Takeoff pro support (#12070)

**Description:**
This PR adds support for the [Pro version of Titan Takeoff
Server](https://docs.titanml.co/docs/category/pro-features). Users of
the Pro version will have to import the TitanTakeoffPro model, which is
different from TitanTakeoff.

**Issue:**
Also minor fixes to docs for Titan Takeoff (Community version)

**Dependencies:**
No additional dependencies

 **Twitter handle:** @becoming_blake

@baskaryan @hwchase17

docs/docs/integrations/llms/titan_takeoff.ipynb

@@ -108,7 +108,7 @@
     "from langchain.llms import TitanTakeoff\n",
     "\n",
     "llm = TitanTakeoff(\n",
-    "    baseURL=\"http://localhost:8000\",\n",
+    "    base_url=\"http://localhost:8000\",\n",
     "    generate_max_length=128,\n",
     "    temperature=1.0\n",
     ")\n",

docs/docs/integrations/llms/titan_takeoff_pro.ipynb

@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titan Takeoff Pro\n",
+    "\n",
+    "`TitanML` helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform.\n",
+    "\n",
+    ">Note: These docs are for the Pro version of Titan Takeoff. For the community version, see the page for Titan Takeoff.\n",
+    "\n",
+    "Our inference server, [Titan Takeoff (Pro Version)](https://docs.titanml.co/docs/titan-takeoff/pro-features/feature-comparison) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example usage\n",
+    "Here are some helpful examples to get started using the Pro version of Titan Takeoff Server.\n",
+    "No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and generation parameters can be supplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import TitanTakeoffPro\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "\n",
+    "# Example 1: Basic use\n",
+    "llm = TitanTakeoffPro()\n",
+    "output = llm(\"What is the weather in London in August?\")\n",
+    "print(output)\n",
+    "\n",
+    "\n",
+    "# Example 2: Specifying a port and other generation parameters\n",
+    "llm = TitanTakeoffPro(\n",
+    "    base_url=\"http://localhost:3000\",\n",
+    "    min_new_tokens=128,\n",
+    "    max_new_tokens=512,\n",
+    "    no_repeat_ngram_size=2,\n",
+    "    sampling_topk= 1,\n",
+    "    sampling_topp= 1.0,\n",
+    "    sampling_temperature= 1.0,\n",
+    "    repetition_penalty= 1.0,\n",
+    "    regex_string= \"\",\n",
+    ")\n",
+    "output = llm(\"What is the largest rainforest in the world?\")\n",
+    "print(output)\n",
+    "\n",
+    "\n",
+    "# Example 3: Using generate for multiple inputs\n",
+    "llm = TitanTakeoffPro()\n",
+    "rich_output = llm.generate([\"What is Deep Learning?\", \"What is Machine Learning?\"])\n",
+    "print(rich_output.generations)\n",
+    "\n",
+    "\n",
+    "# Example 4: Streaming output\n",
+    "llm = TitanTakeoffPro(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n",
+    "prompt = \"What is the capital of France?\"\n",
+    "llm(prompt)\n",
+    "\n",
+    "# Example 5: Using LCEL\n",
+    "llm = TitanTakeoffPro()\n",
+    "prompt = PromptTemplate.from_template(\"Tell me about {topic}\")\n",
+    "chain = prompt | llm\n",
+    "chain.invoke({\"topic\": \"the universe\"})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}