justin
af44d7a102
## 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>
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83 lines
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# Reranker sidecar — llama.cpp + jina-reranker-v2-base
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Phase 6 setup. The MCP server reads `RERANK_URL` and, when set, pipes
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the top-50 dense (or hybrid) chunks through this sidecar before
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returning to the LLM. See `docs_mcp/server.py:_rerank_pool`.
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## Production deploy — trashpanda (Tesla P4, 8 GB VRAM)
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This is where the reranker lives. Same box that runs the Drawbar
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backend + Cloudflare Tunnel, so the MCP server can reach it on the
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internal LAN.
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```bash
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ssh justin@10.10.1.65 \
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'docker run -d --name llama-rerank --restart unless-stopped --gpus all \
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-p 8082:8080 \
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ghcr.io/ggml-org/llama.cpp:server-cuda \
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-hf gpustack/jina-reranker-v2-base-multilingual-GGUF:Q8_0 \
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--reranking --host 0.0.0.0 --port 8080 -ngl 99'
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```
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Key flags:
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- `--gpus all` — pass through the Tesla P4
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- `server-cuda` image — CUDA-built llama.cpp (not the CPU-only `:server`)
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- `-ngl 99` — offload all layers to GPU
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- `-hf <repo>` — auto-download from HuggingFace on first start (~280 MB,
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cached in the container volume)
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- `--reranking` — enables `/v1/rerank` endpoint
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- `--restart unless-stopped` — survives reboot
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VRAM usage: ~280 MB model + CUDA context. Well under the 8 GB the
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Tesla P4 has, leaves room for nomic-embed-text (~560 MB) if you
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later co-host it.
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## Configure the MCP server
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```bash
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export RERANK_URL=http://10.10.1.65:8082
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# search_docs now reranks the hybrid pool through the GPU before returning
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```
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In production (the MetaMCP-fronted Drawbar deploy), this is baked
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into the MCP server's container env.
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## Verify
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```bash
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curl http://10.10.1.65:8082/v1/rerank -H 'Content-Type: application/json' -d '{
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"query": "soybean herbicide for waterhemp",
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"documents": [
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"Roundup Custom for fallow burndown",
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"Sencor metribuzin controls waterhemp in soybean pre-emergence"
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]
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}'
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```
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Expect index=1 (the Sencor doc) at score ~0.8, index=0 at a strongly
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negative score, in under 1 s.
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## Performance reference
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| Mode | Pool | Wall time |
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|---|---|---|
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| CPU (local 28-thread Xeon) | 50 docs | ~23 s |
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| GPU (Tesla P4 on trashpanda) | 50 docs | ~0.7-1.5 s |
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| GPU (Tesla P4) | 20 docs | ~0.4 s |
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The Tesla P4 is Pascal-era (8.1 TFLOPs FP32) so a modern Ampere or
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Ada Lovelace GPU would be ~3-5× faster, but for the row-crop label
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corpus query rate the P4 is plenty.
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## Troubleshooting
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- **Model not on GPU?** Check `docker logs llama-rerank | grep CUDA` —
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you should see `CUDA0 : Tesla P4 (8109 MiB, ... free)` and tensor
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load lines. If you see CPU-only init, you forgot `--gpus all` or
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used `:server` instead of `:server-cuda`.
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- **Conflict with Ollama on the same GPU?** No — both processes can
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share the GPU, CUDA handles VRAM partitioning. nomic-embed-text +
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jina-reranker-v2-base together use ~840 MB on the 8 GB card.
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- **First rerank call is slow (~4 s)?** Warm-up. Subsequent calls are
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~0.7 s for 50 docs.
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