added gita.txt, removed faiss

app.py

@@ -1,9 +1,11 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 from sentence_transformers import SentenceTransformer
-import faiss
+#import faiss
 import numpy as np
 
+
+
 # =========================
 # Load and Prepare Gita Text
 # =========================
@@ -28,17 +30,27 @@ embedder = SentenceTransformer("all-MiniLM-L6-v2")
 doc_embeddings = embedder.encode(documents)
 dimension = doc_embeddings.shape[1]
 
-index = faiss.IndexFlatL2(dimension)
-index.add(np.array(doc_embeddings))
-
+doc_embeddings = embedder.encode(documents)
 
 def retrieve(query, top_k=4):
-    query_embedding = embedder.encode([query])
-    distances, indices = index.search(np.array(query_embedding), top_k)
-    results = [documents[i] for i in indices[0]]
+    query_embedding = embedder.encode([query])[0]
+    scores = np.dot(doc_embeddings, query_embedding)
+    top_indices = np.argsort(scores)[-top_k:][::-1]
+    results = [documents[i] for i in top_indices]
     return "\n\n".join(results)
 
 
+# index = faiss.IndexFlatL2(dimension)
+# index.add(np.array(doc_embeddings))
+
+
+# def retrieve(query, top_k=4):
+#     query_embedding = embedder.encode([query])
+#     distances, indices = index.search(np.array(query_embedding), top_k)
+#     results = [documents[i] for i in indices[0]]
+#     return "\n\n".join(results)
+
+
 # =========================
 # RAG Chat Function
 # =========================