143 Semantic CF code search with JARVIS (No Arc Reactor Budget required) with Monte Chan

Monte Chan talks about “Semantic CF code search with JARVIS (No Arc Reactor Budget required)” in this episode of ColdFusion Alive Podcast with host Michaela Light.
“...With this tool, kind of like an AI bot, if you will, that will give a jump start on the onboarding process… the new employees can just use this bot to get a familiarity with the code base…” 
https://youtu.be/w2LeYZB8LA8
Show notes
What if you could ask questions about your codebase and get answers instantly?
Motivated by CodeQL by GitHub (Paid tool)
“What if a new developer (or a CTO) could just ask the codebase ‘where is the session timeout set?’ and get a real answer instead of grepping for three hours?”
“You called this a poor man’s J.A.R.V.I.S. Ben Nadel said ‘with great power comes… great self-indulgence.’ Are we about to be very self-indulgent today?
What is J.A.R.V.I.S.?
J.A.R.V.I.S. is the fictional artificial intelligence assistant created by Tony Stark in Marvel's Iron Man and Avengers comics and films.
J.A.R.V.I.S stands for "Just A Rather Very Intelligent System".
Codebase ingestion using vector databases
Walk the repo, split files into chunks (functions, classes, docs), turn each chunk into an embedding (a numeric vector that captures meaning), and store those vectors with metadata (path, line range, language). That index is what makes “where is session timeout set?” searchable by meaning, not just IDE search.
A vector database is a store optimized for “find the N most similar vectors.” Examples: Pinecone, Qdrant, Milvus, Chroma, LanceDB, plus CF’s built-in / in-memory store for demos. Code search is usually hybrid: vectors for meaning + keyword/BM25 for exact symbols.
How you chunk CFM/CFC/JS/SQL differently
Why you store a summary + metadata and embed that
The current gap: CF’s built-in document ingestion doesn’t yet understand CFC/CFML structure, so custom scripts are required
PDFs can be ingessed directly
Updating the vector store when your codebase changes 
Prompt-driven code exploration
Prompt-driven code exploration: You ask in English. The system retrieves the relevant chunks, stuffs them into the prompt, and the model answers from that context. That pattern is RAG (retrieval-augmented generation): retrieve first, then generate, so the model is grounded in your code instead of guessing
 
What you need” slide with practical commentary:
Piece
Why it matters
CIO translation
CF 2025 Update 8
Native AI, vector stores, agents, MCP
No new language or Python sidecar tax
Vector store (Qdrant / Milvus / Pinecone / Chroma)
In-memory is too small for real apps
Persistent, scalable “memory” for the AI
LLM (Anthropic, OpenAI, or Ollama local)
The brain
Cloud vs on-prem / air-gapped choice
Ingestion script
CF can ingest docs today; CFC/CFML still need custom work
One-time + ongoing cost of keeping the index fresh
MCP (optional but powerful)
File-system or other tools the agent can call
Controlled agency—AI can look without unrestricted access
Key terms
Vector store / embeddings -  numbers that capture the meaning of code so similarity search works
Chunking by logical entity (whole function, not random 500-token slices) -  this is the quality differentiator
Summary-first embeddings -  embed a rich description (purpose, path, callers, callees) rather than raw code only
MCP (Model Context Protocol) -  open standard so the agent can safely call tools (filesystem, DB, etc.)
Ongoing ingestion -  full re-index vs git-diff / pipeline-after-merge
Adobe folded LLM, embedding, vector-store, RAG, agent, and MCP APIs into the CF engine (commonly described as CF 2025 Update 8 / the “CF 2026” AI wave). You call models from CFML (ChatModel(), agent(), simpleRAG(), VectorStore(), etc.) instead of wiring every provider by hand.
MCP (Model Context Protocol)
An open standard so tools/resources are exposed the same way to Claude, Cursor, VS Code, ChatGPT, etc. CF can host MCP servers or call MCP tools. Community projects like MCPCFC wrap existing CF functions as MCP tools.

Similar commercial tools
Sourcegraph Cody, Continue.dev, custom LanceDB/Qdrant indexers
A fascinating look at how AI can help developers understand and navigate complex applications. 
CIO / risk / value questions
Time-to-answer for audits, compliance questions, or new-hire onboarding
Reducing “bus factor” / key-person risk
Security posture:
Local Ollama option (data never leaves the building)
Guardrails and “excessive agency” concerns (echo Pete Freitag’s CF Summit security talk)
Prompt injection surface when the AI can call tools
Cost model: vector DB + LLM tokens + ongoing ingestion vs. developer hours spent hunting code
Build vs buy: this is a capability you can own inside the CF platform rather than another SaaS
Practical next steps & gotchas (5- 6 min)
Minimum viable version a team could stand up in a sprint
Biggest surprise or pain point he hit while building it
What CF 2026 still needs (better CFC/CFML-aware ingestion is the obvious one)
Where the slides and sample code live: github.com/knittingguy (JARVIS repo + the earlier mongoDB-langchain / aiagent_cf work)
Close & resources
One sentence each for the CF developer and the CIO listening
Ben Nadel quote callback
Point people to the GitHub, MCP servers list (mcpservers.org), and Monte’s Facebook/Geek Talk channel
Invite questions from the live or later audience
Why are you proud to use CF?
WWIT to make CF more alive this year?
What are you looking forward to at the Adobe CF Summit East?
Mentioned in this episode
133 GitHub Copilot & AI-Assisted Coding (Unlocking ColdFusion’s AI Potential) with Monte Chan
Qdrant vector database
CF 2026 features (TT blog)
MCP servers
CF Summit East
Monte's GitHub
Listen to the Audio
Bio
Monte Chan
Monte Chan is currently a Senior Web Programmer at Shoes For Crews. He has been programming in ColdFusion since version 4.5 back in 1999. He was a co-manager of the Alamo Area ColdFusion User Group from 2008 to 2010. In his free time, he enjoys learning any web development related technologies or just programming languages he can put his hands on. He also enjoys running marathons; he does not run fast; he just runs🙂. He currently resides in San Antonio, TX with his beautiful wife and three rescue dogs (two chiweenies and one pure-bred chihuahua).
Links
Facebook 
You can email me at monte (at) monteandjanicechan.com but you may get a quicker response if you send me a message in Facebook Messenger.
LinkedIn
YouTube channel, Geek Talk.
GitHub
Interview transcript
Michaela Light
So welcome back to the show. I'm here with Monte Chan and we're going to be talking about semantic ColdFusion code search with Jarvis. And you don't need an Arc Reactor budget to do this because it's all written in ColdFusion. So welcome, Monte.
Monte Chan
Oh, thank you.
Michaela Light
And it's been a few years since you've been on the show when you were talking about your AI coding experience. And I'll put that episode in the show notes at TeraTech.com. But let me introduce you to folks who haven't met you before. You're currently a senior web programmer at Shoes for Crews, and you've been programming ColdFusion since version 4.5 back in the late '90s. You used to be the co-manager of the Alamo Area ColdFusion User Group.
And in your free time, I don't know how you have free time because I've seen you presenting at CF Summit and I know you work hard. You enjoy learning any web development-related technologies or just programming languages that you can get your hands on. And also, you enjoy running marathons, though you don't have any world-record times there. You just enjoy it, do it for fun.
Currently, you're in San Antonio, Texas, and you have a beautiful wife and three rescue dogs. Two chiweenies and one purebred Chihuahua. What is a chiweenie? I've never heard of that. Chihuahua is the little dog, right?
Read more
 
Michaela Light is the host of the CF Alive Podcast and has interviewed more than 100 ColdFusion experts. In each interview, she asks "What Would It Take to make CF more alive this year?" The answers still inspire her to continue to write and interview new speakers.
Michaela has been programming in ColdFusion for more than 20 years. She founded TeraTech in 1989. The company specializes in ColdFusion application development, security and optimization. She has also founded the CFUnited Conference and runs the annual State of the CF Union Survey.
 
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