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>
2026-05-25 12:28:49 -04:00
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+"""SQLite FTS5-backed BM25 retrieval over the same chunks Chroma indexes.
+
+Hybrid retrieval (BM25 + dense + Reciprocal Rank Fusion) addresses a
+limit of single-tower dense embeddings: when a query has specific
+technical terms (filenames, language names, error codes, API paths),
+the dense embedding doesn't bridge from the query into a short
+code-focused chunk. The chunk loses to the much larger crowd of
+prose chunks that semantically match the query topic.
+
+BM25 handles this directly. Lexical overlap on rare terms ("python",
+"create_vpg.py", "PROTECTED_SITE_ID", "applyUpgrade") scores those
+chunks high. Fused with the dense ranking via RRF, the hybrid result
+is strictly better than either alone for the queries we've seen
+fail.
+
+Why SQLite FTS5:
+  - In the stdlib. Zero new deps.
+  - On-disk. Same persistence model as Chroma — Docker COPY the dir,
+    `rag.index --rebuild` regenerates from corpus.
+  - Built-in `bm25()` ranking function. No knobs to tune that matter
+    for our use case (k1=1.2, b=0.75 defaults are fine).
+  - Builds 70k+ chunks in seconds. Faster than the Chroma rebuild's
+    embedding step by 100×, so it adds basically nothing to the
+    full-rebuild cycle.
+
+Schema is two tables to keep filtering clean. FTS5 doesn't filter
+nicely on its own columns; the content_rowid pattern keeps an
+external metadata table joinable by rowid:
+
+    CREATE TABLE chunks_meta (
+        rowid INTEGER PRIMARY KEY AUTOINCREMENT,
+        id TEXT UNIQUE,
+        bundle_id TEXT, page_id TEXT, version TEXT,
+        platform TEXT, product TEXT, ordinal INTEGER
+    );
+    CREATE VIRTUAL TABLE chunks_fts USING fts5(
+        text,
+        tokenize = 'porter unicode61 remove_diacritics 2',
+        content = 'chunks_meta',
+        content_rowid = 'rowid'
+    );
+
+Queries:
+
+    SELECT m.id, bm25(chunks_fts) AS score
+    FROM chunks_meta m
+    JOIN chunks_fts  f ON m.rowid = f.rowid
+    WHERE f MATCH ?
+      AND m.version = ?            -- optional metadata filter
+    ORDER BY bm25(chunks_fts)      -- lower = better in FTS5
+    LIMIT ?;
+"""
+from __future__ import annotations
+
+import logging
+import re
+import sqlite3
+from pathlib import Path
+from typing import Any
+
+log = logging.getLogger(__name__)
+
+# Default location: bm25/<product>_docs.db at the repo root, next to chroma/.
+ROOT = Path(__file__).resolve().parent.parent
+DEFAULT_DB_DIR = ROOT / "bm25"
+DEFAULT_DB_NAME = "<product>_docs.db"
+
+# Columns we expose as filterable metadata. Mirrors what _build_where in
+# docs_mcp/server.py accepts so the same filter dicts work for both
+# Chroma and BM25 without per-retriever translation in the caller.
+FILTER_COLUMNS = ("bundle_id", "page_id", "version", "platform", "product", "ordinal")
+
+
+# Allowlist tokenizer for free-text queries. FTS5's parser chokes on lots
+# of punctuation we routinely see in user queries (".10.9", "?", "VPG's",
+# em-dash, etc.). Rather than blocklist every operator, just keep
+# alphanumerics + a few separators and replace everything else with a
+# space. This loses the ability to phrase-search ("exact match") but we
+# don't expose that to users anyway — they ask natural-language questions
+# and want the answer, not a Boolean DSL.
+_KEEP_RE = re.compile(r"[^A-Za-z0-9_\s]")
+# FTS5 reserves these Boolean operator KEYWORDS at the token level —
+# stripping them avoids accidental phrase-query behavior when a user
+# query happens to contain bare "AND", "OR", "NOT", "NEAR".
+_BOOLEAN_KW_RE = re.compile(r"(?<!\w)(AND|OR|NOT|NEAR)(?!\w)")
+
+
+def _sanitize_query(text: str) -> str:
+    """Reduce a natural-language query to an FTS5 OR-of-tokens query.
+
+    Two transformations:
+
+    1. Non-alphanumeric → space (drops punctuation; "10.9?" becomes
+       "10 9"). Lets us handle versions, parens, question marks, etc.
+       without inviting FTS5 parse errors.
+    2. Boolean keywords stripped (FTS5 reserves AND/OR/NOT/NEAR).
+    3. Tokens explicitly OR'd. FTS5's default is AND-of-tokens — for
+       any non-trivial natural-language query that means zero hits
+       (no chunk contains every word). OR semantics is what we want:
+       BM25 already weights documents containing more query terms
+       higher, so we don't lose precision, but we DO gain recall.
+    """
+    cleaned = _KEEP_RE.sub(" ", text)
+    cleaned = _BOOLEAN_KW_RE.sub(" ", cleaned)
+    tokens = cleaned.split()
+    if not tokens:
+        return ""
+    return " OR ".join(tokens)
+
+
+def _where_to_sql(where: dict | None) -> tuple[str, list[Any]]:
+    """Translate a Chroma-shaped filter dict into a SQL fragment + params.
+
+    Accepts the same shapes ``docs_mcp.server._build_where`` produces:
+
+        None                          → ("", [])
+        {"version": "10.9"}           → ("AND m.version = ?", ["10.9"])
+        {"$and": [{...}, {...}]}      → ("AND m.X = ? AND m.Y = ?", [...])
+
+    Unknown keys are silently dropped (defensive — better to over-match
+    than to crash on a filter we don't know).
+    """
+    if not where:
+        return "", []
+    parts: list[str] = []
+    params: list[Any] = []
+
+    def _emit_eq(cond: dict[str, Any]) -> None:
+        for k, v in cond.items():
+            if k in FILTER_COLUMNS:
+                parts.append(f"m.{k} = ?")
+                params.append(v)
+
+    if "$and" in where:
+        for sub in where["$and"]:
+            _emit_eq(sub)
+    else:
+        _emit_eq(where)
+    if not parts:
+        return "", []
+    return "AND " + " AND ".join(parts), params
+
+
+class BM25Index:
+    """Thin wrapper around an FTS5-backed sqlite db.
+
+    Single-writer model. Reads are connection-per-call (sqlite handles
+    concurrency through file locks; for our read-heavy workload that's
+    fine and avoids cross-thread connection sharing issues with the MCP
+    server's request handlers).
+    """
+
+    def __init__(self, db_path: Path | None = None):
+        self.db_path = Path(db_path) if db_path else (DEFAULT_DB_DIR / DEFAULT_DB_NAME)
+
+    # -- build ----------------------------------------------------------
+
+    def build(self, records: list[dict]) -> int:
+        """Rebuild the index from scratch from `records`.
+
+        `records` is the same list ``rag.index.page_records`` produces:
+        ``[{"id": ..., "text": ..., "metadata": {...}}, ...]``. Bulk
+        insert wrapped in a transaction — single-digit seconds for the
+        full 73k-chunk corpus.
+        """
+        self.db_path.parent.mkdir(parents=True, exist_ok=True)
+        # Drop and recreate. Idempotent rebuild.
+        if self.db_path.exists():
+            self.db_path.unlink()
+        with sqlite3.connect(self.db_path) as con:
+            con.executescript(self._schema_sql())
+            con.executemany(
+                "INSERT INTO chunks_meta (id, bundle_id, page_id, version, "
+                "platform, product, ordinal) VALUES (?, ?, ?, ?, ?, ?, ?)",
+                [
+                    (
+                        r["id"],
+                        r["metadata"].get("bundle_id") or "",
+                        r["metadata"].get("page_id") or "",
+                        r["metadata"].get("version") or "",
+                        r["metadata"].get("platform") or "",
+                        r["metadata"].get("product") or "",
+                        int(r["metadata"].get("ordinal") or 0),
+                    )
+                    for r in records
+                ],
+            )
+            # Populate the FTS5 contentless-ish table by rowid. We populated
+            # chunks_meta first; rowids align with insertion order.
+            con.executemany(
+                "INSERT INTO chunks_fts (rowid, text) VALUES (?, ?)",
+                [
+                    (i + 1, r["text"])
+                    for i, r in enumerate(records)
+                ],
+            )
+            con.commit()
+        log.info("bm25: indexed %d chunks → %s", len(records), self.db_path)
+        return len(records)
+
+    # -- query ----------------------------------------------------------
+
+    def query(
+        self,
+        text: str,
+        n: int = 200,
+        where: dict | None = None,
+    ) -> list[tuple[str, float]]:
+        """Return up to `n` (chunk_id, bm25_score) pairs, lowest score first.
+
+        FTS5's bm25() returns NEGATIVE numbers — more relevant docs have
+        smaller (more negative) scores. We order ASC so the first row is
+        the most relevant. Callers that need a "rank" should enumerate
+        the returned list.
+        """
+        sanitized = _sanitize_query(text)
+        if not sanitized:
+            return []
+        where_sql, params = _where_to_sql(where)
+        # FTS5 MATCH wants the unaliased table name on its left, so we use
+        # chunks_fts (no alias) and JOIN by rowid against chunks_meta.
+        sql = (
+            "SELECT m.id, bm25(chunks_fts) AS score "
+            "FROM chunks_fts "
+            "JOIN chunks_meta m ON m.rowid = chunks_fts.rowid "
+            f"WHERE chunks_fts MATCH ? {where_sql} "
+            "ORDER BY bm25(chunks_fts) "
+            "LIMIT ?"
+        )
+        try:
+            with sqlite3.connect(self.db_path) as con:
+                cur = con.execute(sql, [sanitized, *params, n])
+                return [(row[0], float(row[1])) for row in cur.fetchall()]
+        except sqlite3.OperationalError as e:
+            # FTS5 syntax error (rare after sanitization) or db missing.
+            # Caller decides whether to fall back to dense-only.
+            log.warning("bm25 query failed (%s); query=%r", e, sanitized[:80])
+            return []
+
+    def exists(self) -> bool:
+        """Cheap probe — does the index file exist on disk?"""
+        return self.db_path.exists()
+
+    def count(self) -> int:
+        """Number of chunks indexed. 0 if the db is missing or empty."""
+        if not self.exists():
+            return 0
+        try:
+            with sqlite3.connect(self.db_path) as con:
+                return con.execute("SELECT COUNT(*) FROM chunks_meta").fetchone()[0]
+        except sqlite3.OperationalError:
+            return 0
+
+    # -- schema ---------------------------------------------------------
+
+    @staticmethod
+    def _schema_sql() -> str:
+        return """
+        CREATE TABLE chunks_meta (
+            rowid     INTEGER PRIMARY KEY AUTOINCREMENT,
+            id        TEXT UNIQUE NOT NULL,
+            bundle_id TEXT,
+            page_id   TEXT,
+            version   TEXT,
+            platform  TEXT,
+            product   TEXT,
+            ordinal   INTEGER
+        );
+        CREATE INDEX idx_meta_version  ON chunks_meta(version);
+        CREATE INDEX idx_meta_platform ON chunks_meta(platform);
+        CREATE INDEX idx_meta_bundle   ON chunks_meta(bundle_id);
+
+        CREATE VIRTUAL TABLE chunks_fts USING fts5(
+            text,
+            tokenize = 'porter unicode61 remove_diacritics 2'
+        );
+        """