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<article id="content">
<header>
<h1 class="title">Module <code>tinytroupe.validation.tiny_person_validator</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import os
import json
import chevron
import logging
from tinytroupe import openai_utils
from tinytroupe.agent import TinyPerson
from tinytroupe import config
import tinytroupe.utils as utils
default_max_content_display_length = config["OpenAI"].getint("MAX_CONTENT_DISPLAY_LENGTH", 1024)
class TinyPersonValidator:
@staticmethod
def validate_person(person, expectations=None, include_agent_spec=True, max_content_length=default_max_content_display_length) -> tuple[float, str]:
"""
Validate a TinyPerson instance using OpenAI's LLM.
This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM.
The method returns a float value representing the confidence score of the validation process.
If the validation process fails, the method returns None.
Args:
person (TinyPerson): The TinyPerson instance to be validated.
expectations (str, optional): The expectations to be used in the validation process. Defaults to None.
include_agent_spec (bool, optional): Whether to include the agent specification in the prompt. Defaults to False.
max_content_length (int, optional): The maximum length of the content to be displayed when rendering the conversation.
Returns:
float: The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails.
str: The justification for the validation score, or None if the validation process fails.
"""
# Initiating the current messages
current_messages = []
# Generating the prompt to check the person
check_person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/check_person.mustache')
with open(check_person_prompt_template_path, 'r') as f:
check_agent_prompt_template = f.read()
system_prompt = chevron.render(check_agent_prompt_template, {"expectations": expectations})
# use dedent
import textwrap
user_prompt = textwrap.dedent(\
"""
Now, based on the following characteristics of the person being interviewed, and following the rules given previously,
create your questions and interview the person. Good luck!
""")
if include_agent_spec:
user_prompt += f"\n\n{json.dumps(person._persona, indent=4)}"
else:
user_prompt += f"\n\nMini-biography of the person being interviewed: {person.minibio()}"
logger = logging.getLogger("tinytroupe")
logger.info(f"Starting validation of the person: {person.name}")
# Sending the initial messages to the LLM
current_messages.append({"role": "system", "content": system_prompt})
current_messages.append({"role": "user", "content": user_prompt})
message = openai_utils.client().send_message(current_messages)
# What string to look for to terminate the conversation
termination_mark = "```json"
max_iterations = 10 # Limit the number of iterations to prevent infinite loops
cur_iteration = 0
while cur_iteration < max_iterations and message is not None and not (termination_mark in message["content"]):
cur_iteration += 1
# Appending the questions to the current messages
questions = message["content"]
current_messages.append({"role": message["role"], "content": questions})
logger.info(f"Question validation:\n{questions}")
# Asking the questions to the person
person.listen_and_act(questions, max_content_length=max_content_length)
responses = person.pop_actions_and_get_contents_for("TALK", False)
logger.info(f"Person reply:\n{responses}")
# Appending the responses to the current conversation and checking the next message
current_messages.append({"role": "user", "content": responses})
message = openai_utils.client().send_message(current_messages)
if message is not None:
json_content = utils.extract_json(message['content'])
# read score and justification
score = float(json_content["score"])
justification = json_content["justification"]
logger.info(f"Validation score: {score:.2f}; Justification: {justification}")
return score, justification
else:
return None, None</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="tinytroupe.validation.tiny_person_validator.TinyPersonValidator"><code class="flex name class">
<span>class <span class="ident">TinyPersonValidator</span></span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TinyPersonValidator:
@staticmethod
def validate_person(person, expectations=None, include_agent_spec=True, max_content_length=default_max_content_display_length) -> tuple[float, str]:
"""
Validate a TinyPerson instance using OpenAI's LLM.
This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM.
The method returns a float value representing the confidence score of the validation process.
If the validation process fails, the method returns None.
Args:
person (TinyPerson): The TinyPerson instance to be validated.
expectations (str, optional): The expectations to be used in the validation process. Defaults to None.
include_agent_spec (bool, optional): Whether to include the agent specification in the prompt. Defaults to False.
max_content_length (int, optional): The maximum length of the content to be displayed when rendering the conversation.
Returns:
float: The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails.
str: The justification for the validation score, or None if the validation process fails.
"""
# Initiating the current messages
current_messages = []
# Generating the prompt to check the person
check_person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/check_person.mustache')
with open(check_person_prompt_template_path, 'r') as f:
check_agent_prompt_template = f.read()
system_prompt = chevron.render(check_agent_prompt_template, {"expectations": expectations})
# use dedent
import textwrap
user_prompt = textwrap.dedent(\
"""
Now, based on the following characteristics of the person being interviewed, and following the rules given previously,
create your questions and interview the person. Good luck!
""")
if include_agent_spec:
user_prompt += f"\n\n{json.dumps(person._persona, indent=4)}"
else:
user_prompt += f"\n\nMini-biography of the person being interviewed: {person.minibio()}"
logger = logging.getLogger("tinytroupe")
logger.info(f"Starting validation of the person: {person.name}")
# Sending the initial messages to the LLM
current_messages.append({"role": "system", "content": system_prompt})
current_messages.append({"role": "user", "content": user_prompt})
message = openai_utils.client().send_message(current_messages)
# What string to look for to terminate the conversation
termination_mark = "```json"
max_iterations = 10 # Limit the number of iterations to prevent infinite loops
cur_iteration = 0
while cur_iteration < max_iterations and message is not None and not (termination_mark in message["content"]):
cur_iteration += 1
# Appending the questions to the current messages
questions = message["content"]
current_messages.append({"role": message["role"], "content": questions})
logger.info(f"Question validation:\n{questions}")
# Asking the questions to the person
person.listen_and_act(questions, max_content_length=max_content_length)
responses = person.pop_actions_and_get_contents_for("TALK", False)
logger.info(f"Person reply:\n{responses}")
# Appending the responses to the current conversation and checking the next message
current_messages.append({"role": "user", "content": responses})
message = openai_utils.client().send_message(current_messages)
if message is not None:
json_content = utils.extract_json(message['content'])
# read score and justification
score = float(json_content["score"])
justification = json_content["justification"]
logger.info(f"Validation score: {score:.2f}; Justification: {justification}")
return score, justification
else:
return None, None</code></pre>
</details>
<h3>Static methods</h3>
<dl>
<dt id="tinytroupe.validation.tiny_person_validator.TinyPersonValidator.validate_person"><code class="name flex">
<span>def <span class="ident">validate_person</span></span>(<span>person, expectations=None, include_agent_spec=True, max_content_length=4000) ‑> tuple[float, str]</span>
</code></dt>
<dd>
<div class="desc"><p>Validate a TinyPerson instance using OpenAI's LLM.</p>
<p>This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM.
The method returns a float value representing the confidence score of the validation process.
If the validation process fails, the method returns None.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>person</code></strong> : <code>TinyPerson</code></dt>
<dd>The TinyPerson instance to be validated.</dd>
<dt><strong><code>expectations</code></strong> : <code>str</code>, optional</dt>
<dd>The expectations to be used in the validation process. Defaults to None.</dd>
<dt><strong><code>include_agent_spec</code></strong> : <code>bool</code>, optional</dt>
<dd>Whether to include the agent specification in the prompt. Defaults to False.</dd>
<dt><strong><code>max_content_length</code></strong> : <code>int</code>, optional</dt>
<dd>The maximum length of the content to be displayed when rendering the conversation.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>float</code></dt>
<dd>The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails.</dd>
<dt><code>str</code></dt>
<dd>The justification for the validation score, or None if the validation process fails.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def validate_person(person, expectations=None, include_agent_spec=True, max_content_length=default_max_content_display_length) -> tuple[float, str]:
"""
Validate a TinyPerson instance using OpenAI's LLM.
This method sends a series of questions to the TinyPerson instance to validate its responses using OpenAI's LLM.
The method returns a float value representing the confidence score of the validation process.
If the validation process fails, the method returns None.
Args:
person (TinyPerson): The TinyPerson instance to be validated.
expectations (str, optional): The expectations to be used in the validation process. Defaults to None.
include_agent_spec (bool, optional): Whether to include the agent specification in the prompt. Defaults to False.
max_content_length (int, optional): The maximum length of the content to be displayed when rendering the conversation.
Returns:
float: The confidence score of the validation process (0.0 to 1.0), or None if the validation process fails.
str: The justification for the validation score, or None if the validation process fails.
"""
# Initiating the current messages
current_messages = []
# Generating the prompt to check the person
check_person_prompt_template_path = os.path.join(os.path.dirname(__file__), 'prompts/check_person.mustache')
with open(check_person_prompt_template_path, 'r') as f:
check_agent_prompt_template = f.read()
system_prompt = chevron.render(check_agent_prompt_template, {"expectations": expectations})
# use dedent
import textwrap
user_prompt = textwrap.dedent(\
"""
Now, based on the following characteristics of the person being interviewed, and following the rules given previously,
create your questions and interview the person. Good luck!
""")
if include_agent_spec:
user_prompt += f"\n\n{json.dumps(person._persona, indent=4)}"
else:
user_prompt += f"\n\nMini-biography of the person being interviewed: {person.minibio()}"
logger = logging.getLogger("tinytroupe")
logger.info(f"Starting validation of the person: {person.name}")
# Sending the initial messages to the LLM
current_messages.append({"role": "system", "content": system_prompt})
current_messages.append({"role": "user", "content": user_prompt})
message = openai_utils.client().send_message(current_messages)
# What string to look for to terminate the conversation
termination_mark = "```json"
max_iterations = 10 # Limit the number of iterations to prevent infinite loops
cur_iteration = 0
while cur_iteration < max_iterations and message is not None and not (termination_mark in message["content"]):
cur_iteration += 1
# Appending the questions to the current messages
questions = message["content"]
current_messages.append({"role": message["role"], "content": questions})
logger.info(f"Question validation:\n{questions}")
# Asking the questions to the person
person.listen_and_act(questions, max_content_length=max_content_length)
responses = person.pop_actions_and_get_contents_for("TALK", False)
logger.info(f"Person reply:\n{responses}")
# Appending the responses to the current conversation and checking the next message
current_messages.append({"role": "user", "content": responses})
message = openai_utils.client().send_message(current_messages)
if message is not None:
json_content = utils.extract_json(message['content'])
# read score and justification
score = float(json_content["score"])
justification = json_content["justification"]
logger.info(f"Validation score: {score:.2f}; Justification: {justification}")
return score, justification
else:
return None, None</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="tinytroupe.validation" href="index.html">tinytroupe.validation</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="tinytroupe.validation.tiny_person_validator.TinyPersonValidator" href="#tinytroupe.validation.tiny_person_validator.TinyPersonValidator">TinyPersonValidator</a></code></h4>
<ul class="">
<li><code><a title="tinytroupe.validation.tiny_person_validator.TinyPersonValidator.validate_person" href="#tinytroupe.validation.tiny_person_validator.TinyPersonValidator.validate_person">validate_person</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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