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Changelog

Unreleased

[0.1.0] - 2023-12-20

Added

  • Base feature

[0.1.1] - 2023-12-24

Added

  • Changelog file

Fixed

  • Image display and description text in README.md

Removed

  • Unused files

[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

[0.1.5] - 2024-01-23

Fixed

  • Improvements in async survey running

[0.1.6] - 2024-01-24

Fixed

  • Improvements in async survey running

[0.1.7] - 2024-01-25

Fixed

  • Improvements in async survey running
  • Added logging

[0.1.8] - 2024-01-26

Fixed

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

[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

[0.1.11] - 2024-02-01

Added

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)

Removed

[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.

Removed

[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

Removed

[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.

Fixed

Removed

[0.1.15] - 2024-03-09

Added

Fixed

  • Various fixes and small improvements

Removed

[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.

Removed

Fixed

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

[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.

[0.1.18] - 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.

[0.1.19] - 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.

[0.1.20] - 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.

[0.1.21] - 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

[0.1.22] - 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()

[0.1.23] - 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.

[0.1.24] - 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).

[0.1.25] - 2024-TBD

Added

Changed

  • [In progress] Survey run exceptions are now optionally displayed in an html report.

  • [In progress] New prompt visibility features.

  • [In progress] New methods for piping responses to questions into other questions.

  • [In progress] QuestionMultipleChoice is being modified allow non-responsive answers. Previously, an error was thrown if the agent did not select one of the given options. Details TBD.

Fixed

Deprecated

Removed