Hey all new here, new to AI and my wife needs your help! She has a terrible memory and is always getting her facts wrong during our arguments. I’m creating a fun workflow where I can upload a PDF document of the last 5 years of text messages 300 page 35mb) between us, extract the data while retaining timestamps, sender ID, and message content. I then query an agent to pull messages based on prompts such as “list all messages that talk about the kids school (keyword)”, “list all messages that have an aggressive tone (sentiment)”, or “summarize the conversation between x date and y date” (analysis)
I am familiar with parsing, chunking, embedding, vector databases, and context window limitations, but am overwhelmed by the sheer volume and variety of tools, pipelines and solutions.
Currently I am running everything locally (privacy) in docker on a MacBook Pro silicon, ollama chat model (llama3.2: 7b), default text extractor, ollama default embeddings, Postgres with pgvector.
Right now it’s doing a horrible job even extracting the text from the pdf and groups all message info such as date/time and sender, and then groups all the message content, and even splits messages further breaking the connection. Proud I’ve even got this far but just kinda stuck, and been seeing that RAG is challenging for these reasons.
I’m not a software engineer but I can navigate fairly well and write some basic code, but I can’t even tell if I’m over engineering or under engineering this project. I’m having a blast, my wife thinks It’s fun, I’m passionate about learning and would love to hear all your thoughts and advice.
Much love
-WarcrY