local embedding

rag.py

@@ -12,7 +12,12 @@ df['content']=df['product']+"; "+df['purpose']+"; "+df['benefit']+"; "+df['fee']
 
 corpus = [row['content'] for i,row in df.iterrows()]
 
-embedder = dspy.Embedder('huggingface/BAAI/bge-small-en-v1.5')
+'huggingface/BAAI/bge-small-en-v1.5'
+from sentence_transformers import SentenceTransformer
+
+# Load an extremely efficient local model for retrieval
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2", device="cpu")
+embedder = dspy.Embedder(model.encode)
 class RecommendProduct(dspy.Signature):
   """
   Recommend RBC financial product based on verbatim

requirements.txt

@@ -2,6 +2,7 @@ markdownify
 requests
 duckduckgo_search
 pandas
+sentence_transformers
 langchain
 langgraph
 litellm==1.63