"
+
+# Search for the most similar document using FAISS
+query_embedding = model.encode([query])
+D, I = faiss_index.search(query_embedding, k=1)  # k=1 for the most similar document
+
+# Use the retrieved document as context for the generative model
+retrieved_doc = documents[I[0][0]]
+
+# Generate a response using the retrieved document as context
+prompt = f"Context: {retrieved_doc}\nQuestion: {query}\nAnswer:"
+answer = generator(prompt, max_length=50)
+print(answer[0]['generated_text'])