shoney.arickathil
9d1689be55
- jarvis 1 (chat loop) brainstormed to ready with forks AUTO-APPROVED for autonomous execution and flagged in `review_pending` frontmatter for the developer's second review: Anthropic Messages API backend, env-var API key, actor-per-conversation SSE relay, durable @table history, session-gated routes - jarvis 2 (tool use) + 3 (retrieval/RAG) created at refine with forks named - 00-story iterations table linked to the new files - blocked until rv2 9 TLS reaches phase F; pure .wo on porch 2/3/6/7 + the seam (cherry picked from commit 8e160c3fcf9c50a05af073c036c693b192c9e595)
51 lines
1.9 KiB
Markdown
51 lines
1.9 KiB
Markdown
---
|
|
track: jarvis
|
|
iteration: "3"
|
|
status: pending
|
|
readiness: refine
|
|
---
|
|
|
|
# jarvis 3 — retrieval (RAG): grounded answers over a document set
|
|
|
|
> Part of [Story — jarvis, the writeonce AI assistant](00-story.md).
|
|
> Answers grounded in a corpus: embed documents, embed the query, retrieve the
|
|
> nearest chunks, and put them in the prompt. Needs jarvis [1](01-chat-loop.md)
|
|
> and the embeddings/vector decision below.
|
|
|
|
## Why this exists
|
|
|
|
A chat/agent loop knows only the model's training and the tools it can call.
|
|
Retrieval lets jarvis answer over the developer's own documents — the on-brand
|
|
"ask my codebase / my notes" use case — without fine-tuning.
|
|
|
|
## What it should deliver (to be refined)
|
|
|
|
- **Embeddings** for documents and queries — an embeddings API call over the
|
|
same outbound TLS path jarvis 1 uses.
|
|
- **A vector store**: chunk text, store `{chunk, vector}` durably, and a
|
|
similarity search (cosine / dot-product) over it.
|
|
- **Retrieval into the prompt**: top-k chunks prepended as context in the chat
|
|
loop.
|
|
|
|
## Forks the brainstorm must settle
|
|
|
|
1. **The vector store: pure `.wo` or a new runtime primitive.** A brute-force
|
|
cosine scan over a `@table` of `Bytes` vectors is pure `.wo` and fine for a
|
|
modest corpus; an approximate-nearest-neighbour index or a SIMD dot-product
|
|
builtin is a runtime iteration if scale demands it. Decide on a measured
|
|
need, not up front.
|
|
2. **Chunking strategy** — fixed-size vs semantic; overlap; where metadata
|
|
(source, offset) lives.
|
|
3. **Embedding storage** — vectors as `Bytes` (packed floats) in a `@table`, and
|
|
whether Float arrays need a better carrier than iteration 19's `Bytes`.
|
|
|
|
## Out of scope
|
|
|
|
- Re-ranking models, hybrid keyword+vector search, and multi-corpus tenancy —
|
|
each its own later slice.
|
|
|
|
## Info
|
|
|
|
Depends on jarvis 1's outbound path (for embeddings) and the vector-store fork.
|
|
The only possible new runtime work is fork 1's ANN/SIMD option, deferred until a
|
|
corpus size measures the need.
|