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

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    "---\n",
    "sidebar_label: Bedrock\n",
    "---"
   ]
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    "# 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."
   ]
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     "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,
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   "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",
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   "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": []
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   "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": {},
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    }
   ],
   "source": [
    "messages = [\n",
    "    HumanMessage(\n",
    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
    "    )\n",
    "]\n",
    "chat.invoke(messages)"
   ]
  },
  {
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   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36575b3",
   "metadata": {},
   "source": [
    "### LLM Caching with OpenSearch Semantic Cache\n",
    "\n",
    "Use OpenSearch as a semantic cache to cache prompts and responses and evaluate hits based on semantic similarity.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "375d4e56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.globals import set_llm_cache\n",
    "from langchain_aws import BedrockEmbeddings, ChatBedrock\n",
    "from langchain_community.cache import OpenSearchSemanticCache\n",
    "from langchain_core.messages import HumanMessage\n",
    "\n",
    "bedrock_embeddings = BedrockEmbeddings(\n",
    "    model_id=\"amazon.titan-embed-text-v1\", region_name=\"us-east-1\"\n",
    ")\n",
    "\n",
    "chat = ChatBedrock(\n",
    "    model_id=\"anthropic.claude-3-haiku-20240307-v1:0\", model_kwargs={\"temperature\": 0.5}\n",
    ")\n",
    "\n",
    "# Enable LLM cache. Make sure OpenSearch is set up and running. Update URL accordingly.\n",
    "set_llm_cache(\n",
    "    OpenSearchSemanticCache(\n",
    "        opensearch_url=\"http://localhost:9200\", embedding=bedrock_embeddings\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%%time\n",
    "# The first time, it is not yet in cache, so it should take longer\n",
    "messages = [HumanMessage(content=\"tell me about Amazon Bedrock\")]\n",
    "response_text = chat.invoke(messages)\n",
    "\n",
    "print(response_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cfb3086",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# The second time, while not a direct hit, the question is semantically similar to the original question,\n",
    "# so it uses the cached result!\n",
    "\n",
    "messages = [HumanMessage(content=\"what is amazon bedrock\")]\n",
    "response_text = chat.invoke(messages)\n",
    "\n",
    "print(response_text)"
   ]
  }
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