langchain/docs/extras/integrations/text_embedding/edenai.ipynb

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   "source": [
    "# EDEN AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eden AI is an AI consulting company that was founded to use its resources to empower people and create impactful products that use AI to improve the quality of life for individuals, businesses and societies at large."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example goes over how to use LangChain to interact with Eden AI embedding models\n",
    "\n",
    "-----------------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing the EDENAI's API requires an API key, \n",
    "\n",
    "which you can get by creating an account https://app.edenai.run/user/register  and heading here https://app.edenai.run/admin/account/settings\n",
    "\n",
    "Once we have a key we'll want to set it as an environment variable by running:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "export EDENAI_API_KEY=\"...\""
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you'd prefer not to set an environment variable you can pass the key in directly via the edenai_api_key named parameter\n",
    "\n",
    " when initiating the EdenAI embedding class:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings.edenai import EdenAiEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = EdenAiEmbeddings(edenai_api_key=\"...\",provider=\"...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calling a model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The EdenAI API brings together various providers.\n",
    "\n",
    "To access a specific model, you can simply use the \"provider\" when calling.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = EdenAiEmbeddings(provider=\"openai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = [\"It's raining right now\", \"cats are cute\"]\n",
    "document_result = embeddings.embed_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"my umbrella is broken\"\n",
    "query_result = embeddings.embed_query(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine similarity between \"It's raining right now\" and query: 0.849261496107252\n",
      "Cosine similarity between \"cats are cute\" and query: 0.7525900655705218\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "query_numpy = np.array(query_result)\n",
    "for doc_res, doc in zip(document_result, docs):\n",
    "    document_numpy = np.array(doc_res)\n",
    "    similarity = np.dot(query_numpy, document_numpy) / (\n",
    "        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)\n",
    "    )\n",
    "    print(f'Cosine similarity between \"{doc}\" and query: {similarity}')"
   ]
  }
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