I'm developing a CRM and need help creating a scanner that extracts key information from text or emails, focusing on capturing details like brands, compensation, deliverables, or deadlines. I've been using regex to detect keywords like 'collab' or 'paid partnership', but this method isn't flexible enough as the language in these emails varies greatly. I'm looking for insights on how to blend keyword detection with a language model to enhance accuracy. Is there a particular framework or approach that combines regex, embeddings, and reasoning? I'm not an expert, but I'm eager to learn how to structure this project or what topics to study to grasp the concepts better.
4 Answers
Check out Google's NotebookLM; it seems to already do what you're trying to achieve!
You're on the right track wanting to transform unstructured data into structured formats. If this is just a proof of concept, I recommend experimenting with the Gemini AI Pro API. It’s user-friendly for trying out concepts. For instance, you can set a prompt like, "add the following data point to the data set: [insert email text]" and see how it processes. You can also dive into building your models in Python that are tailored to your specific use cases. Choose between getting your hands dirty or starting off with simple testing first!
Have you considered LlamaParse or Solr? They might be beneficial for your needs!
Consider using the Aho-Corasick algorithm for searching text. It allows you to add multiple synonyms and variations while maintaining efficient performance. Regex isn't ideal for this task due to its limitations when dealing with varied language. For connecting it with a language model, I’d need more detail about what you're aiming for, but that could be crucial to your setup.
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