community[minor]: Add Datahareld tool (#19680)

**Description:** Integrate [dataherald](https://www.dataherald.com)
tool, It is a natural language-to-SQL tool.
**Dependencies:** Install dataherald sdk to use it,
```
pip install dataherald
```

---------

Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Christophe Bornet <cbornet@hotmail.com>

docs/docs/integrations/providers/dataherald.mdx

@@ -0,0 +1,64 @@
+# Dataherald
+
+>[Dataherald](https://www.dataherald.com) is a natural language-to-SQL.
+
+This page covers how to use the `Dataherald API` within LangChain.
+
+## Installation and Setup
+- Install requirements with 
+```bash
+pip install dataherald
+```
+- Go to dataherald and sign up [here](https://www.dataherald.com)
+- Create an app and get your `API KEY`
+- Set your `API KEY` as an environment variable `DATAHERALD_API_KEY`
+
+
+## Wrappers
+
+### Utility
+
+There exists a DataheraldAPIWrapper utility which wraps this API. To import this utility:
+
+```python
+from langchain_community.utilities.dataherald import DataheraldAPIWrapper
+```
+
+For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/dataherald).
+
+### Tool
+
+You can use the tool in an agent like this:
+```python
+from langchain_community.utilities.dataherald import DataheraldAPIWrapper
+from langchain_community.tools.dataherald.tool import DataheraldTextToSQL
+from langchain_openai import ChatOpenAI
+from langchain import hub
+from langchain.agents import AgentExecutor, create_react_agent, load_tools
+
+api_wrapper = DataheraldAPIWrapper(db_connection_id="<db_connection_id>")
+tool = DataheraldTextToSQL(api_wrapper=api_wrapper)
+llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
+prompt = hub.pull("hwchase17/react")
+agent = create_react_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+agent_executor.invoke({"input":"Return the sql for this question: How many employees are in the company?"})
+```
+
+Output
+```shell
+> Entering new AgentExecutor chain...
+I need to use a tool that can convert this question into SQL.
+Action: dataherald
+Action Input: How many employees are in the company?Answer: SELECT
+    COUNT(*) FROM employeesI now know the final answer
+Final Answer: SELECT
+    COUNT(*)
+FROM
+    employees
+
+> Finished chain.
+{'input': 'Return the sql for this question: How many employees are in the company?', 'output': "SELECT \n    COUNT(*)\nFROM \n    employees"}
+```
+
+For more information on tools, see [this page](/docs/modules/tools/).

docs/docs/integrations/tools/dataherald.ipynb

@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "245a954a",
+   "metadata": {},
+   "source": [
+    "# Dataherald\n",
+    "\n",
+    "This notebook goes over how to use the dataherald component.\n",
+    "\n",
+    "First, you need to set up your Dataherald account and get your API KEY:\n",
+    "\n",
+    "1. Go to dataherald and sign up [here](https://www.dataherald.com/)\n",
+    "2. Once you are logged in your Admin Console, create an API KEY\n",
+    "3. pip install dataherald\n",
+    "\n",
+    "Then we will need to set some environment variables:\n",
+    "1. Save your API KEY into DATAHERALD_API_KEY env variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "961b3689",
+   "metadata": {
+    "vscode": {
+     "languageId": "shellscript"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pip install dataherald"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "34bb5968",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"DATAHERALD_API_KEY\"] = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ac4910f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.utilities.dataherald import DataheraldAPIWrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "84b8f773",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataherald = DataheraldAPIWrapper(db_connection_id=\"65fb766367dd22c99ce1a12d\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "068991a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'select COUNT(*) from employees'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataherald.run(\"How many employees are in the company?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "53f3bc57609c7a84333bb558594977aa5b4026b1d6070b93987956689e367341"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}