langchain/docs/versioned_docs/version-0.2.x/integrations/text_embedding/bedrock.ipynb

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    "# Bedrock\n",
    "\n",
    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
    "> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
    "> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
    "> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
    "> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
    "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
    "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
    "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
    "> generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
    "\n"
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   "source": [
    "%pip install --upgrade --quiet  boto3"
   ]
  },
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   "id": "282239c8-e03a-4abc-86c1-ca6120231a20",
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   "source": [
    "from langchain_community.embeddings import BedrockEmbeddings\n",
    "\n",
    "embeddings = BedrockEmbeddings(\n",
    "    credentials_profile_name=\"bedrock-admin\", region_name=\"us-east-1\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19a46868-4bed-40cd-89ca-9813fbfda9cb",
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   "outputs": [],
   "source": [
    "embeddings.embed_query(\"This is a content of the document\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf0349c4-6408-4342-8691-69276a388784",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings.embed_documents(\n",
    "    [\"This is a content of the document\", \"This is another document\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f6b364d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# async embed query\n",
    "await embeddings.aembed_query(\"This is a content of the document\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9240a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# async embed documents\n",
    "await embeddings.aembed_documents(\n",
    "    [\"This is a content of the document\", \"This is another document\"]\n",
    ")"
   ]
  }
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