pinecone: Add embedding Inference Support (#24515)

**Description**

Add support for Pinecone hosted embedding models as
`PineconeEmbeddings`. Replacement for #22890

**Dependencies**
Add `aiohttp` to support async embeddings call against REST directly

- [x] **Add tests and docs**: If you're adding a new integration, please
include

Added `docs/docs/integrations/text_embedding/pinecone.ipynb`


- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/

Twitter: `gdjdg17`

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/text_embedding/pinecone.ipynb

@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Pinecone Embeddings\n",
+    "\n",
+    "Pinecone's inference API can be accessed via `PineconeEmbeddings`. Providing text embeddings via the Pinecone service. We start by installing prerequisite libraries:"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "f4b5d823fee826c2"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "!pip install -qU \"langchain-pinecone>=0.2.0\" "
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "3bc5d3a5ed7f5ce3",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Next, we [sign up / log in to Pinecone](https://app.pinecone.io) to get our API key:"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "62a77d25c3fd8bd5"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"PINECONE_API_KEY\"] = os.getenv(\"PINECONE_API_KEY\") or getpass(\n",
+    "    \"Enter your Pinecone API key: \"\n",
+    ")"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "8162dbcbcf7d3d55",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Check the document for available [models](https://docs.pinecone.io/models/overview). Now we initialize our embedding model like so:"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "98d860a0a2d8b907"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "from langchain_pinecone import PineconeEmbeddings\n",
+    "\n",
+    "embeddings = PineconeEmbeddings(model=\"multilingual-e5-large\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "2b3adb72786a5275",
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "From here we can create embeddings either sync or async, let's start with sync! We embed a single text as a query embedding (ie what we search with in RAG) using `embed_query`:"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "11e24da855517230"
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "docs = [\n",
+    "    \"Apple is a popular fruit known for its sweetness and crisp texture.\",\n",
+    "    \"The tech company Apple is known for its innovative products like the iPhone.\",\n",
+    "    \"Many people enjoy eating apples as a healthy snack.\",\n",
+    "    \"Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.\",\n",
+    "    \"An apple a day keeps the doctor away, as the saying goes.\",\n",
+    "]"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "2da515e2a61ef7e9",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "doc_embeds = embeddings.embed_documents(docs)\n",
+    "doc_embeds"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "2897e0d570c90b2f",
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "outputs": [],
+   "source": [
+    "query = \"Tell me about the tech company known as Apple\"\n",
+    "query_embed = embeddings.embed_query(query)\n",
+    "query_embed"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "510784963c0e17a",
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}