Langchain sql agent examples. Build resilient language agents as graphs.

Langchain sql agent examples. 开发：使用 LangChain 的开源组件和第三方集成构建您的应用程序。使用 LangGraph 来构建支持一流流式传输和人工干预的有状态智能体。生产化：使用 LangSmith 来检查、监控和评估您的应用程序，以便您可以持续优化并自信地部署。部署：使用 LangGraph Platform 将您的 LangGraph 应用程序转化为可用于生产的 API 和助手。 LangChain 为大型语言模型及相关技术（如嵌入模型和向量存储）实现了标准接口，并集成了数百家提供商。有关更多信息，请参阅集成页面。. LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. Learn the essentials of LangSmith — our platform for LLM application development, whether you're building with LangChain or not. LangChain 是一个用于开发由大型语言模型（LLMs）驱动的应用程序的框架。 LangChain 简化了 LLM 应用程序生命周期的每个阶段. sql_database. SQL Database Agent # This notebook showcases an agent designed to interact with a sql databases. utilities. Example Input: table1, table2, table3', db=<langchain_community. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source components and third-party integrations. Unless the user specifies in his question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. from langchain_core. SQLDatabase object at 0x103d5fa60>), ListSQLDatabaseTool(db=<langchain_community. LangChain 是一个用于开发由语言模型驱动的应用程序的框架。我们相信，最强大和不同的应用程序不仅将通过 API 调用语言模型，还将：数据感知：将语言模型与其他数据源连接在一起。主动性：允许语言模型与其环境进行交互。因此，LangChain 框架的设计目标是为了实现这些类型的应用程序。组件：LangChain 为处理语言模型所需的组件提供模块化的抽象。 LangChain 还为所有这些抽象提供了实现的集合。这些组件旨在易于使用，无论您是否使用 LangChain 框架的其余部分。用例特定链：链可以被看作是以特定方式组装这些组件，以便最好地完成特定用例。这旨在成为一个更高级别的接口，使人们可以轻松地开始特定的用例。这些链也旨在可定制化。 LangChain is a framework for building LLM-powered applications. Agents LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. Aug 21, 2023 · A step-by-step guide to building a LangChain enabled SQL database question answering agent. Dec 13, 2024 · In this post, we’ll walk you through creating a LangChain agent that can understand questions in natural language (NLP), dynamically generate SQL queries based on your input, fetch results from Jun 21, 2023 · 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 to connect and ask questions Construct a SQL agent from an LLM and toolkit or database. prompts import ChatPromptTemplate system_message = """ Given an input question, create a syntactically correct {dialect} query to run to help find the answer. Mar 10, 2025 · LangChain is an excellent framework equipped with components and third-party integrations for developing applications that leverage LLMs. Note that, as this agent is in active development, all answers might not be correct. It can recover from errors by running a generated query, catching the traceback and regenerating it Sep 12, 2023 · Under the hood, the LangChain SQL Agent uses a MRKL (pronounced Miracle)-based approach, and queries 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. Build resilient language agents as graphs. We are excited to announce the launch of the LangChainHub, a place where you can find and submit commonly used prompts, chains, agents, and more! This obviously draws a lot of inspiration from Hugging Face's Hub, which we believe has done an incredible job of fostering an amazing community. Jun 17, 2025 · Build an Agent LangChain supports the creation of agents, or systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. The agent builds off of SQLDatabaseChain and is designed to answer more general questions about a database, as well as recover from errors. This repository demonstrates the use of LangChain and LangGraph for SQL query generation, execution and validation. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis. Continuously improve your application with LangSmith's tools for LLM observability, evaluation, and prompt engineering. This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. 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). After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. LangChain is a framework for developing applications powered by large language models (LLMs). It provides a standard interface for chains, many integrations with other tools, and end-to-end chains for common applications. In this article, we will build an AI workflow using LangChain and construct an AI agent workflow by issuing SQL queries on CSV data with DuckDB. In this tutorial we LangChain's products work seamlessly together to provide an integrated solution for every step of the application development journey. When you use all LangChain products, you'll build better, get to production quicker, and grow visibility -- all with less set up and friction. This is often achieved via tool-calling. Jul 23, 2025 · LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). It helps you chain together interoperable components and third-party integrations to simplify AI application development — all while future-proofing decisions as the underlying technology evolves. Contribute to langchain-ai/langgraph development by creating an account on GitHub. SQLDatabase object at 0x103d5fa60>), QuerySQLCheckerTool(description='Use this tool to double check if your query is correct before executing it. apyr wuvvy rsuusez eoappj dyddus jchg fzgqd mkv tim uzu