Langchain sql agent source code

Langchain sql agent source code. The LLM model is designed for interacting with Large Language Models (like GPT-4). 194. If this is your first time using playwright, you’ll have to install a browser executable. This is a agent is defined as follows: `toolkit = SQLDatabaseToolkit(db=db, llm=llm) toolkit. agents import create_sql_agent from langchain. “force” returns a string saying that it stopped because it met a. The package allows you to connect to any SQL database that you can otherwise connect to with Python; Choose your front end. RAG is a methodology that assists LLMs generate accurate and up-to-date information. Here, we use gpt-3. This works for really specific and simple questions: But breaks down when it gets more complicated. from functions import sql_db_list_tables, sql_db_schema# List the tables in the databaseprint(sql_db_list . Apr 27, 2023 · LLMs. Two RAG use cases which we cover elsewhere are: Q&A over SQL data; Q&A over code (e. Creating this separately from #1026 because the SQL injection issue and the Python exec issues are separate. code-block:: python from langchain_openai import ChatOpenAI from langchain_experimental. prompts import PromptTemplate from langchain. db", sample_rows_in_table_info=2) llm = OpenAI(temperature=0, verbose=True) db_chain = SQLDatabaseChain. agent_toolkits import create_sql_agent. ; sql_db_checker Correct question to SQL pairs are stored for future reference and make the future results more accurate; Supports any SQL database. Create a new model by parsing and validating input 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. toolkit. “generate” calls the agent’s LLM Chain one final time to generate. language_models import BaseLanguageModel from langchain_core. It interprets and analyzes its intention, and with all its knowledge, then generates what it needs to perform the first step. SQLDatabaseToolkit¶ class langchain_community. In the end, my LLM agent can do a much better job with several customizes. openai import OpenAI. gguf. After defining the Teradata Search tool, you can create an SQL Agent using the following code. agent_toolkits. The advent of large language models (LLMs), such as OpenAI’s GPT-3, has ushered in a new era of possibilities in the realm of natural language processing. SQL. from langchain. conversational_retrieval. chat_models import ChatOpenAI from langchain. To add memory to the SQL agent in LangChain, you can use the save_context method of the ConversationBufferMemory class. Class hierarchy: 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. Feb 23, 2024 · The agents will use Tavily to get access to data, and also PythonREPL, a Langchain library for executing code. from_agent_and_tools(agent=agent, tools=tools, verbose=True) agent_executor. It takes as input all the same input variables as the prompt passed in does. 5-turbo model. agents import AgentType, tool, create_sql_agent @tool def my_first_awesome_tool(human_message: str) -> list: """ Searches for my CSV. Quickstart For a quick start to working with agents, please check out this getting started guide. A typical RAG workflow follows the 3 steps below: Relevant knowledge (or data) is retrieved from the knowledge base (typically a vector search DB) A prompt, containing retrieved knowledge above, is constructed. sql 4 days ago · Source code for langchain. The method to use for early stopping if the agent never returns AgentFinish. Aug 16, 2023 · This code snippet demonstrates how to use the LangChain library, specifically the `Cohere` module and the `cx_Oracle` library, to interact with an Oracle Database using natural language queries. Jun 17, 2023 · DIY, Search Engine: How LangChain SQL Agent Simplifies Data Extraction. 0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. The code to create the ChatModel and give it tools is really simple, you can check it all in the Langchain doc. history import RunnableWithMessageHistory from langchain. The autoreload extension is already loaded. Current configured baseUrl = / (default value) We suggest trying baseUrl = / LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks and components. 0rc2¶ langchain. ttk as ttk from langchain. This covers basics like initializing an agent, creating tools, and adding memory. py and base. instead. . LLMs are great for building question-answering systems over various types of data sources. from_uri ( "sqlite:///Chinook. 2. Another user suggested editing the prompt of the SQL agent, which seemed to work Dec 6, 2023 · 🤖. from_uri (sql_uri) model_path = ". %load_ext autoreload %autoreload 2. In this section we'll go over how to build Q&A systems over data stored in a CSV file(s). I am sure that this is a bug in LangChain rather than my code. sql_db_list_tables : Returns table names inside the database. This method allows you to save the context of a conversation, which can be used to respond to queries, retain history, and remember context for subsequent queries. SQLDatabaseToolkit [source] ¶ Bases: BaseToolkit. When using database agent this is how I am initiating things: `db = SQLDatabase. this code ``` from langchain_community. llms import HuggingFaceEndpoint. n_batch = 512 # Should be between 1 Aug 19, 2023 · LangChain 101: Build Your Own GPT-Powered Applications Tools, Agents, and Toolkits in LangChain. sql_database import SQLDatabase. SqlDatabase from langchain/sql_db; createSqlAgent from langchain/agents Jun 21, 2023 · SQL-Free chatbot: Langchain SQL Agent Bridges Any DB with LLMs, Crafting Queries in Plain English! The above video shows how SQL LLM agent is interacting with sqlite DB 2 min read · Mar 31, 2024 ⚡ Repository focus on course and application for agent of Langchain. chat_message_histories import ChatMessageHistory from langchain_core. prompts import PromptTemplate. Use the most basic and common components of LangChain: prompt templates, models, and output parsers. Dec 25, 2023 · Once install you can use below code to start using langchain. agents import create_pandas_dataframe_agent import pandas as pd df = pd. At present, there’s a growing buzz Check out some other full examples of apps that utilize LangChain + Streamlit: Auto-graph - Build knowledge graphs from user-input text (Source code) Web Explorer - Retrieve and summarize insights from the web (Source code) LangChain Teacher - Learn LangChain from an LLM tutor (Source code) 2 days ago · A Runnable sequence representing an agent. But first, what are LLM Agent and Langchain? The ChatGPT and other LLMs are really powerful and we all know that. Chat Models. For example SQL injection cannot be solved with running inside an isolated container. Go to prompt flow in your workspace, then go to connections tab. Hello @rajesh-b-rao!I'm Dosu, a bot here to assist you. ; sql_db_schema: Provides detailed structure information about a table. Source Code. Here's how you can run the chain without manually formatting the prompt: sql_prompt = PromptTemplate ( input_variables= [ "input", "table_info", "dialect" ], template=sql Google BigQuery. 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 2 days ago · Either 'force' or 'generate'. This notebook goes through how to create your own custom agent. sql. 5-turbo" to the OpenAI attributes, but it didn't work for you. JSON Agent - fo Aug 7, 2023 · The Agent receives a user request in natural language. cpp: C++ implementation of llama inference code with weight optimization / quantization; gpt4all: Optimized C backend for inference; Ollama: Bundles model weights and environment into an app that runs on device and serves the LLM Nov 24, 2023 · I am using local LLM with langchain: openhermes-2. n_gpu_layers = 1 # Change this value based on your model and your GPU VRAM pool. from_llm(llm, db, verbose=True) #db_chain. from langchain_core. In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. getsource (function_name) Next, we’ll create a custom prompt template that takes in the function name as input, and formats the prompt template to provide the source code of the function. %pip install --upgrade --quiet langchain-google-community[bigquery] Sep 14, 2023 · Text-to-SQL Query and Execution. agents ¶ Agent is a class that uses an LLM to choose a sequence of actions to take. In this notebook, we will create an example of an agent that uses Bearly to interact with data from langchain_community . Q8_0. Additionally, you can include the `teradata_search_tool 4 days ago · langchain. To adjust logging level use sc. Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining. llms. However, if you have complex security requirements - you may want to use Azure Active Directory. These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through Aug 7, 2023 · The SQL agent code is currently experimental — so it’s likely you may encounter some bugs! We then dived into a basic demo using the open source LangChain SQL agent & Azure OpenAI Jul 11, 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. Something like: from langchain. llms import OpenAI from langchain_experimental. The Agent component of LangChain is a wrapper around LLM, which decides the best steps or actions to take to solve a problem. The two main ways to do this are to either: Oct 12, 2023 · Let’s start building the Langchain SQL agent. While this is downloading, create a new file called . Specify your Connery Runner credentials. 5-mistral-7b. pip install langchain openai python-dotenv requests duckduckgo-search. g. This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Google BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. Apr 24, 2023 · Discover how you can harness the power of LangChain, SQL Agents, and OpenAI LLMs to query databases using natural language. NOTE: this agent calls the Pandas DataFrame agent under the hood, which in turn calls the Python agent, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Upon asking questions that might involve joining tables, ordering and filtering. Jul 21, 2023 · Based on my understanding, you were encountering errors when trying to implement memory with the SQL agent toolkit in the LangChain framework. In Chains, a sequence of actions is hardcoded. Here are some relevant links: Python SQL Chains; Python SQL Agents; Javascript SQL Chains; Javascript SQL Agents; Introduction. A. run("How many employees are there?") db_chain. Based on the information you've provided and the context from the LangChain repository, the 'suffix' parameter in the create_sql_agent function should be an optional string (Optional[str]). Tools are functions that perform specific duties, such as Google Search, database lookups, or In this video, we look at different agent toolkits for Langchain, including:1. Note: Here we focus on Q&A for unstructured data. If the table is slightly bigger with complex question, It throws InvalidRequestError: This model's maximum context length is 4097 tokens, however you requested 13719 tokens (13463 in your prompt; 256 for the completion). from lcforecast. Make natural language queries to a SQL Database using LangChain & LLM's. To use Google BigQuery, you would need to create an instance of SQLDatabase that Mar 9, 2016 · from langchain. The main advantages of using the SQL Agent are: It can answer questions based on the databases’ schema as well as on the databases’ content (like describing a specific table). It provides a standardised interface so you can interchange different models while keeping the rest of your code the same. Select the LLM to use. agents. LangChain is a very large library so that may take a few minutes. agents import AgentType , initialize_agent Jan 21, 2024 · Let’s write a sample agent that will summarize the meeting notes and preserve the action items. 5-turbo-instruct. The package provides a generic interface to many foundation models, enables prompt management, and acts as a central interface to other components like prompt templates, other LLMs, external data, and other tools via agents Mar 10, 2010 · However, according to the LangChain codebase, the agent_executor function does not take a parameter named 'functions'. , Python) RAG Architecture A typical RAG application has two main components: Jun 9, 2023 · SQLDatabaseChain should have a facility to intercept and review the SQL before sending it to the database. Here's how to implement it: pip install langchain openai pymysql --upgrade -q. In this example, we will use OpenAI Tool Calling to create this agent. import os. Most people start in a Jupyter Notebook. AgentTokenBufferMemory¶ class langchain. Import libraries. Jul 3, 2023 · param early_stopping_method: str = 'force' ¶. You can find the source code here. Import the necessary libraries: from langchain. 23/05/18 16:03:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform using builtin-java classes where applicable Mar 26, 2023 · Build LLM Agent combining Reasoning and Action (ReAct) framework using LangChain Most of the people see LLM as a knowledge source which is having good understanding of language and is built using Custom agent. This notebook shows how to use agents to interact with data in CSV format. It also supports large language models Dec 20, 2023 · Create SQL Agent with customize tools. agents import ZeroShotAgent. The repo comes with a setup script that loads a sqlite database with some sample data. sql_database import SQLDatabase from langchain. It returns as output either an AgentAction or AgentFinish. You have access to a Microsoft SQL Server database. LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Pandas DataFrame agent - for interrogating Pandas DataFrames2. /openhermes-2. LangChain is a framework for developing applications powered by large language models (LLMs). from langchain_community. ''' FORMAT_INSTRUCTIONS = """Please use the following SQL Agent Toolkit. Either ‘force’ or ‘generate’. messages import SystemMessage from langchain_core. It is mostly optimized for question answering. 4 days ago · langchain_community. Apr 24, 2023 · V. Always use this tool before executing a query with sql_db_query! ' ¶. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. sql import SQLDatabaseChain db = SQLDatabase. For SparkR, use setLogLevel(newLevel). Your Docusaurus site did not load properly. Deprecated since version 0. run("List all the Sep 22, 2023 · Use the combination of the prefix variable and the tool function description. 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. Hit the ground running using third-party integrations and Templates. agent_toolkits import SQLDatabaseToolkit from langchain. It is designed to answer more general questions about a database, as well as recover from errors. openai_functions_agent. In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe. csv Quickstart. Most of an enterprise’s data is traditionally stored in SQL databases. from_llm(prompt=prompt, llm=llm, db=db, verbose=True) return sql_chain. One such use case is the capacity to search for pertinent data effectively. llms import OpenAI llm = OpenAI (model_name = "text-davinci-003") # 告诉他我们生成的内容需要哪些字段，每个字段类型式啥 response_schemas = [ ResponseSchema (name = "bad_string Aug 15, 2023 · Finally, python-dotenv will be used to load the OpenAI API keys into the environment. run("generate a short blog post to review the plot of the movie Avatar 2. In this video, you will discover how you can harness the power of LangChain, SQL Age Mar 15, 2024 · from langchain_community. ⚡. Part of this could be the result of using an open source model but you can copy the exact prompt into Chat-GPT and often get the same result I searched the LangChain documentation with the integrated search. Note that, as this agent is in active development, all answers might not be correct. Load a BigQuery query with one document per row. We will first create it WITHOUT memory, but we will then show how to add memory in. It includes four tools. It could be an SQL query, that is sent to the tool that the Agent knows will execute SQL queries. agent_toolkits import SQLDatabaseToolkit from AgentRun: The easiest, and fastest way to run AI-generated Python code safely ; LLama Cpp Agent: The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models ; FinRobot: An Open-Source AI Agent Platform for Financial Applications using LLMs ; Groq Ruby: Groq Cloud runs LLM models fast and cheap. agent_executor = create_sql_agent(llm=your_llm, toolkit=your_toolkit, callback_manager=your_callback_manager, callbacks=your 3 days ago · Returns: An AgentExecutor with the specified agent_type agent and access to a PythonAstREPLTool with the DataFrame(s) and any user-provided extra_tools. It leverages LangChain's capabilities to convert human language into SQL queries for execution on the Oracle Database. get_tools() PREFIX = '''You are a SQL expert. A few frameworks for this have emerged to support inference of open-source LLMs on various devices: llama. 2 days ago · langchain 0. Example: . You made changes to the Prompt. from langchain_experimental. 1. llms import OpenAI. openai import OpenAI Jul 11, 2023 · sql_chain = SQLDatabaseChain. Select Create and select a connection type to store your credentials. gguf". utilities import SQLDatabase from langchain. Here is how you can use this method: Sep 28, 2023 · L angchain is an open source framework for developing applications which can process natural language using LLMs (Large Language Models). read_csv("titanic. LangChain is a software framework designed to help create applications that utilize large language models (LLMs). time or iteration limit. output_parsers import StructuredOutputParser, ResponseSchema from langchain. I am currently testing on my laptop that has 8GB of Ram and a decent CPU. 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. agents import AgentExecutor from langchain. Notes. LLM receives the prompt above to generate a text from langchain. prompts import (. Encode the query LangServe - deploy LangChain runnables and chains as a REST API (Python) OpenGPTs - Open-source effort to create a similar experience to OpenAI's GPTs and Assistants API (Python) LangGraph - build language agents as graphs (Python) Feb 14, 2024 · I want you to give me a code snippet of how to use. Here are the 4 key steps that take place: Load a vector database with encoded documents. While we wait for human maintainers, I'll do my best to help you with your issue. The agent analyzes if the received response is what the user wants. For querying with SQL Agent I want to leverage GPT-4 this time. agents import load_tools. LangChain Agents - Joining Tools and Chains with Decisions; Relative Colab; Building Custom Tools and Agents with LangChain (gpt-3. run but I would like to get the various observations and graph the results. Introduction. The following code will create an agent that has the forecast tool. First export to the shell the OPENAI_API_KEY and your database connection string as DB_CONN. See sample output below: 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. 3 days ago · Examples using initialize_agent ¶. LLM Agent with Tools: Extend the agent with access to multiple tools and test that it uses them to answer questions. from typing import Any, List, Optional # noqa: E501 from langchain_core. Memory is needed to enable conversation. runnables. This example shows how to load and use an agent with a SQL toolkit. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. tools import tool from typing import Annotated from langchain Apr 21, 2023 · import inspect def get_source_code (function_name): # Get the source code of the function return inspect. Identify which tables can be used to answer the user's question and write and execute a SQL query accordingly. Create a new model by parsing and validating input data from keyword arguments. prompts import May 1, 2023 · I am using SQL agent from langchain, for some context I have a large postgres data source. Assuming you have imported the necessary modules and initialized the required variables. `"generate"` calls the agent's LLM Chain one final time to generate a final answer based on the previous steps. LangChain’s strength lies in its wide array of integrations and capabilities. So here are my experiences. This step-by-step tutorial will g May 17, 2023 · So, I decide to modify and optimize the Langchain agent with local LLMs. 📺📽️ Video and Colab. 5 days ago · The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work. memory import BaseMemory from langchain_core. The function create_sql_agent you've used in your code is designed to construct a SQL agent from a language model and a toolkit or database. 5-turbo", temperature=0) tools = [retriever_tool] agent = create Dec 1, 2023 · There are two ways you can authenticate to Azure OpenAI: - API Key - Azure Active Directory (AAD) Using the API key is the easiest way to get started. agents import AgentExecutor, create_tool_calling_agent, tool from langchain_anthropic import ChatAnthropic from langchain_core. Create a new model by parsing and validating input data from Apr 25, 2023 · LangChain is an open-source Python library that enables anyone who can write code to build LLM-powered applications. Create a connection that securely stores your credentials, such as your LLM API KEY or other required credentials. Setting default log level to "WARN". The agent gets hit to its maximum iterations. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a separate document. A very common reason is a wrong site baseUrl configuration. Used to tell the model how/when/why to use the tool. That’s why I allow myself a little more power Mar 28, 2024 · Create a connection. agents import create_openai_functions_agent llm = ChatOpenAI(model="gpt-3. Let's get started. 0. This notebook showcases an agent designed to interact with a SQL databases. The LLM model takes a text string input and returns a text string ouput. memory import TextMemory. You mentioned that you tried different approaches, including adding model_name="gpt-3. Python version Apr 29, 2023 · From what I understand, you were having trouble using the SQL Database Agent with the OpenAI gpt-3. AgentTokenBufferMemory [source] ¶ Bases: BaseChatMemory. `"force"` returns a string saying that it stopped because it met a time or iteration limit. 5-turbo) Relative Colab Code writing. Toolkit for interacting with SQL databases. sql import SQLDatabaseChain. BigQuery is a part of the Google Cloud Platform. Agents select and use Tools and Toolkits for actions. Example Code. prompts. The Agent typically has access to a set of functions called Tools (or Toolkit) and Jan 24, 2024 · Running agents with LangChain. This is generally the most reliable way to create agents. agent_token_buffer_memory. You can provide few-shot examples as a part of the description. """handle_parsing_errors:Union[bool,str,Callable[ [OutputParserException],str]]=False"""How to handle errors In chains, a sequence of actions is hardcoded (in code). agentkit import ForecastToolkit. You can find your API key in the Azure portal under your Azure OpenAI resource. 5 days ago · param description: str = ' Use this tool to double check if your query is correct before executing it. It should be a Nov 14, 2023 · Here’s a high-level diagram to illustrate how they work: High Level RAG Architecture. We have just integrated a ChatHuggingFace wrapper that lets you create agents based on open-source models in 🦜🔗LangChain. Mar 10, 2010 · This is a simple App for testing LLM to SQL commands on a sqlite database using Langchain SQL Agent. llm = OpenAI(temperature=0, verbose=True) Mar 7, 2024 · As you would expect from LangChain, the code is very clear, even as it performs complex operations behind the scenes. openai_functions. Mar 13, 2023 · 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. %pip install --upgrade --quiet langchain-core langchain-experimental langchain-openai. import langchain. llm) agent_executor = create_sql_agent(prefix=MSSQL_AGENT_PREFIX, May 2, 2023 · Knowledge Base: Create a knowledge base of "Stuff You Should Know" podcast episodes, to be accessed through a tool. Here, llm is an instance of BaseLanguageModel, tools is a list of BaseTool instances, verbose is a boolean that defaults to False, and include_task_in_prompt is also a boolean that defaults to False. py files, and received assistance from me, who provided potential solutions involving modifying the create_sql_agent function and passing inputs as a dictionary Yes, it is indeed possible to create an SQL agent in the latest version of LangChain to query tables on Google BigQuery. Code example shows just the "final answer" May 14, 2023 · agent_executor = AgentExecutor. setLogLevel(newLevel). import os import pyodbc import tkinter as tk import tkinter. # Function to create a consistent LLM connector. [LangChain version: 0. Dec 4, 2023 · Database Toolkit. It includes API wrappers, web scraping subsystems, code analysis tools, document summarization tools, and more. env and paste your API key in. toolkit = SQLDatabaseToolkit(db=db, llm=self. This repo is intended to be a starter for Langchain and also a starter for programming with co-pilot GPT-4. output_parsers import StrOutputParser. Memory used to save agent output AND intermediate steps. Example of how to use LCEL to write Python code. May 7, 2023 · If I ask straightforward question on a tiny table that has only 5 records, Then the agent is running well. chat import Aug 26, 2023 · I need to connect my langchain database agent with my sqlserver database so that my agent can access the data, yet in the documention it does not explain how to do so, it only shows how to connect to sqlite like the following code: from langchain. The data volume is manageable compared to the extensive processing required for importing a multitude of documents. Mar 11, 2024 · when using handle parsing errors i get the following agent executor chain: Entering new SQL Agent Executor chain Could not parse tool input: {'arguments': '# I will use the sql_db_list_tables and sql_db_schema tools to see what tables are in the database and their schemas. Like working with SQL databases, the key to working with CSV files is to give an LLM access to tools for querying and interacting with the data. tools import BearlyInterpreterTool from langchain . I used the GitHub search to find a similar question and didn't find it. ff uc ng yt ed by eh ow hp lw