Delete rag_pipeline.py

rag_pipeline.py

@@ -1,35 +0,0 @@
-from datasets import load_dataset
-import pandas as pd
-from sentence_transformers import SentenceTransformer
-import faiss
-from transformers import pipeline
-
-class RAGPipeline:
-    def __init__(self):
-        self.embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-        self.generator = pipeline("text2text-generation", model="google/flan-t5-base")
-
-        # Load dataset directly
-        ds = load_dataset("pubmed_qa", "pqa_labeled", split="train[:500]")
-        self.documents = ds["context"]
-        self.questions = ds["question"]
-
-        self.index = self.build_faiss_index()
-
-    def build_faiss_index(self):
-        embeddings = self.embedder.encode(self.documents, convert_to_numpy=True)
-        index = faiss.IndexFlatL2(embeddings.shape[1])
-        index.add(embeddings)
-        return index
-
-    def retrieve(self, query, top_k=5):
-        query_embedding = self.embedder.encode([query], convert_to_numpy=True)
-        scores, indices = self.index.search(query_embedding, top_k)
-        return [self.documents[i] for i in indices[0]]
-
-    def generate_answer(self, query):
-        docs = self.retrieve(query)
-        context = " ".join(docs)
-        prompt = f"Answer the following medical question using the context:\nContext: {context}\nQuestion: {query}"
-        result = self.generator(prompt, max_length=200, do_sample=True)
-        return result[0]['generated_text']