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
parent 278fe5f456
commit af44d7a102
3 changed files with 410 additions and 27 deletions
@@ -599,16 +599,107 @@ def corpus_status() -> str:


 # ---------------------------------------------------------------------------
-# Stubs for later phases — keep the signatures in this file so refactors
-# don't lose the contracts. Implementations come per phase.
+# Phase 11 — Curated agronomy / label-handling knowledge
+# ---------------------------------------------------------------------------
+
+LESSONS_PATH = Path(__file__).resolve().parent / "lessons.md"
+_lessons_cache: tuple[str, list[tuple[str, str]]] | None = None  # (full, sections)
+
+
+def _load_lessons() -> tuple[str, list[tuple[str, str]]]:
+    """Read lessons.md once, split into (topic_slug, body) sections."""
+    global _lessons_cache
+    if _lessons_cache is not None:
+        return _lessons_cache
+    if not LESSONS_PATH.exists():
+        _lessons_cache = ("", [])
+        return _lessons_cache
+    full = LESSONS_PATH.read_text(encoding="utf-8")
+    sections: list[tuple[str, str]] = []
+    # Split on lines like "## Topic: <slug>" (case-sensitive marker)
+    parts = re.split(r"^## Topic:\s+(\S+)\s*$", full, flags=re.MULTILINE)
+    # parts = [preamble, slug1, body1, slug2, body2, ...]
+    for i in range(1, len(parts), 2):
+        slug = parts[i].strip()
+        body = parts[i + 1].strip() if i + 1 < len(parts) else ""
+        sections.append((slug, body))
+    _lessons_cache = (full, sections)
+    return _lessons_cache
+
+
+@mcp.tool()
+def ppls_api_lessons(
+    topic: Annotated[
+        str | None,
+        Field(description="OPTIONAL: topic slug or substring (e.g., "
+                          "'rup-handling', 'dicamba', 'rei'). Omit to get "
+                          "the full table of contents."),
+    ] = None,
+) -> str:
+    """Surface curated agronomy + label-handling knowledge that supplements
+    the raw label corpus.
+
+    **Call this proactively whenever you're about to give a pesticide
+    recommendation from search_docs results.** The lessons cover:
+    EPA signal words, REI/PHI fundamentals, RUP handling, 2(ee)/24(c)
+    supplemental labels, tank-mix and resistance-management
+    fundamentals (HRAC/FRAC/IRAC groups), product-specific application
+    rules (glufosinate, dicamba), Lake Erie watershed considerations
+    for Ohio, SCN context for soybean, drift management, and the
+    canonical recommendation format the farmer expects.
+
+    Without these lessons, your recommendations risk being technically
+    correct but missing the regulatory framing, resistance group
+    callouts, RUP applicator requirements, or off-target-damage
+    warnings that make them actionable. Call this first; cite specific
+    lessons in your response.
+    """
+    with TimedCall("ppls_api_lessons", {"topic": topic}) as _call:
+        full, sections = _load_lessons()
+        if not sections:
+            _call.set(sections=0)
+            return "_(lessons.md not found — Phase 11 knowledge layer not populated)_"
+
+        if not topic:
+            _call.set(sections=len(sections), returned="toc")
+            toc_lines = [
+                "# PPLS API lessons — table of contents",
+                "",
+                f"Call `ppls_api_lessons(topic='<slug>')` to fetch a specific section.",
+                "",
+            ]
+            for slug, body in sections:
+                # First non-blank, non-list line as the headline summary
+                summary = ""
+                for line in body.splitlines():
+                    s = line.strip()
+                    if s and not s.startswith(("-", "*", "|", "```")):
+                        summary = s[:140]
+                        break
+                toc_lines.append(f"- **`{slug}`** — {summary}")
+            return "\n".join(toc_lines)
+
+        topic_lc = topic.lower()
+        matched = [(slug, body) for slug, body in sections if topic_lc in slug.lower()]
+        if not matched:
+            _call.set(sections=0, returned="no-match")
+            available = ", ".join(f"`{s}`" for s, _ in sections)
+            return (f"_(no lesson matched topic `{topic}`. Available: {available})_")
+
+        _call.set(sections=len(matched), returned="match")
+        out: list[str] = []
+        for slug, body in matched:
+            out.append(f"## Topic: {slug}\n\n{body}")
+        return "\n\n---\n\n".join(out)
+
+
+# ---------------------------------------------------------------------------
+# Stubs for later phases
 # ---------------------------------------------------------------------------

 # @mcp.tool()  # Phase 12
 # def find_doc_inconsistencies(scope_query: str, ...) -> str: ...

-# @mcp.tool()  # Phase 11
-# def ppls_label_lessons(topic: str | None = None) -> str: ...
-

 # ===========================================================================
 # Entry point