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app.py

@@ -149,7 +149,21 @@ def expand_query(state: AgentState) -> AgentState:
     state['expanded_query'] = chain.invoke({"query": state['query'], "question": state['query'], "query_feedback": state.get('query_feedback', '')}) # Pass all required variables
     return state
 
+db_loc = 'nutritional_db'
 
+# Initialize the Chroma vector store for retrieving documents
+vector_store = Chroma(
+    collection_name="nutritional_hypotheticals",
+    persist_directory=db_loc,
+    embedding_function=embedding_model
+
+)
+
+# Create a retriever from the vector store
+retriever = vector_store.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 3}
+)
 
 def retrieve_context(state: AgentState) -> AgentState:
     """