seed-mcp scaffold: clone docs-mcp-template, customize for crop_seed PRODUCT_NAME

Sibling project to crop-chem-docs, same MCP-template lineage. Corpus is
seed/hybrid varieties across 6 vendors instead of pesticide labels.

What's customized vs. the template:
- CLAUDE.md: vendor matrix, build priority, Pioneer fallback policy,
  canonical sidecar schema (per-crop), Golden Harvest disease-scale
  reversal gotcha, no-IPv6 / HTTPS-clone note
- README.md: vendor coverage table, tool list, phase status
- Dockerfile: PRODUCT_NAME=crop_seed default, sources.json (not
  bundles.json), HYBRID_SEARCH=true, OLLAMA_URL + RERANK_URL Docker
  DNS defaults (same llama-rerank sidecar as crop-chem-docs)
- .gitea/workflows/refresh.yml: monthly cron (seed catalogs move
  slowly), 5 GREEN scraper steps, corpus-YYYY.MM.DD tag for Drawbar
  pinning, continue-on-error on GC step
- .gitea/workflows/image-only.yml: paths filter + cancel-in-progress
  concurrency group
- scripts/registry_gc.py: lifted from crop-chem-docs (correct Gitea
  packages API URL + UA header to bypass CF block on default
  Python-urllib UA)
- sources.json: catalog of 6 sources + scope_filter + per-source
  schema notes + Pioneer-exclusion rationale
- scrape/runner.py: dispatcher with --all = GREEN-only
- scrape/sources/{bayer_seeds,golden_harvest,nk,agripro,becks_pfr,
  becks_products}.py: stub modules with implementation notes
- docs_mcp/server.py: PRODUCT_NAME default → crop_seed,
  PRODUCT_DOCS_URL → repo URL

Pioneer is intentionally NOT a source. ToS bans automation; dealer
locator is login-gated. The MCP returns a curated fallback lesson
directing the user to pioneer.com.

Next phases:
- Phase 1: implement bayer_seeds (lift-and-shift from crop-chem-docs
  Bayer scraper; same __NEXT_DATA__ infra)
- Phase 7: curate eval/queries.jsonl
- Phase 11: lessons.md with Pioneer fallback + disease-scale notes

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

rag/embeddings.py

@@ -0,0 +1,72 @@
+"""Embedding function for Chroma — Ollama-hosted nomic-embed-text by default.
+
+Swappable: implement the same `embedding_function()` interface returning
+a Chroma `EmbeddingFunction` and the rest of the pipeline doesn't care.
+
+Defaults (override via env):
+  OLLAMA_URL    one or more comma-separated URLs (load-balanced)
+  EMBED_MODEL   model name; default 'nomic-embed-text'
+  EMBED_DIM     expected embedding dim; default 768 (nomic-embed-text)
+"""
+from __future__ import annotations
+
+import os
+import logging
+from typing import Any
+
+import httpx
+from chromadb import EmbeddingFunction, Documents, Embeddings
+
+log = logging.getLogger(__name__)
+
+OLLAMA_URLS = [u.strip() for u in os.environ.get("OLLAMA_URL",
+               "http://localhost:11434").split(",") if u.strip()]
+EMBED_MODEL = os.environ.get("EMBED_MODEL", "nomic-embed-text")
+EMBED_DIM = int(os.environ.get("EMBED_DIM", "768"))
+
+
+class OllamaEmbeddings(EmbeddingFunction):
+    """Calls /api/embed across N Ollama endpoints, naive round-robin.
+
+    For indexing throughput on multiple GPUs, run one Ollama container
+    per GPU (pinned via NVIDIA_VISIBLE_DEVICES) and pass all their URLs
+    in OLLAMA_URL — the embedder picks the next endpoint per batch.
+    """
+
+    def __init__(self, urls: list[str] = OLLAMA_URLS, model: str = EMBED_MODEL):
+        self.urls = urls
+        self.model = model
+        self._next = 0
+
+    def __call__(self, input: Documents) -> Embeddings:
+        url = self.urls[self._next % len(self.urls)]
+        self._next += 1
+        with httpx.Client(timeout=300) as c:
+            r = c.post(f"{url}/api/embed",
+                       json={"model": self.model, "input": list(input)})
+            r.raise_for_status()
+            data = r.json()
+        return data.get("embeddings") or []
+
+    def name(self) -> str:                  # newer chromadb requires this
+        return f"ollama:{self.model}"
+
+    @staticmethod
+    def build_from_config(config: dict) -> "OllamaEmbeddings":  # newer chromadb
+        return OllamaEmbeddings(
+            urls=config.get("urls", OLLAMA_URLS),
+            model=config.get("model", EMBED_MODEL),
+        )
+
+    def get_config(self) -> dict:           # newer chromadb
+        return {"urls": self.urls, "model": self.model}
+
+    def default_space(self) -> str:
+        return "cosine"
+
+    def supported_spaces(self) -> list[str]:
+        return ["cosine", "l2", "ip"]
+
+
+def embedding_function() -> EmbeddingFunction:
+    return OllamaEmbeddings()