add about embedding cache

Author: Eric Liu

app/rag_system.py

@@ -21,19 +21,26 @@ def __init__(self, knowledge_base_path='./data/knowledge_base.json'):
 
         # load existing embeddings if available
         logging.info("Embedding knowledge base...")
-        if os.path.exists('./data/doc_embeddings.npy'):
+
+        if os.path.exists('./data/doc_about_embeddings.npy') and os.path.exists('./data/doc_embeddings.npy'):
+            self.doc_about_embeddings = np.load('./data/doc_about_embeddings.npy')
+            logging.info("Loaded existing about document about embeddings from disk.")
             self.doc_embeddings = np.load('./data/doc_embeddings.npy')
             logging.info("Loaded existing document embeddings from disk.")
         else:
             self.rebuild_embeddings()
+
         logging.info("Knowledge base embeddings created")
         self.conversation_history = []
 
     def rebuild_embeddings(self):
         logging.info("No existing document embeddings found, creating new embeddings.")
         self.doc_embeddings = self.embed_knowledge_base()
+        self.doc_about_embeddings = self.embed_knowledge_base_about()
         # cache doc_embeddings to disk
         np.save('./data/doc_embeddings.npy', self.doc_embeddings.cpu().numpy())
+        np.save('./data/doc_about_embeddings.npy', self.doc_about_embeddings.cpu().numpy())
+
 
     def load_knowledge_base(self):
         with open(self.knowledge_base_path, 'r') as kb_file:
@@ -43,6 +50,9 @@ def embed_knowledge_base(self):
         docs = [f'{doc["about"]}. {doc["text"]}' for doc in self.knowledge_base]
         return self.model.encode(docs, convert_to_tensor=True)
 
+    def embed_knowledge_base_about(self):
+        return self.model.encode([doc["about"] for doc in self.knowledge_base], convert_to_tensor=True)
+
     def normalize_query(self, query):
         return query.lower().strip()
 
@@ -55,13 +65,12 @@ def get_query_embedding(self, query):
     def get_doc_embeddings(self):
         return self.doc_embeddings
 
-    def compute_document_scores(self, query_embedding, doc_embeddings, high_match_threshold):
-        text_similarities = cosine_similarity(query_embedding, doc_embeddings)[0]
-        about_similarities = []
-        for doc in self.knowledge_base:
-            about_similarity = cosine_similarity(query_embedding, self.model.encode([doc["about"]]))[0][0]
-            about_similarities.append(about_similarity)
+    def get_doc_about_embeddings(self):
+        return self.doc_about_embeddings
 
+    def compute_document_scores(self, query_embedding, doc_embeddings, doc_about_embeddings, high_match_threshold):
+        text_similarities = cosine_similarity(query_embedding, doc_embeddings)[0]
+        about_similarities = cosine_similarity(query_embedding, doc_about_embeddings)[0]
         relevance_scores = self.compute_relevance_scores(text_similarities, about_similarities, high_match_threshold)
 
         result = [
@@ -82,8 +91,9 @@ def compute_document_scores(self, query_embedding, doc_embeddings, high_match_th
     def retrieve(self, query, similarity_threshold=0.4, high_match_threshold=0.8, max_docs=5):
         query_embedding = self.get_query_embedding(query)
         doc_embeddings = self.get_doc_embeddings()
+        doc_about_embeddings = self.get_doc_about_embeddings()
 
-        doc_scores = self.compute_document_scores(query_embedding, doc_embeddings, high_match_threshold)
+        doc_scores = self.compute_document_scores(query_embedding, doc_embeddings, doc_about_embeddings, high_match_threshold)
         retrieved_docs = self.get_top_docs(doc_scores, similarity_threshold, max_docs)
 
         if not retrieved_docs:

app/test_rag_system.py

@@ -80,9 +80,10 @@ def test_compute_document_scores(self):
         query = "Does Defang have an MCP sample?"
         query_embedding = self.rag_system.get_query_embedding(query)
         doc_embeddings = self.rag_system.get_doc_embeddings()
+        doc_about_embeddings = self.rag_system.doc_about_embeddings()
 
         # call function and get results
-        result = self.rag_system.compute_document_scores(query_embedding, doc_embeddings, high_match_threshold=0.8)
+        result = self.rag_system.compute_document_scores(query_embedding, doc_embeddings, doc_about_embeddings, high_match_threshold=0.8)
         # sort the result by relevance score in descending order
         result = sorted(result, key=lambda x: x["relevance_score"], reverse=True)