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langchain/docs/docs/integrations/chat/bedrock.ipynb

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"source": [
"---\n",
"sidebar_label: Bedrock\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "bf733a38-db84-4363-89e2-de6735c37230",
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"source": [
"# ChatBedrock\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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d51edc81",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install --upgrade --quiet langchain-aws"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain_aws import ChatBedrock\n",
"from langchain_core.messages import HumanMessage"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"chat = ChatBedrock(\n",
" model_id=\"anthropic.claude-3-sonnet-20240229-v1:0\",\n",
" model_kwargs={\"temperature\": 0.1},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content=\"Voici la traduction en français :\\n\\nJ'aime la programmation.\", additional_kwargs={'usage': {'prompt_tokens': 20, 'completion_tokens': 21, 'total_tokens': 41}}, response_metadata={'model_id': 'anthropic.claude-3-sonnet-20240229-v1:0', 'usage': {'prompt_tokens': 20, 'completion_tokens': 21, 'total_tokens': 41}}, id='run-994f0362-0e50-4524-afad-3c4f5bb11328-0')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"messages = [\n",
" HumanMessage(\n",
" content=\"Translate this sentence from English to French. I love programming.\"\n",
" )\n",
"]\n",
"chat.invoke(messages)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "a4a4f4d4",
"metadata": {},
"source": [
"### Streaming\n",
"\n",
"To stream responses, you can use the runnable `.stream()` method."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d9e52838",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voici la traduction en français :\n",
"\n",
"J'aime la programmation."
]
}
],
"source": [
"for chunk in chat.stream(messages):\n",
" print(chunk.content, end=\"\", flush=True)"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
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"nbformat": 4,
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