Bedrock llm and embeddings (#5464)

# Bedrock LLM and Embeddings
This PR adds a new LLM and an Embeddings class for the
[Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes
example notebooks for using the LLM class in a conversation chain and
embeddings usage in creating an embedding for a query and document.

**Note**: AWS is doing a private release of the Bedrock service on
05/31/2023; users need to request access and added to an allowlist in
order to start using the Bedrock models and embeddings. Please use the
[Bedrock Home Page](https://aws.amazon.com/bedrock) to request access
and to learn more about the models available in Bedrock.

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docs/modules/models/llms/integrations/bedrock.ipynb

@@ -0,0 +1,86 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amazon Bedrock"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install boto3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms.bedrock import Bedrock\n",
+    "\n",
+    "llm = Bedrock(credentials_profile_name=\"bedrock-admin\", model_id=\"amazon.titan-tg1-large\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using in a conversation chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import ConversationChain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "\n",
+    "conversation = ConversationChain(\n",
+    "    llm=llm,\n",
+    "    verbose=True,\n",
+    "    memory=ConversationBufferMemory()\n",
+    ")\n",
+    "\n",
+    "conversation.predict(input=\"Hi there!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/modules/models/text_embedding/examples/bedrock.ipynb

@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "75e378f5-55d7-44b6-8e2e-6d7b8b171ec4",
+   "metadata": {},
+   "source": [
+    "# Bedrock Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2dbe40fa-7c0b-4bcb-a712-230bf613a42f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install boto3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "282239c8-e03a-4abc-86c1-ca6120231a20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import BedrockEmbeddings\n",
+    "\n",
+    "embeddings = BedrockEmbeddings(credentials_profile_name=\"bedrock-admin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19a46868-4bed-40cd-89ca-9813fbfda9cb",
+   "metadata": {},
+   "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([\"This is a content of the document\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}