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Changelog

[0.1.38] - TBD

Added

Changed

  • In progress: The progress bar is now web-based. It is still called in the same way: run(progress_bar=True)

[0.1.37] - 2024-11-14

Added

  • EDSL auth token: It will automatically retrieve your EXPECTED_PARROT_API_KEY and write it to your .env file. How it works: If you try to run a survey remotely without storing your EXPECTED_PARROT_API_KEY, you will see a message with a Coop login link that will automatically write your key to your .env file when you click on the link and log in. Example message:
You're missing some of the API keys needed to run this job:
     🔑 OPENAI_API_KEY

You can either add the missing keys to your .env file, or use remote inference.
Remote inference allows you to run jobs on our server.

🚀 To use remote inference, sign up at the following link:
    https://www.expectedparrot.com/login?edsl_auth_token=gVSxKvDcuB1x6d_dqa7xrA

Once you log in, we will automatically retrieve your Expected Parrot API key and continue your job remotely.
⠇ Waiting for login. Last checked: 2024-11-13 11:36:44 AM

Once you have logged in you will see a confirmation message:

✨ API key retrieved and written to .env file.

Changed

  • The AgentList method from_csv() now allows you to (optionally) automatically specify the name parameters for agents by including a column "name" in the CSV. Other columns are (still) passed as agent traits. See an example: https://docs.expectedparrot.com/en/latest/agents.html#from-a-csv-file

  • The Job method run() now takes a parameter remote_inference_results_visibility to set the visibility of results of jobs that are being run remotely. The allowed visibility settings are public, private and unlisted (the default setting is unlisted). This parameter has the same effect as passing the parameter visibility to the push() and patch() methods for posting and updating objects at the Coop. For example, these commands have the same effect when remote inference activated:

Survey.example().run() 

Survey.example().run(remote_inference_visibility="unlisted")

Fixed

[0.1.36] - 2024-10-28

Added

  • Method show_prompts() can be called on a Survey to display the user prompt and system prompt. This is in addition to the existing method prompts() that is called on a Job which will return the prompts and additional information about the questions, agents, models and estimated costs. Learn more: https://docs.expectedparrot.com/en/latest/prompts.html

  • Documentation on storing API keys as "secrets" for using EDSL in Colab.

Changed

  • Conversation module works with multiple models at once.

  • Improved features for adding new models.

[0.1.35] - 2024-10-17

Fixed

  • Access to Open AI o1 models

[0.1.34] - 2024-10-15

Added

  • Survey Builder is a new interface for creating and launching hybrid human-AI surveys. It is fully integrated with EDSL and Coop. Get access by activating beta features from your Coop account profile page. Learn more: https://docs.expectedparrot.com/en/latest/survey_builder.html

  • Jobs method show_prompts() returns a table showing the user and system prompts that will be used with a survey, together with information about the agent and model and estimated cost for each interview. Jobs method prompts returns the information in a dataset.

  • Scenario objects can contain multiple images to be presented to a model at once (works with Google models).

Fixed

  • Bug in piping a ScenarioList containing multiple lists of question_options to use with questions.

[0.1.33] - 2024-09-26

Added

  • Optional parameters for Question objects:

    • include_comment = False prevents a comment field from being added to a question (default is True: all question types other than free text automatically include a field for the model to comment on its answer, unless this parameter is passed)
    • use_code = True modifies user prompts for question types that take question_options to instruct the model to return the integer code for an option instead of the option value (default is False)
    • answering_instructions and question_presentation allow you to control exact prompt language and separate instructions for the presentation of a question
    • permissive = True turns off enforcement of question constraints (e.g., if min/max selections for a checkbox question have been specified, you can set permissive = True to allow responses that contain fewer or greater selections) (default is False)

  • Methods for Question objects:

  • Methods for ScenarioList objects:

    • unpivot() expands a scenario list by specified identifiers
    • pivot() undoes unpivot(), collapsing scenarios by identifiers
    • give_valid_names() generates valid Pythonic identifiers for scenario keys
    • group_by() groups scenarios by identifiers or applies a function to the values of the specified variables
    • from_wikipedia_table() converts a Wikipedia table into a scenario list. See examples: https://docs.expectedparrot.com/en/latest/notebooks/scenario_list_wikipedia.html
    • to_docx() exports scenario lists as structured Docx documents

  • Optional parameters for Model objects:

    • raise_validation_errors = False causes exceptions to only be raised (interrupting survey execution) when a model returns nothing at all (default: raise_validation_errors = True)
    • print_exceptions = False causes exceptions to not be printed at all (default: print_exceptions = True)

  • Columns in Results for monitoring token usage:

    • generated_tokens shows the tokens that were generated by the model
    • raw_model_response.<question_name>_cost shows the cost of the result for the question, applying the token quanities & prices
    • raw_model_response.<question_name>_one_usd_buys shows the number of results for the question that 1USD will buy
    • raw_model_response.<question_name>_raw_model_response shows the raw response for the question

  • Methods for Results objects:

    • tree() displays a nested tree for specified components
    • generate_html() and save_html() generate and save HTML code for displaying results

Changed

  • General improvements to exceptions reports.

  • General improvements to the progress bar: survey.run(progress_bar=True)

  • Question validation methods no longer use JSON. This will eliminate exceptions relating to JSON errors previously common to certain models.

  • Base agent instructions template is not added to a job if no agent is used with a survey (reducing tokens).

  • The select() method (for Results and ScenarioList) now allows partial match on key names to save typing.

Fixed

  • Bug in enforcement of token/rate limits.

  • Bug in generation of exceptions report that excluded agent information.

[0.1.32] - 2024-08-19

Added

  • Models: AWS Bedrock & Azure

  • Question: New method loop() allows you to create versions of questions when you are constructing a survey. It takes a ScenarioList() as a parameter and returns a list of Question objects.

Fixed

  • Bug in Survey question piping prevented you from adding questions after piping.

[0.1.31] - 2024-08-15

  • ScenarioList.from_sqlite allows you to create a list of scenarios from a SQLite table.

  • Added LaTeX support to SQL outputs and ability to write to files: Results.print(format="latex", filename="example.tex")

  • Options that we think of as "terminal", such as sql(), print(), html(), etc., now take a tee boolean that causes them to return self. This is useful for chaining, e.g., if you run print(format = "rich", tee = True) it will return self, which allows you do also run print(format = "rich", tee = True).print(format = "latex", filename = "example.tex").

[0.1.30] - 2024-07-28

Added

  • Ability to create a Scenario for question_options. Example:
from edsl import QuestionMultipleChoice, Scenario

q = QuestionMultipleChoice(
    question_name = "capital_of_france",
    question_text = "What is the capital of France?", 
    question_options = "{{question_options}}"
)

s = Scenario({'question_options': ['Paris', 'London', 'Berlin', 'Madrid']})

results = q.by(s).run()

[0.1.29] - 2024-07-21

Added

  • Prompts visibility: Call prompts() on a Jobs object for a survey to inspect the prompts that will be used in a survey before running it. For example:
from edsl import Model, Survey
j = Survey.example().by(Model()) 
j.prompts().print(format="rich")

  • Piping: Use agent traits and components of questions (question_text, answer, etc.) as inputs to other questions in a survey (e.g., question_text = "What is your last name, {{ agent.first_name }}?" or question_text = "Name some examples of {{ prior_q.answer }}" or question_options = ["{{ prior_q.answer[0]}}", "{{ prior_q.answer[1] }}"]). Examples: https://docs.expectedparrot.com/en/latest/surveys.html#id2

  • Agent traits: Call agent traits directly (e.g., Agent.example().age will return 22).

Fixed

[0.1.28] - 2024-07-09

Added

  • Method add_columns() allows you to add columns to Results.

  • Class ModelList allows you to create a list of Model objects, similar to ScenarioList and AgentList.

Changed

Fixed

Deprecated

Removed

[0.1.27] - 2024-06-28

Added

Changed

  • Method rename() allows you to rename questions, agents, scenarios, results.

Fixed

  • New language models from OpenAI, Anthropic, Google will be added automatically when they are released by the platforms.

[0.1.26] - 2024-06-10

Fixed

  • Removed an errant break point in language models module.

[0.1.25] - 2024-06-10

Added

  • Scenario.rename() allows you to rename fields of a scenario.

  • Scenario.chunk() allows you to split a field into chunks of a given size based on num_word or num_lines, creating a ScenarioList.

  • Scenario.from_html() turns the contents of a website into a scenario.

  • Scenario.from_image() creates an image scenario to use with a vision model (e.g., GPT-4o).

  • ScenarioList.sample() allows you to take a sample from a scenario list.

  • ScenarioList.tally() allows you to tally fields in scenarios.

  • ScenarioList.expand() allows you to expand a scenario by a field in it, e.g., if a scenario field contains a list the method can be used to break it into separate scenarios.

  • ScenarioList.mutate() allows you to add a key/value to each scenario.

  • ScenarioList.order_by() allows you to order the scenarios.

  • ScenarioList.filter() allows you to filter the scenarios based on a logical expression.

  • ScenarioList.from_list() allows you to create a ScenarioList from a list of values and specified key.

  • ScenarioList.add_list() allows you to use a list to add values to individual scenarios.

  • ScenarioList.add_value() allows you to add a value to all the scenarios.

  • ScenarioList.to_dict() allows you to turn a ScenarioList into a dictionary.

  • ScenarioList.from_dict() allows you to create a ScenarioList from a dictionary.

  • Results.drop() complements Results.select() for identifying the components that you want to print in a table.

  • ScenarioList.drop() similarly complements ScenarioList.select().

Changed

  • Improvements to exceptions reports: Survey run exceptions now include the relevant job components and are optionally displayed in an html report.

[0.1.24] - 2024-05-28

Added

  • We started a blog! https://blog.expectedparrot.com

  • Agent/AgentList method remove_trait(<trait_key>) allows you to remove a trait by name. This can be useful for comparing combinations of traits.

  • Agent/AgentList method translate_traits(<codebook_dict>) allows you to modify traits based on a codebook passed as dictionary. Example:

agent = Agent(traits = {"age": 45, "hair": 1, "height": 5.5})
agent.translate_traits({"hair": {1:"brown"}})

This will return: Agent(traits = {'age': 10, 'hair': 'brown', 'height': 5.5})

  • AgentList method get_codebook(<filename>) returns the codebook for a CSV file.

  • AgentList method from_csv(<filename>) loads an AgentList from a CSV file with the column names as traits keys. Note that the CSV column names must be valid Python identifiers (e.g., current_age and not current age).

  • Results method to_scenario_list() allows you to turn any components of results into a list of scenarios to use with other questions. A default parameter remove_prefixes=True will remove the results component prefixes agent., answer., comment., etc., so that you don't have to modify placeholder names for the new scenarios. Example: https://docs.expectedparrot.com/en/latest/scenarios.html#turning-results-into-scenarios

  • ScenarioList method to_agent_list() converts a ScenarioList into an AgentList.

  • ScenarioList method from_pdf(<filename>) allows you to import a PDF and automatically turn the pages into a list of scenarios. Example: https://docs.expectedparrot.com/en/latest/scenarios.html#turning-pdf-pages-into-scenarios

  • ScenarioList method from_csv(<filename>) allows you to import a CSV and automatically turn the rows into a list of scenarios.

  • ScenarioList method from_pandas(<dataframe>) allows you to import a pandas dataframe and automatically turn the rows into a list of scenarios.

  • Scenario method from_image(<image_path>) creates a scenario with a base64 encoding of an image. The scenario is formatted as follows: "file_path": <filname / url>, "encoded_image": <generated_encoding> Note that you need to use a vision model (e.g., model = Model('gpt-4o')) and you do not need to add a {{ placeholder }} for the scenario (for now--this might change!). Example:

from edsl.questions import QuestionFreeText
from edsl import Scenario, Model

model = Model('gpt-4o')

scenario = Scenario.from_image('general_survey.png') # Image from this notebook: https://docs.expectedparrot.com/en/latest/notebooks/data_labeling_agent.html 
# scenario

q = QuestionFreeText(
    question_name = "example",
    question_text = "What is this image showing?" # We do not need a {{ placeholder }} for this kind of scenario
)

results = q.by(scenario).by(model).run(cache=False)

results.select("example").print(format="rich")

Returns:

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ answer                                                                                                          ┃
┃ .example                                                                                                        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ This image is a flowchart showing the process of creating and administering a survey for data labeling tasks.   │
│ The steps include importing data, creating data labeling tasks as questions about the data, combining the       │
│ questions into a survey, inserting the data as scenarios of the questions, and administering the same survey to │
│ all agents.                                                                                                     │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Changed

  • Question and Survey method html() generates an improved html page representation of the object. You can optionally specify the filename and css. See default css:

    edsl/edsl/surveys/SurveyExportMixin.py

    Line 10 in 9d981fa

    def css(self):

  • QuestionMultipleChoice now takes numbers and lists as question_options (e.g., question_options = [[1,2,3], [4,5,6]] is allowed). Previously options had to be a list of strings (i.e., question_options = ['1','2','3'] is still allowed but not required).

[0.1.23] - 2024-05-18

Added

  • Optional parameter in Results method to_list() to flatten a list of lists (eg, responses to QuestionList): results.to_list(flatten=True)

Fixed

  • Erroneous error messages about adding rules to a survey.

[0.1.22] - 2024-05-14

Added

  • New Survey method to export a survey to file. Usage: generated_code = survey.code("example.py")

Fixed

  • A bug in Survey method add_skip_logic()

[0.1.21] - 2024-05-13

Added

  • New methods for adding, sampling and shuffling Results objects: dup_results = results + results results.shuffle() results.sample(n=5)

Changed

  • Optional parameter survey.run(cache=False) if you do not want to access any cached results in running a survey.

  • Instructions passed to an agent at creation are now a column of results: agent_instruction

[0.1.20] - 2024-05-09

Added

  • Methods for setting session caches New function set_session_cache will set the cache for a session:
from edsl import Cache, set_session_cache
set_session_cache(Cache())

The cache can be set to a specific cache object, or it can be set to a dictionary or SQLite3Dict object:

from edsl import Cache, set_session_cache
from edsl.data import SQLiteDict
set_session_cache(Cache(data = SQLiteDict("example.db")))
# or
set_session_cache(Cache(data = {}))

The unset_session_cache function is used to unset the cache for a session:

from edsl import unset_session_cache
unset_session_cache()

This will unset the cache for the current session, and you will need to pass the cache object to the run method during the session.

Details: https://docs.expectedparrot.com/en/latest/data.html#setting-a-session-cache

Changed

  • Answer comments are now a separate component of results The "comment" field that is automatically added to each question (other than free text) is now stored in Results as comment.<question_name>. Prior to this change, the comment for each question was stored as answer.<question_name>_comment, i.e., if you ran results.columns the list of columns would include answer.<question_name> and answer.<question_name>_comment for each question. With this change, the columns will now be answer.<question_name> and comment.<question_name>_comment. This change is meant to make it easier to select only the answers, e.g., running results.select('answer.*').print() will no longer also include all the comments, which you may not want to display. (The purpose of the comments field is to allow the model to add any information about its response to a question, which can help avoid problems with JSON formatting when the model does not want to return just the properly formatted response.)

  • Exceptions We modified exception messages. If your survey run generates exceptions, run results.show_exceptions() to print them in a table.

Fixed

  • A package that was missing for working with Anthropic models.

[0.1.19] - 2024-05-03

Added

  • Results objects now include columns for question components. Call the .columns method on your results to see a list of all components. Run results.select("question_type.*", "question_text.*", "question_options.*").print() to see them.

  • Survey objects now also have a .to_csv() method.

Changed

  • Increased the maximum number of multiple choice answer options to 200 (previously 20) to facilitate large codebooks / data labels.

[0.1.18] - 2024-05-01

Fixed

  • A bug in in Survey.add_rule() method that caused an additional question to be skipped when used to apply a skip rule.

[0.1.17] - 2024-04-29

Added

  • New models: Run Model.available() to see a complete current list.

Fixed

  • A bug in json repair methods.

[0.1.16] - 2024-04-11

Added

  • New documentation: https://docs.expectedparrot.com

  • Progress bar: You can now pass progress_bar=True to the run() method to see a progress bar as your survey is running. Example:

from edsl import Survey 
results = Survey.example().run(progress_bar=True)

                            Job Status                             
                                                                   
  Statistic                                            Value       
 ───────────────────────────────────────────────────────────────── 
  Elapsed time                                         1.1 sec.    
  Total interviews requested                           1           
  Completed interviews                                 1           
  Percent complete                                     100 %       
  Average time per interview                           1.1 sec.    
  Task remaining                                       0           
  Estimated time remaining                             0.0 sec.    
  Model Queues                                                     
  gpt-4-1106-preview;TPM (k)=1200.0;RPM (k)=8.0                    
  Number question tasks waiting for capacity           0           
   new token usage                                                 
   prompt_tokens                                       0           
   completion_tokens                                   0           
   cost                                                $0.00000    
   cached token usage                                              
   prompt_tokens                                       104         
   completion_tokens                                   35          
   cost                                                $0.00209
  • New language models: We added new models from Anthropic and Databricks. To view a complete list of available models see edsl.enums.LanguageModelType or run:
from edsl import Model
Model.available()

This will return:

['claude-3-haiku-20240307', 
'claude-3-opus-20240229', 
'claude-3-sonnet-20240229', 
'dbrx-instruct', 
'gpt-3.5-turbo',
'gpt-4-1106-preview',
'gemini_pro',
'llama-2-13b-chat-hf',
'llama-2-70b-chat-hf',
'mixtral-8x7B-instruct-v0.1']

For instructions on specifying models to use with a survey see new documentation on Language Models. Let us know if there are other models that you would like us to add!

Changed

  • Cache: We've improved user options for caching LLM calls.

Old method: Pass a use_cache boolean parameter to a Model object to specify whether to access cached results for the model when using it with a survey (i.e., add use_cache=False to generate new results, as the default value is True).

How it works now: All results are (still) cached by default. To avoid using a cache (i.e., to generate fresh results), pass an empty Cache object to the run() method that will store everything in it. This can be useful if you want to isolate a set of results to share them independently of your other data. Example:

from edsl.data import Cache
c = Cache() # create an empty Cache object

from edsl.questions import QuestionFreeText
results = QuestionFreeText.example().run(cache = c) # pass it to the run method

c # inspect the new data in the cache

We can inspect the contents:

Cache(data = {‘46d1b44cd30e42f0f08faaa7aa461d98’: CacheEntry(model=‘gpt-4-1106-preview’, parameters={‘temperature’: 0.5, ‘max_tokens’: 1000, ‘top_p’: 1, ‘frequency_penalty’: 0, ‘presence_penalty’: 0, ‘logprobs’: False, ‘top_logprobs’: 3}, system_prompt=‘You are answering questions as if you were a human. Do not break character. You are an agent with the following persona:\n{}’, user_prompt=‘You are being asked the following question: How are you?\nReturn a valid JSON formatted like this:\n{“answer”: “<put free text answer here>“}‘, output=’{“id”: “chatcmpl-9CGKXHZPuVcFXJoY7OEOETotJrN4o”, “choices”: [{“finish_reason”: “stop”, “index”: 0, “logprobs”: null, “message”: {“content”: “```json\\n{\\“answer\\“: \\“I\‘m doing well, thank you for asking! How can I assist you today?\\“}\\n```“, “role”: “assistant”, “function_call”: null, “tool_calls”: null}}], “created”: 1712709737, “model”: “gpt-4-1106-preview”, “object”: “chat.completion”, “system_fingerprint”: “fp_d6526cacfe”, “usage”: {“completion_tokens”: 26, “prompt_tokens”: 68, “total_tokens”: 94}}’, iteration=0, timestamp=1712709738)}, immediate_write=True, remote=False)

For more details see new documentation on Caching LLM Calls.

Coming soon: Automatic remote caching options.

  • API keys: You will no longer be prompted to enter your API keys when running a session. We recommend storing your keys in a private .env file in order to avoid having to enter them at each session. Alternatively, you can still re-set your keys whenever you run a session. See instructions on setting up an .env file in our Starter Tutorial.

The Expected Parrot API key is coming soon! It will let you access all models at once and come with automated remote caching of all results. If you would like to test it out, please let us know!

  • Prompts: We made it easier to modify the agent and question prompts that are sent to the models. For more details see new documentation on Prompts.

Deprecated

  • Model attribute use_cache is now deprecated. See details above about how caching now works.

Fixed

  • .run(n = ...) now works and will run your survey with fresh results the specified number of times.

[0.1.15] - 2024-03-09

Fixed

  • Various fixes and small improvements

[0.1.14] - 2024-03-06

Added

  • The raw model response is now available in the Results object, accessed via "raw_model_response" keyword. There is one for each question. The key is the question_name + _raw_response_model
  • The .run(progress_bar = True) returns a much more informative real-time view of job progress.

[0.1.13] - 2024-03-01

Added

  • The answer component of the Results object is printed in a nicer format.

Fixed

  • trait_name descriptor was not working; it is now fixed.
  • QuestionList is now working properly again

[0.1.12] - 2024-02-12

Added

  • Results now provides a .sql() method that can be used to explore data in a SQL-like manner.
  • Results now provides a .ggplot() method that can be used to create ggplot2 visualizations.
  • Agent now admits an optional name argument that can be used to identify the Agent.

Fixed

  • Fixed various issues with visualizations. They should now work better.

[0.1.11] - 2024-02-01

Fixed

  • Question options can now be 1 character long or more (down from 2 characters)
  • Fixed a bug where prompts displayed were incorrect (prompts sent were correct)

[0.1.9] - 2024-01-27

Added

  • Report functionalities are now part of the main package.

Fixed

  • Fixed a bug in the Results.print() function

Removed

  • The package no longer supports a report extras option.
  • Fixed a bug in EndofSurvey

[0.1.8] - 2024-01-26

Fixed

  • Better handling of async failures
  • Fixed bug in survey logic

[0.1.7] - 2024-01-25

Fixed

  • Improvements in async survey running
  • Added logging

[0.1.6] - 2024-01-24

Fixed

  • Improvements in async survey running

[0.1.5] - 2024-01-23

Fixed

  • Improvements in async survey running

[0.1.4] - 2024-01-22

Added

  • Support for several large language models
  • Async survey running
  • Asking for API keys before they are used

Fixed

  • Bugs in survey running
  • Bugs in several question types

Removed

  • Unused files
  • Unused package dependencies

[0.1.1] - 2023-12-24

Added

  • Changelog file

Fixed

  • Image display and description text in README.md

Removed

  • Unused files

[0.1.0] - 2023-12-20

Added

  • Base feature