Developed Embedding and Smart Tagging Pipeline

📅 2025-05-06 — Session: Developed Embedding and Smart Tagging Pipeline

🕒 17:00–17:30
🏷️ Labels: Embedding, Data Processing, Python, Automation, Metadata
📂 Project: Dev
⭐ Priority: MEDIUM

The session aimed to enhance the data processing pipeline by developing embedding, smart tagging, and storage solutions.

Outlined the next steps in the data processing pipeline, focusing on merging daily logs, embedding for semantic search, and implementing smart tagging and storage solutions.
Developed a robust merge strategy for log files using Python scripts, which included merging original log files with screening results.
Created a flattened script for merging JSONL files, ensuring the original structure is retained and including a screening_result field.
Designed an embedding and metadata indexing pipeline using merged JSONL files, detailing steps for text extraction, metadata preparation, embedding computation, and storage in a vector database.
Implemented a full pipeline for merging logs and embedding, storing results in ChromaDB with a JSONL backup.
Set up an incremental embedding system in Python using Langchain, including package installation and environment verification.
Developed an embedding pipeline for merged logs, extracting relevant content and saving it into a vector store.

Successfully developed and tested a comprehensive embedding and smart tagging pipeline ready for vectorization, including enhancements like skipping already embedded chunks and utilizing OpenAI embeddings with rich metadata tracking.

Further testing and optimization of the pipeline for production use.
Exploration of potential user interface options for enhanced search and retrieval of annotated data.