mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-16 20:42:01 +00:00
240190db3f
- updated titles and descriptions of the `integrations/memory` notebooks into consistent and laconic format; - removed `docs/extras/integrations/memory/motorhead_memory_managed.ipynb` file as a duplicate of the `docs/extras/integrations/memory/motorhead_memory.ipynb`; - added `integrations/providers` Integration Cards for `dynamodb`, `motorhead`. - updated `integrations/providers/redis.mdx` with links - renamed several notebooks; updated `vercel.json` to reroute new names.
372 lines
12 KiB
Plaintext
372 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "91c6a7ef",
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"metadata": {},
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"source": [
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"# AWS DynamoDB\n",
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"\n",
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">[Amazon AWS DynamoDB](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/dynamodb/index.html) is a fully managed `NoSQL` database service that provides fast and predictable performance with seamless scalability.\n",
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"\n",
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"This notebook goes over how to use `DynamoDB` to store chat message history."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3f608be0",
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"metadata": {},
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"source": [
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"First make sure you have correctly configured the [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html). Then make sure you have installed `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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"id": "3a7e89c2-4c55-4a66-91ec-9bf9a37467eb",
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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": "markdown",
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"id": "030d784f",
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"metadata": {},
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"source": [
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"Next, create the `DynamoDB` Table where we will be storing messages:"
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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": 10,
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"id": "93ce1811",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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}
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],
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"source": [
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"import boto3\n",
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"\n",
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"# Get the service resource.\n",
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"dynamodb = boto3.resource(\"dynamodb\")\n",
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"\n",
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"# Create the DynamoDB table.\n",
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"table = dynamodb.create_table(\n",
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" TableName=\"SessionTable\",\n",
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" KeySchema=[{\"AttributeName\": \"SessionId\", \"KeyType\": \"HASH\"}],\n",
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" AttributeDefinitions=[{\"AttributeName\": \"SessionId\", \"AttributeType\": \"S\"}],\n",
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" BillingMode=\"PAY_PER_REQUEST\",\n",
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")\n",
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"\n",
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"# Wait until the table exists.\n",
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"table.meta.client.get_waiter(\"table_exists\").wait(TableName=\"SessionTable\")\n",
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"\n",
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"# Print out some data about the table.\n",
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"print(table.item_count)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a9b310b",
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"metadata": {},
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"source": [
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"## DynamoDBChatMessageHistory"
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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": 11,
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"id": "d15e3302",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n",
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"\n",
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"history = DynamoDBChatMessageHistory(table_name=\"SessionTable\", session_id=\"0\")\n",
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"\n",
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"history.add_user_message(\"hi!\")\n",
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"\n",
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"history.add_ai_message(\"whats up?\")"
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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": 12,
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"id": "64fc465e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
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" AIMessage(content='whats up?', additional_kwargs={}, example=False),\n",
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" HumanMessage(content='hi!', additional_kwargs={}, example=False),\n",
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" AIMessage(content='whats up?', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"history.messages"
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]
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},
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{
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"cell_type": "markdown",
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"id": "955f1b15",
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"metadata": {},
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"source": [
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"## DynamoDBChatMessageHistory with Custom Endpoint URL\n",
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"\n",
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"Sometimes it is useful to specify the URL to the AWS endpoint to connect to. For instance, when you are running locally against [Localstack](https://localstack.cloud/). For those cases you can specify the URL via the `endpoint_url` parameter in the constructor."
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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": 13,
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"id": "225713c8",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n",
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"\n",
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"history = DynamoDBChatMessageHistory(\n",
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" table_name=\"SessionTable\",\n",
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" session_id=\"0\",\n",
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" endpoint_url=\"http://localhost.localstack.cloud:4566\",\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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"id": "97f8578a",
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"metadata": {},
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"source": [
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"## DynamoDBChatMessageHistory With Different Keys Composite Keys\n",
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"The default key for DynamoDBChatMessageHistory is ```{\"SessionId\": self.session_id}```, but you can modify this to match your table design.\n",
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"\n",
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"### Primary Key Name\n",
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"You may modify the primary key by passing in a primary_key_name value in the constructor, resulting in the following:\n",
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"```{self.primary_key_name: self.session_id}```\n",
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"\n",
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"### Composite Keys\n",
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"When using an existing DynamoDB table, you may need to modify the key structure from the default of to something including a Sort Key. To do this you may use the ```key``` parameter.\n",
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"\n",
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"Passing a value for key will override the primary_key parameter, and the resulting key structure will be the passed value.\n"
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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": 14,
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"id": "088c037c",
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[HumanMessage(content='hello, composite dynamodb table!', additional_kwargs={}, example=False)]"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n",
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"\n",
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"composite_table = dynamodb.create_table(\n",
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" TableName=\"CompositeTable\",\n",
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" KeySchema=[{\"AttributeName\": \"PK\", \"KeyType\": \"HASH\"}, {\"AttributeName\": \"SK\", \"KeyType\": \"RANGE\"}],\n",
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" AttributeDefinitions=[{\"AttributeName\": \"PK\", \"AttributeType\": \"S\"}, {\"AttributeName\": \"SK\", \"AttributeType\": \"S\"}],\n",
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" BillingMode=\"PAY_PER_REQUEST\",\n",
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")\n",
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"\n",
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"# Wait until the table exists.\n",
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"composite_table.meta.client.get_waiter(\"table_exists\").wait(TableName=\"CompositeTable\")\n",
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"\n",
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"# Print out some data about the table.\n",
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"print(composite_table.item_count)\n",
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"\n",
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"my_key = {\n",
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" \"PK\": \"session_id::0\",\n",
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" \"SK\": \"langchain_history\",\n",
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"}\n",
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"\n",
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"composite_key_history = DynamoDBChatMessageHistory(\n",
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" table_name=\"CompositeTable\",\n",
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" session_id=\"0\",\n",
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" endpoint_url=\"http://localhost.localstack.cloud:4566\",\n",
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" key=my_key,\n",
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")\n",
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"\n",
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"composite_key_history.add_user_message(\"hello, composite dynamodb table!\")\n",
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"\n",
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"composite_key_history.messages"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3b33c988",
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"metadata": {},
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"source": [
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"## Agent with DynamoDB Memory"
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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": 15,
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"id": "f92d9499",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import Tool\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.agents import initialize_agent\n",
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"from langchain.agents import AgentType\n",
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"from langchain.utilities import PythonREPL\n",
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"from getpass import getpass\n",
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"\n",
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"message_history = DynamoDBChatMessageHistory(table_name=\"SessionTable\", session_id=\"1\")\n",
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"memory = ConversationBufferMemory(\n",
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" memory_key=\"chat_history\", chat_memory=message_history, return_messages=True\n",
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")"
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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": 16,
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"id": "1167eeba",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_repl = PythonREPL()\n",
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"\n",
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"# You can create the tool to pass to an agent\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"python_repl\",\n",
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" description=\"A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\",\n",
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" func=python_repl.run,\n",
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" )\n",
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"]"
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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": 17,
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"id": "fce085c5",
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"metadata": {},
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"outputs": [
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{
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"ename": "ValidationError",
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"evalue": "1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m llm \u001b[38;5;241m=\u001b[39m \u001b[43mChatOpenAI\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtemperature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m agent_chain \u001b[38;5;241m=\u001b[39m initialize_agent(\n\u001b[1;32m 3\u001b[0m tools,\n\u001b[1;32m 4\u001b[0m llm,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 7\u001b[0m memory\u001b[38;5;241m=\u001b[39mmemory,\n\u001b[1;32m 8\u001b[0m )\n",
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"File \u001b[0;32m~/Documents/projects/langchain/libs/langchain/langchain/load/serializable.py:74\u001b[0m, in \u001b[0;36mSerializable.__init__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 74\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lc_kwargs \u001b[38;5;241m=\u001b[39m kwargs\n",
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"File \u001b[0;32m~/Documents/projects/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;31mValidationError\u001b[0m: 1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)"
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]
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}
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],
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"source": [
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"llm = ChatOpenAI(temperature=0)\n",
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"agent_chain = initialize_agent(\n",
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" tools,\n",
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" llm,\n",
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" agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,\n",
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" verbose=True,\n",
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" memory=memory,\n",
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")"
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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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"id": "952a3103",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Hello!\")"
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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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"id": "54c4aaf4",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Who owns Twitter?\")"
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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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"id": "f9013118",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"My name is Bob.\")"
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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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"id": "405e5315",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_chain.run(input=\"Who am I?\")\n"
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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": "3.10.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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