toolkit docs (langchain-ai#174)

* toolkit docs

* Add sidebar label

---------

Co-authored-by: Nuno Campos <[email protected]>

docs/docs/modules/agents/agent_toolkits/_category_.yml

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+label: "Agent Toolkits"
+collapsible: true # make the category collapsible
+collapsed: false # keep the category open by default

docs/docs/modules/agents/agent_toolkits/json.md

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+# JSON Agent Toolkit
+
+This example shows how to load and use an agent with a JSON toolkit.
+
+```typescript
+import { OpenAI } from "langchain";
+import * as fs from "fs";
+import * as yaml from "js-yaml";
+import { JsonSpec, JsonObject } from "langchain/tools";
+import { JsonToolkit, createJsonAgent } from "langchain/agents";
+
+export const run = async () => {
+  let data: JsonObject;
+  try {
+    const yamlFile = fs.readFileSync("openai_openapi.yaml", "utf8");
+    data = yaml.load(yamlFile) as JsonObject;
+    if (!data) {
+      throw new Error("Failed to load OpenAPI spec");
+    }
+  } catch (e) {
+    console.error(e);
+    return;
+  }
+
+  const toolkit = new JsonToolkit(new JsonSpec(data));
+  const model = new OpenAI({ temperature: 0 });
+  const executor = createJsonAgent(model, toolkit);
+
+  const input = `What are the required parameters in the request body to the /completions endpoint?`;
+
+  console.log(`Executing with input "${input}"...`);
+
+  const result = await executor.call({ input });
+
+  console.log(`Got output ${result.output}`);
+
+  console.log(
+    `Got intermediate steps ${JSON.stringify(
+      result.intermediateSteps,
+      null,
+      2
+    )}`
+  );
+};
+```

docs/docs/modules/agents/agent_toolkits/openapi.md

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+# OpenAPI Agent Toolkit
+
+This example shows how to load and use an agent with a OpenAPI toolkit.
+
+```typescript
+import * as fs from "fs";
+import * as yaml from "js-yaml";
+import { JsonSpec, JsonObject } from "langchain/tools";
+import { createOpenApiAgent, OpenApiToolkit } from "langchain/agents";
+import { OpenAI } from "langchain";
+
+export const run = async () => {
+  let data: JsonObject;
+  try {
+    const yamlFile = fs.readFileSync("openai_openapi.yaml", "utf8");
+    data = yaml.load(yamlFile) as JsonObject;
+    if (!data) {
+      throw new Error("Failed to load OpenAPI spec");
+    }
+  } catch (e) {
+    console.error(e);
+    return;
+  }
+
+  const headers = {
+    "Content-Type": "application/json",
+    Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
+  };
+  const model = new OpenAI({ temperature: 0 });
+  const toolkit = new OpenApiToolkit(new JsonSpec(data), model, headers);
+  const executor = createOpenApiAgent(model, toolkit);
+
+  const input = `Make a POST request to openai /completions. The prompt should be 'tell me a joke.'`;
+  console.log(`Executing with input "${input}"...`);
+
+  const result = await executor.call({ input });
+  console.log(`Got output ${result.output}`);
+
+  console.log(
+    `Got intermediate steps ${JSON.stringify(
+      result.intermediateSteps,
+      null,
+      2
+    )}`
+  );
+};
+```

docs/docs/modules/agents/agent_toolkits/sql.md

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+# SQL Agent Toolkit
+
+This example shows how to load and use an agent with a SQL toolkit.
+
+```typescript
+import { OpenAI } from "langchain";
+import { SqlDatabase } from "langchain/tools";
+import { createSqlAgent, SqlToolkit } from "langchain/agents";
+import sqlite3 from "sqlite3";
+
+/** This example uses Chinook database, which is a sample database available for SQL Server, Oracle, MySQL, etc.
+ * To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the .db file
+ * in the examples folder.
+ */
+export const run = async () => {
+  const db = await new sqlite3.Database("Chinook.db");
+  const tookit = new SqlToolkit(new SqlDatabase(db));
+  const model = new OpenAI({ temperature: 0 });
+  const executor = createSqlAgent(model, tookit);
+
+  const input = `List the total sales per country. Which country's customers spent the most?`;
+
+  console.log(`Executing with input "${input}"...`);
+
+  const result = await executor.call({ input });
+
+  console.log(`Got output ${result.output}`);
+
+  console.log(
+    `Got intermediate steps ${JSON.stringify(
+      result.intermediateSteps,
+      null,
+      2
+    )}`
+  );
+
+  db.close();
+};
+```

docs/docs/modules/agents/overview.md

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+---
+sidebar_position: 1
+---
+
 # Agent Overview
 
 Agents use an LLM to determine which actions to take and in what order. An action can either be using a tool and observing its output, or returning to the user.