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Standard-Labs / real-intent

Python SDK and abstracted interface for Real Intent operations.
https://realintent.co
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Insight generation with validation context #40

Closed preritdas closed 3 months ago

preritdas commented 3 months ago

Create a ValidatedInsightsGenerator that takes a BaseProcessor object, reads its required and fallback validators, and uses AI to incorporate these into the general understanding of insights.

Creates a validation_insight, a human-readable high-level description of the validation that went into lead generation. Displayed in the final string.

Example

On validation: These leads have been stripped of unreachable phones and emails, not having repeated entries of the same person, and are ensured to be contactable. These are fine-tuned to a specific location, ensuring the most relevant and actionable dataset possible with strong signals in real estate interests.

  1. 68% of leads are associated with the Pre-Movers category, indicating a high potential for engagement with real estate agencies or moving services. This group includes NAME_REDACTED and NAME_REDACTED, who might be primed for moving or buying opportunities.
  2. A significant 74% of leads have a household net worth greater than $499,999 and overlap with mortgage interests. These individuals, like NAME_REDACTED and NAME_REDACTED, could be approached with luxury property listings and high-value financial products.
  3. 56% of the leads possess credit scores of 750-799, suggesting they are likely eligible for competitive mortgage rates or refinance options. NAME_REDACTED and NAME_REDACTED fall into this segment, showing strong potential for mortgage product marketing.
  4. Among the male leads, 66% are married and associated with high net worth categories, indicating an opportunity to target family-oriented housing solutions or upscale investment properties. This includes individuals like NAME_REDACTED and NAME_REDACTED.
  5. 33% of the leads are females who are single homeowners with net worths exceeding $499,999. This group, which includes NAME_REDACTED and NAME_REDACTED, could be targeted with personalized financial planning services or high-end real estate investments.
  6. There are several leads like NAME_REDACTED and NAME_REDACTED who demonstrate a multichannel potential through both email and phone contact methods, maximizing outreach strategies for effective marketing campaigns.

Summary by CodeRabbit

coderabbitai[bot] commented 3 months ago

[!CAUTION]

Review failed

The pull request is closed.

Walkthrough

The recent changes enhance the bigdbm module's functionality by introducing a new validation framework for lead data. Key updates include the addition of classes for generating validated insights and improved documentation across validators to clarify their roles in processing Personally Identifiable Information (PII). This reorganization not only improves the module's capability to handle data validation but also ensures greater clarity and maintainability in the codebase.

Changes

Files Change Summary
bigdbm/analyze/__init__.py Added import for ValidatedInsightsGenerator, enhancing insight generation capabilities.
bigdbm/analyze/insights/__init__.py Introduced ValidatedLeadInsights and ValidatedInsightsGenerator classes, improving lead data analysis with validation mechanisms. Updated existing methods for comprehensive insight generation.
bigdbm/analyze/insights/validator_prompt.py Created a structured prompt for analyzing leads, detailing expected input and validation processes for generating actionable insights.
bigdbm/validate/base.py Updated BaseValidator to focus on lead validation instead of MD5 hashes, modifying associated method parameters and docstrings.
bigdbm/validate/email.py Clarified EmailValidator and HasEmailValidator documentation, specifying criteria for valid emails.
bigdbm/validate/home_attrs.py Enhanced NotRenterValidator documentation to clearly describe its purpose in filtering leads based on home ownership status.
bigdbm/validate/phone.py Improved clarity in PhoneValidator and related classes' documentation on handling phone numbers and validation criteria.
bigdbm/validate/pii.py Revised GenderValidator, AgeValidator, MNWValidator, and HNWValidator descriptions for better clarity on their functionalities and standardizing terminology.
bigdbm/validate/simple.py Updated documentation for ZipCodeValidator, ContactableValidator, and others to enhance clarity and consistency in terminology across validators.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant DataValidator
    participant InsightsGenerator
    participant OpenAI

    User->>DataValidator: Submit lead data
    DataValidator->>InsightsGenerator: Validate and prepare data
    InsightsGenerator->>OpenAI: Request insights with validation info
    OpenAI-->>InsightsGenerator: Return generated insights
    InsightsGenerator-->>DataValidator: Send validated insights
    DataValidator-->>User: Provide insights report

🐰 In the fields where the lead data plays,
New insights bloom in wondrous ways!
With validations firm and bright,
A valid path to data's light.
So hop along, let's celebrate,
For every lead, now validated! 🌼✨

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