mirror of
https://github.com/hwchase17/langchain
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89 lines
1.9 KiB
Plaintext
89 lines
1.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bedrock"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"[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"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install boto3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.llms.bedrock import Bedrock\n",
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"\n",
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"llm = Bedrock(\n",
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" credentials_profile_name=\"bedrock-admin\",\n",
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" model_id=\"amazon.titan-tg1-large\",\n",
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" endpoint_url=\"custom_endpoint_url\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Using in a conversation chain"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.chains import ConversationChain\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"\n",
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"conversation = ConversationChain(\n",
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" llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
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")\n",
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"\n",
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"conversation.predict(input=\"Hi there!\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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