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justin af44d7a102 Phase 11 + Phase 6 GPU move

## Phase 11 — Curated agronomy / label-handling knowledge layer

docs_mcp/lessons.md: 13 topic-anchored markdown sections covering
the LLM-side context a farmer-advisor needs alongside the raw
label corpus —
  - how-to-use-this-corpus
  - epa-signal-words
  - rei-phi-fundamentals
  - rup-handling
  - supplemental-labels-24c-2ee
  - tank-mix-fundamentals
  - resistance-management-hrac-frac-irac
  - glufosinate-application-rules
  - dicamba-application-rules
  - lake-erie-watershed-ohio
  - scn-and-other-seed-treatment-context
  - drift-management-essentials
  - how-to-format-recommendations

Each Topic block is independently retrievable via the new MCP tool:

  ppls_api_lessons(topic="rup-handling")

Or with no topic to get the full TOC, or with a substring to
match-and-return matching sections ("dicamba" → dicamba-application-rules).

Tool docstring instructs the LLM to call this proactively before any
pesticide recommendation so the recommendation lands with regulatory
framing, resistance-group callouts, RUP applicator language, and the
canonical recommendation format — not just a rate from a label.

## Phase 6 — Reranker moved to GPU on trashpanda

Stopped the local CPU container and started on trashpanda's Tesla P4
(8 GB VRAM) via:

  docker run -d --name llama-rerank --restart unless-stopped --gpus all \
    -p 8082:8080 \
    ghcr.io/ggml-org/llama.cpp:server-cuda \
    -hf gpustack/jina-reranker-v2-base-multilingual-GGUF:Q8_0 \
    --reranking --host 0.0.0.0 --port 8080 -ngl 99

The :server-cuda image variant (not :server) is required for CUDA
backend; -ngl 99 offloads all layers to GPU.

Latency: 50-doc rerank dropped from ~23 s on CPU to ~0.7-1.5 s on
the Tesla P4 — production-grade interactive speeds.

deploy/rerank-docker.md updated with the trashpanda deploy recipe,
troubleshooting (mostly "did you use server-cuda?"), and a perf
reference table. The MCP server's RERANK_URL just points at
http://10.10.1.65:8082 now.

GPU eval still completing in background; results land in
eval/results/with_rerank_gpu.md as a follow-up commit.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

2026-05-24 12:10:09 -04:00

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Reranker sidecar — llama.cpp + jina-reranker-v2-base

Phase 6 setup. The MCP server reads RERANK_URL and, when set, pipes the top-50 dense (or hybrid) chunks through this sidecar before returning to the LLM. See docs_mcp/server.py:_rerank_pool.

Production deploy — trashpanda (Tesla P4, 8 GB VRAM)

This is where the reranker lives. Same box that runs the Drawbar backend + Cloudflare Tunnel, so the MCP server can reach it on the internal LAN.

ssh justin@10.10.1.65 \
  'docker run -d --name llama-rerank --restart unless-stopped --gpus all \
     -p 8082:8080 \
     ghcr.io/ggml-org/llama.cpp:server-cuda \
     -hf gpustack/jina-reranker-v2-base-multilingual-GGUF:Q8_0 \
     --reranking --host 0.0.0.0 --port 8080 -ngl 99'

Key flags:

--gpus all — pass through the Tesla P4
server-cuda image — CUDA-built llama.cpp (not the CPU-only :server)
-ngl 99 — offload all layers to GPU
-hf <repo> — auto-download from HuggingFace on first start (~280 MB, cached in the container volume)
--reranking — enables /v1/rerank endpoint
--restart unless-stopped — survives reboot

VRAM usage: ~280 MB model + CUDA context. Well under the 8 GB the Tesla P4 has, leaves room for nomic-embed-text (~560 MB) if you later co-host it.

Configure the MCP server

export RERANK_URL=http://10.10.1.65:8082
# search_docs now reranks the hybrid pool through the GPU before returning

In production (the MetaMCP-fronted Drawbar deploy), this is baked into the MCP server's container env.

Verify

curl http://10.10.1.65:8082/v1/rerank -H 'Content-Type: application/json' -d '{
  "query": "soybean herbicide for waterhemp",
  "documents": [
    "Roundup Custom for fallow burndown",
    "Sencor metribuzin controls waterhemp in soybean pre-emergence"
  ]
}'

Expect index=1 (the Sencor doc) at score ~0.8, index=0 at a strongly negative score, in under 1 s.

Performance reference

Mode	Pool	Wall time
CPU (local 28-thread Xeon)	50 docs	~23 s
GPU (Tesla P4 on trashpanda)	50 docs	~0.7-1.5 s
GPU (Tesla P4)	20 docs	~0.4 s

The Tesla P4 is Pascal-era (8.1 TFLOPs FP32) so a modern Ampere or Ada Lovelace GPU would be ~3-5× faster, but for the row-crop label corpus query rate the P4 is plenty.

Troubleshooting

Model not on GPU? Check docker logs llama-rerank | grep CUDA — you should see CUDA0 : Tesla P4 (8109 MiB, ... free) and tensor load lines. If you see CPU-only init, you forgot --gpus all or used :server instead of :server-cuda.
Conflict with Ollama on the same GPU? No — both processes can share the GPU, CUDA handles VRAM partitioning. nomic-embed-text + jina-reranker-v2-base together use ~840 MB on the 8 GB card.
First rerank call is slow (~4 s)? Warm-up. Subsequent calls are ~0.7 s for 50 docs.

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