langchain chat2db

Mastering Natural Language to SQL with LangChain | NL2SQL

https://blog.futuresmart.ai/mastering-natural-language-to-sql-with-langchain-nl2sql

 

By the end of this post, you'll have a solid understanding of:

1. Building a Basic NL2SQL Model: The foundation of translating natural language queries into SQL commands.

2. Incorporating Few-Shot Learning: Enhancing model accuracy with examples.

3. Dynamic Few-Shot Example Selection: Tailoring examples to the query context for improved relevance.

4. Dynamic Relevant Table Selection: Automatically identifying which tables to query based on the natural language input.

5. Customizing Prompts and Responses: Fine-tuning the model's interaction to provide clear, concise, and relevant answers.

6. Adding Memory to Chatbots: Enabling the model to handle follow-up questions by remembering the context of the conversation.

 

 

How to convert natural language text to SQL using LangChain

https://www.gettingstarted.ai/tutorial-on-how-to-convert-natural-language-text-to-sql-using-langchain/

Text-to-SQL Query

In this code example, we'll see how we can create our SQL statement from text without execution:

from langchain.chat_models import ChatOpenAI
from langchain.chains import create_sql_query_chain

chain = create_sql_query_chain(ChatOpenAI(temperature=0), db)
response = chain.invoke({"question":"How many employees are there"

print(response)

Code sample of text-to-SQL query without execution

Text-to-SQL Query and Execution

To create and execute a query that will retrieve the number of employees from our SQL table using chains, and then execute it:

from langchain.llms import OpenAI
from langchain_experimental.sql import SQLDatabaseChain

llm = OpenAI(temperature=0, verbose=True)

db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("How many employees are there?")

Code sample of text-to-SQL query with execution

 

SQL

https://langchain-opentutorial.gitbook.io/langchain-opentutorial/14-chains/02-sql

 

Overview

This tutorial covers how to use create_sql_query_chain to generate SQL queries, execute them, and derive answers.

Additionally, we'll explore the differences between this method and the SQL Agent.

sql-chain-work-flow

 

Table of Contents

•  

  Overview

•  

  Environment Setup

•  

  Loading SQL Databases

•  

  SQL Query Chain

•  

  Using SQL Query Chain with an Agent

•  

  Appendix : Chain with gpt-4o and a Post-Processing Function

 

langchain-ai/sql-query-system-prompt

Public

https://smith.langchain.com/hub/langchain-ai/sql-query-system-prompt

Given an input question, create a syntactically correct {dialect} query to run to help find the answer. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.

Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.

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. Also, pay attention to which column is in which table.

Only use the following tables:
{table_info}

Question: {input}

 

SQLDatabase Toolkit

https://python.langchain.com/docs/integrations/tools/sql_database/

 

How to deal with large databases when doing SQL question-answering

https://python.langchain.com/docs/how_to/sql_large_db/

 

Build a Question/Answering system over SQL data

https://python.langchain.com/docs/tutorials/sql_qa/#application-state

 

 

1. NL2SQL-using-Langchain

 

https://github.com/Yogeshwari0509/NL2SQL-using-Langchain/blob/main/main.ipynb