What is ChatGPT?

A definition of Artificial Intelligence

There is a lot of hype, misinformation and nonsense surrounding ChatGPT and machine learning (ML) in all its forms. NOTE: I’m using the term machine learning specifically to include artificial intelligence (AI) and related topics. Let’s begin with some definitions; using Wikipedia because it’s readily available, subject to crowd sourced peer review and generally considered a relatively trusted source of information.

from https://en.wikipedia.org/wiki/Artificial_intelligence

Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to intelligence of humans and other animals.

This definition is bounded by machines, as opposed to biological or alien type definitions. Hence my preference to use the term machine learning. Those without a STEM background and some segments of the media seem to have a broader view of artificial intelligence.

What is ChatGPT?

ChatGPT is a portmanteau of Chat Generative Pre-trained Transformer. It is a collection of three technologies:

• chat functionality, or the front end where humans can ask questions
• Generative, pre trained model - this is the ‘knowledge’ piece
• Transformer, which is a deep learning model that is an evolution of Recurrent Neural Networks (RNN).

Primarily ChatGPT is trained on large data sets (internet based) and it is good at making associations between terms and phrases. It’s not intelligent, it’s not sentient and it’s not a general purpose AI. It is however quite good at certain tasks in assisting humans. The remainder of this post will highlight three examples of ChatGPT generating computer code for AWS Cloudformation, R and the Amazon Kendra API.

ChatGPT - Generating Code

TL;DR

Here’s a summary of ChatGPT pros and cons: Pros

• Generally very effective at scaffolding many 10s of lines of code and abiding by language specific syntax, layout and structure
• Able to produce error free R code sometimes
• Is quite good at generating AWS CLI code including JQ query syntax and simple bash scripting
• Readily generates improved code when advised of errors in code it has already generated in the current session
• Is much more focussed in it’s answers than an equivalent web search. Which makes it useful for getting up to speed with programming constructs.

Cons

• It generates non functional code for AWS CloudFormation with incorrect yaml syntax and nesting constructs
• It frequently fails to process requiring a browser refresh; but this acceptable in the free demonstrator
• Generates incorrect parameter options like csv as an output for AWS CLI
• Will produce inconsistent output for the same question in different sessions. This a feature of deep learning models and with syntax like R there are often many different ways of solving a problem.
• Seems to have no comprehension of language and syntax versions, often using obsolete libraries and code output
• If you provide it with code with secrets and personally identifiable information (PII) it will return results with that information. It will not obfuscate unless you tell it to. Presumably those secrets and PII will then be accessible or captured in logs and retained information.

In summary ChatGPT, and generative ML is useful for:

• Returning much more contextually useful websearch than current web search engines
• Reliably useful for scaffolding contextually relevant code
• Understanding error messages and likely fixes for code it has already generated or is provided with

Continue reading articles in my Amazon Web Services series

• Data Warehousing on AWS
• Migrating to AWS
• AWS Business Essentials
• IAM Demo
• Architecting on AWS
• SysOps on AWS
• S3 Demo
• Predict the Future
• AWS Tech Essentials
• Developing on AWS
• DevOps on AWS
• Advanced Architecting on AWS
• Big Data on AWS
• AWS Deep Dive Toolbox
• Security Engineering on AWS
• Deep Learning on AWS
• AWS List of Services
• Networking on Aws
• AWS Data and Analytics
• Microsoft Immersion Day
• Adelaide Deep Racer Hints and Tips
• Deep Racer Awards
• Windows on AWS
• AWS Ask Me Anything
• Cloudwatch and Systems Manager Workshop
• Containers Immersion Day
• Redshift Immersion Day
• Innovation in Ambiguity
• AWS Contingency Planning
• AWS CLI Examples
• Migrating to Cloud in 2023
• Chaos Engineering Workshop
• Chatgpt Friend or Foe

Code Generation Examples

R and GGPLOT

write an R script to count number of occurences of text in a dataframe column and plot using ggplot

Sure! Here’s an R script that will count the number of occurrences of text in a dataframe column and plot the results using ggplot:

R

library(ggplot2)

# Create a sample dataframe
df <- data.frame(
  text_col = c("apple", "banana", "apple", "orange", "banana", "pear")
)

# Use table() to count the number of occurrences of each text in the column
text_counts <- table(df$text_col)

# Convert the counts to a dataframe for plotting
counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts))

# Plot the counts using ggplot
ggplot(counts_df, aes(x = text, y = count)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(x = "Text", y = "Count", title = "Number of Occurrences of Text in Column")

In this script, we first create a sample dataframe with a column called text_col. We then use the table() function to count the number of occurrences of each text in this column. We convert these counts to a dataframe using data.frame(), and then plot the results using ggplot(). The geom_bar() function is used to create a bar chart, with the fill parameter set to “blue” to color the bars. Finally, we use labs() to add labels to the x and y axes, as well as a title for the plot.

rewrite to extract certain text strings only

Sure! To extract only certain text strings from the dataframe column, you can use the grep() or grepl() function in R. Here’s an updated script that extracts text strings containing “apple” or “banana” and plots the results using ggplot:

R

library(ggplot2)

# Create a sample dataframe
df <- data.frame(
  text_col = c("apple", "banana", "apple", "orange", "banana", "pear")
)

# Extract only text strings containing "apple" or "banana"
df_subset <- df[grepl("apple|banana", df$text_col), ]

# Use table() to count the number of occurrences of each text in the column
text_counts <- table(df_subset$text_col)

# Convert the counts to a dataframe for plotting
counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts))

# Plot the counts using ggplot
ggplot(counts_df, aes(x = text, y = count)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(x = "Text", y = "Count", title = "Number of Occurrences of Text in Column")

In this updated script, we use grepl() to extract only the text strings that contain “apple” or “banana” from the text_col column of the df dataframe. We then count the number of occurrences of each text string using table(), and plot the results using ggplot(). The rest of the code remains the same as in the previous script.

rewrite to ignore text case

Sure! To ignore the case of text strings in the dataframe column, you can convert them all to lowercase using the tolower() function in R. Here’s an updated script that converts all text strings to lowercase before extracting those containing “apple” or “banana”, and plots the results using ggplot:

R

library(ggplot2)

# Create a sample dataframe
df <- data.frame(
  text_col = c("Apple", "banana", "APPLE", "orange", "Banana", "pear")
)

# Convert all text strings to lowercase
df$text_col <- tolower(df$text_col)

# Extract only text strings containing "apple" or "banana"
df_subset <- df[grepl("apple|banana", df$text_col), ]

# Use table() to count the number of occurrences of each text in the column
text_counts <- table(df_subset$text_col)

# Convert the counts to a dataframe for plotting
counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts))

# Plot the counts using ggplot
ggplot(counts_df, aes(x = text, y = count)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(x = "Text", y = "Count", title = "Number of Occurrences of Text in Column")

In this updated script, we use tolower() to convert all text strings in the text_col column of the df dataframe to lowercase. We then use grepl() to extract only the text strings that contain “apple” or “banana” (ignoring case) from the text_col column of the df dataframe. We then count the number of occurrences of each text string using table(), and plot the results using ggplot(). The rest of the code remains the same as in the previous script.

Update this R script to plot each text string as a seperate bar. library(ggplot2) # Create a sample dataframe df <- data.frame( text_col = c(“Apple”, “banana”, “APPLE”, “orange”, “Banana”, “pear”) ) # Convert all text strings to lowercase df$text_col <- tolower(df$text_col) # Extract only text strings containing “apple” or “banana” df_subset <- df[grepl(“apple|banana”, df$text_col), ] # Use table() to count the number of occurrences of each text in the column text_counts <- table(df_subset$text_col) # Convert the counts to a dataframe for plotting counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts)) # Plot the counts using ggplot ggplot(counts_df, aes(x = text, y = count)) + geom_bar(stat = “identity”, fill = “blue”) + labs(x = “Text”, y = “Count”, title = “Number of Occurrences of Text in Column”)

To plot each text string as a separate bar, you can modify the code to create a new variable that contains a unique identifier for each string. Here’s the updated code:

R

# Create a sample dataframe
df <- data.frame(
  text_col = c("Apple", "banana", "APPLE", "orange", "Banana", "pear")
)

# Convert all text strings to lowercase
df$text_col <- tolower(df$text_col)

# Extract only text strings containing "apple" or "banana"
df_subset <- df[grepl("apple|banana", df$text_col), ]

# Use table() to count the number of occurrences of each text in the column
text_counts <- table(df_subset$text_col)

# Convert the counts to a dataframe for plotting
counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts))

# Add a new variable with a unique identifier for each text string
counts_df$id <- seq_len(nrow(counts_df))

# Plot the counts using ggplot
ggplot(counts_df, aes(x = id, y = count, fill = text)) +
  geom_bar(stat = "identity") +
  scale_fill_discrete(name = "Text") +
  labs(x = "Text", y = "Count", title = "Number of Occurrences of Text in Column") +
  theme(legend.position = "bottom")

In the updated code, a new variable called “id” is created using the seq_len() function, which generates a sequence of numbers from 1 to the number of rows in the dataframe. This creates a unique identifier for each text string, which is used as the x-axis variable in the ggplot. The fill argument in the aes() function is set to “text”, so each bar is colored based on the text string it represents. The scale_fill_discrete() function is used to create a legend for the fill colors. Finally, the theme() function is used to move the legend to the bottom of the plot.

rewrite so that each text string is counted and plotted so that the number of bars equals the number of text strings searched

To plot each text string separately and have the number of bars equal the number of text strings searched, you can modify the code as follows:

R

# Create a sample dataframe
df <- data.frame(
  text_col = c("Apple", "banana", "APPLE", "orange", "Banana", "pear")
)

# Convert all text strings to lowercase
df$text_col <- tolower(df$text_col)

# Define the text strings to search
search_text <- c("apple", "banana")

# Filter the dataframe to include only rows that contain the search text
df_subset <- df[grepl(paste(search_text, collapse = "|"), df$text_col), ]

# Use table() to count the number of occurrences of each text in the column
text_counts <- table(df_subset$text_col)

# Convert the counts to a dataframe for plotting
counts_df <- data.frame(text = names(text_counts), count = as.numeric(text_counts))

# Plot the counts using ggplot
ggplot(counts_df, aes(x = text, y = count)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(x = "Text", y = "Count", title = "Number of Occurrences of Text in Column") +
  scale_x_discrete(limits = search_text)

In the updated code, the search_text variable is defined to contain the text strings to search for. The paste() function is used to concatenate the search text into a single regular expression pattern, which is passed to the grepl() function to filter the rows in the dataframe that contain the search text. The limits argument in the scale_x_discrete() function is set to search_text, which limits the x-axis to the search text strings, ensuring that each text string is counted and plotted as a separate bar. Note that the fill argument is removed since each text string is plotted with a different color by default.

Write an R script to search for specific text strings in a data frame column, count the occurence and add to a new column titled counts, plot using ggplot where each text string is a seperate bar

Assuming you have a data frame named df with a column named text_column that contains the text you want to search, the following R script will search for specific text strings in the column, count the occurrences, and create a new column called counts. Then it will plot a bar chart using ggplot where each text string is a separate bar:

R

library(dplyr)
library(ggplot2)

# Define the text strings you want to search for
search_strings <- c("text1", "text2", "text3")

# Use dplyr's mutate function to create a new column called "counts" that counts the occurrences of each search string in the text_column
df <- df %>% mutate(counts = sapply(strsplit(as.character(text_column), "\\s+"), function(x) sum(x %in% search_strings)))

# Use ggplot to create a bar chart of the counts for each search string
ggplot(df, aes(x = "", y = counts, fill = text_column)) +
  geom_bar(stat = "identity", width = 0.8) +
  coord_flip() +
  scale_fill_brewer(palette = "Set1") +
  labs(title = "Counts of Specific Text Strings in Text Column", fill = "Text Strings", y = "Counts", x = "") +
  theme(plot.title = element_text(hjust = 0.5))

In the above code, the search_strings variable should be replaced with the actual text strings you want to search for in the text_column. The strsplit function is used to split the text_column into individual words, and sum(x %in% search_strings) counts the number of times each search string appears in the text. The resulting counts are stored in the new counts column. The ggplot code creates a bar chart with each search string as a separate bar, and the fill aesthetic is used to color each bar based on the original text string. The coord_flip() function is used to rotate the chart so that the bars are horizontal, and various theme functions are used to adjust the appearance of the chart.

write an R script to check for specific text in a data frame column. create a new data frame, with all original columns but only the rows that contain the specific text

Sure, here’s an R script that should do what you’re looking for:

R

# First, create a sample data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Dave"),
  city = c("New York", "Los Angeles", "San Francisco", "Boston")
)

# Specify the specific text you want to search for
specific_text <- "San"

# Use grep to find rows that contain the specific text
matches <- grep(specific_text, df$city)

# Create a new data frame with only the matching rows
new_df <- df[matches, ]

# Print the new data frame
print(new_df)

In this example, we first create a sample data frame with two columns: “name” and “city”. We then specify the specific text we want to search for (in this case, “San”). We use the grep function to find all rows where the “city” column contains the specific text, and store the resulting row numbers in a variable called “matches”. Finally, we create a new data frame by subsetting the original data frame using the “matches” variable, and print the new data frame to the console. Note that this script assumes that you want to search for the specific text in a single column of the data frame. If you want to search for the text in multiple columns, you may need to modify the script accordingly.

add additional text search strings

Sure thing! Here’s an updated version of the R script that allows you to specify multiple search strings:

R

# First, create a sample data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Dave"),
  city = c("New York", "Los Angeles", "San Francisco", "Boston")
)

# Specify the search strings you want to use
search_strings <- c("San", "York")

# Use grepl to find rows that contain any of the search strings
matches <- grepl(paste(search_strings, collapse = "|"), df$city)

# Create a new data frame with only the matching rows
new_df <- df[matches, ]

# Print the new data frame
print(new_df)

In this version of the script, we start by creating a sample data frame just like before. We then specify the search strings we want to use by creating a character vector called “search_strings”. To find rows that contain any of the search strings, we use the grepl function instead of grep. We use paste to concatenate all of the search strings into a single regular expression pattern, with the | symbol indicating “or”. We then pass this pattern to grepl, along with the “city” column of the data frame, and store the resulting logical vector in a variable called “matches”. Finally, we create a new data frame by subsetting the original data frame using the “matches” variable, and print the new data frame to the console. This should give you a data frame with only the rows that contain any of the specified search strings in the “city” column.

explain this error Error in geom_bar(): ! Problem while computing aesthetics. ℹ Error occurred in the 1st layer. Caused by error in tapply(): ! total number of levels >= 2^31

This error message is related to a problem in the geom_bar() function, which is used to create bar charts in the ggplot2 package in R. The error message indicates that there was an issue while computing aesthetics, which refers to the graphical properties of the plot such as color, size, and shape. Specifically, the error occurred in the first layer of the plot, meaning it is likely related to the data being plotted. The error is caused by a limitation in the tapply() function, which is used to summarize the data before plotting. This limitation arises because the total number of levels in the data exceeds the maximum number of levels that can be handled by R, which is 2^31 (or 2,147,483,648). This is a very large number and is unlikely to be reached in most cases. To resolve this issue, you can try reducing the number of levels in the data by aggregating or summarizing the data differently. Alternatively, you could try using a different plotting function or package that can handle larger datasets with many levels.

AWS CLI Code Generation

# Chatgpt kendra query creation
aws kendra query \
--query-text "How reliable is S3" \
--index-id "<obfuscated>" \
--query-result-type-filter ANSWER \
--page-size 1 \
--output text \
--query 'ResultSet.Results[0].DocumentExcerpt.Text'

fix the Unknown options: –size, 1 error

ChatGPT require a restart

write an amazon kendra query using the cli query call to retrieve only the answer text aws kendra query –index-id –query-text “how reliable is S3” –query-result-type-filter ANSWER

extract just the text

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query-result-type-filter ANSWER --query 'ResultItems[0].DocumentText.Text'

no, just the text

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query-result-type-filter ANSWER --query 'ResultItems[0].DocumentText.Text' --output text

Had to write this by hand as Chatgpt couldn’t write correct jmespath

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].AdditionalAttributes[].Value[].TextWithHighlightsValue[].Text'

but need to restructrure to extract document excerpt

Now extract document excerpt

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{DocumentTitle: DocumentTitle, DocumentExcerpt: DocumentExcerpt}'

Now use jq to write results to csv

jq

| jq -r '.[] | [ .DocumentTitle, .DocumentExcerpt ] | @csv'

as

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{DocumentTitle: DocumentTitle, DocumentExcerpt: DocumentExcerpt}' | jq -r '.[] | [ .DocumentTitle, .DocumentExcerpt ] | @csv'

Hmmm errors

This works

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{Title: DocumentTitle.Text, Excerpt: DocumentExcerpt.Text}'

now push to a CSV file using ChatGpt

modify this code to create a csv file

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{Title: DocumentTitle.Text, Excerpt: DocumentExcerpt.Text}'

To modify the code to create a CSV file, you can use the following command:

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{Title: DocumentTitle.Text, Excerpt: DocumentExcerpt.Text}' --output csv > output.csv

This command uses the –output flag to specify that the output format should be CSV, and redirects the output to a file called output.csv using the > operator. The resulting CSV file will contain two columns: Title and Excerpt, with one row for each result item returned by the query.

wrong do this using jq

Sure! Here’s how you can modify the command using jq to output the results to a CSV file:

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query 'ResultItems[*].{Title: DocumentTitle.Text, Excerpt: DocumentExcerpt.Text}' | jq -r '(.[0] | keys_unsorted) as $keys | $keys, map([.[ $keys[] ]])[] | @csv' > output.csv

This command uses the jq command to convert the JSON output of the aws kendra query command to CSV format. The resulting CSV file will contain two columns: Title and Excerpt, with one row for each result item returned by the query. Here’s a breakdown of what each part of the jq command does: (.[0] | keys_unsorted) as $keys: extracts the keys from the first object in the JSON array (which should contain all the keys for all the objects), and saves them to a variable called $keys. $keys: outputs the keys as the first row of the CSV. map([.[ $keys[] ]])[]: for each object in the JSON array, extracts the values for each key specified in $keys. @csv: formats the extracted values as a CSV row. > output.csv: redirects the output to a file called output.csv.

rename the csv output

modify by naming the csv file with the –query-text

Sure! Here’s how you can modify the command to name the CSV file with the –query-text parameter:

bash

query="how reliable is S3"

aws cli

aws kendra query --index-id <obfuscated> --query-text "$query" --query 'ResultItems[*].{Title: DocumentTitle.Text, Excerpt: DocumentExcerpt.Text}' | jq -r '(.[0] | keys_unsorted) as $keys | $keys, map([.[ $keys[] ]])[] | @csv' > "$query".csv

This command sets a variable called query to the value of the –query-text parameter, and uses that variable to name the CSV file. The resulting CSV file will be named after the value of the –query-text parameter and will contain two columns: Title and Excerpt, with one row for each result item returned by the query.

Here is the first attempt where ChatGPT could not generate functional code

write an amazon kendra query using the cli query call to retrieve only the answer text

To retrieve only the answer text from Amazon Kendra using the CLI query call, you can use the –query-result-type-filter parameter with the value ANSWER. Here is an example command:

aws cli

aws kendra query --index-id your_index_id --query-text "your_query_text" --query-result-type-filter ANSWER --region your_region

Replace your_index_id with the ID of your Amazon Kendra index, your_query_text with the text you want to search for, and your_region with the region where your Amazon Kendra index is located. This command will return only the answer text from the search results, filtering out any other types of results such as documents or questions.

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query-result-type-filter ANSWER

The command aws kendra query –index-id –query-text “how reliable is S3” –query-result-type-filter ANSWER

will search the Amazon Kendra index with ID for the query “how reliable is S3”, and will filter the results to only return answers.

This command will return the answer text from the search results that best matches the query “how reliable is S3”. The answer text may provide information on the reliability of Amazon S3, a highly scalable and durable cloud storage service offered by Amazon Web Services (AWS).

extract just the text

To extract just the answer text from the output of the aws kendra query command, you can use the –query parameter with a JMESPath expression that selects the Text field of the first item in the ResultItems array. Here is an example command:

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query-result-type-filter ANSWER --query 'ResultItems[0].DocumentText.Text' --region your_region

Replace your_region with the region where your Amazon Kendra index is located.

This command will return only the answer text from the search results in a plain text format, filtering out any other information such as the score or source document information. The output will be the text of the first item in the ResultItems array, which should be the answer text that best matches the query “how reliable is S3”.

no, just the text

To extract just the answer text from the output of the aws kendra query command, you can use the –query parameter with a JMESPath expression that selects the Text field of the first item in the ResultItems array and wraps it with double quotes. Here is an example command:

aws cli

aws kendra query --index-id <obfuscated> --query-text "how reliable is S3" --query-result-type-filter ANSWER --query 'ResultItems[0].DocumentText.Text' --output text --region your_region

Replace your_region with the region where your Amazon Kendra index is located.

This command will return only the answer text from the search results in a plain text format, filtering out any other information such as the score or source document information, and wrapping the text with double quotes. The output will be the text of the first item in the ResultItems array, which should be the answer text that best matches the query “how reliable is S3”.

AWS CloudFormation Code Generation

write a cloudformation template that uses an existing cloudfront distribution and accepts a http GET with query parameters and returns a response of 200 and OK. Include api gateway, lambda and use python

CloudFormation Template for HTTP GET Response in Python Add prod stage to API. CloudFormation API Gateway. Create CloudFormation template. HTTP GET 200 OK Python Route to AWS. CloudFormation template creation. Create CloudFormation Template. Build Serverless API KeyError accessing dictionary. Test writing to DynamoDB. DynamoDB KeyError Solution. Python Test for DynamoDB Write Torque data to DynamoDB Presto SQL time conversion. Count rows from specific date. Unix Timestamp to Varchar. R code for Amazon Athena Python AWS Lambda 200. Python Lambda with Bearer

Write a cloudformation template that uses an existing cloudfront distribution and accepts a http GET with query parameters and returns a response of 200 and OK. Include api gateway, lambda, IAM roles and use python

Sure, here’s a CloudFormation template that sets up an API Gateway, a Lambda function, and an IAM Role, which accepts an HTTP GET request with query parameters and returns a response of 200 and OK using Python:

yaml

---
AWSTemplateFormatVersion: 2010-09-09
Resources:
  LambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: lambda-function
      Handler: lambda_function.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Runtime: python3.8
      Code:
        ZipFile: |
          import json

          def lambda_handler(event, context):
              response = {
                  "statusCode": 200,
                  "body": "OK"
              }
              return response

  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - lambda.amazonaws.com
            Action:
              - sts:AssumeRole
      Path: "/"
      Policies:
        - PolicyName: LambdaExecutionPolicy
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                Resource: arn:aws:logs:*:*:*

  ApiGatewayRestApi:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: api-gateway-rest-api

  ApiGatewayResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      ParentId: !GetAtt ApiGatewayRestApi.RootResourceId
      PathPart: my-resource

  ApiGatewayMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      HttpMethod: GET
      AuthorizationType: NONE
      Integration:
        Type: AWS_PROXY
        IntegrationHttpMethod: POST
        Uri: !Sub "arn:aws:apigateway:${AWS::Region}:cloudfront:path/${DistributionId}"
        PassthroughBehavior: WHEN_NO_TEMPLATES
      ResourceId: !Ref ApiGatewayResource
      RestApiId: !Ref ApiGatewayRestApi

  Deployment:
    Type: AWS::ApiGateway::Deployment
    DependsOn:
      - ApiGatewayMethod
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      StageName: prod

Parameters:
  DistributionId:
    Type: String
    Description: The ID of the CloudFront distribution to use as the integration endpoint.

This template sets up a CloudFormation stack that includes an API Gateway Rest API, a Lambda function, and an IAM Role. The API Gateway Rest API is configured to use a CloudFront distribution as the integration endpoint for the GET method, and the Lambda function returns a response of 200 and OK for any incoming requests. You can use the template by deploying it to an AWS account via the AWS Console or using the AWS CLI. Once the stack is created, you can use the API Gateway endpoint to send GET requests with query parameters, and the Lambda function will return a response of 200 and OK.

Include an api gateway stage called dev

Sure, here’s an updated version of the CloudFormation template that includes an API Gateway stage called “dev”:

yaml

---
AWSTemplateFormatVersion: 2010-09-09
Resources:
  LambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: lambda-function
      Handler: lambda_function.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Runtime: python3.8
      Code:
        ZipFile: |
          import json

          def lambda_handler(event, context):
              response = {
                  "statusCode": 200,
                  "body": "OK"
              }
              return response

  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - lambda.amazonaws.com
            Action:
              - sts:AssumeRole
      Path: "/"
      Policies:
        - PolicyName: LambdaExecutionPolicy
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                Resource: arn:aws:logs:*:*:*

  ApiGatewayRestApi:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: api-gateway-rest-api

  ApiGatewayResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      ParentId: !GetAtt ApiGatewayRestApi.RootResourceId
      PathPart: my-resource

  ApiGatewayMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      HttpMethod: GET
      AuthorizationType: NONE
      Integration:
        Type: AWS_PROXY
        IntegrationHttpMethod: POST
        Uri: !Sub "arn:aws:apigateway:${AWS::Region}:cloudfront:path/${DistributionId}"
        PassthroughBehavior: WHEN_NO_TEMPLATES
      ResourceId: !Ref ApiGatewayResource
      RestApiId: !Ref ApiGatewayRestApi

  Deployment:
    Type: AWS::ApiGateway::Deployment
    DependsOn:
      - ApiGatewayMethod
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      StageName: prod

  DevStage:
    Type: AWS::ApiGateway::Stage
    Properties:
      StageName: dev
      RestApiId: !Ref ApiGatewayRestApi
      DeploymentId: !Ref Deployment

Parameters:
  DistributionId:
    Type: String
    Description: The ID of the CloudFront distribution to use as the integration endpoint.

This updated template includes a new “DevStage” resource of type AWS::ApiGateway::Stage, which creates an API Gateway stage called “dev”. You can use the “dev” stage to deploy changes to your API Gateway configuration without affecting the production stage. Once the stack is created, you can access the “dev” stage by appending “/dev” to the end of the API Gateway URL. For example, if the API Gateway URL is “https://api-gateway-url.execute-api.region.amazonaws.com/prod", the “dev” stage URL would be “https://api-gateway-url.execute-api.region.amazonaws.com/dev".

fix this error Integrations of type ‘AWS_PROXY’ currently only supports Lambda function and Firehose stream invocations.

Sure!

This error occurs because AWS_PROXY integration is only supported for Lambda functions or Firehose streams. If you want to use AWS_PROXY integration with a CloudFront distribution, you can do so by invoking a Lambda function and passing the request through it. Here is an updated version of the CloudFormation template that includes a Lambda function as an intermediary between API Gateway and CloudFront:

yaml

---
AWSTemplateFormatVersion: 2010-09-09
Resources:
  LambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: lambda-function
      Handler: lambda_function.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Runtime: python3.8
      Code:
        ZipFile: |
          import json

          def lambda_handler(event, context):
              response = {
                  "statusCode": 200,
                  "body": "OK"
              }
              return response

  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service:
                - lambda.amazonaws.com
            Action:
              - sts:AssumeRole
      Path: "/"
      Policies:
        - PolicyName: LambdaExecutionPolicy
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                Resource: arn:aws:logs:*:*:*

  ApiGatewayRestApi:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: api-gateway-rest-api

  ApiGatewayResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      ParentId: !GetAtt ApiGatewayRestApi.RootResourceId
      PathPart: my-resource

  ApiGatewayMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      HttpMethod: GET
      AuthorizationType: NONE
      Integration:
        Type: AWS_PROXY
        IntegrationHttpMethod: POST
        Uri: !Sub "arn:aws:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${LambdaFunction.Arn}/invocations"
        PassthroughBehavior: WHEN_NO_TEMPLATES
      ResourceId: !Ref ApiGatewayResource
      RestApiId: !Ref ApiGatewayRestApi

  Deployment:
    Type: AWS::ApiGateway::Deployment
    DependsOn:
      - ApiGatewayMethod
    Properties:
      RestApiId: !Ref ApiGatewayRestApi
      StageName: prod

  DevStage:
    Type: AWS::ApiGateway::Stage
    Properties:
      StageName: dev
      RestApiId: !Ref ApiGatewayRestApi
      DeploymentId: !Ref Deployment

Parameters:
  DistributionId:
    Type: String
    Description: The ID of the CloudFront distribution to use as the integration endpoint.

In this updated template, we are using the Lambda function as an intermediary between API Gateway and CloudFront by invoking the Lambda function from API Gateway using AWS_PROXY integration. The Lambda function can then be used to modify or transform the request and response as needed before passing it on to CloudFront. Note that you will need to update the Lambda function code to handle the incoming event and transform the response as needed.

Cloudfront errors with missing authentication token

If you are getting a “Missing Authentication Token” error when using CloudFront with API Gateway, it’s likely that the CloudFront distribution is not passing the authentication token to API Gateway. To fix this error, you need to make sure that CloudFront is configured to pass the authentication token to API Gateway. Here’s how you can do it:

1. Go to the CloudFront distribution that you’re using with API Gateway and click on “Behaviors” tab.
2. Select the behavior that you’re using with API Gateway and click on “Edit”.
3. Under “Lambda Function Associations”, click on “Edit”.
4. In the “Origin Request” event, add the following code to include the Authorization header in the request:

yaml

"headers": {
    "Authorization": {
        "value": "$http_authorization"
    }
}

This configuration will ensure that the authentication token is passed from the client to CloudFront, and then from CloudFront to API Gateway, which should resolve the “Missing Authentication Token” error. Note that the $http_authorization variable should be used exactly as shown, with the $ symbol and the curly braces. This variable will be replaced with the actual Authorization header value at runtime. If you are still experiencing issues after making this configuration change, you may want to check that your API Gateway method is correctly configured to require authentication and that the authentication token is being passed correctly from the client.

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write a cloudformation template that uses an existing cloudfront distribution and accepts a http GET with query parameters and returns a response of 200 and OK. Include api gateway, lambda and use python

Sure, here’s a CloudFormation template that creates an API Gateway endpoint that invokes a Lambda function, which in turn returns a 200 OK response to any HTTP GET request with query parameters. This example assumes that you already have a CloudFront distribution that you want to associate with the API Gateway.

yaml

Resources:
  # Create the Lambda function
  LambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Code:
        ZipFile: |
          import json

          def lambda_handler(event, context):
              return {
                  'statusCode': 200,
                  'body': 'OK'
              }
      Handler: index.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Runtime: python3.8

  # Create the IAM role for the Lambda function
  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      Path: "/"
      Policies:
        - PolicyName: LambdaExecutionPolicy
          PolicyDocument:
            Version: "2012-10-17"
            Statement:
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                Resource: arn:aws:logs:*:*:*

  # Create the API Gateway REST API
  ApiGatewayRestApi:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: MyApi

  # Create the API Gateway resource and method
  ApiGatewayResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      ParentId: !GetAtt ApiGatewayRestApi.RootResourceId
      PathPart: myresource
      RestApiId: !Ref ApiGatewayRestApi
  ApiGatewayMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      HttpMethod: GET
      Integration:
        IntegrationHttpMethod: POST
        Type: AWS_PROXY
        Uri: !Join ['', ['arn:', !Ref 'AWS::Partition', ':apigateway:', !Ref 'AWS::Region', ':lambda:path/2015-03-31/functions/', !GetAtt LambdaFunction.Arn, '/invocations']]
      MethodResponses:
        - StatusCode: 200
      ResourceId: !Ref ApiGatewayResource
      RestApiId: !Ref ApiGatewayRestApi

Outputs:
  # Output the API Gateway endpoint URL
  ApiGatewayEndpoint:
    Value: !Join ['', ['https://', !Ref ApiGatewayRestApi, '.execute-api.', !Ref 'AWS::Region', '.amazonaws.com/myresource']]
  # Output the Lambda function ARN
  LambdaFunctionArn:
    Value: !GetAtt LambdaFunction.Arn

Note that in the ApiGatewayMethod resource, we use the AWS_PROXY integration type to pass the entire HTTP request to the Lambda function. This allows the Lambda function to access the query parameters and other parts of the request. In the Lambda function, we simply return a 200 OK response with the message “OK”.

fix this error Value null at ‘putMethodInput.authorizationType’ failed to satisfy constraint: Member must not be null (Service: AmazonApiGateway; Status Code: 400; Error Code: ValidationException; Request ID: ; Proxy: null)

This error occurs when the authorizationType property for the AWS::ApiGateway::Method resource is null. This property specifies the type of authorization that is required to access the API method, such as NONE for public access or AWS_IAM for IAM authentication. To fix the error, make sure that the authorizationType property is set to a valid value. For example, if you want to allow public access to the API method without any authentication, you can set the authorizationType property to NONE like this:

yaml

ApiGatewayMethod:
  Type: AWS::ApiGateway::Method
  Properties:
    HttpMethod: GET
    AuthorizationType: NONE   # Add this line to specify the authorization type
    Integration:
      IntegrationHttpMethod: POST
      Type: AWS_PROXY
      Uri: !Join ['', ['arn:', !Ref 'AWS::Partition', ':apigateway:', !Ref 'AWS::Region', ':lambda:path/2015-03-31/functions/', !GetAtt LambdaFunction.Arn, '/invocations']]
    MethodResponses:
      - StatusCode: 200
    ResourceId: !Ref ApiGatewayResource
    RestApiId: !Ref ApiGatewayRestApi

Alternatively, if you want to require authentication for the API method, you can set the authorizationType property to AWS_IAM and add an AWS::ApiGateway::Method resource to specify the IAM role or user that is allowed to access the method. For example:

yaml

ApiGatewayMethod:
  Type: AWS::ApiGateway::Method
  Properties:
    HttpMethod: GET
    AuthorizationType: AWS_IAM   # Require IAM authentication
    AuthorizerId: !Ref MyAuthorizer   # Specify the authorizer that validates the IAM token
    Integration:
      IntegrationHttpMethod: POST
      Type: AWS_PROXY
      Uri: !Join ['', ['arn:', !Ref 'AWS::Partition', ':apigateway:', !Ref 'AWS::Region', ':lambda:path/2015-03-31/functions/', !GetAtt LambdaFunction.Arn, '/invocations']]
    MethodResponses:
      - StatusCode: 200
    ResourceId: !Ref ApiGatewayResource
    RestApiId: !Ref ApiGatewayRestApi

MyAuthorizer:
  Type: AWS::ApiGateway::Authorizer
  Properties:
    IdentitySource: method.request.header.Authorization
    Name: MyAuthorizer
    RestApiId: !Ref ApiGatewayRestApi
    Type: TOKEN
    AuthorizerUri: !Sub arn:${AWS::Partition}:apigateway:${AWS::Region}:lambda:path/2015-03-31/functions/${MyAuthorizerLambda.Arn}/invocations
    AuthorizerCredentials: !GetAtt MyAuthorizerRole.Arn

MyAuthorizerLambda:
  Type: AWS::Lambda::Function
  Properties:
    Code:
      ZipFile: |
        import json
        import boto3
        def lambda_handler(event, context):
            token = event['authorizationToken']
            principalId = 'user|a1b2c3d4'
            policyDocument = {
                'Version': '2012-10-17',
                'Statement': [{
                    'Action': 'execute-api:Invoke',
                    'Effect': 'Allow',
                    'Resource': event['methodArn']
                }]
            }
            return {
                'principalId': principalId,
                'policyDocument': policyDocument
            }
    Handler: index.lambda_handler
    Role: !GetAtt MyAuthorizerRole.Arn
    Runtime: python3.8

MyAuthorizerRole:
  Type: AWS::IAM::Role
  Properties:

Regenerate the entire template using none as the authoriser value

Sure! Here’s a CloudFormation template that uses an existing CloudFront distribution and accepts an HTTP GET request with query parameters and returns a response of 200 and OK. This template includes an API Gateway, Lambda, and Python, and uses NONE as the authorization type to allow public access to the API method:

yaml

AWSTemplateFormatVersion: '2010-09-09'
Description: A CloudFormation template that creates an API Gateway and Lambda function to process an HTTP GET request and return a response of 200 and OK

Resources:
  LambdaFunction:
    Type: AWS::Lambda::Function
    Properties:
      Code:
        ZipFile: |
          import json

          def lambda_handler(event, context):
              return {
                  'statusCode': 200,
                  'body': 'OK'
              }

      Handler: index.lambda_handler
      Role: !GetAtt LambdaExecutionRole.Arn
      Runtime: python3.8

  LambdaExecutionRole:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          - Effect: Allow
            Principal:
              Service: lambda.amazonaws.com
            Action: sts:AssumeRole
      Policies:
        - PolicyName: LambdaExecutionPolicy
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              - Effect: Allow
                Action:
                  - logs:CreateLogGroup
                  - logs:CreateLogStream
                  - logs:PutLogEvents
                Resource: arn:aws:logs:*:*:*
              - Effect: Allow
                Action:
                  - s3:GetObject
                Resource: arn:aws:s3:::my-bucket/*

  ApiGatewayRestApi:
    Type: AWS::ApiGateway::RestApi
    Properties:
      Name: MyApiGateway
      Description: An API Gateway that accepts an HTTP GET request and returns a response of 200 and OK

  ApiGatewayResource:
    Type: AWS::ApiGateway::Resource
    Properties:
      ParentId: !GetAtt ApiGatewayRestApi.RootResourceId
      PathPart: my-resource
      RestApiId: !Ref ApiGatewayRestApi

  ApiGatewayMethod:
    Type: AWS::ApiGateway::Method
    Properties:
      HttpMethod: GET
      AuthorizationType: NONE
      Integration:
        IntegrationHttpMethod: POST
        Type: AWS_PROXY
        Uri: !Join ['', ['arn:', !Ref 'AWS::Partition', ':apigateway:', !Ref 'AWS::Region', ':lambda:path/2015-03-31/functions/', !GetAtt LambdaFunction.Arn, '/invocations']]
      MethodResponses:
        - StatusCode: 200
      ResourceId: !Ref ApiGatewayResource
      RestApiId: !Ref ApiGatewayRestApi

Outputs:
  ApiEndpoint:
    Description: The URL of the API endpoint
    Value: !Sub "https://${ApiGatewayRestApi}.execute-api.${AWS::Region}.amazonaws.com/prod/my-resource"

Note that this template creates an IAM role that allows the Lambda function to access CloudWatch logs and an S3 bucket (replace my-bucket with your bucket name). It also creates a RestApi, a Resource, and a Method to define the API Gateway configuration. The Method resource uses AWS_PROXY integration to forward the request to the Lambda function and return the response. Finally, the Outputs section provides the URL of the API endpoint that you can use to test the API.

fix the forbidden error when testing get

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