Sql database agent langchain. sql_database import SQLDatabase: from langchain.
Sql database agent langchain from_llm(llm, db, verbose=True LangChain offers basic, general agents but also several different optimized agents for various use-cases. query. sql import SQLDatabaseChain from langchain. toolkit (Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the agent to use. agent_types import AgentType langchain_community. tool import QuerySQLDataBaseTool execute_query = QuerySQLDataBaseTool(db=db) execute_query. get_usable_table_names ()) table_info = self. from_databricks(catalog="", schema="") command and the agent and required tools are created by SQLDatabaseToolkit(db=db, llm=llm) and create_sql_agent(llm=llm, toolkit=toolkit, **kwargs), respectively. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. Creating SQL Agents with LangChain LangChain provides a robust framework for creating SQL Agents that enhance interaction with SQL databases. Company. tools. It uses LLamA2-13b hosted by Replicate, but can be adapted to any API that supports LLaMA2 including Fireworks. 5) This is what my initial code looks like to return all views. The language model (for use with QuerySQLCheckerTool) Instantiate: Create a new model by parsing and validating input data from keyword arguments. openai import OpenAI from langchain. InfoSQLDatabaseTool [source] ¶. Large-language models (LLMs), such as ChatGPT and Llama, I have used Langchain - create_sql_agent to generate SQL queries with a database and get the output result of the generated SQL query. ; sql_db_schema: Provides detailed structure information about a table. Where possible, schemas are inferred from runnable. invoke({"input": "How many resources are there in XYZ Convert question to a graph database query: Model converts user input to a graph database query (e. SQLDatabaseToolkit. from langchain_openai import ChatOpenAI from langchain_community. utilities import SQLDatabaseChain db_chain = SQLDatabaseChain. The SQL database. This database agent is equipped with the following tools out of the box: QuerySQLDataBaseTool: Tool for querying the database Agent 1: SQL Query Agent This agent interprets the user’s question, formulates an SQL query and retrieves the data from the database. When coupled with Amazon Bedrock's single API, which interfaces with a variety of chat models, LangChain accelerates the creation of POCs and the deployment of Graphs. \nGiven an input question, create a syntactically correct Spark SQL query to run, This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. import streamlit as st from google. spark_sql import SparkSQL from langchain_openai import class langchain_community. A. LangChain provides a robust framework for creating SQL Agents that enhance In the LangChain codebase, we have two types of agents you mentioned: the Pandas Dataframe agent and the CSV agent. ::: This notebook shows how to use the utility to access an SQLite database. Top comments (17) Subscribe. A big use case for LangChain is creating agents. When attempting to use the SQL Database agent with LLMs that aren't from OpenAI, it produces this loop caused by back slash escape sequences. Setting Up the SQL Database Agent. Restack. chat import ChatPromptTemplate final_prompt = ChatPromptTemplate. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. prompts. sql_agent. 3. you just need to create a python file and import streamlit. When there are many tables, columns, and/or high-cardinality columns, it becomes impossible for us to dump the full information about our database in every prompt. toolkit import SQLDatabaseToolkit from langchain. tools. Let’s explore the basics of how it works. The first step in a SQL chain or agent is to take the user input and convert it to a SQL query. To set it up, follow these instructions, placing the . Photo by Emily Morter on Unsplash. sql_database import SQLDatabase from langchain import OpenAI from langchain. Answer the question: Model responds to user input using the query results. LangChain comes with a built-in Connecting to a SQLite database; Configuring the LangChain SQL Toolkit; Creating a custom prompt template with few-shot examples; Building and running the SQL agent; By the end of this tutorial, you’ll have a functional SQL agent that can answer questions about your data using natural language. Environment I'm trying to do sql retrieval using a langchain sql agent, pretty much as done in the following snippet: from sqlalchemy import create_engine from langchain_huggingface import HuggingFaceEndpoint from langchain_community. Follow instructions here to download Ollama. Large-language models (LLMs), such as ChatGPT and Llama, You signed in with another tab or window. agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit (db = db, llm = llm) 文章浏览阅读1. agents import AgentExecutor from langchain. (temperature=0, model_name="gpt-3. Use Azure OpenAI Service’s function calling feature to use pre-built functions for sending queries to databases, improving the efficiency and security of your SQL agent. \nGiven an input question, create a syntactically correct Spark SQL query to run, def create_sql_query_chain (llm: BaseLanguageModel, db: SQLDatabase, prompt: Optional [BasePromptTemplate] = None, k: int = 5,)-> Runnable [Union [SQLInput, SQLInputWithTables, Dict [str, Any]], str]: """Create a chain that generates SQL queries. 28. Explore the capabilities of Langchain's SQL agent with few-shot learning techniques for enhanced data interaction. The recent framework LangChain reduces the technical barrier of interacting with data due to its advanced language processing capabilities, it thus potentially presents new opportunities for businesses. ### Runing the app Run the SQL database chain In order to write valid queries against a database, we need to feed the model the table names, table schemas, and feature values for it to query over. In this blog, I’ve detailed how the SQL Agent utilizes tools like sql_db_list_tables to interact with the database. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) This notebook showcases an agent designed to interact with a sql databases. agents import create_sql_agent from langchain. db in the same directory as this notebook: Save this file as Chinook_Sqlite. agents import AgentExecutor, AgentType from langchain_community. g. toolkit import SQLDatabaseToolkit from langchain_community. chat_models import ChatOpenAI Create a BaseTool from a Runnable. This Unlock the power of LLMs like ChatGPT and Ollama to effortlessly query and analyze your SQL database using natural language. Make sure that your database connection permissions are always scoped as narrowly as possible for your chain/agent’s needs. cloud import bigquery from sqlalchemy import create_engine import os from langchain. sql-ollama. Q4. Use langchain sql agent to talk to your database. Classes. For talking to SQL databases, it uses the SQLAlchemy Core API . The main difference SQL Databases: The backbone holding the data you'll be querying. With this, you can develop an SQL agent for each database since currently each agent only supports one database connection. agent_toolkits import SparkSQLToolkit, create_spark_sql_agent from langchain_community. Key init args: db: SQLDatabase. I am using SQL agent from langchain, for some context I have a large Key init args: db: SQLDatabase. prompts import BasePromptTemplate, PromptTemplate The below example will use a SQLite connection with Chinook database. vw_YYYYYYYYY where XXXXXXXXXX is the schema and vw_YYYYYYYYY . run()` method. Learn to set up and use LangChain for complex queries, making data-driven decisions easier and accessible to all, even without technical expertise. _InfoSQLDatabaseToolInput'> ¶. prompts import FewShotPromptTemplate from langchain. the graph extracts memories from chat interactions and persists them to a database. ; Single step: Evaluate any agent step in isolation (e. The main difference between the two is that our agent can query the database in a loop as many time as it needs to answer the question. It’s adept at interpreting table In the following example the database instance is created within the SQLDatabase. sql; Run sqlite3 Chinook. However, there is no SQL Agent in the current The SQL agent can be also integrated in a more general architecture, with Retrieval Augmented Generation (RAG), enabling users to combine analysis on structured and unstructured data. By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) like GPT, we have created an application that enable users to query databases using NLP backup database stored-procedures t-sql sql-agent database-maintenance database-mail. Session Management How-to guides. In this article, we will explore how to use LangChain and OpenAI to interact with an SQL database. """Cohere SQL agent. By themselves, language models can't take actions - they just output text. create_sql_agent (llm: BaseLanguageModel, toolkit: SQLDatabaseToolkit Construct a SQL agent from an LLM and toolkit or database. , whether it selects the appropriate tool). from langchain_community. LangChain comes with a built-in from langchain. The agent takes a dbengine object which is tied to one connection alone. Not with the current implementation. The wrapper provides a simple interface to execute SQL queries and fetch results. There are many ways to connect to a SQL database from LangChain. Parameters. ignore_tables (Optional[List[str]]) – . tavily_search import TavilySearchResults Source code for langchain_community. Essentially, langchain makes it easier to build chatbots from langchain. In this post, basic LangChain components (toolkits, chains, agents) will be used to create a natural language to SQL prompt that will allow interactions with an Azure SQL Database; just ask the database what you want as if speaking This notebook shows how to use agents to interact with Spark SQL. One of the chain-of-thought reasoning steps is to predict which table the data will come from, and then get the Explore the Langchain SQL agent in JavaScript, enabling seamless database interactions and efficient data handling. Newer LangChain version out! You are currently viewing the old v0. Download your LLM of interest: sql_agent. agent_toolkits import SQLDatabaseToolkit from langchain. chat_models import ChatOpenAI from langchain_community. get_table_info ([table_names]) (command[, fetch, include_columns, ]) Execute a SQL command and return a string representing the results. LangChain is a revolutionary technology that serves as a bridge between natural language processing (NLP), ChatGPT and databases. Agent-based approaches coupled with large language models (LLMs) are quickly transforming how we interact with databases and data warehouses. We will cover implementations using both chains and agents. include_tables (Optional from langchain. Please let me know if it is possible or any other way. Contact. It utilizes the LangChain library and various language models, such as ChatGroq and ChatOpenAI, to generate SQL queries and provide responses. ValidationError] if the input data cannot be validated to form a valid model. chat_models import ChatAnthropic from langchain_experimental. langchain. Can I use this approach with databases other than MySQL? A. Initialize the tool. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, LangChain offers a number of tools and functions that allow you to create SQL Agents which can provide a more flexible way of interacting with SQL databases. The documents should contain data with a bunch of specifications, alongside more fluid, natural language descriptions. Alternatively (e. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. ; sql_db_checker: Checks the query before AI & NLP database integration. and displaying the responses generated by the LangChain SQL agent. This agent uses a toolkit: langchain. read Chinook_Sqlite. SQL Database Agent This example demonstrates the use of the SQL Database Agent for answering questions over a CnosDB. Database Toolkit. Legal. langchain sql agentallows you to use an agent to explore your database, the agent is powered by an llm model, it could be openai or some open source models like llama2. Skip to main content. param db: SQLDatabase [Required] # param llm: BaseLanguageModel [Required] # get_context → dict [source] # from composio_langchain import App, ComposioToolSet from langchain import hub from langchain. Return db context that you may want in agent prompt. param db: SQLDatabase [Required] # param llm: BaseLanguageModel [Required] # get_context → dict [source] # In simple terms, langchain is a framework and library of useful templates and tools that make it easier to build large language model applications that use custom data and external tools. Execute graph database query: Execute the graph database query. I have been doing this and is working okay. You switched accounts on another tab or window. execute_query("SELECT * FROM my_table") # process the result for row in create_sql_agent# langchain_cohere. utilities. Setup First, get required packages and set environment variables. Combined, these technologies enable natural language queries to data in your application or business, eliminating the need for SQL expertise to interact with data and even facilitating seamless queries across LangChain SQL Database Agent. agents. The main advantages of using the SQL Agent are: You are an agent designed to interact with a SQL database. spark_sql. engine (Engine) – . Updated Nov 29, 2024; TSQL; brokenthorn / mssql-database from langchain. Below is a code snippet that demonstrates how to create an SQL agent using the Langchain library: Source code for langchain_cohere. In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. Final Response: Evaluate the agent's final response. Must provide exactly one of ‘toolkit’ or SQL Database Agent This example demonstrates the use of the SQL Database Agent for answering questions over a CnosDB. Instead, we must find ways to dynamically insert into the prompt Action: sql_db_query Action Input: SELECT * FROM table2 Observation: 2 rows in table2 Thought: I should query the database to see if there are any rows in the tables. tool import (InfoSQLDatabaseTool, One of the most common types of databases that we can build Q&A systems for are SQL databases. agent_types import AgentType from langchain. Learn to use LangChain's SQL Database Chain and Agent with large language models to perform natural language queries (NLQ) of Amazon RDS for We will use a handy SQL database wrapper available in the langchain_community package to interact with the database. This toolkit provides the necessary tools to interact with your SQL database effectively. The SQLDatabase class You signed in with another tab or window. include_tables (Optional LangChain’s SQL Database Agent. sql; Test SELECT * Source code for the upcoming blog post, Generative AI for Analytics: Performing Natural Language Queries on Amazon RDS using SageMaker, LangChain, and LLMs. Session Management sql-llama2. agent_toolkits import SQLDatabaseToolkit from langchain_community. SQL_PREFIX = """You are an agent designed to interact with a SQL database. llm (BaseLanguageModel) – Language model to use for the agent. import os from langchain. By leveraging the langchain sql agent toolkit, developers can create agents that not only answer queries but also understand the underlying database schema. utilities import SQLDatabase from langchain. metadata (Optional[MetaData]) – . agent_toolkits. NOTE: it is not guaranteed that the agent won’t perform DML statements on your database given certain questions. In today’s blog post, we’re diving into an exciting project: creating a Streamlit app that allows us to extract insights from a SQL database using natural language. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. from langchain. tool import (InfoSQLDatabaseTool, You are an agent designed to interact with a SQL database. com Including examples of natural language questions being converted class langchain_community. Here’s an example of how to use the Databricks SQL Implement LangChain agents to connect to a provided SQL database, and how to build a DB agent that translates natural language to SQL code. run_no_throw (command[, fetch, ]) Execute a SQL command and return a string representing the This notebook shows how to use agents to interact with Spark SQL. LangChain SQL: Advanced Query Capabilities - September 2024. They enable use cases such as: LangChain is an open-source framework for creating applications that use and are powered by language models (LLM/MLM/SML). The language model (for use with QuerySQLCheckerTool) Instantiate: A User can have multiple Orders (one-to-many) A Product can be in multiple Orders (one-to-many) An Order belongs to one User and one Product (many-to-one for both, not unique) In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. sql_database import SQLDatabase: from langchain. 16; agent_toolkits # Toolkits are sets of tools that can be used to interact with various services and APIs. langchain_cohere. create_sql_agent (llm: BaseLanguageModel, Construct a SQL agent from an LLM and toolkit or database. LangChain’s SQL Database Agent. agent_types import AgentType from Construct a SQL agent from an LLM and toolkit or database. 5-turbo") # Creating a SQL Database Chain from langchain_community. There are inherent risks in doing this. The issue I run into is that there are about 500 players. Be sure that the tables actually exist by from langchain. agents import create_sql_agent from langchain_community. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, In our last blog post we discussed the topic of connecting a PostGres database to Large Language Model (LLM) and provided an example of how to use LangChain SQLChain Explore the capabilities of LangChain SQL Agent for seamless database interactions and advanced data analysis. So, embrace the future of database management with LangChain and experience the ease and efficiency of conversing with your data. Agent Setup. If agent_type is “tool-calling” then llm is expected to In this article, I will show you how we can use LangChain Agent and Azure OpenAI gpt-35-turbo model to query your SQL database using natural language (without writing any SQL at all!) and get useful data insights. One of the chain-of-thought reasoning steps is to predict which table the data will come from, and then get the Use the combination of the prefix variable and the tool function description. Here are some relevant links: Python SQL Chains; Python SQL Agents; Javascript SQL Chains; Javascript SQL Agents; Introduction. """ table_names = list (self. Create template Templates let you quickly answer FAQs or store snippets for re-use. Below is a step-by-step guide to set up your SQL Database Agent. agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit(db=db, llm=llm) agent = create_sql_agent(llm=llm, In order to write valid queries against a database, we need to feed the model the table names, table schemas, and feature values for it to query over. This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. agent_toolkits To create an Azure SQL database using the Azure Portal, you can follow these steps: import os from langchain. Be The model interprets user queries and formulates SQL queries to interact with the database. It’s adept at interpreting table structures and crafting SQL Build an Agent. The main advantages of using Natural language querying allows users to interact with databases more intuitively and efficiently. utilities import SQLDatabase from langchain_openai import ChatOpenAI # Set OpenAI API key securely using @classmethod def from_databricks (cls, catalog: str, schema: str, host: Optional [str] = None, api_token: Optional [str] = None, warehouse_id: Optional [str] = None from langchain import SQLDatabase from constants import anthropic_key from langchain. Conveniently for us, they provide an SQL Database Agent, which is optimized for creating database access chats. After executing actions, the results can be fed back into the LLM to determine whether more actions LangChain Python API Reference; langchain-community: 0. llms. It uses Zephyr-7b via Ollama to run inference locally on a Mac laptop. def get_context (self)-> Dict [str, Any]: """Return db context that you may want in agent prompt. Pydantic Afterwards, we can set up the llm chain that will generate SQL queries, for this , we will use LangChain’s in-built create_sql_query_chain which will generate sql queries based on the user’s I am trying to use the Langchain sql agent to connect to a table in databricks unity catalog. utilities. ?” types of questions. Personal Trusted User. Reload to refresh your session. 4. Explore LangChain SQL's powerful features for complex data analysis and manipulation in language models. sql. , of This section provides a detailed overview of how to utilize the SQL Database Agent effectively. Additionally, it integrates with Langsmith for tracing and feedback collection. Pydantic LangChain SQL Agent¶. With the basic NL2SQL model set up, you've taken the first step towards transforming how we interact with databases. 5k次，点赞22次，收藏22次。可以自定义所使用的prompt提示模板，这是使用官方的一个prompt示例# 导入langchain的实用工具和相关的模块# 连接到demo数据库# 创建LLM# 创建一个生成 SQL 查询的链# 运行查询问题response = db_chain. Setup This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. What is an Agent in LangChain? Some applications will require not just a predetermined chain of This notebook showcases an agent designed to interact with a sql databases. These systems will allow us to We will use a handy SQL database wrapper available in the langchain_community package to interact with the database. Now, my concept is to designate RAG as another tool. However, this is just How-to guides. LangChain’s SQL Agent provides a dynamic way of interacting with SQL Databases. Follow these installation steps to create Chinook. According to LangChain documentation, the SQL Database Agent “builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. The language model (for use with QuerySQLCheckerTool) Instantiate: To create an Azure SQL database using the Azure Portal, you can follow these steps: import os from langchain. Tools are functions that perform specific duties, such as Google Search, database lookups, or This example shows how to load and use an agent with a SQL toolkit. toolkit import SQLDatabaseToolkit This works extremely well with synthetic data. To begin using the SQL Database Agent, you need to set up the necessary components. code-block:: python from Photo by Emily Morter on Unsplash. Most of an enterprise’s data is traditionally stored in SQL databases. , Neo4j, MemGraph, Amazon Neptune, Kùzu, OntoText, Tigergraph). 1 docs. messages import AIMessage, class SQLDatabaseToolkit (BaseToolkit): """SQLDatabaseToolkit for interacting with SQL databases. Something like: from langchain. ainetwork. db; Run . agents import create_sql_agent from langchain. sql_database import SQLDatabase db = SQLDatabase. In this example, we first create an SQL database with a ‘countries’ table, and subsequently, we will use LangChain Agent to make queries against it. Tools within the SQLDatabaseToolkit are designed to interact with a SQL database. The model interprets user queries and formulates SQL queries to interact with the database. You'll need to first install typeorm: See this section for general instructions on installing integration packages. tool. Parameters: llm (BaseLanguageModel) – Language model to use for the agent. use tool. How LangChain works. You signed out in another tab or window. 2. However, this toolkit is in active development and does come with some issues. agent_types import AgentType from create_sql_agent# langchain_cohere. Here is how my code looks like, it is working pretty well. We will also use the langchain_openai package to interact with the OpenAI API for language models later in the tutorial. It allows users to interact with their databases using natural language, making it easier to retrieve, manipulate, and manage data without the need for intricate SQL queries. agents Source code for langchain_cohere. agents import AgentType, create_sql_agent from langchain. The LangChain SQL Agent represents a pivotal component in the Natural language querying allows users to interact with databases more intuitively and efficiently. Introduction to LangChain. Agent 2: Email Composition Agent This agent takes the processed data from the SQL Query Agent and composes a coherent and informative email. The wrapper provides a simple interface to execute SQL queries Construct a SQL agent from an LLM and toolkit or database. execute_query("SELECT * FROM my_table") # process the result for row in I want to connect LLM to more than 2 SQL database with SQLDatabaseChain. For direct access, users can access the LangChain SQL database toolkit, which provides functionality to ingest the database schema, query the database, and recover from any potential errors. chat_models import ChatOpenAI I have used Langchain - create_sql_agent to generate SQL queries with a database and get the output result of the generated SQL query. SqlDatabase from langchain/sql_db; createSqlAgent from SQL Database Agent The SQL Database Agent is designed to interact with SQL databases, allowing users to ask questions in natural language and receive answers. Source code for langchain_community. Yes, LangChain’s SQL toolkit is designed to work with various relational databases. agent_toolkits import SQLDatabaseToolkit toolkit = SQLDatabaseToolkit (db = db, llm = llm) LangChain 101: Build Your Own GPT-Powered Applications Tools, Agents, and Toolkits in LangChain. 0 from langchain. LangChain has a pre-built SQL Database Agent which is a good start. from_messages( [ ("system", """ You are a helpful AI assistant expert in querying SQL Database to find answers to user's question about database view: XXXXXXXXXX. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. openai import OpenAI from langchain. For comprehensive descriptions of every class and function see the API Reference. LangChain offers SQL Chains and Agents to build and run SQL queries based on natural language prompts. The language model (for use with QuerySQLCheckerTool) Instantiate: from langchain. . For more information on how to build this database, see here. It runs the SQL database chain using `db_chain. The function create_sql_agent you've used in your code is The SQL Database Agent is a component within LangChain that acts as a bridge between users and SQL databases. create_sql_agent# langchain_cohere. Cypher). agents import create_sql_agent class langchain_community. Instead, we must find ways to dynamically insert into the prompt To implement a SQL Database Agent using LangChain, you will leverage the create_sql_agent function along with the SQLDatabaseToolkit. chains. Given an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. from langchain_aws import ChatBedrock from langchain. """ from __future__ import annotations from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union, cast,) from langchain_core. It includes four tools. The documents should contain data with a bunch of specifications, alongside more fluid, LangChain’s SQL Agent provides a dynamic way of interacting with SQL Databases. agent = create_sql_agent( llm=llm, db=db, verbose=True, agent_type= "openai-tools", ) response = agent. base. query import create_sql_query_chain from langchain. spark_sql import SparkSQL from langchain_openai import create_sql_agent# langchain_cohere. """SQL agent. Here you’ll find answers to “How do I. Suggestion to improve chat with SQL database using Langchain. They enable use cases such as: This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. One of the common types of databases that we can build Q&A systems for are graph databases. invoke(query) Moving Forward. Set up a streamlit app. The agent successfully utilized the Dataherald text-to-SQL tool to generate the SQL query and then proceeded to generate a plot based on the results obtained from executing the SQL query. These agents, particularly within the LangChain framework, leverage SQL databases to answer complex queries efficiently, demonstrating their capabilities through the SQL Database Agent example. from_databricks(catalog="samples", schema="nyctaxi") This code initializes a connection to the specified catalog and schema within Databricks, enabling you to execute SQL queries directly through LangChain. chains. sql_database # If anybody using Sybase SQL anywhere database then it should not # go to else condition. create_sql_agent (llm[, ]). By leveraging the power of LangChain, SQL Agents, and OpenAI's Large This example shows how to load and use an agent with a SQL toolkit. This template enables a user to interact with a SQL database using natural language. This is useful for usecases, such as retrieving stock data or interacting with a cloud platform. The agent can translate natural language queries coming from users into SQL, and execute them against Yes, it is indeed possible to create an SQL agent in the latest version of LangChain to query tables on Google BigQuery. For end-to-end walkthroughs see Tutorials. Like you said Agents are very slow and when you use gpt-4 its even slower. Instead, we will leverage LangChain’s SQL Agent to generate complex database queries from human text. agent_types import AgentType from langchain. These are compatible with any SQL dialect supported by SQLAlchemy (e. * To set it This tutorial demonstrated how to create a SQL agent using Cohere and LangChain. We asked Agent 2 to email me (Samir Saci, Control Tower Manager) to LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. prompt import SQL_PROMPTS list (SQL_PROMPTS) ['crate', 3 Peacock Jane Sales Support Agent 2 1973-08-29 00:00:00 2002-04-01 00:00:00 1111 6 Ave SW Calgary AB Canada T2P 5M5 +1 (403) 262-3443 +1 (403) 262-6712 jane@chinookcorp. agents import create_sql_agent One thing you can do is create an Agent Executor and provide other agents as tools to this executor. It enables users to ask questions in natural language, Explore the Langchain SQL Chain Agent for efficient data handling and integration in your applications. besides sql agent , you can also use sql chain which in my Source code for the upcoming blog post, Generative AI for Analytics: Performing Natural Language Queries on Amazon RDS using SageMaker, LangChain, and LLMs. AI & NLP database integration. db file in the directory where your code lives. You signed in with another tab or window. execute_query("SELECT * FROM my_table") # process the result for row in Create a BaseTool from a Runnable. tool import ( InfoSQLDatabaseTool, ListSQLDatabaseTool, QueryCheckerTool, QuerySQLDataBaseTool, ) # assign your llm and db info_sql_database_tool_description = """Input to this tool is a comma separated list of tables, output is the schema and sample rows for those tables. For today, we’ll use a SQLite database. While this guide focuses on MySQL, you can adapt the code to connect to Since LangChain uses SQLAlchemy to connect to SQL databases, we can use any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, Databricks, or SQLite. sql_database. Combined, these technologies enable natural language queries Instead, we will leverage LangChain’s SQL Agent to generate complex database queries from human text. sql_database import SQLDatabase from langchain. , if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. Interested in your specific use case. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). These guides are goal-oriented and concrete; they're meant to help you complete a specific task. agent_toolkits import create_sql_agent from langchain This project is an AI-powered SQL query agent that can answer natural language questions by querying a SQLite database. The template includes an example database of 2023 NBA rosters. I've went ahead and created an NBA database with the game logs of every player for the season. We’ll walk through a Python script that leverages these technologies to convert natural language queries into SQL commands, execute those queries, and then return the results in a human-readable format. chat_models import ChatOpenAI from langchain. get_table_info The LangChain Agent uses a MRKL-based approach to query the database schema and example rows, and uses these to generate SQL queries, which it then executes to pull back the results you’re asking for. invoke({"input": "How many resources are there in XYZ Hi, I'm trying to have a chat model retrieve SQL data and use it for question answering. create_sql_query_chain (llm: BaseLanguageModel, db: Security Note: This chain generates SQL queries for the given database. com Including examples of natural language questions being converted Interacting with APIs LangChain’s chain and agent features enable users to include LLMs in a longer workflow with other API calls. LangChain comes with a number of built-in chains and agents that are compatible with graph query language dialects like Cypher, SparQL, and others (e. agent. from langchain_sql_database_agent. Raises [ValidationError][pydantic_core. py and lib\python\site-package\langchain_community\tools\sql_database\tool. *Security Note*: This chain generates SQL queries for the given database. sql_database import SQLDatabase from langchain. messages import AIMessage, SystemMessage from langchain_core. "Memory" in this tutorial will be represented in two ways: from langchain_community. In this guide we'll go over the basic ways to create a Q&A system over tabular data in databases. ; Trajectory: Evaluate whether the agent took the expected path (e. The discrepancy you're experiencing with LangChain and DuckDB, particularly with the get_usable_table_names() method returning an empty list, might be attributed to how LangChain's SQLDatabase class and DuckDB handle in-memory databases and schemas. agents. callout-note} The SQLDatabase adapter utility is a wrapper around a database connection. agent_toolkits. agent_toolkits import SQLDatabaseToolkit: from langchain. llms. toolkit. """ from __future__ import annotations from typing import (TYPE_CHECKING, Any, Dict, Optional, Sequence, Union,) from langchain_community. agent_types import AgentType # Set up the OpenAI LLM: llm = ChatOpenAI(openai_api_key=OPENAI_API_KEY, model_name = "gpt-3. Evaluate an agent. Bases: BaseSQLDatabaseTool, BaseTool Tool for getting metadata about a SQL database. Explore the capabilities of LangChain SQL Agent for seamless database interactions and advanced data analysis. It uses the example Chinook Database, and demonstrates those features: Query using SQL Build an Agent. schema (Optional[str]) – . Note that, as this agent is in active development, all answers might not be correct. sql_database import SQLDatabase from langchain_openai import ChatOpenAI import streamlit as st from langchain_core. agent_toolkits import sql_agent. Setup: Install ``langchain-community`` code-block:: bash pip install -U langchain-community Key init args: db: SQLDatabase The SQL database. sql_db_list_tables : Returns table names inside the database. invoke("总共有多少用 from langchain. llms import AzureOpenAI import openai from langchain. Building Q&A systems of SQL databases requires executing model-generated SQL queries. [ ] I am trying to access all data from pre-defined VIEWS in my MS SQL database using Langchain - SQLDatabaseToolkit and Langchain - create_sql_agent. (Langchain version 0. sql_agent_executor import SQLAgentExecutor # create an instance of SQLAgentExecutor using the SQLDatabase instance created earlier agent_executor = SQLAgentExecutor(db) # execute a SQL query result = agent_executor. LangChain comes with a built-in LLMs make it possible to interact with SQL databases using natural langugae. py copy in In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. agents import AgentExecutor from langchain. Environment Setup Before using this template, you need to set up Ollama and SQL database. Large-language models (LLMs), such as ChatGPT and Llama, Since LangChain uses SQLAlchemy to connect to SQL databases, we can use any SQL dialect supported by SQLAlchemy, such as MS SQL, MySQL, MariaDB, PostgreSQL, Oracle SQL, Databricks, or SQLite. class langchain_community. Given an input question, create a syntactically correct SQLite query to run, then look at the results of the query and return from langchain_sql_database_agent. llm: BaseLanguageModel The language model (for use with QuerySQLCheckerTool) Instantiate:. openai import OpenAI Use the combination of the prefix variable and the tool function description. agents import AgentType, tool, create_sql_agent @tool def from langchain. This notebook showcases an agent designed to interact with a sql databases. copy lib\python\site-package\langchain_community\agen_toolkits\sql\toolkit. In this tutorial, we will walk through 3 evaluation strategies LLM agents, building on the conceptual points shared in our evaluation guide. agents import AgentType, tool, create_sql_agent @tool def The LangChain library has multiple SQL chains and even an SQL agent aimed at making interacting with data stored in SQL as easy as possible. LangChain Agent: Created with create_sql_agent, which handles the logic of converting natural language into SQL queries. agent_types import class SQLDatabaseToolkit (BaseToolkit): """SQLDatabaseToolkit for interacting with SQL databases. Here’s an example of how to use the Databricks SQL SQL Agents in data management play a crucial role in automating tasks, optimizing queries, and ensuring data integrity across databases. For conceptual explanations see the Conceptual guide. LangChain's SQLDatabase class uses SQLAlchemy to initialize a database connection and to RAG as a tool. Action: sql_db_query Action Input: SELECT * FROM table3 Observation: 1 row in table3 Thought: I should query the database to see if there are any rows in the tables. agents import create_sql_agent from langchain_community. Toolkit: Uses SQLDatabaseToolkit for database interactions. A common application is to enable agents to answer questions using data in a relational database, In this tutorial, we will walk through step-by-step, the creation of a LangChain enabled, large language model (LLM) driven, agent that can use a SQL database to answer questions. This will mitigate though not eliminate the risks of building a model-driven system. sql_database import SQLDatabase class SQLDatabaseToolkit(BaseToolkit): """SQLDatabaseToolkit for interacting with SQL databases. If agent_type is “tool-calling” then llm is expected to support tool calling. The SQLDatabase class provides a get_table_info method that can be used to get column information as well as sample data from the table. llm: BaseLanguageModel. Solution: LangChain provides an easy-to-use open-source framework with multiple SQL chains, SQL agents, and tools optimized for querying SQL databases, which accelerates the development process. Setting Up the SQL Database Toolkit Parameters. Construct a SQL agent from an LLM and toolkit or database. agents import AgentExecutor, create_openai_functions_agent from langchain_openai import ChatOpenAI SQL Database Agent#. It’s adept at interpreting table This toolkit is useful for asking questions, performing queries, validating queries and more on a SQL database. Similar to SQL Database Agent, it is designed to address general inquiries about Spark SQL and from langchain_community. The idea is that in some fixed setting, the user will ask questions relevant to some business area, and the model will subsequently query from langchain_sql_database_agent. After executing actions, the results can be fed back into the LLM to determine whether more actions class langchain_community. AINetworkToolkit. 5-turbo") # Set up the Instead, we will leverage LangChain’s SQL Agent to generate complex database queries from human text. create_spark_sql_agent (llm: BaseLanguageModel, toolkit: SparkSQLToolkit, callback_manager: BaseCallbackManager | None = None, callbacks: Callbacks = None, prefix: str = 'You are an agent designed to interact with Spark SQL. Here’s a basic example of how to create a SQL Database Agent: from langchain. agent_toolkits import SQLDatabaseToolkit from langchain. py in workspace. agent_toolkits import create_sql_agent from langchain_community. SQLDatabaseToolkit [source] Key init args: db: SQLDatabase. /** This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. Learn to use LangChain's SQL Database Chain and Agent with large How-to guides. Create a new model by parsing and validating input data from keyword arguments. I'm trying to put together a proof of concept when it comes to looking over a very large database with hundreds of tables. get_input_schema. sql_database. Action: sql Database Queries with LangChain’s SQL Toolkit. The agent can store, retrieve, and use memories to enhance its interactions with users. prompt import PROMPT_SUFFIX, _mysql_prompt #print(PROMPT_SUFFIX) few_shot_prompt = FewShotPromptTemplate from langchain_community. agents import create_sql_agent: from langchain. as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. So when using a SQL agent or SQL chain, I run into an issue. , of The SQL Database Agent is a powerful tool within the LangChain framework that enables users to interact with CnosDB effectively. param args_schema: Type [BaseModel] = <class 'langchain_community. agents import create_sql_agent from SQL Database::: {. self is explicitly positional-only to allow self as a field name. qxodwwiqgyyabudmfycydtqdibzjmawcinuzjrbubqmiagnd