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from sentence_transformers import SentenceTransformer
import numpy as np
import logging, pickle,os
import faiss
import gradio as gr
INDEX_PATH = r"data/faiss_index.idx"
DOCUMENT_PATH = r"data/faiss_documents.pk1"
MODEL_NAME = 'all-MiniLM-L6-v2'
TOP_K_DEFAULT = 1
model = SentenceTransformer(MODEL_NAME)
def query_engine(query, top_k = TOP_K_DEFAULT):
logging.info("Loading faiss index and documents")
loaded_index = faiss.read_index(INDEX_PATH)
with open(DOCUMENT_PATH,'rb') as f:
docs = pickle.load(f)
q_emb = model.encode([query],convert_to_numpy= True)
q_emb = np.asarray(q_emb,dtype = np.float32)
q_emb = np.ascontiguousarray(q_emb)
faiss.normalize_L2(q_emb)
D,I = loaded_index.search(q_emb, top_k)
# print("FAISS score (inner product, larger is better)",D)
# print("I is ",I)
results = []
for idx in I[0]:
if idx < 0:
continue
results.append({
'idx':int(idx),
'instruction': docs[idx]['instruction'],
'output': docs[idx]['output'],
'text_for_embedding': docs[idx]['text_for_embedding']
})
return results[0]['output']
demo = gr.Interface(
fn = query_engine,
inputs = [gr.Textbox(label = "Question", lines = 4, max_lines = 8)],
outputs = [gr.Textbox(label = "Answer", lines = 4, max_lines = 8)],
title = "DSCM KNOWLEDGE BOT"
)
if __name__ == "__main__":
demo.launch()
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