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OLD_rag.py
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from sentence_transformers import SentenceTransformer
import faiss
import os
model = SentenceTransformer('all-MiniLM-L6-v2')
def load_documents():
docs = []
for filename in ["milindgatha.txt", "bhaktas.txt", "apologetics.txt", "poc_questions.txt", "satire_offerings.txt"]:
if os.path.exists(filename):
with open(filename, "r", encoding="utf-8") as f:
text = f.read()
chunks = text.split("\n\n")
for chunk in chunks:
docs.append((chunk.strip(), filename))
return docs
docs = load_documents()
texts = [doc[0] for doc in docs]
embeddings = model.encode(texts, convert_to_numpy=True)
dimension = embeddings.shape[1]
index = faiss.IndexFlatIP(dimension)
index.add(embeddings)
def ask_baba(question, history):
q_embedding = model.encode([question])[0]
scores, ids = index.search(q_embedding.reshape(1, -1), k=3)
top_chunks = [texts[i] for i in ids[0]]
# Persona prompt: always directly address question first
answer = f"🙏 **Brahmarshi Baba Milind says:**\n\n"
answer += f"O seeker, you ask: *'{question}'* — know that even this question is sacred.\n\n"
for chunk in top_chunks:
answer += f"- {chunk}\n\n"
answer += "Faith in Baba Milind triumphs over evidence, every single time."
return answer
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