From d5febd7a4e197d354cbc04b51d730d9f51780c9e Mon Sep 17 00:00:00 2001
From: "0xThresh.eth" <0xthresh@protonmail.com>
Date: Tue, 2 Jul 2024 09:51:51 -0700
Subject: [PATCH] Added valves and name to text-to-SQL pipeline

---
 .../pipelines/rag/text_to_sql_pipeline.py     | 111 ++++++++++++++++++
 1 file changed, 111 insertions(+)
 create mode 100644 examples/pipelines/rag/text_to_sql_pipeline.py

diff --git a/examples/pipelines/rag/text_to_sql_pipeline.py b/examples/pipelines/rag/text_to_sql_pipeline.py
new file mode 100644
index 00000000..ab30e72b
--- /dev/null
+++ b/examples/pipelines/rag/text_to_sql_pipeline.py
@@ -0,0 +1,111 @@
+"""
+title: Llama Index DB Pipeline
+author: 0xThresh
+date: 2024-07-01
+version: 1.0
+license: MIT
+description: A pipeline for using text-to-SQL for retrieving relevant information from a database using the Llama Index library.
+requirements: llama_index, sqlalchemy, psycopg2-binary
+"""
+
+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
+from sqlalchemy import create_engine
+
+
+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.name = "Database RAG Pipeline"
+        self.engine = None
+        self.nlsql_response = ""
+
+        # 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):
+        # 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()
+
+    async def on_shutdown(self):
+        # This function is called when the server is stopped.
+        pass
+
+    def pipe(
+        self, user_message: str, model_id: str, messages: List[dict], body: dict
+    ) -> Union[str, Generator, Iterator]:
+        # 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.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.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 = """
+        Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. 
+        You can order the results by a relevant column to return the most interesting examples in the database.
+        Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per Postgres. You can order the results to return the most informative data in the database.
+        Never query for all the columns from a specific table, only ask for a few relevant columns given the question.
+        You should use DISTINCT statements and avoid returning duplicates wherever possible.
+        Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Pay attention to which column is in which table. Also, qualify column names with the table name when needed. You are required to use the following format, each taking one line:
+
+        Question: Question here
+        SQLQuery: SQL Query to run
+        SQLResult: Result of the SQLQuery
+        Answer: Final answer here
+
+        Only use tables listed below.
+        {schema}
+
+        Question: {query_str}
+        SQLQuery: 
+        """
+
+        text_to_sql_template = PromptTemplate(text_to_sql_prompt)
+
+        query_engine = NLSQLTableQueryEngine(
+            sql_database=sql_database, 
+            tables=self.valves.DB_TABLES, 
+            llm=llm, 
+            embed_model="local", 
+            text_to_sql_prompt=text_to_sql_template, 
+            streaming=True
+        )
+
+        response = query_engine.query(user_message)
+
+        return response.response_gen