docs: moving FireworksEmbeddings documentation to docs folder

docs/docs/integrations/text_embedding/fireworks.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b14a24db",
+   "metadata": {},
+   "source": [
+    "# FireworksEmbeddings\n",
+    "\n",
+    "This notebook explains how to use Fireworks Embeddings, which is included in the langchain_fireworks package, to embed texts in langchain. We use the default nomic-ai v1.5 model in this example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ab948fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install -qU langchain-fireworks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67c637ca",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5709b030",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_fireworks import FireworksEmbeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d81e58c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if \"FIREWORKS_API_KEY\" not in os.environ:\n",
+    "    os.environ[\"FIREWORKS_API_KEY\"] = getpass.getpass(\"Fireworks API Key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a2a098d",
+   "metadata": {},
+   "source": [
+    "# Using the Embedding Model\n",
+    "With `FireworksEmbeddings`, you can directly use the default model 'nomic-ai/nomic-embed-text-v1.5', or set a different one if available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "584b9af5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding = FireworksEmbeddings(mode=\"nomic-ai/nomic-embed-text-v1.5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "be18b873",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.01367950439453125, 0.0103607177734375, -0.157958984375, -0.003070831298828125, 0.05926513671875]\n",
+      "[0.0369873046875, 0.00545501708984375, -0.179931640625, -0.018707275390625, 0.0552978515625]\n"
+     ]
+    }
+   ],
+   "source": [
+    "res_query = embedding.embed_query(\"The test information\")\n",
+    "res_document = embedding.embed_documents([\"test1\", \"another test\"])\n",
+    "print(res_query[:5])\n",
+    "print(res_document[1][:5])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv-2",
+   "language": "python",
+   "name": "poetry-venv-2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}