From 0588a0301c4472c555600ede677e2eeb9cc8d0c5 Mon Sep 17 00:00:00 2001 From: josh-ashkinaze Date: Tue, 24 Sep 2024 16:33:55 +0000 Subject: [PATCH] deploy: 20a5fe6dba164a12c4cecde8eb8f17b619d29606 --- _sources/tutorial_3_agents.rst | 4 ++-- _sources/tutorial_4_structures.rst | 2 +- _sources/tutorial_5_moderators.rst | 4 ++-- index.html | 6 +++--- searchindex.js | 2 +- tutorial_3_agents.html | 14 +++++++------- tutorial_4_structures.html | 6 +++--- tutorial_5_moderators.html | 12 ++++++------ 8 files changed, 25 insertions(+), 25 deletions(-) diff --git a/_sources/tutorial_3_agents.rst b/_sources/tutorial_3_agents.rst index 4d62b8b..57f3157 100644 --- a/_sources/tutorial_3_agents.rst +++ b/_sources/tutorial_3_agents.rst @@ -303,7 +303,7 @@ If you make ``persona=='random'`` then we will randomly sample a row from ANES a task = "Write a paragraph about the importance of the environment to America." agent = Agent(persona='random', model='gpt-4o', task=task) -ANES Option 3: Searching ANES using a pandas query string +ANES Option 3: Searching ANES using a Pandas query string ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you want to get more specific, you can pass in a query string that will be used to filter the ANES dataset. Now, you may not know the exact variables in ANES, so we have a helper function that will print out the demographic/political columns we are using so you know what values to pass in. @@ -367,7 +367,7 @@ on more than just ideology and state. the south. You live in a small town. You live in the state of west virginia. -Using Different LLM Models +Using different LLMs --------------------------------------------------- Plurals supports various LLMs by using LiteLLM to make API calls. Agents also support LiteLLM's ``kwargs`` arguments diff --git a/_sources/tutorial_4_structures.rst b/_sources/tutorial_4_structures.rst index df91eec..9dbb145 100644 --- a/_sources/tutorial_4_structures.rst +++ b/_sources/tutorial_4_structures.rst @@ -18,7 +18,7 @@ Every Structure can be moderated, which means that a Moderator Agent will overse Structures will return ``responses`` as a list or you can access the final response by calling the ``final_response`` attribute. -Combination Instructions +Combination instructions ------------------------ diff --git a/_sources/tutorial_5_moderators.rst b/_sources/tutorial_5_moderators.rst index ef199a5..d6829e2 100644 --- a/_sources/tutorial_5_moderators.rst +++ b/_sources/tutorial_5_moderators.rst @@ -12,7 +12,7 @@ Moderators are a subclass of Agents who summarize multi-agent deliberation. Any - **Model:** Moderators are initialized to be a particular LLM. -Setting a Moderator’s System Instructions +Setting a Moderator’s system instructions ----------------------------------------- Via personas @@ -60,7 +60,7 @@ templates, so you cannot access any pre-defined moderator instructions when using ``system_instructions``. ``system_instructions='default'`` will not access the default template like ``persona='default'`` would. -Auto-Moderators +Auto-moderators ~~~~~~~~~~~~~~~ We have a special option where, if the ``system_instructions`` of a diff --git a/index.html b/index.html index 17b86cf..52a8250 100644 --- a/index.html +++ b/index.html @@ -362,13 +362,13 @@

Contents
  • Example combining ANES and templates
  • Process tasks
  • Different ways to set up system instructions
    • -
  • Using Different LLM Models
    • +
  • Using different LLMs
  • Inspecting the exact prompts that an Agent is doing
  • Structures
  • Moderators
    • diff --git a/searchindex.js b/searchindex.js index 0500182..8d6bf8b 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Safe String Formatting": [[2, "safe-string-formatting"]], "2. Avoid Double Periods": [[2, "avoid-double-periods"]], "3. 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6, "instal": 5, "instruct": [6, 7, 8], "integr": 5, "kei": 5, "llm": 6, "moder": [4, 5, 8], "modul": [0, 1, 2], "nation": 6, "none": 2, "option": 6, "overview": 7, "own": 7, "panda": 6, "period": 2, "persona": [6, 8], "plural": 3, "process": 6, "prompt": 6, "queri": 6, "quick": 5, "random": 6, "replac": 2, "repres": 6, "safe": 2, "sampl": 6, "search": 6, "set": [5, 6, 7, 8], "start": 5, "string": [2, 6], "structur": [4, 5, 7], "system": [6, 8], "tabl": 3, "task": 6, "templat": [4, 6, 7], "trace": 7, "tutori": 3, "up": 6, "us": [6, 7], "user": 6, "via": 8, "wai": 6, "welcom": 3, "what": 7, "your": 7}}) \ No newline at end of file diff --git a/tutorial_3_agents.html b/tutorial_3_agents.html index ce61718..9986650 100644 --- a/tutorial_3_agents.html +++ b/tutorial_3_agents.html @@ -324,10 +324,10 @@

    Contents

  • Using ANES for nationally representative personas
  • ANES Option 1: ideology initializer
  • ANES Option 2: Random sampling
    • -
  • ANES Option 3: Searching ANES using a pandas query string
    • +
  • ANES Option 3: Searching ANES using a Pandas query string
    • -
  • Using Different LLM Models
    • +
  • Using different LLMs
  • Inspecting the exact prompts that an Agent is doing
    • @@ -598,7 +598,7 @@

    ANES Option 2: Random sampling -

    ANES Option 3: Searching ANES using a pandas query string#

    +

    ANES Option 3: Searching ANES using a Pandas query string#

    If you want to get more specific, you can pass in a query string that will be used to filter the ANES dataset. Now, you may not know the exact variables in ANES, so we have a helper function that will print out the demographic/political columns we are using so you know what values to pass in.

    from plurals.helpers import print_anes_mapping
 
@@ -649,8 +649,8 @@ 
    ANES Option 3: Searching ANES using a pandas query string
-
    Using Different LLM Models#
    
+
    
+
    Using different LLMs#
    
 
    Plurals supports various LLMs by using LiteLLM to make API calls. Agents also support LiteLLM’s kwargs arguments
 so you can (e.g) pass in a temperature or max tokens. Here are some examples of using different models:
    
 
    from plurals.agent import Agent
@@ -741,10 +741,10 @@ 
    Inspecting the exact prompts that an Agent is doingUsing ANES for nationally representative personas
 
  • ANES Option 1: ideology initializer
    • 
 
  • ANES Option 2: Random sampling
    • 
-
  • ANES Option 3: Searching ANES using a pandas query string
    • 
+
  • ANES Option 3: Searching ANES using a Pandas query string
    • 
 
 
-
  • Using Different LLM Models
    • 
+
  • Using different LLMs
    • 
 
  • Inspecting the exact prompts that an Agent is doing
    • 
 
   
    
diff --git a/tutorial_4_structures.html b/tutorial_4_structures.html
index 2f5f709..b838527 100644
--- a/tutorial_4_structures.html
+++ b/tutorial_4_structures.html
@@ -316,7 +316,7 @@ 
     Contents 
    
             
    
 
    
-
    Combination Instructions#
    
+
    Combination instructions#
    
 
    Combination instructions describe how agents are instructed to combine information in the structure. It is a special kind of instruction that only kicks in when there are previous responses from an agent’s view. These instructions allow you to customize how Agents deliberate. There are two ways to set combination_instructions:
    
 
    
 
    Using a template for combination instructions#
    
@@ -702,7 +702,7 @@ 
    Tracing what is going on in Structures