Langchain sql agent example

Langchain sql agent example. Memory is needed to enable conversation. com. from langchain_community. Specify 'toolkit' if you want to use a different model for the agent and the toolkit. env file in the root of the folder. py file: from sql_llama2 import chain as sql_llama2_chain. agent_type: One of "tool-calling Dialect-specific prompting. agent_toolkits. create table [dbo]. If agent_type is “tool-calling” then llm is expected to support tool calling. To use agents, we require three things: A base LLM, In this section we will review several agents and see how they 'think' and what they can do. This ensures that the guidance provided to the model is tailored to the specific requirements of the query pip install -U langchain-cli. history import RunnableWithMessageHistory from langchain. run("Do you have a record with email is myFirstName. Intended Model Type. Here's how to implement it: pip install langchain openai pymysql --upgrade -q. llms import HuggingFaceEndpoint. Sep 28, 2023 · L angchain is an open source framework for developing applications which can process natural language using LLMs (Large Language Models). chains import create_sql_query_chain. SQL-Free chatbot: Langchain SQL Agent Bridges Any DB with LLMs, Crafting Queries in Plain English! Sep 14, 2023 · Text-to-SQL Query and Execution. Using one of langchain's pre-built agents involves three variables: defining the tools or the toolkit. In this guide we focus on adding logic for incorporating historical messages. Import the necessary libraries: from langchain. Given an input question, create a syntactically correct Spark SQL Mar 28, 2024 · For custom connection, you need to follow the steps: Import library from promptflow. py: Simple app using StreamlitChatMessageHistory for LLM conversation memory (View the app) mrkl_demo. openAI. runnables. ["graphql"], Aug 26, 2023 · from langchain. The Agent component of LangChain is a wrapper around LLM, which decides the best steps or actions to take to solve a problem. If you don't have a database, you can 4 days ago · langchain_community. env and add the openai key as follows. 2 days ago · Args: llm: Language model to use for the agent. chat_message_histories import ChatMessageHistory from langchain_core. llm = OpenAI(temperature=0, verbose=True) agent is defined as follows: `toolkit = SQLDatabaseToolkit(db=db, llm=llm) toolkit. Query checker. invoke ({ "input": prompt }, { "callbacks": [ handler ]})[ 'output'] This should allow the on_agent_action method in your SQLHandler class to capture the SQL query when the sql_db_query tool For example, you can invoke a prompt template with prompt variables and retrieve the generated prompt as a string or a list of messages. The agent gets hit to its maximum iterations. The output of this function is an AgentExecutor object, which is used to execute the agent's actions. import pyodbc. agents import AgentExecutor. Given an input question, first create a syntactically correct postgresql query to run, then look at the results of the query and return the answer. As a part of the launch, we highlighted two simple runtimes: one that is the equivalent of the Sep 22, 2023 · Use the combination of the prefix variable and the tool function description. If agent_type is "tool-calling" then llm is expected to support tool calling. The list of messages per example corresponds to: 1) HumanMessage: contains the content from which content should be extracted. It can search for information and even query a SQL database. This example shows how to load and use an agent with a SQL toolkit. Whether this agent is intended for Chat Models (takes in messages, outputs message) or LLMs (takes in string, outputs string). pip install langchain openai python-dotenv requests duckduckgo-search. py: Simple streaming app with langchain. After the code has finished executing, here is the final output. Something like: from langchain. You can find more details in the LangChain repository. QUERY = """. In the end, my LLM agent can do a much better job with several customizes. custom_suffix = """ I should first get the similar examples I know. The Agent typically has access to a set of functions called Tools (or Toolkit) and May 23, 2023 · When creating a create_sql_agent() how do you get the prompt, thought, and observation? I know how to get the final answer which is just the response of the agent_executor. Note the use of gpt-3. Let’s take a look at a straightforward example of this. May 9, 2023 · Open platform. This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc. openai import OpenAI from langchain. Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. Databricks SQL Agent. spark_sql. Agents. agents import AgentType, tool, create_sql_agent @tool def my_first_awesome_tool(human_message: str) -> list: """ Searches for my LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. To create a new LangChain project and install this as the only package, you can do: langchain app new my-app --package gemini-functions-agent. . LangSmith will help us trace, monitor and debug Apr 21, 2023 · This notebook showcases an agent designed to interact with a sql databases. run but I would like to get the various observations and graph the results. Mar 11, 2024 · Integrating with LangChain: Integrate the example selector with your LangChain workflow. The input_variables parameter is set to ["Product"], meaning the template expects a product name as input. When a new query is received, the selector determines the most relevant few-shot examples before the model generates the SQL query. The code to create the ChatModel and give it tools is really simple, you can check it all in the Langchain doc. from langchain_experimental. chat_models. toolkit. Mar 13, 2023 · The main issue that exists is hallucination. Additionally, you can include the `teradata_search_tool Custom Agents. Agents and Tools. env and replace with your keys. llms import OpenAI. Perhaps the simplest strategy is to ask the model itself to check the original query for common mistakes. . agent_toolkits 3mCREATE TABLE langchain_example. Jun 21, 2023 · danielvi1787 commented on May 30, 2023. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). SQLDatabaseToolkit [source] ¶ Bases: BaseToolkit. And add the following code to your server. This is all really easy to do in langchain, as we will see in the following example. toolkit: SQLDatabaseToolkit for the agent to use. defining the llm. chat_models import ChatOpenAI from langchain. It worked for me. If you can not find it reply with NONE") edited Oct 26, 2023 at 11:27. May 25, 2023 · In this video we discover how to set up the LangChain SQL Database Agent with PostgreSQL and OpenAI using LangChain. env. py file: from sql_ollama import chain as sql_ollama_chain add_routes ( app If you want to add this to an existing project, you can just run: langchain app add sql-llama2. py file: Sep 28, 2023 · Example of the prompt generated by LangChain. To do so, we can use the pyodbc library in Python, which you can easily install via pip install pyodc. agents import AgentExecutor from langchain. But first, what are LLM Agent and Langchain? The ChatGPT and other LLMs are really powerful and we all know that. prompts. from langchain_openai import OpenAI. By default, most of the agents return a single string. example into . Here are the 4 key steps that take place: Load a vector database with encoded documents. Learn to use LangChain's SQL Database Chain and Agent with large language models to perform natural language queries (NLQ) of Amazon RDS for PostgreSQL. By translating these natural language queries into SQL queries and executing them against the database, the SQL Database Agent May 1, 2023 · I am using SQL agent from langchain, for some context I have a large postgres data source. Click on create new secret key button to create a new openai key. If you have a match reply with the ID. copy the environmental variables from . May 2, 2023 · Using create_sql_agent(), specify agent_executor_kwargs like so: agent_executor_kwargs={"return_intermediate_steps": True}. Mar 13, 2024 · Finally, the agent is executed with an AgentExecutor that invokes the agent for running the tools depending on the input. "allowing you to automate essential data Mar 9, 2012 · It looks like you encountered a "ModuleNotFoundError" for 'MySQLdb' and 'ConfigParser' while trying to run the SQL Database Agent example in Langchain. Now, let’s create a BaseGraphQLTool instance with the specified Star Wars API endpoint and initialize an Agent with the tool. {SqlDatabase } from "langchain/sql_db"; import {createSqlAgent, SqlToolkit basic_streaming. Cap the max number of iterations. Instead, we will leverage LangChain’s SQL Agent to generate complex database queries from human text. Aug 19, 2023 · The SQL Agent provided by LangChain is a tool that allows you to interact with SQL databases using natural language. ChatOpenAI (View the app) basic_memory. py file: from sql_ollama import chain as sql Databricks SQL Agent. Source code for the upcoming blog post, Generative AI for Analytics: Performing Natural Language Queries on Amazon RDS using SageMaker, LangChain, and LLMs. With the Databricks SQL Agent any Databricks users can interact with a specified schema in Unity Catalog and generate insights on their data. SQL Database Agent #. This step shows an instance in which an agent can actually issue queries, be it for greeting the user or getting certain information. titanic (PassengerId INT, Survived Custom agent. In this example, we will use OpenAI Tool Calling to create this agent. "SQLQuery": "SQL Query to run". env and paste your API key in. Add OpenAI key and data base connection as environment variables. It is designed to be more flexible and more powerful than the standard Sep 24, 2023 · llm = ChatAnthropic(temperature=0) # Create db chain. The core idea of agents is to use a language model to choose a sequence of actions to take. agents import AgentType, initialize_agent, load_tools. We will begin with a “zero-shot” agent (more on this later) that allows our LLM to use a calculator. Create a new model by parsing and validating input data from keyword arguments. When using the built-in create_sql_query_chain and SQLDatabase , this is handled for you for any of the following dialects: from langchain. prompt import PromptTemplate. 3) ToolMessage: contains confirmation to the model that the model requested a tool correctly. The first thing we want to do is import one of our SQL tables into a pandas dataframe. In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of “memory” of past questions and answers, and some logic for incorporating those into its current thinking. So here are my experiences. Click on your name or icon option which is located on the top right corner of the page and select “API Keys” or click on the link — Account API Keys — OpenAI API. This notebook goes through how to create your own custom agent. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. To start, we will set up the retriever we want to use, and then turn it into a retriever tool. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. This notebook walks through how to cap an agent at taking a certain number of steps. If you want to add this to an existing project, you can just run: langchain app add gemini-functions-agent. Quick install. In order to add a memory with an external message store to an agent we are going to do the following steps: We are going to create a RedisChatMessageHistory to connect to an external database to store the messages in. Using agents. In chains, a sequence of actions is hardcoded (in code). So one of the big challenges we face is how to ground the LLM in reality so that it produces valid SQL. from langchain. The Databricks SQL Agent is a variant of the standard SQL Database Agent that LangChain provides and is considered a more powerful variant of the Spark DataFrame Agent. Jun 21, 2023 · Below is the code I found on the internet: from langchain import OpenAI, SQLDatabase. Using agents, an LLM can write and execute Python code. After you clone the repo, follow these instructions: Install packages npm install. Jul 12, 2023 · If you alter the structure of the prompt, the language model might struggle to generate the correct output, and the SQLDatabaseChain might have difficulty parsing the output. This notebook showcases an agent designed to interact with a sql databases. To connect with my Azure SQL DB, I used an ODBC connection. I want 100% match ignoring the case. Create a file named . Additionally, it is not guaranteed that the Apr 14, 2023 · LangChain library installed (you can do so via pip install langchain) Quickstart Demo. py: An agent that replicates the MRKL demo (View the app) minimal_agent. With the Databricks SQL Agent any Azure Databricks users can interact with a specified schema in Unity Catalog and generate insights on their data. database = 'XYZ'. I am currently testing on my laptop that has 8GB of Ram and a decent CPU. Must provide exactly one of 'toolkit' or 'db'. [langtable] (id int Identity, username nvarchar(100)) GO. Feb 22, 2024 · Guys, the key to adapt it its to create the full prompt from scratch, following this format: This is a printscreen from the original source of the langchain "create_sql_agent" method, it creates the prompt if no prompt is provided, but if it is, the prompt must be complete and in proper form, different from the "openai_tools" method that gets your prompt and insert it in the ReAct prompt somehow. Jupyter Notebooks to help you get hands-on with Pinecone vector databases - pinecone-io/examples GPTCache: A Library for Creating Semantic Cache for LLM Queries ; Gorilla: An API store for LLMs ; LlamaHub: a library of data loaders for LLMs made by the community ; EVAL: Elastic Versatile Agent with Langchain. You can use an agent with a different type of model than it is intended for, but it likely won't produce results of the same quality. Regarding multi-agent communication, it can be implemented in the LangChain framework by creating multiple instances of the AgentExecutor class, each with its own agent and set of tools. openai. agents import AgentExecutor, create_react_agent. driver = 'ODBC Driver 17 for SQL Server'. chains import SQLDatabaseSequentialChain. 5-turbo", temperature=0) chain = create_sql_query Jun 17, 2023 · Finally, we feed the Agent information about our data source, including table schemas, sample data, field descriptions, etc. My new query is: agent_executor. We have just integrated a ChatHuggingFace wrapper that lets you create agents based on open-source models in 🦜🔗LangChain. sql_database import SQLDatabase from langchain. We will first create it WITHOUT memory, but we will then show how to add memory in. This works because in this file , it's apparent that AgentExecutor. ''' FORMAT_INSTRUCTIONS = """Please use the following May 27, 2023 · For example, in order for an agent to interact with a SQL database in the best way it may need access to one tool to execute queries and another tool to inspect tables. from sqlalchemy import Column, Integer, String, Table, Date, May 14, 2023 · You would do something like this: from langchain. create_spark_sql_agent(llm: BaseLanguageModel, toolkit: SparkSQLToolkit, callback_manager: Optional[BaseCallbackManager] = None, callbacks: Callbacks = None, prefix: str = 'You are an agent designed to interact with Spark SQL. In the next section, we will explore the different ways you can run prompt templates in LangChain and how you can leverage the power of prompt templates to generate high-quality prompts for your language models. Two RAG use cases which we cover elsewhere are: Q&A over SQL data; Q&A over code (e. chains import LLMChain. SQL Database Agent. _DEFAULT_TEMPLATE = """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. One of the simplest things we can do is make our prompt specific to the SQL dialect we’re using. Upon asking questions that might involve joining tables, ordering and filtering. Use the following format: "Question": "Question here". Agent Executor: this wraps an agent and a list of tools. 4 days ago · langchain_community. #. 5-turbo'), database=db, verbose=True) Now running a query is just a matter of the following line: May 3, 2023 · By leveraging the power of LangChain, SQL Agents, and OpenAI’s Large Language Models (LLMs) like ChatGPT, we can create applications that enable users to query databases using natural language. Links. Apr 24, 2023 · V. 5-turbo as a model which I will get into later on. g. agents import create_openai_functions_agent llm = ChatOpenAI(model="gpt-3. Lastname@mydomain. from langchain_openai import ChatOpenAI. Understanding the Magic: Deconstructing Langchain’s SQL Agent. We are going to create an LLMChain using that chat history as memory. Sep 27, 2023 · 1. 5-turbo", temperature=0) tools = [retriever_tool] agent = create Feb 19, 2024 · The last setup section for the LangChain with Azure SQL Database example is the table creation script. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. db_chain = SQLDatabaseChain(llm=ChatOpenAI(temperature=0, model_name='gpt-3. Here's how you can run the chain without manually formatting the prompt: sql_prompt = PromptTemplate ( input_variables= [ "input", "table_info", "dialect" ], template=sql In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. This example demonstrates the use of Runnables with questions and more on a SQL database. In this notebook, we’ll cover the stream Aug 21, 2023 · This is done by providing the sample_rows_in table and the include_tables parameters. add_routes(app, sql_llama2_chain, path="/sql-llama2") (Optional) Let's now configure LangSmith. prompt import SQL_PROMPTS. While this is downloading, create a new file called . Streaming is an important UX consideration for LLM apps, and agents are no exception. Jan 23, 2024 · This Python code defines a prompt template for an LLM to act as an IT business idea consultant. Follow the steps to create a new openai key. will execute all your requests. If you want to add this to an existing project, you can just run: langchain app add sql-ollama. And finally, we Feb 14, 2024 · As for the create_sql_agent function, it is used to construct an SQL agent from a language model and tools. This can be useful to ensure that they do not go haywire and take too many steps. pip install -U langchain-cli. LangChain Expression Language (LCEL) LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together. Note: Here we focus on Q&A for unstructured data. Suppose we have the following SQL query chain: from langchain. This is an agent specifically optimized for doing retrieval when necessary and also holding a conversation. The question: {question} """. This is responsible for the loop of running the agent iteratively until the stopping criteria is met. ¶. SQLDatabaseToolkit¶ class langchain_community. , Python) RAG Architecture A typical RAG application has two main components: May 9, 2023 · Openai key is required to access langchain. Streaming with agents is made more complicated by the fact that it’s not just tokens of the final answer that you will want to stream, but you may also want to stream back the intermediate steps an agent takes. toolkit ( Optional[SQLDatabaseToolkit]) – SQLDatabaseToolkit for the Aug 15, 2023 · Finally, python-dotenv will be used to load the OpenAI API keys into the environment. llm ( BaseLanguageModel) – Language model to use for the agent. This is useful for avoiding query results that exceed the prompt max length or consume tokens unnecessarily. connections import CustomConnection, and define an input parameter of type CustomConnection in the tool function. Jan 24, 2024 · Running agents with LangChain. You can find the source code here. agents import create_sql_agent from langchain. agent_executor = AgentExecutor(agent=agent SQL Agent Toolkit. Dec 20, 2023 · Create SQL Agent with customize tools. agents. sql_database. After defining the Teradata Search tool, you can create an SQL Agent using the following code. LLMs can write SQL, but they are often prone to making up tables, making up fields, and generally just writing SQL that if executed against your database would not actually be valid. create a . When using NL2SQL agents, its best to connect with a user with as few roles/privileges as possible for maximum security. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. 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. 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. from_agent_and_tools() doesn't use kwargs , but rather agent_executor_kwargs . LangChain is a very large library so that may take a few minutes. To create a new LangChain project and install this as the only package, you can do: langchain app new my-app --package sql-ollama. Note that, as this agent is in active development, all answers might not be correct. Memory in Agent. The main thing this affects is the prompting strategy used. Identify which tables can be used to answer the user's question and write and execute a SQL query accordingly. agent_types import AgentType from langchain. Mar 15, 2024 · from langchain_community. This is generally the most reliable way to create agents. Code example shows just the "final answer" Jun 15, 2023 · Given an input question, first create a syntactically correct postgresql query to run, then look at the results of the query and return the answer. server = 'XYZ'. llms. Copy. Mar 7, 2024 · These aren’t the typical questions that you could solve by just using RAG. Construct a SQL agent from an LLM and toolkit or database. The issue has been resolved by me, who provided a detailed response explaining the likely cause of the errors and suggested alternative packages to use. agents import create_spark_sql_agent from langchain_community. Returning Structured Output. It enables users to ask questions in natural language, eliminating the need for writing complex SQL queries. llm = ChatOpenAI(model="gpt-3. defining the agent type. llm = OpenAI(temperature=0) tools = load_tools(. Using an example set Create the example set from langchain. Chart by self LangChain’s default prompt for the SQL toolkit is designed to answer user’s questions by generating SQL queries, retrieving the final result, and converting that result into natural language. chains. The documents should contain data with a bunch of specifications, alongside more fluid, natural language descriptions. A good example of this is an agent tasked with doing question-answering over some sources. The Hugging Face Model Hub hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together. password = 'XYZ'. It can often be useful to have an agent return something with more structure. Parse the input to the input section, then select your target custom connection in the value dropdown. You have access to a Microsoft SQL Server database. from langchain import hub. Nov 14, 2023 · Here’s a high-level diagram to illustrate how they work: High Level RAG Architecture. Toolkit for interacting with SQL databases. Apr 8, 2024 · How to use the Python langchain agent to update data in the SQL table? I'm using the below py-langchain code for creating an SQL agent. May 17, 2023 · So, I decide to modify and optimize the Langchain agent with local LLMs. These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through If you are querying for several rows of a table you can select the maximum number of results you want to get by using the ‘top_k’ parameter (default is 10). However, without more context, it's hard to say how this function is related to your issue. base. sql. username = 'XYZ'. 2) AIMessage: contains the extracted information from the model. agents import load_tools. db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, top_k=3) db Jan 26, 2024 · You should initialize the handler as an instance of SQLHandler before passing it to the invoke method: handler = SQLHandler () response = agent_executor. chat_models import ChatOpenAI db Jan 23, 2024 · LangGraph: Multi-Agent Workflows. Each agent can then be run in a loop, with the output of one agent being passed as input to the next agent. Encode the query May 3, 2023 · At this point, we are able to create a SQLDatabaseChain as below. that can be fed into a chat model. NotTheDr01ds. py: A minimal agent with search (requires setting OPENAI_API_KEY 2 days ago · langchain_community. create_sql_agent. If the examples are enough to construct the query, I can build it. Hi, I was struggling with this too, but I could resolve it, on Azure AI Studio you can create a Deployment with a name different to the model name, if you do this, the code line llm = AzureOpenAI (deployment_name="deployment name", model_name="model name") fails with the Resource not found error, if you Jul 11, 2023 · The SQL Database Agent is a component within LangChain that acts as a bridge between users and SQL databases. agent_toolkits import SQLDatabaseToolkit from langchain. This notebook covers how to have an agent return a structured output. Add chat history. I managed to change the query and sort this out. The template parameter is a string that defines Nov 21, 2023 · Modify the custom_suffix variable like below. Next, we will use the high level constructor for this type of agent. The Prompt Template class from the LangChain module is used to create a new prompt template. SQL. get_tools() PREFIX = '''You are a SQL expert. sql import SQLDatabaseChain. wm lz og iu zp zh cc rg bt gj