updating docs

docs/questions.rst

@@ -211,18 +211,19 @@ We can combine multiple questions into a survey by passing them as a list to a `
 
 .. code-block:: python 
 
-   from edsl import QuestionLinearScale, QuestionFreeText, QuestionNumerical, Survey 
+   from edsl import QuestionLinearScale, QuestionList, QuestionNumerical, Survey 
 
    q1 = QuestionLinearScale(
-      question_name = "likely_to_vote",
-      question_text = "On a scale from 1 to 5, how likely are you to vote in the upcoming U.S. election?",
+      question_name = "dc_state",
+      question_text = "How likely is Washington, D.C. to become a U.S. state?",
       question_options = [1, 2, 3, 4, 5],
       option_labels = {1: "Not at all likely", 5: "Very likely"}
    )
 
-   q2 = QuestionFreeText(
-      question_name = "largest_us_city",
-      question_text = "What is the largest U.S. city?"
+   q2 = QuestionList(
+      question_name = "largest_us_cities",
+      question_text = "What are the largest U.S. cities by population?",
+      max_list_items = 3
    )
 
    q3 = QuestionNumerical(
@@ -232,8 +233,6 @@ We can combine multiple questions into a survey by passing them as a list to a `
 
    survey = Survey(questions = [q1, q2, q3])
 
-   results = survey.run()
-
 
 This allows us to administer multiple questions at once, either asynchronously (by default) or according to specified logic (e.g., skip or stop rules).
 To learn more about designing surveys with conditional logic, please see the :ref:`surveys` section.
@@ -247,29 +246,37 @@ This is done by calling the `run` method for the question:
 
 .. code-block:: python
 
+   from edsl import QuestionCheckBox
+
+   q = QuestionCheckBox(
+      question_name = "primary_colors",
+      question_text = "Which of the following colors are primary?",
+      question_options = ["Red", "Orange", "Yellow", "Green", "Blue", "Purple"]
+   )
+
    results = q.run()
 
 
 This will generate a `Results` object that contains a single `Result` representing the response to the question and information about the model used.
-If the model to be used has not been specified (as in the above example), the `run` method delivers the question to the default LLM (GPT 4).
+If the model to be used has not been specified (as in the above example), the `run` method delivers the question to the default LLM (run `Model()` to check the current default LLM).
 We can inspect the response and model used by calling the `select` and `print` methods on the components of the results that we want to display.
 For example, we can print just the `answer` to the question:
 
 .. code-block:: python 
 
-   results.select("answer.favorite_primary_color").print(format="rich")
+   results.select("primary_colors").print(format="rich")
 
 
 Output:
 
 .. code-block:: text
 
-   ┏━━━━━━━━━━━━━━━━━━━━━━━━━┓
-   ┃ answer                  ┃
-   ┃ .favorite_primary_color ┃
-   ┡━━━━━━━━━━━━━━━━━━━━━━━━━┩
-   │ blue                    │
-   └─────────────────────────┘
+   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+   ┃ answer                    ┃
+   ┃ .primary_colors           ┃
+   ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+   │ ['Red', 'Yellow', 'Blue'] │
+   └───────────────────────────┘
 
 
 Or to inspect the model:
@@ -283,18 +290,40 @@ Output:
 
 .. code-block:: text
 
-   ┏━━━━━━━━━━━━━━━━━━━━┓
-   ┃ model              ┃
-   ┃ .model             ┃
-   ┡━━━━━━━━━━━━━━━━━━━━┩
-   │ gpt-4-1106-preview │
-   └────────────────────┘
+   ┏━━━━━━━━┓
+   ┃ model  ┃
+   ┃ .model ┃
+   ┡━━━━━━━━┩
+   │ gpt-4o │
+   └────────┘
 
 
 If questions have been combined in a survey, the `run` method is called directly on the survey instead:
 
 .. code-block:: python
 
+   from edsl import QuestionLinearScale, QuestionList, QuestionNumerical, Survey 
+
+   q1 = QuestionLinearScale(
+      question_name = "dc_state",
+      question_text = "How likely is Washington, D.C. to become a U.S. state?",
+      question_options = [1, 2, 3, 4, 5],
+      option_labels = {1: "Not at all likely", 5: "Very likely"}
+   )
+
+   q2 = QuestionList(
+      question_name = "largest_us_cities",
+      question_text = "What are the largest U.S. cities by population?",
+      max_list_items = 3
+   )
+
+   q3 = QuestionNumerical(
+      question_name = "us_pop",
+      question_text = "What was the U.S. population in 2020?"
+   )
+
+   survey = Survey(questions = [q1, q2, q3])
+
    results = survey.run()
 
    results.select("answer.*").print(format="rich")
@@ -304,12 +333,12 @@ Output:
 
 .. code-block:: text
 
-   ┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
-   ┃ answer          ┃ answer                                                ┃ answer    ┃
-   ┃ .likely_to_vote ┃ .largest_us_city                                      ┃ .us_pop   ┃
-   ┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
-   │ 4               │ The largest U.S. city by population is New York City. │ 331449281 │
-   └─────────────────┴───────────────────────────────────────────────────────┴───────────┘
+   ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓
+   ┃ answer                                 ┃ answer    ┃ answer    ┃
+   ┃ .largest_us_cities                     ┃ .dc_state ┃ .us_pop   ┃
+   ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩
+   │ ['New York', 'Los Angeles', 'Chicago'] │ 2         │ 331449281 │
+   └────────────────────────────────────────┴───────────┴───────────┘
 
 
 For a survey, each `Result` represents a response for the set of survey questions. 
@@ -474,7 +503,8 @@ To learn more about designing agents, please see the :ref:`agents` section.
 
 Specifying language models
 --------------------------
-In the above examples we did not specify a language model for the question or survey, so the default model (GPT 4) was used.
+
+In the above examples we did not specify a language model for the question or survey, so the default model was used (run `Model()` to check the current default model).
 Similar to the way that we optionally passed scenarios to a question and added AI agents, we can also use the `by` method to specify one or more LLMs to use in generating results.
 This is done by creating `Model` objects for desired models and optionally specifying model parameters, such as temperature.
 
@@ -486,6 +516,7 @@ To check available models:
 
    Model.available()
 
+
 This will return a list of names of models that we can choose from.
 
 We can also check the models for which we have already added API keys:
@@ -494,6 +525,7 @@ We can also check the models for which we have already added API keys:
 
    Model.check_models()
 
+
 See instructions on storing :ref:`api_keys` for the models that you want to use, or activating :ref:`remote_inference` to use the Expected Parrot server to access available models.
 
 To specify models for a survey we first create `Model` objects:
@@ -503,7 +535,7 @@ To specify models for a survey we first create `Model` objects:
    from edsl import ModelList, Model 
 
    models = ModelList(
-      Model(m) for m in ['claude-3-opus-20240229', 'llama-2-70b-chat-hf']
+      Model(m) for m in ['gpt-4o', 'gemini-1.5-pro']
    )
 
 
@@ -573,7 +605,7 @@ An example can also created using the `example` method:
    :show-inheritance:
    :special-members: __init__
    :exclude-members: purpose, question_type, question_options, main
-   
+
 
 QuestionCheckBox class
 ^^^^^^^^^^^^^^^^^^^^^^