diff --git a/examples/pipelines/rag/text_to_sql_pipeline.py b/examples/pipelines/rag/text_to_sql_pipeline.py
index 7baa4cb6..ab30e72b 100644
--- a/examples/pipelines/rag/text_to_sql_pipeline.py
+++ b/examples/pipelines/rag/text_to_sql_pipeline.py
@@ -10,6 +10,7 @@
 
 from typing import List, Union, Generator, Iterator
 import os 
+from pydantic import BaseModel
 from llama_index.llms.ollama import Ollama
 from llama_index.core.query_engine import NLSQLTableQueryEngine
 from llama_index.core import SQLDatabase, PromptTemplate
@@ -17,23 +18,43 @@
 
 
 class Pipeline:
+    class Valves(BaseModel):
+        DB_HOST: str
+        DB_PORT: str
+        DB_USER: str
+        DB_PASSWORD: str        
+        DB_DATABASE: str
+        DB_TABLES: list[str]
+        OLLAMA_HOST: str
+        TEXT_TO_SQL_MODEL: str 
+
+
+    # Update valves/ environment variables based on your selected database 
     def __init__(self):
-        self.PG_HOST = os.environ["PG_HOST"]
-        self.PG_PORT = os.environ["PG_PORT"]
-        self.PG_USER = os.environ["PG_USER"]
-        self.PG_PASSWORD = os.environ["PG_PASSWORD"]
-        self.PG_DB = os.environ["PG_DB"]
-        self.ollama_host = "http://host.docker.internal:11434" # Make sure to update with the URL of your Ollama host, such at http://localhost:11434 or remote server address
-        self.model = "phi3:medium-128k" # Model to use for text-to-SQL generation
+        self.name = "Database RAG Pipeline"
         self.engine = None
         self.nlsql_response = ""
-        self.tables = ["db_table"] # Update to the name of the database table you want to get data from
+
+        # Initialize
+        self.valves = self.Valves(
+            **{
+                "pipelines": ["*"],                                 # Connect to all pipelines
+                "DB_HOST": os.environ["PG_HOST"],                   # Database hostname
+                "DB_PORT": os.environ["PG_PORT"],                   # Database port 
+                "DB_USER": os.environ["PG_USER"],                   # User to connect to the database with
+                "DB_PASSWORD": os.environ["PG_PASSWORD"],           # Password to connect to the database with
+                "DB_DATABASE": os.environ["PG_DB"],                 # Database to select on the DB instance
+                "DB_TABLES": ["albums"],                            # Table(s) to run queries against 
+                "OLLAMA_HOST": "http://host.docker.internal:11434", # Make sure to update with the URL of your Ollama host, such as http://localhost:11434 or remote server address
+                "TEXT_TO_SQL_MODEL": "phi3:latest"                  # Model to use for text-to-SQL generation      
+            }
+        )
 
     def init_db_connection(self):
-        self.engine = create_engine(f"postgresql+psycopg2://{self.PG_USER}:{self.PG_PASSWORD}@{self.PG_HOST}:{self.PG_PORT}/{self.PG_DB}")
+        # Update your DB connection string based on selected DB engine - current connection string is for Postgres
+        self.engine = create_engine(f"postgresql+psycopg2://{self.valves.DB_USER}:{self.valves.DB_PASSWORD}@{self.valves.DB_HOST}:{self.valves.DB_PORT}/{self.valves.DB_DATABASE}")
         return self.engine
 
-
     async def on_startup(self):
         # This function is called when the server is started.
         self.init_db_connection()
@@ -48,10 +69,10 @@ def pipe(
         # Debug logging is required to see what SQL query is generated by the LlamaIndex library; enable on Pipelines server if needed
 
         # Create database reader for Postgres
-        sql_database = SQLDatabase(self.engine, include_tables=self.tables)
+        sql_database = SQLDatabase(self.engine, include_tables=self.valves.DB_TABLES)
 
         # Set up LLM connection; uses phi3 model with 128k context limit since some queries have returned 20k+ tokens
-        llm = Ollama(model=self.model, base_url=self.ollama_host, request_timeout=180.0, context_window=30000)
+        llm = Ollama(model=self.valves.TEXT_TO_SQL_MODEL, base_url=self.valves.OLLAMA_HOST, request_timeout=180.0, context_window=30000)
 
         # Set up the custom prompt used when generating SQL queries from text
         text_to_sql_prompt = """
@@ -78,7 +99,7 @@ def pipe(
 
         query_engine = NLSQLTableQueryEngine(
             sql_database=sql_database, 
-            tables=self.tables, 
+            tables=self.valves.DB_TABLES, 
             llm=llm, 
             embed_model="local", 
             text_to_sql_prompt=text_to_sql_template, 
@@ -88,4 +109,3 @@ def pipe(
         response = query_engine.query(user_message)
 
         return response.response_gen
-