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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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