Layer

Selection

Justification

MCP Framework

FastMCP

Clean Python decorators for tool registration while adhering strictly to standard MCP JSON-RPC protocol

PDF Extraction

PyMuPDF (fitz)

High-speed C-backed page parsing. Benchmarked faster than pdfplumber and preserves page boundaries reliably

Embeddings

sentence-transformers (all-MiniLM-L6-v2)

Generates 384-dimensional dense vectors locally on CPU with minimal latency (~90MB model size)

Vector Store

ChromaDB

Persistent on-disk vector database using cosine distance metrics with zero extra server setup

Local LLM

Ollama (llama3.2)

Runs locally on CPU/GPU. Setting temperature to 0.1 ensures factual responses grounded strictly in context

Tool Name

Purpose

Key Inputs

Expected Output

query_documents

Grounded Q&A over indexed PDFs

question (str), document_filter (optional str)

Answer string + list of source citations (document, page, score)

get_nexset_schema

Inspect auto-generated Nexset profile

document_name (str)

Metadata JSON with topic tags, word metrics, headings, and governance tags

list_documents

List index registry & statistics

None

JSON summary of indexed documents, total pages, and chunk counts

get_document_summary

Executive document summary

document_name (str)

High-level summary of main topics and referenced page numbers

Real-World Challenge

Problem Impact

Technical Solution Implemented

Nested Directory Hierarchies

Enterprise document drops use nested subfolders (data/dept_a/, data/0/), causing flat file parsers to miss data.

Implemented recursive pattern discovery (data/**/*.pdf) in ingestion.py to traverse complex folder structures automatically.

Vector Memory Overflow on Large Files

Indexing multi-hundred page PDFs in a single call causes memory spikes and payload exceptions in vector stores.

Implemented batched upserts (batch_size = 100) combined with deterministic chunk hashing (doc_name::page::chunk) in retriever.py for memory-safe, idempotent indexing.

Metadata Invisibility & Token Waste

Basic RAG models treat PDFs as dumb text strings, forcing LLMs to spend expensive context tokens just to discover document structure.

Built the NexsetMetadataProfile engine and exposed get_nexset_schema in server.py, allowing AI agents to inspect structural headings, topic tags, and read metrics instantly.

Context Fragmentation from Naive Chunking

Slicing text at rigid character limits cuts sentences in half, causing fragmented context and degraded embedding quality.

Built a sentence-aware chunker in ingestion.py that finds period (. ) and newline (\n) boundaries before breaking text.

Task / Focus Area

AI Assistance

My Engineering Decisions & Overrides

Directory Discovery

Generated flat data/*.pdf search

Modified to recursive globbing (data/**/*.pdf) to support real enterprise nested folder datasets

Vector Indexing

Generated unbatched collection.add() calls

Refactored to batch upserts (batch_size = 100) with deterministic chunk IDs (doc_name::page::chunk) to prevent memory crashes

Metadata Engine

Defaulted to plain text chunking

Designed NexsetMetadataProfile abstraction to auto-infer topic categories, heading structures, and read metrics

Windows Console I/O

Hardcoded Unicode emojis in print statements

Sanitized console print statements to standard text to avoid UnicodeEncodeError on Windows cp1252 terminals