Boost Training With Enhanced Retrieval
Hey everyone! Let's dive into how we can supercharge our training sessions by enhancing the way we retrieve information. We're going to build upon the TrainingKnowledgeBase to make our AI roleplay trainer even more awesome. This enhancement will focus on improving the retrieval interface, making it smarter and more efficient. Ready to level up your training game? Let's get started!
Refine the TrainingKnowledgeBase for Smarter Information Retrieval
Our mission is to create a robust and intelligent TrainingKnowledgeBase that can quickly and accurately provide the information needed during training sessions. We'll be focusing on the key methods within the TrainingKnowledgeBase class: retrieve_company_facts, retrieve_policies, retrieve_customer_behaviors, and retrieve_coaching_tips. Each of these methods plays a crucial role in different training scenarios, from answering factual questions about the company to simulating customer interactions and providing coaching advice. The goal is to make these methods more effective in retrieving relevant information, which will significantly enhance the quality of our training simulations.
Retrieving Company Facts: Accuracy First
First up, let's talk about retrieve_company_facts. This method is designed to answer questions like "What does Plus Enhancement include?" To make sure this is top-notch, we need to ensure the query to retrieve the company's knowledge is very accurate. We want to be certain that the most pertinent and correct information is provided. Let's make sure the way we query self.company_knowledge is smart and doesn't miss any critical details. By refining the query process, we're setting the foundation for reliable information retrieval. The objective here is to have a robust method for fetching company facts, ensuring that the AI trainer always has the correct and current information at its disposal. This involves using the query functionality correctly and adjusting it so that it always provides the best results for questions.
Policy Retrieval: Context Matters
Next, let’s tackle retrieve_policies. This is crucial for verifying scenarios like, "Can I offer a refund?" Here, we're not just looking for content; we also need to understand the context surrounding the policies. That’s where the filter_metadata comes into play. This filter allows us to narrow down our search based on specific criteria, like authority level. It’s super important to return both the policy content and its metadata. This way, we not only get the policy details, but also the context in which it applies. For instance, the authority level of the user making the query is crucial when determining whether a refund can be offered. Ensuring our method correctly handles metadata, like the user’s role or the policy's applicability, is key. This guarantees the AI trainer offers the right advice and respects all the relevant rules and conditions.
Customer Behavior Simulation: Understanding Emotions
For simulating customer interactions, the retrieve_customer_behaviors method becomes our friend. This method handles queries like, "How does a frustrated customer act?" The key here is the emotion parameter. This method needs to pull out the right customer behavior based on the emotion specified, be it frustration, anger, or excitement. Moreover, we add an intensity parameter to help fine-tune the behavior. Think of it as differentiating between a slightly annoyed customer versus an absolutely furious one. The goal is to make the simulation as realistic as possible by including different levels of emotion. We're providing the trainer with a varied range of customer behaviors so it can adapt to different emotional scenarios. This ensures that the training is not just about what a customer might say but also about how they're likely to behave. This comprehensive approach is what elevates the simulation from basic to dynamic.
Coaching Tips: Guiding Interactions
Last, but not least, we have retrieve_coaching_tips. This method is essential for providing coaching during training sessions. It addresses situations like, "How to handle an escalating customer?" The inclusion of the category parameter here gives us an extra way to refine our search. For instance, you could search for tips specific to "de-escalation" or "active listening." This targeted approach helps to give trainers highly relevant advice that meets the current needs. We're setting up the trainer with focused guidance by adding the category parameter to refine searches. This means the AI can provide clear and useful tips. The metadata allows the inclusion of coaching tips that are more relevant to the scenario, providing the best assistance to the trainee.
Enhancing Retrieval with Code
Now, let's look at the code and see how we can make our retrieval methods even more effective and accurate. We will refine each of the methods to ensure they offer the best possible results.
class TrainingKnowledgeBase:
# ... (initialization per parent issue)
def retrieve_company_facts(self, query: str, n_results: int = 3) -> List[str]:
"""For reference questions: 'What does Plus Enhancement include?'"""
results = self.company_knowledge.query(
query_texts=[query],
n_results=n_results
)
return results['documents'][0] if results['documents'] else []
def retrieve_policies(self, query: str, filter_metadata: Dict = None, n_results: int = 3) -> List[Dict]:
"""
For validation: 'Can I offer a refund?'
Returns both content and metadata
"""
results = self.policies.query(
query_texts=[query],
n_results=n_results,
where=filter_metadata # e.g., {"authority_level": "tier1"}
)
if not results['documents']:
return []
# Return structured results with metadata
return [
{
"content": doc,
"metadata": meta
}
for doc, meta in zip(results['documents'][0], results['metadatas'][0])
]
def retrieve_customer_behaviors(self, emotion: str, intensity: str = None) -> List[str]:
"""For customer simulation: 'How does frustrated customer act?'"""
where_filter = {"emotion": emotion}
if intensity:
where_filter["intensity"] = intensity
results = self.customer_behaviors.query(
query_texts=[f"customer behavior {emotion}"],
n_results=5,
where=where_filter
)
return results['documents'][0] if results['documents'] else []
def retrieve_coaching_tips(self, situation: str, category: str = None) -> List[Dict]:
"""For coaching: 'How to handle escalating customer?'"""
where_filter = {}
if category:
where_filter["category"] = category
results = self.coaching_tips.query(
query_texts=[situation],
n_results=3,
where=where_filter
)
if not results['documents']:
return []
return [
{
"content": doc,
"metadata": meta
}
for doc, meta in zip(results['documents'][0], results['metadatas'][0])
]
Code Deep Dive: Each Method Explained
Let’s go through each method one by one, with a focus on their specific characteristics.
retrieve_company_facts
This method uses the company_knowledge to answer questions. The query parameter passes the exact question asked by the user, for example, "What does Plus Enhancement include?" The n_results parameter defines how many results should be returned. The code then retrieves these results using self.company_knowledge.query. After fetching the query results, the method extracts the relevant documents and returns them. If no results are found, it provides an empty list. The main function of this method is to fetch the key company information quickly and with precision, enabling the AI to answer specific questions accurately.
retrieve_policies
The retrieve_policies method addresses policy-related queries, such as "Can I offer a refund?" It employs the policies object to search for the policies, using a specific query string and filters using filter_metadata. The filter_metadata helps refine the search, focusing on metadata such as the user’s authority level. The results include both the policy content and the related metadata. This approach provides not only what the policy says, but also the context in which the policy applies. This is important for determining the correct answer, based on the specific situation. The structure of the returned results, including both content and metadata, is designed to ensure the AI has everything required to assess and answer the queries properly.
retrieve_customer_behaviors
This method, retrieve_customer_behaviors, simulates customer behavior. It takes an emotion parameter, like