docs[patch]: Add canonical URLs pointing at 0.2 docs for popular pages (#22250)

@efriis
This commit is contained in:
Jacob Lee
2024-05-31 07:55:10 -07:00
committed by GitHub
parent b8550e7d3a
commit f883981446
50 changed files with 276 additions and 6 deletions

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"source": [
"# Agents\n",
"\n",
"```{=mdx}\n",
"<head>\n",
" <link rel=\"canonical\" href=\"https://python.langchain.com/v0.2/docs/tutorials/sql_qa/\" />\n",
"</head>\n",
"```\n",
"\n",
"LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. The main advantages of using the SQL Agent are:\n",
"\n",
"- It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table).\n",

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"source": [
"# SQL\n",
"\n",
"```{=mdx}\n",
"<head>\n",
" <link rel=\"canonical\" href=\"https://python.langchain.com/v0.2/docs/tutorials/sql_qa/\" />\n",
"</head>\n",
"```\n",
"\n",
"One of the most common types of databases that we can build Q&A systems for are SQL databases. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e.g., MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). They enable use cases such as:\n",
"\n",
"* Generating queries that will be run based on natural language questions,\n",

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"source": [
"# Quickstart\n",
"\n",
"```{=mdx}\n",
"<head>\n",
" <link rel=\"canonical\" href=\"https://python.langchain.com/v0.2/docs/tutorials/sql_qa/\" />\n",
"</head>\n",
"```\n",
"\n",
"In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. 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.\n",
"\n",
"## ⚠️ Security note ⚠️\n",