sean-soomgo
1fdd11fa76
`_find_node_tiers` builds two normalizations of the query: `term`, which
tokenizes on \w+ so punctuation becomes a space, and `norm_query`, which
keeps it. The exact tier compared the node id against `term` only, so
`term == nid_lower` was false for every id carrying punctuation, and
`norm_query` — which already held the right form, and is even one of the
two trigram needles — was never compared against the id at all. Comparing
`norm_query` to the folded id closes that half.
It does not reach ids carrying non-ASCII text. `_node_search_text`
indexed the id raw while every query path folds through
`_strip_diacritics`, which NFKD-decomposes. Hangul syllables decompose
into conjoining jamo, and jamo have combining class 0, so they survive
the combining-character filter: the needle's trigrams and the posting's
trigrams were disjoint, `_trigram_candidates` returned a candidate list
without the node, and it was dropped before any predicate ran. The
folded id is now part of the indexed text.
Both halves are additive. An id that resolved before resolves to the
same node; only ids that previously resolved to nothing can now resolve.
The folded field is appended, and only when the fold actually differs,
so field positions do not move and an all-ASCII graph indexes byte for
byte what it indexed before. Index build, median of 7 runs:
graph trigrams postings build
5k all-ASCII before 1723 238876 96ms
5k all-ASCII after 1723 238876 97ms
17k real before 33442 2311784 998ms
17k real after 33490 2312462 990ms
5k half-Hangul before 1735 256381 116ms
5k half-Hangul after 1739 266381 124ms
The real graph is the 17269-node one measured below; 354 of its ids are
non-ASCII, so the index grows 0.03% and the build stays inside run-to-run
noise. The half-Hangul row is a deliberate worst case — every other node
id Korean — and even there the cost is paid once per graph load, on a
graph where id lookup previously returned nothing at all.
On a real 17269-node graph with Korean source filenames, every node id
fed back to itself, full population:
id class total before after
contains punctuation 2326 0 2326
contains Hangul 354 0 354
ASCII, no punctuation 14589 14589 14589
And every query that graph can produce — all 17269 ids plus all 16537
distinct labels — through `_find_node_tiers` on both variants in one
process: 31126 identical, 2680 that returned nothing before and resolve
now, 0 with a changed first result, 0 lost, 0 with a widened exact tier.
Every difference is a query that previously returned nothing.
Left alone deliberately: `_score_query` compares the id raw in the same
way, so `path` and query seeding still cannot take a punctuated id, and
the prefix tier also matches ids against `term` only. Both are behaviour
changes beyond this defect rather than part of it.
One note for the regression tests: the non-ASCII case needs a graph of
at least ~10 nodes. `_trigram_candidates` bails out to a full scan when
`min(present) > int(n * 0.10)`, so on a small synthetic graph the index
path is never taken and the test passes with the defect still present.
2310 lines
105 KiB
Python
2310 lines
105 KiB
Python
# MCP stdio server - exposes graph query tools to Claude and other agents
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from __future__ import annotations
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import json
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import math
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import os
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import re
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import sys
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from array import array
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from collections import OrderedDict
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from pathlib import Path
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import threading
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from typing import NamedTuple
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import networkx as nx
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from networkx.readwrite import json_graph
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from graphify.security import sanitize_label, check_graph_file_size_cap
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from graphify.build import edge_data, edge_datas
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from graphify.paths import default_graph_json as _default_graph_json
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try:
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import jieba as _jieba # type: ignore[import-untyped]
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except ImportError:
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_jieba = None
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def _load_graph(graph_path: str) -> nx.Graph:
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try:
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resolved = Path(graph_path).resolve()
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if resolved.suffix != ".json":
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raise ValueError(f"Graph path must be a .json file, got: {graph_path!r}")
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if not resolved.exists():
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raise FileNotFoundError(f"Graph file not found: {resolved}")
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check_graph_file_size_cap(resolved)
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safe = resolved
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data = json.loads(safe.read_text(encoding="utf-8"))
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if "links" not in data and "edges" in data:
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data = dict(data, links=data["edges"])
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# Stash the on-disk logical flag before the load-time override below:
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# `directed: True` exists only so renderers can recover stored arc
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# order (#2309); tools that care about logical direction (#2487) must
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# not mistake the override for graph truth.
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_logical_directed = bool(data.get("directed", False))
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data = {**data, "directed": True}
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try:
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from graphify.build import graph_has_legacy_ids as _legacy
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if _legacy(data.get("nodes", [])):
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print(
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"[graphify] note: this graph uses the pre-#1504 node-ID scheme; "
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"rebuild with `graphify extract --force` for path-qualified IDs.",
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file=sys.stderr,
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)
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except Exception:
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pass
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try:
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G = json_graph.node_link_graph(data, edges="links")
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except TypeError:
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G = json_graph.node_link_graph(data)
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G.graph["_logical_directed"] = _logical_directed
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# Attach the work-memory overlay (derived sidecar next to graph.json) so
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# the query/MCP read surface can annotate NODE lines display-only. Empty
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# when no sidecar exists, leaving un-annotated output byte-identical.
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try:
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from graphify.reflect import load_learning_overlay as _llo
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G.graph["_learning_overlay"] = _llo(resolved)
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except Exception:
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G.graph["_learning_overlay"] = {}
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return G
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except json.JSONDecodeError as exc:
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print(f"error: graph.json is corrupted ({exc}). Re-run /graphify to rebuild.", file=sys.stderr)
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sys.exit(1)
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except (ValueError, FileNotFoundError) as exc:
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print(f"error: {exc}", file=sys.stderr)
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sys.exit(1)
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def _communities_from_graph(G: nx.Graph) -> dict[int, list[str]]:
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"""Reconstruct community dict from community property stored on nodes."""
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communities: dict[int, list[str]] = {}
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for node_id, data in G.nodes(data=True):
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cid = data.get("community")
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if cid is not None:
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communities.setdefault(int(cid), []).append(node_id)
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return communities
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def _max_server_contexts() -> int:
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"""Return the project-context LRU capacity (default 8, minimum 1).
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``GRAPHIFY_MAX_CONTEXTS`` overrides the default. Invalid or blank values
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use 8; zero and negative values clamp to 1, since each request needs a
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graph context. The server's configured default graph is pinned separately
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and does not count against this limit.
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"""
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raw = os.environ.get("GRAPHIFY_MAX_CONTEXTS", "").strip()
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if not raw:
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return 8
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try:
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return max(1, int(raw))
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except ValueError:
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return 8
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class _GraphContextCache:
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"""Thread-safe graph contexts: one pinned default plus an LRU of projects."""
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def __init__(self, max_contexts: int):
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self._max_contexts = max_contexts
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self._entries: OrderedDict[str, dict] = OrderedDict()
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self._pinned: dict[str, dict] = {}
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self._lock = threading.Lock()
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def _load_entry(self, resolved_path: str, key: tuple[int, int]) -> dict:
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"""Build one entry for an already-resolved path and known file key.
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``_load_graph`` is also used by the CLI, where invalid input terminates
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the process. A client-supplied ``project_path`` must instead become a
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tool error, so the shared MCP server can continue serving other graphs.
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"""
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try:
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graph = _load_graph(resolved_path)
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except SystemExit as exc:
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raise RuntimeError(f"could not load graph.json at {resolved_path}") from exc
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# Warm the index before exposing the graph so its first query does not
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# pay the expensive build cost.
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_get_trigram_index(graph)
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communities = _communities_from_graph(graph)
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entry = {
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"key": key,
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"G": graph,
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"communities": communities,
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}
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return entry
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def load(self, resolved_path: str, *, pinned: bool = False) -> tuple[nx.Graph, dict[int, list[str]]]:
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"""Return a fresh context, retaining project contexts by LRU order.
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``resolved_path`` is resolved by the caller, making this method the
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sole owner of file statting and cache-key construction.
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``pinned=True`` is reserved for the server's configured default graph;
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it remains warm without consuming a project-cache slot.
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"""
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with self._lock:
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try:
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stat_result = Path(resolved_path).stat()
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except FileNotFoundError:
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raise FileNotFoundError(f"graph.json not found: {resolved_path}") from None
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key = (stat_result.st_mtime_ns, stat_result.st_size)
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entries = self._pinned if pinned else self._entries
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entry = entries.get(resolved_path)
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if entry is not None and entry["key"] == key:
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if not pinned:
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self._entries.move_to_end(resolved_path)
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return entry["G"], entry["communities"]
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entry = self._load_entry(resolved_path, key)
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entries[resolved_path] = entry
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if not pinned:
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self._entries.move_to_end(resolved_path)
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while len(self._entries) > self._max_contexts:
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self._entries.popitem(last=False)
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return entry["G"], entry["communities"]
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def _strip_diacritics(text: str | None) -> str:
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import unicodedata
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if not isinstance(text, str):
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text = "" if text is None else str(text)
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nfkd = unicodedata.normalize("NFKD", text)
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return "".join(c for c in nfkd if not unicodedata.combining(c))
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def _search_tokens(text: str) -> list[str]:
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"""Split text into word tokens, stripping punctuation and diacritics."""
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return re.findall(r"\w+", _strip_diacritics(str(text)).lower())
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def _has_chinese(text: str) -> bool:
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return any("一" <= ch <= "鿿" for ch in text)
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def _segment_chinese(text: str) -> list[str]:
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"""Segment Chinese text and keep the original term for exact matching."""
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if _jieba is not None:
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segments = [w for w in _jieba.cut(text) if len(w.strip()) > 0]
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else:
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segments = [text[i:i + 2] for i in range(len(text) - 1)] or [text]
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if len(text) > 1 and text not in segments:
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segments.append(text)
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return segments
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def _is_searchable(term: str) -> bool:
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"""True if term is Chinese, non-English, or an English word longer than 2 chars."""
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if all("a" <= ch <= "z" for ch in term):
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return len(term) > 2
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return True
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# Question/filler words dropped from query terms so content words drive BFS
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# seeding. Without this, "how does the frontier cache work" seeds on "how"/
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# "the"/"work" (which prefix-match prose labels like "Working Principles" at 100x)
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# instead of "frontier"/"cache", and lands in the wrong part of the graph. Applied
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# to query terms only — node text is never filtered, so a symbol literally named
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# `work` stays findable via explain/path. `work`/`works`/`working` are included
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# because "how does X work" / "how X works" is the most common question phrasing.
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#
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# Non-English question words are just as damaging (#1900): in a mostly-English
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# code corpus, German "wie"/"funktioniert" are rare, so they get HIGH IDF weight
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# and out-seed the actual content noun by orders of magnitude. So this also
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# carries a curated German set plus a trimmed French/Spanish/Portuguese/Italian
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# set of question/filler words. Diacritics are kept intact (the query tokenizer
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# does not NFKD-strip).
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#
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# Collision tradeoff: a few foreign stopwords are also English content words.
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# We include high-German-value ones like "die"/"hat" (the all-stopword fallback
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# in _query_terms and the unfiltered find_node path keep an English "die"/"hat"
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# query workable), but deliberately OMIT "war"/"bald" (German was/soon) so
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# English queries about "war" or "bald" are not clobbered. On the Romance side
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# we likewise omit "comment" (FR how), "come" (IT how), "son"/"sin"/"con" (ES),
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# and "pour"/"des" (FR) — all too common as English/code terms.
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_QUERY_STOPWORDS = frozenset({
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# English
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"how", "what", "why", "when", "where", "which", "who", "whom", "whose",
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"does", "did", "is", "are", "was", "were", "be", "been", "being",
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"can", "could", "should", "would", "will", "shall", "may", "might", "must",
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"has", "have", "had", "the", "and", "but", "not", "for", "from", "with",
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"without", "into", "onto", "off", "that", "this", "these", "those", "there",
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"here", "its", "their", "them", "they", "about", "any", "all", "some",
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"work", "works", "working",
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# German (articles/conjunctions/question words/auxiliaries/prepositions)
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"der", "die", "das", "den", "dem", "ein", "eine", "und", "oder", "nicht",
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"wie", "wer", "wann", "wo", "warum", "wieso",
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"welche", "welcher", "welches",
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"ist", "sind", "wird", "wurde", "hat", "haben",
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"kann", "koennen", "können", "soll", "muss", "sich",
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"bei", "mit", "von", "fuer", "für", "ueber", "über", "nach", "aus",
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"gibt", "es",
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"funktioniert", "geaendert", "geändert", "aendert", "ändert",
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# French
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"pourquoi", "quand", "quel", "quelle", "quels", "quelles", "quoi",
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"qui", "que", "est", "sont", "fonctionne", "cette", "dans", "avec", "où",
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# Spanish
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"cómo", "como", "qué", "cuál", "cuáles", "cuándo", "dónde", "donde",
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"porque", "por", "para", "funciona", "está", "están", "hay",
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# Portuguese
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"qual", "quais", "quando", "onde", "são", "estão", "tem", "uma", "não",
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# Italian
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"perché", "cosa", "quale", "quali", "dove", "funziona", "sono", "che",
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"della",
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})
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def _query_terms(question: str) -> list[str]:
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"""Split a query into searchable terms, segmenting Chinese text, then drop
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question/filler words (`_QUERY_STOPWORDS`, English plus common German/
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Romance-language fillers) so content words drive seeding. Falls back to the
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unfiltered terms if the query is all stopwords, so a question like "how does
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it work" or "wie funktioniert das" still seeds on something."""
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terms: list[str] = []
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for raw in question.split():
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if _has_chinese(raw):
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for seg in _segment_chinese(raw.lower().strip()):
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seg = seg.strip()
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if seg and _is_searchable(seg):
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terms.append(seg)
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else:
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# Strip punctuation without touching Unicode characters (avoid NFKD mangling non-Latin scripts)
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for tok in re.findall(r"\w+", raw.lower()):
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if _is_searchable(tok):
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terms.append(tok)
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content = [t for t in terms if t not in _QUERY_STOPWORDS]
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return content or terms
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_EXACT_MATCH_BONUS = 1000.0
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_PREFIX_MATCH_BONUS = 100.0
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_SUBSTRING_MATCH_BONUS = 1.0
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_SOURCE_MATCH_BONUS = 0.5
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def _compute_idf(G: nx.Graph, terms: list[str]) -> dict[str, float]:
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"""IDF weights for query terms, cached in G.graph['_idf_cache'].
|
|
|
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Common terms like 'error' or 'exception' that match hundreds of nodes get
|
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low weights; rare identifiers like 'FooBarService' get high weights.
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Cache is stored on the graph object itself so it auto-invalidates when
|
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a hot-reload replaces G with a new object.
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"""
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cache: dict[str, float] = G.graph.setdefault("_idf_cache", {})
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N = G.number_of_nodes() or 1
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uncached = [t for t in terms if t not in cache]
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if uncached:
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df: dict[str, int] = {t: 0 for t in uncached}
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for _, data in G.nodes(data=True):
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norm_label = (
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data.get("norm_label") or _strip_diacritics(data.get("label") or "")
|
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).lower()
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for t in uncached:
|
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if t in norm_label:
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df[t] += 1
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for t in uncached:
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cache[t] = math.log(1 + N / (1 + df[t]))
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return {t: cache.get(t, math.log(1 + N)) for t in terms}
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|
|
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def _trigrams(text: str) -> set[str]:
|
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"""Character trigrams of `text`; for <3-char text the whole string is the key."""
|
|
if len(text) < 3:
|
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return {text} if text else set()
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return {text[i:i + 3] for i in range(len(text) - 2)}
|
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|
|
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def _node_search_text(data: dict, nid: str) -> str:
|
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"""Concatenate every field _score_nodes / _find_node match a query against, so
|
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one trigram index over this text is a complete candidate generator for both.
|
|
|
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- `norm_label` and `source_file` feed _score_nodes' per-term substring tiers.
|
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- `label_tokens` (the space-joined token form) feeds _find_node's
|
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`term in label_tokens` branch, where a multi-word `term` can span a token
|
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boundary that punctuation hides in `norm_label` (e.g. query "foo bar" matches
|
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label "foo.bar" only via its tokenized form).
|
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- `source_tokens` feeds _find_node's exact source-file path lookup, where a
|
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query like "app/api/example/route.ts" tokenizes to "app api example route ts".
|
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- `nid` feeds the whole-query `joined == nid_lower` tier.
|
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- a trailing diacritic-folded `nid` feeds _find_node's `norm_query == nid_norm`
|
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tier. Every query path folds through `_strip_diacritics` (NFKD), so a raw-only
|
|
id field leaves the needle and the posting under different normal forms and
|
|
the node is dropped before any predicate runs (#2467). Hangul is the common
|
|
case: NFKD decomposes a syllable into conjoining jamo, which have combining
|
|
class 0 and therefore survive the combining-character filter. The field is
|
|
appended only when the fold actually differs, so the text an all-ASCII graph
|
|
indexes — and every field position the other readers rely on — is unchanged.
|
|
|
|
NUL separators stop a trigram from spanning two fields (a query never contains
|
|
NUL, so a cross-field trigram can never be a real match).
|
|
"""
|
|
norm_label = data.get("norm_label") or _strip_diacritics(data.get("label") or "").lower()
|
|
label_tokens = " ".join(_search_tokens(data.get("label") or ""))
|
|
source = (data.get("source_file") or "").lower()
|
|
source_tokens = " ".join(_search_tokens(data.get("source_file") or ""))
|
|
nid_text = str(nid).lower()
|
|
fields = (norm_label, label_tokens, nid_text, source, source_tokens)
|
|
if not nid_text.isascii():
|
|
nid_folded = _strip_diacritics(str(nid)).lower()
|
|
if nid_folded != nid_text:
|
|
fields += (nid_folded,)
|
|
return "\x00".join(fields)
|
|
|
|
|
|
def _get_trigram_index(G: nx.Graph) -> dict:
|
|
"""Lazily build and cache a trigram -> node-position postings map on the graph.
|
|
|
|
Cached on `G.graph` so it auto-invalidates when a hot-reload swaps in a
|
|
fresh graph object, exactly like `_idf_cache`. `set_cache` memoizes per-trigram
|
|
id-sets across queries within one graph generation.
|
|
"""
|
|
idx = G.graph.get("_trigram_index")
|
|
if idx is not None:
|
|
return idx
|
|
ids = list(G.nodes())
|
|
postings: dict[str, array] = {}
|
|
for i, nid in enumerate(ids):
|
|
for g in _trigrams(_node_search_text(G.nodes[nid], nid)):
|
|
bucket = postings.get(g)
|
|
if bucket is None:
|
|
bucket = array("i")
|
|
postings[g] = bucket
|
|
bucket.append(i)
|
|
idx = {"ids": ids, "postings": postings, "set_cache": {}}
|
|
G.graph["_trigram_index"] = idx
|
|
return idx
|
|
|
|
|
|
def _trigram_candidates(G: nx.Graph, needles: list[str], *, guard_frac: float = 0.10) -> list[str] | None:
|
|
"""Node IDs whose text could contain any `needle` as a substring, via the
|
|
trigram index — a *superset* the caller then re-scores with the exact predicates.
|
|
|
|
Returns candidates in graph-iteration order (so order-sensitive callers like
|
|
_find_node stay byte-identical to a full scan), or **None** when the index isn't
|
|
worth it — a needle is too short to trigram, or its rarest trigram is still
|
|
common enough that the candidate set would approach the whole graph. The caller
|
|
falls back to the full scan, preserving the never-worse contract. The guard is
|
|
cheap: postings-length lookups only, no set intersection.
|
|
"""
|
|
idx = _get_trigram_index(G)
|
|
ids, postings, set_cache = idx["ids"], idx["postings"], idx["set_cache"]
|
|
n = len(ids)
|
|
if n == 0:
|
|
return []
|
|
needles = [s for s in needles if s]
|
|
thresh = int(n * guard_frac)
|
|
for s in needles:
|
|
tgs = _trigrams(s)
|
|
if not tgs or any(len(g) < 3 for g in tgs):
|
|
return None # too short to trigram-filter
|
|
present = [len(postings[g]) for g in tgs if g in postings]
|
|
if not present:
|
|
continue # this needle matches nothing — contributes no candidates
|
|
if min(present) > thresh:
|
|
return None # rarest trigram still too common -> not worth the index
|
|
cand: set[int] = set()
|
|
for s in needles:
|
|
sets: list[set] | None = []
|
|
for g in _trigrams(s):
|
|
bucket = postings.get(g)
|
|
if bucket is None:
|
|
sets = None # a trigram absent everywhere -> needle matches nothing
|
|
break
|
|
cached = set_cache.get(g)
|
|
if cached is None:
|
|
cached = set(bucket)
|
|
set_cache[g] = cached
|
|
sets.append(cached)
|
|
if not sets:
|
|
continue
|
|
sets.sort(key=len) # intersect smallest-first
|
|
hit = set(sets[0])
|
|
for other in sets[1:]:
|
|
hit &= other
|
|
if not hit:
|
|
break
|
|
cand |= hit
|
|
return [ids[i] for i in sorted(cand)]
|
|
|
|
|
|
class _QueryScores(NamedTuple):
|
|
"""Per-query scoring result, returned by the private `_score_query` helper.
|
|
|
|
`ranked` is the existing ordered `(score, node_id)` ranking produced by the
|
|
combined query scorer (the value `_score_nodes` always returned). When the
|
|
caller asks for it via `collect_per_term_seeds=True`, `best_seed_by_term`
|
|
additionally carries the winning node id for each normalized search token —
|
|
the seed `_pick_seeds` would have picked for that token via the now-retired
|
|
per-token `_score_nodes([token])` rescoring pass — computed in the *same*
|
|
per-node traversal so the query path makes exactly one graph scoring pass
|
|
regardless of query length. Empty when `collect_per_term_seeds=False`.
|
|
"""
|
|
ranked: list[tuple[float, str]]
|
|
best_seed_by_term: dict[str, str]
|
|
|
|
|
|
def _score_nodes(G: nx.Graph, terms: list[str]) -> list[tuple[float, str]]:
|
|
"""Combined query scorer returning the existing ranked `(score, node_id)` list.
|
|
|
|
Backwards-compatible thin wrapper around `_score_query` for path, explain,
|
|
tests, and every other caller that only needs the combined ranking. The
|
|
per-term seed metadata computed by `_score_query` (when requested) is
|
|
discarded here so existing callers see no API or runtime-cost change.
|
|
"""
|
|
return _score_query(G, terms, collect_per_term_seeds=False).ranked
|
|
|
|
|
|
def _score_query(
|
|
G: nx.Graph, terms: list[str], *, collect_per_term_seeds: bool
|
|
) -> _QueryScores:
|
|
"""Single-pass combined scorer that optionally also records the best seed
|
|
for each normalized query token.
|
|
|
|
The combined ranking is byte-identical to what `_score_nodes` produced
|
|
before the refactor; `_score_nodes` is now a thin wrapper that asks for
|
|
`collect_per_term_seeds=False` and returns only `.ranked`.
|
|
|
|
When `collect_per_term_seeds=True`, the per-token singleton winner is
|
|
computed alongside the combined score in the *same* per-node visit (it
|
|
reuses the same `norm_label` / `label_tokens` / `source` already evaluated
|
|
for the combined tier), so `_query_graph_text` can feed `best_seed_by_term`
|
|
straight into `_pick_seeds` and skip the T additional whole-graph rescoring
|
|
passes the old per-token `_score_nodes([token])` loop ran.
|
|
|
|
Singleton-winner semantics match the legacy per-token path exactly. The
|
|
score itself mirrors `_score_nodes([token])` with `n_terms == 1` (so the
|
|
coverage term is 1 and the per-token tier is unscaled) plus the broader
|
|
joined-singlet tier (which also checks `label_tokens` and `nid_lower`).
|
|
Tie-break order is (1) highest singleton score, (2) highest graph degree,
|
|
(3) shortest displayed label, (4) lexicographically smallest node id —
|
|
exactly what `max(tied, key=degree)` over a sort by `(-score, label_len,
|
|
nid)` produced in the legacy `_pick_seeds` per-token loop. The combined
|
|
trigram candidate set (needles `norm_terms + [joined]`) is a superset of
|
|
each per-token `[t]` candidate set, so iterating combined candidates
|
|
discovers every non-zero singleton-score node for every term.
|
|
"""
|
|
scored: list[tuple[float, str]] = []
|
|
# Dedupe tokens, order-preserving (as _pick_seeds already does): a repeated
|
|
# query word must not double-count every tier, and with coverage scaling
|
|
# below it would also inflate the matched-term ratio (#1602).
|
|
norm_terms = list(dict.fromkeys(tok for t in terms for tok in _search_tokens(t)))
|
|
n_terms = len(norm_terms)
|
|
idf = _compute_idf(G, norm_terms)
|
|
# Whole-query string for full-label matching (mirrors _find_node's `term`).
|
|
joined = " ".join(norm_terms)
|
|
# Weight the full-query bonus by the rarest constituent term so a specific
|
|
# multi-word label still outweighs common-token noise; floor at 1.0.
|
|
joined_w = max((idf.get(t, 1.0) for t in norm_terms), default=1.0)
|
|
# Trigram prefilter: score only nodes whose text could match a term, falling
|
|
# back to the whole graph when the index isn't selective. The result is
|
|
# identical either way — the per-node scoring below is unchanged and a
|
|
# non-candidate node always scores 0. (IDF above stays a whole-graph statistic.)
|
|
candidate_ids = _trigram_candidates(G, norm_terms + ([joined] if joined else []))
|
|
node_iter = (
|
|
G.nodes(data=True) if candidate_ids is None
|
|
else ((nid, G.nodes[nid]) for nid in candidate_ids)
|
|
)
|
|
# Per-token best tracking, only when the caller (the query path) wants the
|
|
# seed metadata. The key tuple is the full multi-key tie-break
|
|
# (`(-singleton_score, -degree, label_len, nid)`), so `min` over the
|
|
# stored key mirrors the legacy `max(tied, key=degree)` over a
|
|
# (-score, label_len, nid)-sorted term_scored list. `None` is comparable
|
|
# as "smaller" than every tuple, so the first non-zero candidate seeds the
|
|
# entry without a separate `if t not in best_by_term` branch.
|
|
best_by_term: dict[str, tuple[tuple, str]] | None = (
|
|
{} if collect_per_term_seeds else None
|
|
)
|
|
for nid, data in node_iter:
|
|
norm_label = data.get("norm_label") or _strip_diacritics(data.get("label") or "").lower()
|
|
bare_label = norm_label.rstrip("()")
|
|
# Tokenized form of the label (punctuation stripped, same transform as the
|
|
# query). norm_label may still carry punctuation like ':' or '-', which a
|
|
# tokenized query can never equal; comparing token-joined forms on both
|
|
# sides makes "uoce: dehumidifier driver" match query "uoce dehumidifier
|
|
# driver".
|
|
label_tokens = " ".join(_search_tokens(data.get("label") or ""))
|
|
source = (data.get("source_file") or "").lower()
|
|
# `nid_lower` is needed both by the full-query tier (`if joined`) and by
|
|
# the per-token singleton tier (joined-singlet exact-match check). When
|
|
# neither runs (`joined` empty AND not collecting seeds) skip the call;
|
|
# this preserves the single-query-time perf where nid_lower was lazy.
|
|
nid_lower = nid.lower() if (joined or collect_per_term_seeds) else ""
|
|
score = 0.0
|
|
# Full-query tier: a multi-word query that equals (or prefixes) the whole
|
|
# label must dominate the per-token bag-of-words sums below, so `path`/
|
|
# `query` resolve the same node `explain` does (via _find_node). Without
|
|
# this, no single token equals a multi-word label, the per-token exact
|
|
# tier never fires, and every node sharing the token set ties -> arbitrary
|
|
# node-id sort -> wrong/disconnected endpoint -> false "No path found".
|
|
if joined:
|
|
if joined in (norm_label, bare_label, label_tokens, nid_lower):
|
|
score += _EXACT_MATCH_BONUS * 10 * joined_w
|
|
elif (
|
|
norm_label.startswith(joined)
|
|
or bare_label.startswith(joined)
|
|
or label_tokens.startswith(joined)
|
|
):
|
|
score += _PREFIX_MATCH_BONUS * 10 * joined_w
|
|
# Term coverage (#1602): scale the per-term exact/prefix tiers by the
|
|
# squared fraction of query terms the node's LABEL matches, so a lone
|
|
# generic word that happens to equal a short label (query term "home"
|
|
# vs. a home() leaf) cannot bury nodes that match several of the
|
|
# query's terms. Squaring matters because the exact tier is 10x the
|
|
# prefix tier: at linear coverage a 1-of-10-terms exact match still
|
|
# outscores a 3-of-10 prefix+substring match. Single-term and
|
|
# full-coverage queries are unchanged (coverage == 1), so identifier
|
|
# lookups keep exact-match dominance. Source-file hits score but do
|
|
# not count as coverage: a colliding leaf whose directory shares
|
|
# tokens with the query (common near the intended target) must not
|
|
# win back its exact tier via path fragments. The substring/source
|
|
# bonuses and the full-query tier above stay unscaled.
|
|
matched = 0
|
|
tiered = 0.0
|
|
for t in norm_terms:
|
|
w = idf.get(t, 1.0)
|
|
# Per-tier contributions for this token, kept separate so the
|
|
# singleton tracking below can reuse them without re-evaluating
|
|
# the same predicates. Three-tier precedence: exact > prefix >
|
|
# substring (take the strongest tier per term so a single term
|
|
# cannot double-count).
|
|
tier_value = 0.0
|
|
substr_value = 0.0
|
|
source_value = 0.0
|
|
if t == norm_label or t == bare_label:
|
|
tier_value = _EXACT_MATCH_BONUS * w
|
|
matched += 1
|
|
elif norm_label.startswith(t) or bare_label.startswith(t):
|
|
tier_value = _PREFIX_MATCH_BONUS * w
|
|
matched += 1
|
|
elif t in norm_label:
|
|
substr_value = _SUBSTRING_MATCH_BONUS * w
|
|
score += substr_value
|
|
matched += 1
|
|
if t in source:
|
|
source_value = _SOURCE_MATCH_BONUS * w
|
|
score += source_value
|
|
tiered += tier_value
|
|
if collect_per_term_seeds and best_by_term is not None:
|
|
# Singleton score for [t] on this node, mirroring
|
|
# `_score_nodes(G, [t])` exactly (n_terms == 1, no coverage
|
|
# scaling). The joined-singlet tier is broader than the per-
|
|
# token tier: it also checks `label_tokens` and `nid_lower`,
|
|
# matching the legacy single-token `_score_nodes([t])` call
|
|
# (where `joined == t`).
|
|
if t in (norm_label, bare_label, label_tokens, nid_lower):
|
|
singleton = _EXACT_MATCH_BONUS * 10 * w
|
|
elif (
|
|
norm_label.startswith(t)
|
|
or bare_label.startswith(t)
|
|
or label_tokens.startswith(t)
|
|
):
|
|
singleton = _PREFIX_MATCH_BONUS * 10 * w
|
|
else:
|
|
singleton = 0.0
|
|
singleton += tier_value + substr_value + source_value
|
|
if singleton > 0:
|
|
# Tie-break key mirrors the legacy sort+max(degree):
|
|
# (-singleton, -degree, label_len, nid) — the minimum
|
|
# tuple wins, exactly matching max(tied, key=degree)
|
|
# over (label_len asc, nid asc)-sorted ties.
|
|
key = (-singleton, -G.degree(nid), len(data.get("label") or nid), nid)
|
|
cur = best_by_term.get(t)
|
|
if cur is None or key < cur[0]:
|
|
best_by_term[t] = (key, nid)
|
|
if tiered:
|
|
score += tiered * (matched / n_terms) ** 2
|
|
if score > 0:
|
|
scored.append((score, nid))
|
|
# Sort by score desc; break ties toward the shorter label so a concise exact
|
|
# match beats a longer superset that happens to share the same score.
|
|
scored.sort(key=lambda s: (-s[0], len(G.nodes[s[1]].get("label") or s[1]), s[1]))
|
|
best_seed_by_term: dict[str, str] = {}
|
|
if collect_per_term_seeds and best_by_term:
|
|
best_seed_by_term = {t: nid for t, (_key, nid) in best_by_term.items()}
|
|
return _QueryScores(ranked=scored, best_seed_by_term=best_seed_by_term)
|
|
|
|
|
|
def _pick_scored_endpoint(G: nx.Graph, scored: list[tuple[float, str]], query: str) -> str:
|
|
"""Pick a path endpoint from a _score_nodes result, preferring full-token matches.
|
|
|
|
The full-query tier in _score_nodes only fires when the query equals or
|
|
prefixes a label, so a query that is a token *subset* of the intended label
|
|
(query "Reject-everything judge" vs. label "Degenerate Reject-Everything
|
|
Judge") gets no bonus, and a node prefix-matching one rare token (label
|
|
"Rejection Summary") can out-score it on IDF alone. Committing to scored[0]
|
|
then anchors the path on an unrelated — often disconnected — node and yields
|
|
a false "No path found". Scan the score-ordered list and take the first
|
|
candidate whose label contains EVERY query token; when the top candidate
|
|
already full-matches, or no candidate does, this is exactly scored[0].
|
|
|
|
`scored` must be non-empty (both callers return early on no match).
|
|
"""
|
|
qtokens = set(_search_tokens(query))
|
|
if not qtokens:
|
|
return scored[0][1]
|
|
for _score, nid in scored:
|
|
if qtokens <= set(_search_tokens(G.nodes[nid].get("label") or nid)):
|
|
return nid
|
|
return scored[0][1]
|
|
|
|
|
|
def _pick_seeds(
|
|
scored: list[tuple[float, str]],
|
|
max_k: int = 3,
|
|
gap_ratio: float = 0.2,
|
|
*,
|
|
G: "nx.Graph | None" = None,
|
|
best_seed_by_term: dict[str, str] | None = None,
|
|
) -> list[str]:
|
|
"""Select BFS seed nodes, stopping when score drops too far below the top.
|
|
|
|
Prevents high-frequency noise terms (error, exception) from stealing seed
|
|
slots from a dominant identifier match. When FooBarService scores 1000 and
|
|
error nodes score 1.0, only FooBarService is seeded — the score gap is 99.9%
|
|
which is well above the 20% threshold that would allow additional seeds.
|
|
|
|
That same gap_ratio cutoff has a failure mode on multi-term natural-language
|
|
queries: if one term happens to hit an EXACT label match on a node that is
|
|
otherwise unrelated to the query's intent (e.g. a common word that is also
|
|
used as an unrelated identifier or field name elsewhere in the corpus), it
|
|
can outscore every SUBSTRING match on the query's other, actually-relevant
|
|
terms by ~1000x (see `_EXACT_MATCH_BONUS` vs. `_SUBSTRING_MATCH_BONUS`).
|
|
The 20%-gap cutoff then silently discards all of those substring-tier
|
|
seeds, so the BFS traversal only ever explores the neighborhood of the one
|
|
unrelated exact match — see #1445.
|
|
|
|
When `G` and `best_seed_by_term` are supplied, this guarantees at least one
|
|
seed per distinct query term that has any match at all, so one term's
|
|
incidental collision cannot starve out the others. The per-token winners
|
|
in `best_seed_by_term` are precomputed by `_score_query` (during the same
|
|
traversal that produced `scored`) so this function no longer rescores the
|
|
graph per term — see #1445 and the `_score_query` docstring.
|
|
|
|
Coverage scaling in _score_nodes (#1602) now dampens a lone collision's
|
|
exact tier on multi-term queries, which brings label-matching relevant
|
|
nodes back inside the gap window; this per-term guarantee remains
|
|
load-bearing for relevant nodes matched only via substrings, whose flat
|
|
scores a dampened collision can still exceed.
|
|
"""
|
|
if not scored:
|
|
return []
|
|
|
|
# Deduplicate seeds by (normalized) label so a generic, homonymous symbol —
|
|
# e.g. dozens of route handlers all labelled `GET`/`POST`, or a `handler`
|
|
# repeated across a framework — contributes at most one seed instead of
|
|
# consuming every slot and flooding the BFS with near-identical neighborhoods
|
|
# (#1766). The key mirrors _score_nodes' normalization so `GET`/`Get`/`get`
|
|
# collapse together. When G is absent we can't read labels, so fall back to
|
|
# the (unique) node id, which is a no-op — preserving the old behavior.
|
|
def _seed_label_key(nid: str) -> str:
|
|
if G is None:
|
|
return nid
|
|
data = G.nodes[nid]
|
|
return (data.get("norm_label")
|
|
or _strip_diacritics(data.get("label") or "").lower()) or nid
|
|
|
|
top_score = scored[0][0]
|
|
seeds: list[str] = []
|
|
seen_labels: set[str] = set()
|
|
for score, nid in scored:
|
|
if len(seeds) >= max_k:
|
|
break
|
|
if seeds and score < top_score * gap_ratio:
|
|
break
|
|
key = _seed_label_key(nid)
|
|
if key in seen_labels:
|
|
continue
|
|
seen_labels.add(key)
|
|
seeds.append(nid)
|
|
|
|
if G is not None and best_seed_by_term:
|
|
# Guarantee one seed per distinct query term that has any match at all,
|
|
# so an incidental exact match on one term cannot starve matches on
|
|
# other terms (#1445). Iterate tokens in a deterministic sorted order
|
|
# so seeds added by this loop have a stable order independent of dict
|
|
# iteration — preserving the legacy `_pick_seeds(terms=...)` behavior
|
|
# which iterated `sorted({tok ...})`. Per-token winners arrive
|
|
# precomputed in `best_seed_by_term` from `_score_query`'s single
|
|
# traversal, so `_pick_seeds` no longer rescoring the graph per term.
|
|
# The per-label dedup cap also gates these additions, so the guarantee
|
|
# cannot reintroduce a second copy of an already-seeded generic label
|
|
# (#1766).
|
|
for term in sorted(best_seed_by_term):
|
|
best_nid = best_seed_by_term[term]
|
|
# Honor the same per-label cap so the per-term guarantee can't
|
|
# reintroduce a second copy of an already-seeded generic label.
|
|
key = _seed_label_key(best_nid)
|
|
if best_nid not in seeds and key not in seen_labels:
|
|
seen_labels.add(key)
|
|
seeds.append(best_nid)
|
|
return seeds
|
|
|
|
|
|
# Verb-shaped tokens that express the RELATION a query asks about ("who calls
|
|
# X", "what uses Y") rather than a symbol to look up. `_query_terms` keeps them
|
|
# on purpose (a corpus can legitimately define an identifier named `calls`, see
|
|
# #1597), but they must not be handed a guaranteed seed slot in `_pick_seeds`:
|
|
# an incidental prefix match (e.g. "calls" prefixing `.callStoreWithAmount()`)
|
|
# would otherwise seat an unrelated decoy as a BFS root (#2507). Demotion
|
|
# happens at the `_query_graph_text` call site, so `_score_query`'s ranking —
|
|
# where such a verb can still win a seat on merit via the gap window — is
|
|
# untouched. Deliberately verbs only; relation NOUNS (module, field, return)
|
|
# stay eligible for the guarantee.
|
|
_RELATIONAL_INTENT_TERMS: frozenset[str] = frozenset({
|
|
"call", "calls", "called", "caller", "callers",
|
|
"invoke", "invokes", "invoked",
|
|
"use", "uses", "used", "using",
|
|
"import", "imports", "imported",
|
|
"export", "exports", "exported",
|
|
"extend", "extends", "extended",
|
|
"implement", "implements", "implemented",
|
|
"depend", "depends",
|
|
"reference", "references", "referenced",
|
|
})
|
|
|
|
|
|
_CONTEXT_HINTS: tuple[tuple[str, tuple[str, ...]], ...] = (
|
|
("call", ("call", "calls", "called", "caller", "callers", "invoke", "invokes", "invoked")),
|
|
("import", ("import", "imports", "imported", "module", "modules")),
|
|
("field", ("field", "fields", "member", "members", "property", "properties")),
|
|
("parameter_type", ("parameter", "parameters", "param", "params", "argument", "arguments")),
|
|
("return_type", ("return", "returns", "returned")),
|
|
("generic_arg", ("generic", "generics", "template", "templates")),
|
|
)
|
|
|
|
|
|
_CONTEXT_FILTER_ALIASES: dict[str, str] = {
|
|
"param": "parameter_type",
|
|
"params": "parameter_type",
|
|
"parameter": "parameter_type",
|
|
"parameters": "parameter_type",
|
|
"argument": "parameter_type",
|
|
"arguments": "parameter_type",
|
|
"arg": "parameter_type",
|
|
"args": "parameter_type",
|
|
"return": "return_type",
|
|
"returns": "return_type",
|
|
"returned": "return_type",
|
|
"generic": "generic_arg",
|
|
"generics": "generic_arg",
|
|
"template": "generic_arg",
|
|
"templates": "generic_arg",
|
|
"annotation": "attribute",
|
|
"annotations": "attribute",
|
|
"decorator": "attribute",
|
|
"decorators": "attribute",
|
|
"calls": "call",
|
|
"called": "call",
|
|
"invoke": "call",
|
|
"invocation": "call",
|
|
"fields": "field",
|
|
"property": "field",
|
|
"properties": "field",
|
|
"member": "field",
|
|
"members": "field",
|
|
"imports": "import",
|
|
"imported": "import",
|
|
"module": "import",
|
|
"modules": "import",
|
|
"exports": "export",
|
|
"exported": "export",
|
|
}
|
|
|
|
|
|
def _normalize_context_filters(filters: list[str] | None) -> list[str]:
|
|
if not filters:
|
|
return []
|
|
normalized: list[str] = []
|
|
seen: set[str] = set()
|
|
for value in filters:
|
|
key = _strip_diacritics(str(value)).strip().lower()
|
|
if not key:
|
|
continue
|
|
key = _CONTEXT_FILTER_ALIASES.get(key, key)
|
|
if key not in seen:
|
|
seen.add(key)
|
|
normalized.append(key)
|
|
return normalized
|
|
|
|
|
|
def _infer_context_filters(question: str) -> list[str]:
|
|
lowered = {
|
|
_strip_diacritics(token).lower()
|
|
for token in question.replace("?", " ").replace(",", " ").split()
|
|
}
|
|
inferred: list[str] = []
|
|
for context, hints in _CONTEXT_HINTS:
|
|
if any(hint in lowered for hint in hints):
|
|
inferred.append(context)
|
|
return inferred
|
|
|
|
|
|
def _resolve_context_filters(question: str, explicit_filters: list[str] | None = None) -> tuple[list[str], str | None]:
|
|
normalized = _normalize_context_filters(explicit_filters)
|
|
if normalized:
|
|
return normalized, "explicit"
|
|
inferred = _infer_context_filters(question)
|
|
if inferred:
|
|
return inferred, "heuristic"
|
|
return [], None
|
|
|
|
|
|
def _filter_graph_by_context(G: nx.Graph, context_filters: list[str] | None) -> nx.Graph:
|
|
filters = set(_normalize_context_filters(context_filters))
|
|
if not filters:
|
|
return G
|
|
H = G.__class__()
|
|
H.add_nodes_from(G.nodes(data=True))
|
|
if isinstance(G, (nx.MultiGraph, nx.MultiDiGraph)):
|
|
for u, v, key, data in G.edges(keys=True, data=True):
|
|
if data.get("context") in filters:
|
|
H.add_edge(u, v, key=key, **data)
|
|
else:
|
|
for u, v, data in G.edges(data=True):
|
|
if data.get("context") in filters:
|
|
H.add_edge(u, v, **data)
|
|
return H
|
|
|
|
|
|
def _complete_induced_edges(G: nx.Graph, visited: set[str], edges_seen: list[tuple]) -> None:
|
|
"""Append edges between visited nodes that the traversal never recorded (#2323).
|
|
|
|
Both traversals only record an edge that *discovers* an unvisited neighbour,
|
|
so what they return is a traversal tree, not the induced subgraph over the
|
|
nodes they return. `_bfs` marks every seed visited up front, so an edge
|
|
between two seeds can never be recorded — the reported symptom, where both
|
|
endpoints render and the edge between them does not. It drops ordinary
|
|
cross-edges for the same reason. `_dfs` appends on push rather than on
|
|
visit, so it already captured those; its one gap is an edge between two
|
|
non-seed hubs, since neither endpoint is ever expanded.
|
|
|
|
Scans only edges incident to `visited`, so cost tracks the subgraph rather
|
|
than the whole graph, bounded by O(2E) overall. A visited hub is rescanned
|
|
in full even though the traversal deliberately did not expand it — that is
|
|
unavoidable, since a hub-to-hub edge is exactly the case `_dfs` misses.
|
|
`G` here is the context-filtered `traversal_graph` (see
|
|
`_query_graph_text`), so a filtered-out relation cannot reappear.
|
|
|
|
Self-loops are skipped. A recursive function legitimately carries one, but
|
|
neither traversal has ever recorded one (`n` is always already visited when
|
|
its own self-loop is examined), and surfacing them is a separate output
|
|
change from the missing edges reported here.
|
|
|
|
Dedup keys on the ordered pair for directed graphs and the unordered pair
|
|
otherwise: on a DiGraph `u->v` and `v->u` are genuinely distinct edges
|
|
(mutual recursion, circular imports), and collapsing them would drop a real
|
|
one. On a multigraph parallel edges collapse to one entry, matching the
|
|
renderer, which already shows only the first (`_subgraph_to_text`).
|
|
|
|
Traversal edges keep their discovery order; completions are appended after.
|
|
"""
|
|
directed = G.is_directed()
|
|
|
|
def _key(u: str, v: str):
|
|
return (u, v) if directed else frozenset((u, v))
|
|
|
|
seen = {_key(u, v) for u, v in edges_seen}
|
|
# sorted() so the appended order can't shift run-to-run with CPython's
|
|
# per-process string-hash seed, the same reason the renderer sorts (#1753).
|
|
for u, v in G.edges(sorted(visited)):
|
|
if u == v or v not in visited:
|
|
continue
|
|
key = _key(u, v)
|
|
if key in seen:
|
|
continue
|
|
seen.add(key)
|
|
edges_seen.append((u, v))
|
|
|
|
|
|
def _bfs(G: nx.Graph, start_nodes: list[str], depth: int) -> tuple[set[str], list[tuple]]:
|
|
# Compute hub threshold: nodes above this degree are not expanded as transit.
|
|
# p99 of degree distribution, floored at 50 to avoid over-blocking small graphs.
|
|
degrees = [G.degree(n) for n in G.nodes()]
|
|
if degrees:
|
|
degrees_sorted = sorted(degrees)
|
|
p99_idx = int(len(degrees_sorted) * 0.99)
|
|
hub_threshold = max(50, degrees_sorted[p99_idx])
|
|
else:
|
|
hub_threshold = 50
|
|
seed_set = set(start_nodes)
|
|
visited: set[str] = set(start_nodes)
|
|
frontier = set(start_nodes)
|
|
edges_seen: list[tuple] = []
|
|
for _ in range(depth):
|
|
next_frontier: set[str] = set()
|
|
for n in frontier:
|
|
# Don't expand through high-degree hubs (except seeds - a hub that
|
|
# is the starting node should still be explored).
|
|
if n not in seed_set and G.degree(n) >= hub_threshold:
|
|
continue
|
|
for neighbor in G.neighbors(n):
|
|
if neighbor not in visited:
|
|
next_frontier.add(neighbor)
|
|
edges_seen.append((n, neighbor))
|
|
visited.update(next_frontier)
|
|
frontier = next_frontier
|
|
_complete_induced_edges(G, visited, edges_seen)
|
|
return visited, edges_seen
|
|
|
|
|
|
def _dfs(G: nx.Graph, start_nodes: list[str], depth: int) -> tuple[set[str], list[tuple]]:
|
|
degrees = [G.degree(n) for n in G.nodes()]
|
|
if degrees:
|
|
degrees_sorted = sorted(degrees)
|
|
p99_idx = int(len(degrees_sorted) * 0.99)
|
|
hub_threshold = max(50, degrees_sorted[p99_idx])
|
|
else:
|
|
hub_threshold = 50
|
|
seed_set = set(start_nodes)
|
|
visited: set[str] = set()
|
|
edges_seen: list[tuple] = []
|
|
stack = [(n, 0) for n in reversed(start_nodes)]
|
|
while stack:
|
|
node, d = stack.pop()
|
|
if node in visited or d > depth:
|
|
continue
|
|
visited.add(node)
|
|
if node not in seed_set and G.degree(node) >= hub_threshold:
|
|
continue
|
|
for neighbor in G.neighbors(node):
|
|
if neighbor not in visited:
|
|
stack.append((neighbor, d + 1))
|
|
edges_seen.append((node, neighbor))
|
|
_complete_induced_edges(G, visited, edges_seen)
|
|
return visited, edges_seen
|
|
|
|
|
|
def _subgraph_to_text(G: nx.Graph, nodes: set[str], edges: list[tuple], token_budget: int = 2000, *, seeds: list[str] | None = None) -> str:
|
|
"""Render subgraph as text, cutting at token_budget (approx 3 chars/token).
|
|
|
|
seeds: exact-match nodes rendered first before the degree-sorted expansion,
|
|
so the queried symbol always appears at the top of the output.
|
|
"""
|
|
char_budget = token_budget * 3
|
|
lines = []
|
|
# Work-memory overlay (derived sidecar) stashed on the graph at load time.
|
|
# Empty when no sidecar exists, so un-annotated output stays byte-identical.
|
|
overlay = getattr(G, "graph", {}).get("_learning_overlay", {}) or {}
|
|
seed_set = set(seeds or [])
|
|
seed_hits = [n for n in (seeds or []) if n in nodes]
|
|
# Rank non-seed nodes by hop distance from the seeds so the node that answers
|
|
# the query (a direct hit or its close neighbors) survives the budget cut
|
|
# instead of being pushed past it by incidental high-degree hubs (#BUG2). BFS
|
|
# discovery order was discarded upstream (_bfs returns a set), so recompute
|
|
# layers here over BOTH edge directions. Deterministic: neighbor iteration is
|
|
# insertion-ordered and the sort key ends in str(n) (no hash-order).
|
|
def _adj(n):
|
|
if G.is_directed():
|
|
yield from G.successors(n)
|
|
yield from G.predecessors(n)
|
|
else:
|
|
yield from G.neighbors(n)
|
|
dist: dict[str, int] = {n: 0 for n in seed_hits}
|
|
frontier, hop = seed_hits, 0
|
|
while frontier:
|
|
hop += 1
|
|
nxt = []
|
|
for n in frontier:
|
|
for nb in _adj(n):
|
|
if nb in nodes and nb not in dist:
|
|
dist[nb] = hop
|
|
nxt.append(nb)
|
|
frontier = nxt
|
|
ordered = seed_hits + sorted(
|
|
nodes - seed_set,
|
|
key=lambda n: (dist.get(n, 1 << 30), -G.degree(n), str(n)),
|
|
)
|
|
for nid in ordered:
|
|
d = G.nodes[nid]
|
|
# Every LLM-derived field passes through sanitize_label before being
|
|
# concatenated into MCP tool output (F-010): an attacker who controls a
|
|
# corpus document can otherwise inject ANSI escapes, fake graphify-out
|
|
# log lines, or prompt-injection markup into the model's context via
|
|
# source_file / source_location / community.
|
|
# The learning= suffix is appended INSIDE the bracket and BEFORE the
|
|
# budget check below, so it counts in char_budget accounting.
|
|
entry = overlay.get(str(nid))
|
|
learning_suffix = ""
|
|
if entry:
|
|
status = sanitize_label(str(entry.get("status", "")))
|
|
if status:
|
|
learning_suffix = f" learning={status}{':stale' if entry.get('stale') else ''}"
|
|
line = (
|
|
f"NODE {sanitize_label(d.get('label', nid))} "
|
|
f"[src={sanitize_label(str(d.get('source_file', '')))} "
|
|
f"loc={sanitize_label(str(d.get('source_location', '')))} "
|
|
f"community={sanitize_label(str(d.get('community_name') or d.get('community', '')))}"
|
|
f"{learning_suffix}]"
|
|
)
|
|
lines.append(line)
|
|
for u, v in edges:
|
|
if u in nodes and v in nodes:
|
|
raw = G[u][v]
|
|
d = next(iter(raw.values()), {}) if isinstance(G, (nx.MultiGraph, nx.MultiDiGraph)) else raw
|
|
# (u, v) is BFS/DFS visit order, not necessarily the true edge
|
|
# direction: on an undirected graph G.neighbors() walks callers
|
|
# and callees alike, so a caller->callee edge renders backwards
|
|
# whenever the callee is visited first. _src/_tgt (stashed on the
|
|
# edge data by the `query` CLI loader) carry the real direction;
|
|
# fall back to (u, v) for graphs/edges that don't set them.
|
|
src = d.get("_src", u)
|
|
tgt = d.get("_tgt", v)
|
|
# Guard against a stray/dangling _src/_tgt (hand-edited or adversarial
|
|
# graph.json): only trust them when they name exactly this edge's
|
|
# endpoints, else fall back to (u, v). Without this, G.nodes[src]
|
|
# would KeyError on an unknown id (#2080 review).
|
|
if {src, tgt} != {u, v}:
|
|
src, tgt = u, v
|
|
context = d.get("context")
|
|
context_suffix = f" context={sanitize_label(str(context))}" if context else ""
|
|
# The relation SITE (call/import/reference line in the source's
|
|
# file), not a def line — so "who calls X" cites a clickable call
|
|
# location, not the caller's def (#BUG1).
|
|
_loc = str(d.get("source_location") or "")
|
|
at_suffix = (
|
|
f" at={sanitize_label(str(d.get('source_file') or ''))}:{sanitize_label(_loc)}"
|
|
if _loc else ""
|
|
)
|
|
line = (
|
|
f"EDGE {sanitize_label(G.nodes[src].get('label', src))} "
|
|
f"--{sanitize_label(str(d.get('relation', '')))} "
|
|
f"[{sanitize_label(str(d.get('confidence', '')))}{context_suffix}]--> "
|
|
f"{sanitize_label(G.nodes[tgt].get('label', tgt))}{at_suffix}"
|
|
)
|
|
lines.append(line)
|
|
output = "\n".join(lines)
|
|
if len(output) > char_budget:
|
|
cut_at = output[:char_budget].rfind("\n")
|
|
cut_at = cut_at if cut_at > 0 else char_budget
|
|
# Never cut the seed nodes: they render first, so if the budget lands
|
|
# inside the seed block, extend the cut to cover it. The symbol the
|
|
# question named must always be in the answer (#BUG2). Seeds are bounded
|
|
# (_pick_seeds max_k + one per term), so the overshoot is a few lines.
|
|
if seed_hits:
|
|
seed_block_end = sum(len(lines[i]) + 1 for i in range(len(seed_hits))) - 1
|
|
cut_at = max(cut_at, min(seed_block_end, len(output)))
|
|
total_nodes = sum(1 for l in lines if l.startswith("NODE "))
|
|
shown_nodes = output[:cut_at].count("\nNODE ") + (1 if output.startswith("NODE ") else 0)
|
|
cut_count = total_nodes - shown_nodes
|
|
# Prominent notice at the TOP so a truncated answer can never be mistaken
|
|
# for a complete one — silence used to read as absence (#BUG2). The
|
|
# notice + end marker sit OUTSIDE char_budget by design (two bounded
|
|
# wrapper lines, like the existing end marker).
|
|
output = (
|
|
f"[!] TRUNCATED: showing {shown_nodes} of {total_nodes} nodes "
|
|
f"(~{token_budget}-token budget). The answer may be among the "
|
|
f"{cut_count} cut nodes — raise the token budget (CLI: --budget) or "
|
|
f"narrow the query (e.g. context_filter=['call'], or get_node for a "
|
|
f"specific symbol).\n\n"
|
|
+ output[:cut_at]
|
|
+ f"\n... (truncated — {cut_count} more nodes cut by ~{token_budget}-token budget."
|
|
f" Narrow with context_filter=['call'] or use get_node for a specific symbol)"
|
|
)
|
|
return output
|
|
|
|
|
|
def _cut_lines_to_budget(lines: list[str], token_budget: int, narrow_hint: str) -> str:
|
|
"""Render pre-built lines under the same ~3-chars/token budget rule as
|
|
_subgraph_to_text; over-budget output is cut at a line boundary with a count and a
|
|
narrowing hint instead of flooding the caller's context window."""
|
|
output = "\n".join(lines)
|
|
char_budget = token_budget * 3
|
|
if len(output) <= char_budget:
|
|
return output
|
|
cut_at = output[:char_budget].rfind("\n")
|
|
cut_at = cut_at if cut_at > 0 else char_budget
|
|
kept = output[:cut_at]
|
|
shown = kept.count("\n") + 1
|
|
cut_count = len(lines) - shown
|
|
# Announce truncation at the TOP as well, matching _subgraph_to_text — a
|
|
# bottom-only marker reads as silence/absence (the BUG-2 fix rationale). The
|
|
# notice sits outside char_budget by design (one bounded wrapper line).
|
|
return (
|
|
f"[!] TRUNCATED: showing {shown} of {len(lines)} lines "
|
|
f"(~{token_budget}-token budget). {narrow_hint}\n\n"
|
|
+ kept
|
|
+ f"\n... (truncated — {cut_count} more lines cut by ~{token_budget}-token budget. "
|
|
+ narrow_hint
|
|
+ ")"
|
|
)
|
|
|
|
|
|
def _query_graph_text(
|
|
G: nx.Graph,
|
|
question: str,
|
|
*,
|
|
mode: str = "bfs",
|
|
depth: int = 3,
|
|
token_budget: int = 2000,
|
|
context_filters: list[str] | None = None,
|
|
) -> str:
|
|
terms = _query_terms(question)
|
|
# One graph scoring pass produces both the combined ranking (used to drive
|
|
# the gap-based seed selection below) and the per-token singleton winners
|
|
# (used by _pick_seeds' per-term guarantee). Previously this was T+1 passes
|
|
# — one combined + one per query token — re-walking the whole graph each
|
|
# time; on a 100k-node, three-term benchmark ~71% of scoring time was
|
|
# spent in those redundant per-term passes.
|
|
qs = _score_query(G, terms, collect_per_term_seeds=True)
|
|
# Relational-intent verbs ("calls", "uses", ...) describe the relation the
|
|
# question asks about, not a symbol to seed from; drop them from the
|
|
# per-term seed GUARANTEE so an incidental verb match cannot seat a decoy
|
|
# BFS root (#2507). They keep their place in `qs.ranked`, so a genuine
|
|
# identifier named after a verb can still win a seat on merit via the gap
|
|
# window — and when the query consists ONLY of intent words (bare "calls"),
|
|
# the guarantee is left intact so such an identifier stays reachable.
|
|
best_seed_by_term = qs.best_seed_by_term
|
|
intent = {t for t in best_seed_by_term if t in _RELATIONAL_INTENT_TERMS}
|
|
if intent and any(t not in _RELATIONAL_INTENT_TERMS for t in terms):
|
|
best_seed_by_term = {
|
|
t: nid for t, nid in best_seed_by_term.items() if t not in intent
|
|
}
|
|
start_nodes = _pick_seeds(qs.ranked, G=G, best_seed_by_term=best_seed_by_term)
|
|
if not start_nodes:
|
|
return "No matching nodes found."
|
|
resolved_filters, filter_source = _resolve_context_filters(question, context_filters)
|
|
traversal_graph = _filter_graph_by_context(G, resolved_filters)
|
|
nodes, edges = _dfs(traversal_graph, start_nodes, depth) if mode == "dfs" else _bfs(traversal_graph, start_nodes, depth)
|
|
header_parts = [
|
|
f"Traversal: {mode.upper()} depth={depth}",
|
|
f"Start: {[G.nodes[n].get('label', n) for n in start_nodes]}",
|
|
]
|
|
if resolved_filters:
|
|
header_parts.append(f"Context: {', '.join(resolved_filters)} ({filter_source})")
|
|
header_parts.append(f"{len(nodes)} nodes found")
|
|
header = " | ".join(header_parts) + "\n\n"
|
|
# Pass the seeds so the queried symbol renders first and survives truncation
|
|
# (#BUG2): a branch merge had silently dropped this argument, leaving the
|
|
# seed-first ordering as dead code.
|
|
return header + _subgraph_to_text(traversal_graph, nodes, edges, token_budget, seeds=start_nodes)
|
|
|
|
|
|
def _find_node_tiers(
|
|
G: nx.Graph, label: str
|
|
) -> tuple[list[str], list[str], list[str], list[str]]:
|
|
"""Return match tiers in precedence order: (source_exact, exact, prefix, substring).
|
|
|
|
Split out of `_find_node` so callers that must not guess between equally-good
|
|
matches can inspect the winning tier alone. `_find_node` flattens these, and
|
|
its consumers take `[0]` — which resolves by graph-iteration order when one
|
|
tier holds several nodes from different files. See `find_node_ambiguity`.
|
|
"""
|
|
term = " ".join(_search_tokens(label))
|
|
if not term:
|
|
return []
|
|
# Punctuation-preserving normalized query. `term` tokenizes on \w+ (so
|
|
# "blockStream.ts" -> "blockstream ts", space where the '.' was), but a node's
|
|
# stored `norm_label` keeps punctuation ("blockstream.ts"). Matching only via
|
|
# `term`/`label_tokens` works when the node label tokenizes the same way, but is
|
|
# fragile if `label` and `norm_label` diverge. `norm_query` matches `norm_label`
|
|
# symmetrically so an exactly-typed punctuated label always resolves (#1704).
|
|
# `nid_norm` below extends that symmetry to node ids, which keep their
|
|
# punctuation too and are compared raw against the tokenized `term` (#2467).
|
|
norm_query = _strip_diacritics(str(label)).lower().strip()
|
|
source_exact: list[str] = []
|
|
exact: list[str] = []
|
|
prefix: list[str] = []
|
|
substring: list[str] = []
|
|
# Trigram prefilter (graph-iteration order preserved so exact/prefix/substring
|
|
# ordering — and thus matches[0] — is byte-identical to the full scan).
|
|
candidate_ids = _trigram_candidates(G, [term, norm_query])
|
|
node_iter = (
|
|
G.nodes(data=True) if candidate_ids is None
|
|
else ((nid, G.nodes[nid]) for nid in candidate_ids)
|
|
)
|
|
for nid, d in node_iter:
|
|
norm_label = d.get("norm_label") or _strip_diacritics(d.get("label") or "").lower()
|
|
bare_label = norm_label.rstrip("()")
|
|
label_tokens = " ".join(_search_tokens(d.get("label") or ""))
|
|
source_tokens = " ".join(_search_tokens(d.get("source_file") or ""))
|
|
nid_lower = nid.lower()
|
|
# `_strip_diacritics` is the identity on ASCII, so the NFKD fold is only
|
|
# paid for ids that actually carry non-ASCII text.
|
|
nid_norm = nid_lower if nid.isascii() else _strip_diacritics(nid).lower()
|
|
if term == source_tokens:
|
|
source_exact.append(nid)
|
|
elif (
|
|
term == norm_label or term == bare_label or term == label_tokens or term == nid_lower
|
|
or norm_query == norm_label or norm_query == bare_label or norm_query == nid_norm
|
|
):
|
|
exact.append(nid)
|
|
elif (
|
|
norm_label.startswith(term)
|
|
or bare_label.startswith(term)
|
|
or label_tokens.startswith(term)
|
|
or nid_lower.startswith(term)
|
|
or norm_label.startswith(norm_query)
|
|
or bare_label.startswith(norm_query)
|
|
):
|
|
prefix.append(nid)
|
|
elif term in norm_label or term in label_tokens or norm_query in norm_label:
|
|
substring.append(nid)
|
|
|
|
if source_exact:
|
|
query_basename = _strip_diacritics(Path(label).name).lower()
|
|
preferred = []
|
|
for nid in source_exact:
|
|
if str(G.nodes[nid].get("source_location", "")) != "L1":
|
|
continue
|
|
# File-node label is the bare basename OR a directory-qualified form
|
|
# from the #2032 disambiguation pass (e.g. "process-order/index.ts").
|
|
lbl = _strip_diacritics(str(G.nodes[nid].get("label") or "")).lower()
|
|
if lbl == query_basename or lbl.endswith("/" + query_basename):
|
|
preferred.append(nid)
|
|
if len(preferred) == 1:
|
|
source_exact = preferred + [nid for nid in source_exact if nid != preferred[0]]
|
|
|
|
return source_exact, exact, prefix, substring
|
|
|
|
|
|
def _find_node(G: nx.Graph, label: str) -> list[str]:
|
|
"""Return node IDs whose label or ID matches the search term (diacritic-insensitive).
|
|
|
|
Results are ordered by precedence: exact source-file path match first, then
|
|
exact (label/ID) match, then prefix match, then substring match. Node-ID exact
|
|
matches are grouped with label exact matches.
|
|
"""
|
|
source_exact, exact, prefix, substring = _find_node_tiers(G, label)
|
|
return source_exact + exact + prefix + substring
|
|
|
|
|
|
def find_node_ambiguity(G: nx.Graph, label: str) -> list[str]:
|
|
"""Return rival candidates when the winning match tier spans several source files.
|
|
|
|
`_find_node` ranks matches but never reports that a tie was broken, so callers
|
|
taking `[0]` present one arbitrary file as the answer. Two workspaces that each
|
|
define `MetricsPort` put both nodes in the same `exact` tier, separated only by
|
|
`G.nodes()` iteration order — reorder the graph and the same query answers with
|
|
a different file, equally confidently.
|
|
|
|
Returns one representative node id per distinct source file when the winning
|
|
tier is split that way, else `[]`. Several matches *within one file* (a file
|
|
node plus its members) are ordinary precedence, not ambiguity, and return `[]`.
|
|
|
|
`_disambiguate_file_node_labels` (#2032) already relabels colliding *file*
|
|
nodes; this covers the symbol case it does not reach.
|
|
"""
|
|
for tier in _find_node_tiers(G, label):
|
|
if not tier:
|
|
continue
|
|
by_source: dict[str, str] = {}
|
|
for nid in tier:
|
|
source = str(G.nodes[nid].get("source_file") or "")
|
|
by_source.setdefault(source, nid)
|
|
return list(by_source.values()) if len(by_source) > 1 else []
|
|
return []
|
|
|
|
|
|
def _shortest_path_text(G: nx.Graph, arguments: dict) -> str:
|
|
"""Body of the `shortest_path` MCP tool (module-level so tests can call it
|
|
without an mcp install).
|
|
|
|
Directed by default (#2487): the returned path must follow stored
|
|
caller→callee direction; pass ``undirected=True`` to ignore it.
|
|
"""
|
|
src_scored = _score_nodes(G, [t.lower() for t in arguments["source"].split()])
|
|
tgt_scored = _score_nodes(G, [t.lower() for t in arguments["target"].split()])
|
|
if not src_scored:
|
|
return f"No node matching source '{arguments['source']}' found."
|
|
if not tgt_scored:
|
|
return f"No node matching target '{arguments['target']}' found."
|
|
src_nid = _pick_scored_endpoint(G, src_scored, arguments["source"])
|
|
tgt_nid = _pick_scored_endpoint(G, tgt_scored, arguments["target"])
|
|
# Ambiguity guard: when both queries resolve to the same node, the
|
|
# shortest path is trivially zero hops, which is almost never what the
|
|
# caller wanted (see bug #828).
|
|
if src_nid == tgt_nid:
|
|
return (
|
|
f"'{arguments['source']}' and '{arguments['target']}' both resolved to "
|
|
f"the same node '{src_nid}'. Use a more specific label or the exact node ID."
|
|
)
|
|
warnings: list[str] = []
|
|
for name, scored, nid in (
|
|
("source", src_scored, src_nid),
|
|
("target", tgt_scored, tgt_nid),
|
|
):
|
|
# Only meaningful when the raw score head is what got picked — a
|
|
# full-token override was chosen on token coverage, not score.
|
|
if len(scored) >= 2 and nid == scored[0][1]:
|
|
top, runner = scored[0][0], scored[1][0]
|
|
if top > 0 and (top - runner) / top < 0.10:
|
|
warnings.append(
|
|
f"warning: {name} match was ambiguous "
|
|
f"(top score {top:g}, runner-up {runner:g})"
|
|
)
|
|
max_hops = int(arguments.get("max_hops", 8))
|
|
undirected = bool(arguments.get("undirected", False))
|
|
try:
|
|
# Deterministic path (#2074): the hash-seeded undirected view picked an
|
|
# arbitrary route among equal-length paths. Build a sorted, materialized
|
|
# graph so the chosen path is canonical. Serve's shared G is left
|
|
# untouched (its degree feeds query-seed tie-breaks).
|
|
if undirected:
|
|
_und = nx.Graph()
|
|
_und.add_nodes_from(sorted(G.nodes))
|
|
_und.add_edges_from(sorted((min(u, v), max(u, v)) for u, v in G.edges()))
|
|
path_nodes = nx.shortest_path(_und, src_nid, tgt_nid)
|
|
else:
|
|
# Directed by default (#2487). True direction is NOT raw arc
|
|
# order: legacy canonicalized files persist a flipped arc with
|
|
# _src/_tgt markers (#2309), so build the digraph from _src/_tgt
|
|
# (falling back to the loaded arc) rather than to_directed().
|
|
_dg = nx.DiGraph()
|
|
_dg.add_nodes_from(sorted(G.nodes))
|
|
_dg.add_edges_from(sorted(
|
|
(d.get("_src", u), d.get("_tgt", v)) for u, v, d in G.edges(data=True)
|
|
))
|
|
path_nodes = nx.shortest_path(_dg, src_nid, tgt_nid)
|
|
except (nx.NetworkXNoPath, nx.NodeNotFound):
|
|
src_label = G.nodes[src_nid].get("label", src_nid)
|
|
tgt_label = G.nodes[tgt_nid].get("label", tgt_nid)
|
|
if undirected:
|
|
return f"No path found between '{src_label}' and '{tgt_label}'."
|
|
return (
|
|
f"No directed path found between '{src_label}' and '{tgt_label}'. "
|
|
"Retry with undirected=true to search ignoring edge direction."
|
|
)
|
|
hops = len(path_nodes) - 1
|
|
if hops > max_hops:
|
|
return f"Path exceeds max_hops={max_hops} ({hops} hops found)."
|
|
segments = []
|
|
for i in range(len(path_nodes) - 1):
|
|
u, v = path_nodes[i], path_nodes[i + 1]
|
|
# Report the actual stored relation(s), never a fabricated `calls`;
|
|
# fall back to an honest "related" when the edge has no relation (#2074).
|
|
# Direction truth lives in the per-link _src/_tgt markers (#2309): a
|
|
# legacy canonicalized file can persist a flipped arc, so classify each
|
|
# hop by _src (falling back to the arc tail) instead of raw arc order.
|
|
fwd, bwd = [], []
|
|
for a, b in ((u, v), (v, u)):
|
|
if G.has_edge(a, b):
|
|
for d in edge_datas(G, a, b):
|
|
(fwd if d.get("_src", a) == u else bwd).append(d)
|
|
datas = fwd or bwd
|
|
forward = bool(fwd)
|
|
rels = sorted({d.get("relation") for d in datas if d.get("relation")})
|
|
rel = "/".join(rels) if rels else "related"
|
|
confs = sorted({d.get("confidence") for d in datas if d.get("confidence")})
|
|
conf_str = f" [{'/'.join(confs)}]" if confs else ""
|
|
if i == 0:
|
|
segments.append(G.nodes[u].get("label", u))
|
|
if forward:
|
|
segments.append(f"--{rel}{conf_str}--> {G.nodes[v].get('label', v)}")
|
|
else:
|
|
segments.append(f"<--{rel}{conf_str}-- {G.nodes[v].get('label', v)}")
|
|
prefix = ("\n".join(warnings) + "\n") if warnings else ""
|
|
return prefix + f"Shortest path ({hops} hops):\n " + " ".join(segments)
|
|
|
|
|
|
def _filter_blank_stdin() -> None:
|
|
"""Filter blank lines from stdin before MCP reads it.
|
|
|
|
Some MCP clients (Claude Desktop, etc.) send blank lines between JSON
|
|
messages. The MCP stdio transport tries to parse every line as a
|
|
JSONRPCMessage, so a bare newline triggers a Pydantic ValidationError.
|
|
This installs an OS-level pipe that relays stdin while dropping blanks.
|
|
"""
|
|
r_fd, w_fd = os.pipe()
|
|
saved_fd = os.dup(sys.stdin.fileno())
|
|
|
|
def _relay() -> None:
|
|
try:
|
|
with open(saved_fd, "rb") as src, open(w_fd, "wb") as dst:
|
|
for line in src:
|
|
if line.strip():
|
|
dst.write(line)
|
|
dst.flush()
|
|
except Exception:
|
|
pass
|
|
|
|
threading.Thread(target=_relay, daemon=True).start()
|
|
os.dup2(r_fd, sys.stdin.fileno())
|
|
os.close(r_fd)
|
|
sys.stdin = open(0, "r", closefd=False)
|
|
|
|
|
|
def _community_header(cid: int, community_name) -> str:
|
|
# Header for get_community: "Community N — Name", matching get_node / query
|
|
# output which read the community_name attribute to_json writes onto nodes.
|
|
# Skip the name when it is just the "Community N" placeholder (written for
|
|
# unnamed communities) so the header never reads "Community 12 — Community 12";
|
|
# also falls back to the bare id when there is no name. Name is sanitised
|
|
# (F-010) like every other LLM-derived field.
|
|
base = f"Community {cid}"
|
|
if community_name:
|
|
clean = sanitize_label(str(community_name))
|
|
if clean and clean != base:
|
|
return f"{base} — {clean}"
|
|
return base
|
|
|
|
|
|
def _build_server(graph_path: str):
|
|
"""Build the configured low-level MCP Server (shared by every transport).
|
|
|
|
All graph query tools and resources are registered here over a single
|
|
``mcp.server.Server`` instance; the caller picks the transport (stdio or
|
|
Streamable HTTP) and runs it. Hot-reload of graph.json works the same way
|
|
regardless of transport, since reloads happen inside the tool handlers.
|
|
"""
|
|
try:
|
|
from mcp.server import Server
|
|
from mcp import types
|
|
except ImportError as e:
|
|
raise ImportError('mcp not installed. Run: pip install "graphifyy[mcp]"') from e
|
|
try:
|
|
from mcp.types import AnyUrl
|
|
except ImportError:
|
|
# mcp >= 2.0 dropped the AnyUrl re-export; it was always pydantic's
|
|
# AnyUrl (pydantic is an mcp dependency, so this import cannot miss).
|
|
from pydantic import AnyUrl
|
|
|
|
from graphify import paths as _paths
|
|
|
|
# Graph contexts comprise one pinned configured default plus a bounded LRU
|
|
# of project_path graphs. This preserves the configured graph's warm index
|
|
# while preventing a shared server from retaining every project it serves.
|
|
_default_graph_path = str(Path(graph_path).resolve())
|
|
_ctx_cache = _GraphContextCache(_max_server_contexts())
|
|
|
|
def _load_ctx(path: str):
|
|
"""Return the current default or project graph context as a tool error.
|
|
|
|
Unlike ``_load_graph``, this never lets a missing or corrupt client
|
|
graph terminate the MCP process; it raises so other projects remain
|
|
available on the same server.
|
|
"""
|
|
resolved_path = str(Path(path).resolve())
|
|
return _ctx_cache.load(resolved_path, pinned=resolved_path == _default_graph_path)
|
|
|
|
def _resolve_graph_path(project_path) -> str:
|
|
"""Map an optional project_path to a concrete graph.json path. ``None``
|
|
keeps the server's default graph (backward-compatible); a project_path
|
|
resolves to ``<project_path>/<GRAPHIFY_OUT>/graph.json``, honouring the
|
|
GRAPHIFY_OUT override so worktree/shared-output setups keep working."""
|
|
if not project_path:
|
|
return _default_graph_path
|
|
return str(Path(project_path) / _paths.GRAPHIFY_OUT / "graph.json")
|
|
|
|
# Active per-request context, rebound by _select_graph() and read by the tool
|
|
# handlers below. No lock needed on the hot path: _select_graph and the
|
|
# handler run in one synchronous stretch of each call_tool coroutine (no
|
|
# await between them), so a concurrent call never observes a half-applied
|
|
# swap.
|
|
active_graph_path = _default_graph_path
|
|
try:
|
|
G, communities = _load_ctx(_default_graph_path)
|
|
except (FileNotFoundError, RuntimeError):
|
|
# No default graph at startup → run as a pure multi-project server. Tools
|
|
# then require project_path; a call without one gets a clear error rather
|
|
# than the process refusing to start (which is what _load_graph would do).
|
|
G, communities = None, {}
|
|
|
|
def _select_graph(project_path) -> None:
|
|
nonlocal G, communities, active_graph_path
|
|
path = _resolve_graph_path(project_path)
|
|
G, communities = _load_ctx(path)
|
|
active_graph_path = str(Path(path).resolve())
|
|
|
|
# NOTE: no decorators here — the handlers below are plain coroutines,
|
|
# bound to the Server at the END of this function in a version-aware way:
|
|
# mcp 1.x exposes the @server.list_tools()/... decorator API, mcp 2.x
|
|
# replaced it with on_list_tools=/... constructor callbacks.
|
|
async def list_tools() -> list[types.Tool]:
|
|
_tools = [
|
|
types.Tool(
|
|
name="query_graph",
|
|
description="Search the knowledge graph using BFS or DFS. Returns relevant nodes and edges as text context.",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"question": {"type": "string", "description": "Natural language question or keyword search"},
|
|
"mode": {"type": "string", "enum": ["bfs", "dfs"], "default": "bfs",
|
|
"description": "bfs=broad context, dfs=trace a specific path"},
|
|
"depth": {"type": "integer", "default": 3, "description": "Traversal depth (1-6)"},
|
|
"token_budget": {"type": "integer", "default": 2000, "description": "Max output tokens"},
|
|
"context_filter": {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
"description": "Optional explicit edge-context filter, e.g. ['call', 'field']",
|
|
},
|
|
},
|
|
"required": ["question"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="get_node",
|
|
description="Get full details for a specific node by label or ID.",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {"label": {"type": "string", "description": "Node label or ID to look up"}},
|
|
"required": ["label"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="get_neighbors",
|
|
description="Get all direct neighbors of a node with edge details.",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"label": {"type": "string"},
|
|
"relation_filter": {"type": "string", "description": "Optional: filter by relation type"},
|
|
"token_budget": {"type": "integer", "default": 2000, "description": "Max output tokens"},
|
|
},
|
|
"required": ["label"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="get_community",
|
|
description="Get all nodes in a community by community ID.",
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"community_id": {"type": "integer", "description": "Community ID (0-indexed by size)"},
|
|
"token_budget": {"type": "integer", "default": 2000, "description": "Max output tokens"},
|
|
},
|
|
"required": ["community_id"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="god_nodes",
|
|
description="Return the most connected nodes - the core abstractions of the knowledge graph.",
|
|
inputSchema={"type": "object", "properties": {"top_n": {"type": "integer", "default": 10}}},
|
|
),
|
|
types.Tool(
|
|
name="graph_stats",
|
|
description="Return summary statistics: node count, edge count, communities, confidence breakdown.",
|
|
inputSchema={"type": "object", "properties": {}},
|
|
),
|
|
types.Tool(
|
|
name="shortest_path",
|
|
description=(
|
|
"Find the shortest path between two concepts in the knowledge graph. "
|
|
"Follows stored edge direction by default; set undirected=true to ignore it."
|
|
),
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"source": {"type": "string", "description": "Source concept label or keyword"},
|
|
"target": {"type": "string", "description": "Target concept label or keyword"},
|
|
"max_hops": {"type": "integer", "default": 8, "description": "Maximum hops to consider"},
|
|
"undirected": {"type": "boolean", "default": False,
|
|
"description": "Ignore stored edge direction when searching"},
|
|
},
|
|
"required": ["source", "target"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="list_prs",
|
|
description=(
|
|
"List open GitHub PRs with CI status, review state, and graph impact "
|
|
"(which communities each PR touches, blast radius). Use this before starting "
|
|
"work to check if a PR already covers the area you're about to change."
|
|
),
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"base": {"type": "string", "description": "Base branch to filter PRs by (auto-detected if omitted)"},
|
|
"repo": {"type": "string", "description": "GitHub repo (owner/repo). Defaults to current repo."},
|
|
},
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="get_pr_impact",
|
|
description=(
|
|
"Get detailed graph impact for a specific PR: which files it changes, "
|
|
"which knowledge-graph communities are affected, and how many nodes are touched. "
|
|
"Use this to assess merge risk or check for overlap with your current work."
|
|
),
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"pr_number": {"type": "integer", "description": "PR number to analyse"},
|
|
"repo": {"type": "string", "description": "GitHub repo (owner/repo). Defaults to current repo."},
|
|
},
|
|
"required": ["pr_number"],
|
|
},
|
|
),
|
|
types.Tool(
|
|
name="triage_prs",
|
|
description=(
|
|
"Return all actionable open PRs (correct base, not stale) with full graph impact data "
|
|
"so you can reason about review priority, merge order, and conflict risk. "
|
|
"Call this when the user asks 'what PRs should I review?' or 'what's ready to merge?'"
|
|
),
|
|
inputSchema={
|
|
"type": "object",
|
|
"properties": {
|
|
"base": {"type": "string", "description": "Base branch to filter PRs by (auto-detected if omitted)"},
|
|
"repo": {"type": "string", "description": "GitHub repo (owner/repo). Defaults to current repo."},
|
|
},
|
|
},
|
|
),
|
|
]
|
|
# Multi-project support: every tool accepts an optional project_path.
|
|
# Injected here (rather than repeated in 11 literal schemas) so the set
|
|
# stays in lockstep as tools are added. Omitting it keeps the historical
|
|
# single-graph behaviour, so this is purely additive for existing callers.
|
|
for _t in _tools:
|
|
# The constructor accepts the camelCase alias in both majors, but
|
|
# attribute access is inputSchema on mcp 1.x and input_schema on 2.x.
|
|
_schema = getattr(_t, "inputSchema", None)
|
|
if _schema is None:
|
|
_schema = _t.input_schema
|
|
_schema.setdefault("properties", {})["project_path"] = {
|
|
"type": "string",
|
|
"description": (
|
|
"Absolute path to a project directory containing "
|
|
"graphify-out/graph.json. Optional — defaults to the graph "
|
|
"this server was started with."
|
|
),
|
|
}
|
|
return _tools
|
|
|
|
def _tool_query_graph(arguments: dict) -> str:
|
|
import time as _time
|
|
from graphify import querylog
|
|
question = arguments["question"]
|
|
mode = arguments.get("mode", "bfs")
|
|
depth = min(int(arguments.get("depth", 3)), 6)
|
|
budget = int(arguments.get("token_budget", 2000))
|
|
context_filter = arguments.get("context_filter")
|
|
_t0 = _time.perf_counter()
|
|
result = _query_graph_text(
|
|
G,
|
|
question,
|
|
mode=mode,
|
|
depth=depth,
|
|
token_budget=budget,
|
|
context_filters=context_filter,
|
|
)
|
|
querylog.log_query(
|
|
kind="mcp_query",
|
|
question=question,
|
|
corpus=str(active_graph_path),
|
|
result=result,
|
|
mode=mode,
|
|
depth=depth,
|
|
token_budget=budget,
|
|
duration_ms=(_time.perf_counter() - _t0) * 1000,
|
|
)
|
|
return result
|
|
|
|
def _tool_get_node(arguments: dict) -> str:
|
|
label = arguments["label"].lower()
|
|
matches = [(nid, d) for nid, d in G.nodes(data=True)
|
|
if label in (d.get("label") or "").lower() or label == nid.lower()]
|
|
if not matches:
|
|
return f"No node matching '{label}' found."
|
|
nid, d = matches[0]
|
|
# Sanitise every LLM-derived field before concatenation (F-010).
|
|
return "\n".join([
|
|
f"Node: {sanitize_label(d.get('label', nid))}",
|
|
f" ID: {sanitize_label(nid)}",
|
|
f" Source: {sanitize_label(str(d.get('source_file', '')))} {sanitize_label(str(d.get('source_location', '')))}",
|
|
f" Type: {sanitize_label(str(d.get('file_type', '')))}",
|
|
f" Community: {sanitize_label(str(d.get('community_name') or d.get('community', '')))}",
|
|
f" Degree: {G.degree(nid)}",
|
|
])
|
|
|
|
def _tool_get_neighbors(arguments: dict) -> str:
|
|
label = arguments["label"].lower()
|
|
rel_filter = arguments.get("relation_filter", "").lower()
|
|
matches = _find_node(G, label)
|
|
if not matches:
|
|
return f"No node matching '{label}' found."
|
|
rivals = find_node_ambiguity(G, label)
|
|
if rivals:
|
|
listing = "\n".join(
|
|
f" {G.nodes[r].get('source_file') or r}\n id: {r}" for r in rivals
|
|
)
|
|
return (
|
|
f"Ambiguous: '{label}' matches {len(rivals)} nodes in different files.\n"
|
|
f"{listing}\n"
|
|
"Retry with the repo-relative path or the full node id."
|
|
)
|
|
nid = matches[0]
|
|
lines = [f"Neighbors of {sanitize_label(G.nodes[nid].get('label', nid))}:"]
|
|
def _edge_at(d: dict) -> str:
|
|
# Edge location = the relation SITE (call/import line) in the source
|
|
# node's file, not a def line (#BUG1).
|
|
loc = str(d.get("source_location") or "")
|
|
return (
|
|
f" at={sanitize_label(str(d.get('source_file') or ''))}:{sanitize_label(loc)}"
|
|
if loc else ""
|
|
)
|
|
for nb in G.successors(nid):
|
|
d = edge_data(G, nid, nb)
|
|
rel = d.get("relation", "")
|
|
if rel_filter and rel_filter not in rel.lower():
|
|
continue
|
|
lines.append(
|
|
f" --> {sanitize_label(G.nodes[nb].get('label', nb))} "
|
|
f"[{sanitize_label(str(rel))}] [{sanitize_label(str(d.get('confidence', '')))}]{_edge_at(d)}"
|
|
)
|
|
for nb in G.predecessors(nid):
|
|
d = edge_data(G, nb, nid)
|
|
rel = d.get("relation", "")
|
|
if rel_filter and rel_filter not in rel.lower():
|
|
continue
|
|
lines.append(
|
|
f" <-- {sanitize_label(G.nodes[nb].get('label', nb))} "
|
|
f"[{sanitize_label(str(rel))}] [{sanitize_label(str(d.get('confidence', '')))}]{_edge_at(d)}"
|
|
)
|
|
budget = int(arguments.get("token_budget", 2000))
|
|
return _cut_lines_to_budget(
|
|
lines, budget, "Narrow with relation_filter or use get_node for a specific symbol"
|
|
)
|
|
|
|
def _tool_get_community(arguments: dict) -> str:
|
|
cid = int(arguments["community_id"])
|
|
nodes = communities.get(cid, [])
|
|
if not nodes:
|
|
return f"Community {cid} not found."
|
|
header = _community_header(cid, G.nodes[nodes[0]].get("community_name"))
|
|
lines = [f"{header} ({len(nodes)} nodes):"]
|
|
for n in nodes:
|
|
d = G.nodes[n]
|
|
# Sanitise label and source_file (F-010).
|
|
lines.append(
|
|
f" {sanitize_label(d.get('label', n))} "
|
|
f"[{sanitize_label(str(d.get('source_file', '')))}]"
|
|
)
|
|
budget = int(arguments.get("token_budget", 2000))
|
|
return _cut_lines_to_budget(
|
|
lines, budget, "Raise token_budget or use get_node for specific members"
|
|
)
|
|
|
|
def _tool_god_nodes(arguments: dict) -> str:
|
|
from graphify.analyze import god_nodes as _god_nodes
|
|
nodes = _god_nodes(G, top_n=int(arguments.get("top_n", 10)))
|
|
lines = ["God nodes (most connected):"]
|
|
lines += [f" {i}. {n['label']} - {n['degree']} edges" for i, n in enumerate(nodes, 1)]
|
|
return "\n".join(lines)
|
|
|
|
def _tool_graph_stats(_: dict) -> str:
|
|
confs = [d.get("confidence", "EXTRACTED") for _, _, d in G.edges(data=True)]
|
|
total = len(confs) or 1
|
|
return (
|
|
f"Nodes: {G.number_of_nodes()}\n"
|
|
f"Edges: {G.number_of_edges()}\n"
|
|
f"Communities: {len(communities)}\n"
|
|
f"EXTRACTED: {round(confs.count('EXTRACTED')/total*100)}%\n"
|
|
f"INFERRED: {round(confs.count('INFERRED')/total*100)}%\n"
|
|
f"AMBIGUOUS: {round(confs.count('AMBIGUOUS')/total*100)}%\n"
|
|
)
|
|
|
|
def _tool_shortest_path(arguments: dict) -> str:
|
|
return _shortest_path_text(G, arguments)
|
|
|
|
def _tool_list_prs(arguments: dict) -> str:
|
|
from graphify.prs import fetch_prs, fetch_worktrees, format_prs_text, _detect_default_branch
|
|
repo = arguments.get("repo") or None
|
|
base = arguments.get("base") or _detect_default_branch(repo)
|
|
try:
|
|
prs = fetch_prs(repo=repo, base=base)
|
|
except RuntimeError as e:
|
|
return f"Error: {e}"
|
|
worktrees = fetch_worktrees()
|
|
for pr in prs:
|
|
pr.worktree_path = worktrees.get(pr.branch)
|
|
return format_prs_text(prs, base)
|
|
|
|
def _tool_get_pr_impact(arguments: dict) -> str:
|
|
from graphify.prs import fetch_pr_files, compute_pr_impact, _gh, _parse_ci
|
|
number = int(arguments["pr_number"])
|
|
repo = arguments.get("repo") or None
|
|
# Use gh pr view directly — works for any base branch, not just the default
|
|
view_args = ["pr", "view", str(number), "--json",
|
|
"title,headRefName,baseRefName,author,isDraft,reviewDecision,statusCheckRollup,updatedAt"]
|
|
if repo:
|
|
view_args += ["--repo", repo]
|
|
pr_data = _gh(*view_args)
|
|
if pr_data is None:
|
|
return f"PR #{number} not found or gh not authenticated."
|
|
files = fetch_pr_files(number, repo)
|
|
if not files:
|
|
return f"PR #{number}: no changed files found (may require gh auth)."
|
|
comms, nodes = compute_pr_impact(files, G)
|
|
ci = _parse_ci(pr_data.get("statusCheckRollup") or [])
|
|
lines = [
|
|
f"PR #{number}: {pr_data['title']}",
|
|
f"CI: {ci} Review: {pr_data.get('reviewDecision') or 'none'}",
|
|
f"Base: {pr_data['baseRefName']} Author: {(pr_data.get('author') or {}).get('login', '?')}",
|
|
f"\nGraph impact: {nodes} nodes across {len(comms)} communities",
|
|
f"Communities touched: {comms}",
|
|
f"Files changed ({len(files)}):",
|
|
]
|
|
lines += [f" {f}" for f in files[:20]]
|
|
if len(files) > 20:
|
|
lines.append(f" … and {len(files) - 20} more")
|
|
return "\n".join(lines)
|
|
|
|
def _tool_triage_prs(arguments: dict) -> str:
|
|
from concurrent.futures import ThreadPoolExecutor, as_completed
|
|
from graphify.prs import fetch_prs, fetch_worktrees, fetch_pr_files, compute_pr_impact, _STATUS_ORDER, _detect_default_branch
|
|
repo = arguments.get("repo") or None
|
|
base = arguments.get("base") or _detect_default_branch(repo)
|
|
try:
|
|
prs = fetch_prs(repo=repo, base=base)
|
|
except RuntimeError as e:
|
|
return f"Error: {e}"
|
|
worktrees = fetch_worktrees()
|
|
for pr in prs:
|
|
pr.worktree_path = worktrees.get(pr.branch)
|
|
actionable = [p for p in prs if p.base_branch == base and p.status not in ("WRONG-BASE", "STALE")]
|
|
if not actionable:
|
|
return f"No actionable PRs targeting {base}."
|
|
# Fetch diffs concurrently then compute graph impact using in-memory G
|
|
workers = min(8, len(actionable))
|
|
with ThreadPoolExecutor(max_workers=workers) as pool:
|
|
future_to_pr = {pool.submit(fetch_pr_files, pr.number, repo): pr for pr in actionable}
|
|
for fut in as_completed(future_to_pr):
|
|
pr = future_to_pr[fut]
|
|
try:
|
|
files = fut.result()
|
|
except Exception:
|
|
files = []
|
|
if files:
|
|
pr.files_changed = files
|
|
pr.communities_touched, pr.nodes_affected = compute_pr_impact(files, G)
|
|
header = (
|
|
f"Actionable PRs targeting {base}: {len(actionable)}\n"
|
|
"Rank these by review priority. Higher blast_radius = more graph communities affected = higher merge risk.\n"
|
|
)
|
|
lines = [header]
|
|
for p in sorted(actionable, key=lambda x: (_STATUS_ORDER.index(x.status) if x.status in _STATUS_ORDER else 99)):
|
|
impact = f" blast_radius={p.blast_radius}" if p.blast_radius else ""
|
|
wt = f" worktree={p.worktree_path}" if p.worktree_path else ""
|
|
lines.append(
|
|
f"PR #{p.number} [{p.status}] CI={p.ci_status} review={p.review_decision or 'none'} "
|
|
f"age={p.days_old}d author={p.author}{impact}{wt}\n title: {p.title}"
|
|
)
|
|
return "\n\n".join(lines)
|
|
|
|
_handlers = {
|
|
"query_graph": _tool_query_graph,
|
|
"get_node": _tool_get_node,
|
|
"get_neighbors": _tool_get_neighbors,
|
|
"get_community": _tool_get_community,
|
|
"god_nodes": _tool_god_nodes,
|
|
"graph_stats": _tool_graph_stats,
|
|
"shortest_path": _tool_shortest_path,
|
|
"list_prs": _tool_list_prs,
|
|
"get_pr_impact": _tool_get_pr_impact,
|
|
"triage_prs": _tool_triage_prs,
|
|
}
|
|
|
|
def _load_community_labels() -> dict[int, str]:
|
|
labels_path = Path(active_graph_path).parent / ".graphify_labels.json"
|
|
if labels_path.exists():
|
|
try:
|
|
return {int(k): v for k, v in json.loads(labels_path.read_text(encoding="utf-8")).items()}
|
|
except Exception:
|
|
pass
|
|
return {cid: f"Community {cid}" for cid in communities}
|
|
|
|
async def list_resources() -> list[types.Resource]:
|
|
# Plain-string URIs on purpose: mcp 1.x types the field as AnyUrl and
|
|
# coerces strings, mcp 2.x types it as str and REJECTS AnyUrl objects.
|
|
return [
|
|
types.Resource(uri="graphify://report", name="Graph Report", description="Full GRAPH_REPORT.md", mimeType="text/markdown"),
|
|
types.Resource(uri="graphify://stats", name="Graph Stats", description="Node/edge/community counts and confidence breakdown", mimeType="text/plain"),
|
|
types.Resource(uri="graphify://god-nodes", name="God Nodes", description="Top 10 most-connected nodes", mimeType="text/plain"),
|
|
types.Resource(uri="graphify://surprises", name="Surprising Connections", description="Cross-community surprising connections", mimeType="text/plain"),
|
|
types.Resource(uri="graphify://audit", name="Confidence Audit", description="EXTRACTED/INFERRED/AMBIGUOUS edge breakdown", mimeType="text/plain"),
|
|
types.Resource(uri="graphify://questions", name="Suggested Questions", description="Suggested questions for this codebase", mimeType="text/plain"),
|
|
]
|
|
|
|
async def read_resource(uri: AnyUrl) -> str:
|
|
_select_graph(None) # resources read the server's default graph
|
|
uri_str = str(uri)
|
|
if uri_str == "graphify://report":
|
|
report_path = Path(active_graph_path).parent / "GRAPH_REPORT.md"
|
|
if report_path.exists():
|
|
return report_path.read_text(encoding="utf-8")
|
|
return "GRAPH_REPORT.md not found. Run graphify extract first."
|
|
if uri_str == "graphify://stats":
|
|
return _tool_graph_stats({})
|
|
if uri_str == "graphify://god-nodes":
|
|
return _tool_god_nodes({"top_n": 10})
|
|
if uri_str == "graphify://surprises":
|
|
try:
|
|
from graphify.analyze import surprising_connections
|
|
surprises = surprising_connections(G, communities, top_n=10)
|
|
if not surprises:
|
|
return "No surprising connections found."
|
|
lines = ["Surprising cross-community connections:"]
|
|
for s in surprises:
|
|
lines.append(f" {s.get('source', '')} <-> {s.get('target', '')} [{s.get('relation', '')}]")
|
|
return "\n".join(lines)
|
|
except Exception as exc:
|
|
return f"Could not compute surprising connections: {exc}"
|
|
if uri_str == "graphify://audit":
|
|
confs = [d.get("confidence", "EXTRACTED") for _, _, d in G.edges(data=True)]
|
|
total = len(confs) or 1
|
|
return (
|
|
f"Total edges: {total}\n"
|
|
f"EXTRACTED: {confs.count('EXTRACTED')} ({round(confs.count('EXTRACTED')/total*100)}%)\n"
|
|
f"INFERRED: {confs.count('INFERRED')} ({round(confs.count('INFERRED')/total*100)}%)\n"
|
|
f"AMBIGUOUS: {confs.count('AMBIGUOUS')} ({round(confs.count('AMBIGUOUS')/total*100)}%)\n"
|
|
)
|
|
if uri_str == "graphify://questions":
|
|
try:
|
|
from graphify.analyze import suggest_questions
|
|
community_labels = _load_community_labels()
|
|
questions = suggest_questions(G, communities, community_labels, top_n=10)
|
|
if not questions:
|
|
return "No suggested questions available."
|
|
lines = ["Suggested questions:"]
|
|
for q in questions:
|
|
if isinstance(q, dict):
|
|
lines.append(f" - {q.get('question', '')}")
|
|
else:
|
|
lines.append(f" - {q}")
|
|
return "\n".join(lines)
|
|
except Exception as exc:
|
|
return f"Could not generate questions: {exc}"
|
|
raise ValueError(f"Unknown resource: {uri_str}")
|
|
|
|
async def call_tool(name: str, arguments: dict) -> list[types.TextContent]:
|
|
arguments = dict(arguments or {})
|
|
project_path = arguments.pop("project_path", None)
|
|
handler = _handlers.get(name)
|
|
if not handler:
|
|
return [types.TextContent(type="text", text=f"Unknown tool: {name}")]
|
|
try:
|
|
_select_graph(project_path) # bind G/communities to the target graph
|
|
return [types.TextContent(type="text", text=handler(arguments))]
|
|
except Exception as exc:
|
|
return [types.TextContent(type="text", text=f"Error executing {name}: {exc}")]
|
|
|
|
if hasattr(Server, "list_tools"):
|
|
# mcp 1.x: decorator-based registration. The SDK wraps the raw returns
|
|
# (list[Tool] -> ListToolsResult, str -> resource contents) itself.
|
|
server = Server("graphify")
|
|
server.list_tools()(list_tools)
|
|
server.call_tool()(call_tool)
|
|
server.list_resources()(list_resources)
|
|
server.read_resource()(read_resource)
|
|
else:
|
|
# mcp 2.x: handlers ride the Server constructor as on_* callbacks with
|
|
# the (ctx, params) -> Result contract, so wrap the same impls and
|
|
# build the result models the 1.x decorators used to build for us.
|
|
async def _on_list_tools(ctx, params) -> types.ListToolsResult:
|
|
return types.ListToolsResult(tools=await list_tools())
|
|
|
|
async def _on_call_tool(ctx, params) -> types.CallToolResult:
|
|
content = await call_tool(params.name, dict(params.arguments or {}))
|
|
return types.CallToolResult(content=content)
|
|
|
|
async def _on_list_resources(ctx, params) -> types.ListResourcesResult:
|
|
return types.ListResourcesResult(resources=await list_resources())
|
|
|
|
async def _on_read_resource(ctx, params) -> types.ReadResourceResult:
|
|
text = await read_resource(params.uri)
|
|
mime = "text/markdown" if str(params.uri).startswith("graphify://report") else "text/plain"
|
|
return types.ReadResourceResult(
|
|
contents=[types.TextResourceContents(uri=params.uri, mimeType=mime, text=text)]
|
|
)
|
|
|
|
try:
|
|
from importlib.metadata import version as _pkg_version
|
|
_version = _pkg_version("graphifyy")
|
|
except Exception:
|
|
_version = "0"
|
|
server = Server(
|
|
"graphify",
|
|
version=_version,
|
|
on_list_tools=_on_list_tools,
|
|
on_call_tool=_on_call_tool,
|
|
on_list_resources=_on_list_resources,
|
|
on_read_resource=_on_read_resource,
|
|
)
|
|
|
|
return server
|
|
|
|
|
|
def serve(graph_path: str | None = None) -> None:
|
|
"""Start the MCP server over stdio (the default, per-developer transport)."""
|
|
graph_path = graph_path or _default_graph_json()
|
|
try:
|
|
from mcp.server.stdio import stdio_server
|
|
except ImportError as e:
|
|
raise ImportError('mcp not installed. Run: pip install "graphifyy[mcp]"') from e
|
|
import asyncio
|
|
|
|
server = _build_server(graph_path)
|
|
|
|
async def main() -> None:
|
|
async with stdio_server() as streams:
|
|
await server.run(streams[0], streams[1], server.create_initialization_options())
|
|
|
|
_filter_blank_stdin()
|
|
asyncio.run(main())
|
|
|
|
|
|
class _MCPASGIApp:
|
|
"""Raw-ASGI wrapper around the Streamable HTTP session manager.
|
|
|
|
Passed to a Starlette ``Route`` as a class instance (not a function) so
|
|
Starlette treats it as an ASGI app: it serves the exact mount path for all
|
|
methods (GET/POST/DELETE) with no request/response wrapping and no
|
|
trailing-slash redirect — mirroring how FastMCP mounts the same manager.
|
|
"""
|
|
|
|
def __init__(self, manager) -> None:
|
|
self._manager = manager
|
|
|
|
async def __call__(self, scope, receive, send) -> None:
|
|
await self._manager.handle_request(scope, receive, send)
|
|
|
|
|
|
class _ApiKeyMiddleware:
|
|
"""Pure-ASGI API-key gate for the HTTP transport.
|
|
|
|
Implemented as raw ASGI (not Starlette's BaseHTTPMiddleware) on purpose:
|
|
BaseHTTPMiddleware buffers responses and breaks the Streamable HTTP SSE
|
|
stream. This short-circuits with 401 before the request ever reaches the
|
|
session manager, leaving the streaming path untouched for authorized calls.
|
|
"""
|
|
|
|
def __init__(self, app, api_key: str) -> None:
|
|
self.app = app
|
|
self._expected = api_key.encode("utf-8")
|
|
|
|
async def __call__(self, scope, receive, send) -> None:
|
|
if scope["type"] != "http":
|
|
await self.app(scope, receive, send)
|
|
return
|
|
import hmac
|
|
headers = dict(scope.get("headers") or [])
|
|
provided = headers.get(b"x-api-key")
|
|
if provided is None:
|
|
# RFC 6750: the auth scheme token is case-insensitive.
|
|
scheme, _, token = headers.get(b"authorization", b"").partition(b" ")
|
|
if scheme.lower() == b"bearer" and token:
|
|
provided = token.strip()
|
|
# Constant-time compare; reject when no key was supplied at all.
|
|
if provided is None or not hmac.compare_digest(provided, self._expected):
|
|
body = b'{"error": "unauthorized"}'
|
|
await send({
|
|
"type": "http.response.start",
|
|
"status": 401,
|
|
"headers": [
|
|
(b"content-type", b"application/json"),
|
|
(b"content-length", str(len(body)).encode("ascii")),
|
|
],
|
|
})
|
|
await send({"type": "http.response.body", "body": body})
|
|
return
|
|
await self.app(scope, receive, send)
|
|
|
|
|
|
def _build_http_app(
|
|
graph_path: str,
|
|
*,
|
|
host: str = "127.0.0.1",
|
|
port: int = 8080,
|
|
api_key: str | None = None,
|
|
path: str = "/mcp",
|
|
json_response: bool = False,
|
|
stateless: bool = False,
|
|
session_timeout: float | None = 3600.0,
|
|
):
|
|
"""Build the Starlette ASGI app for the Streamable HTTP transport.
|
|
|
|
Split out from :func:`serve_http` (which blocks on uvicorn) so the wiring
|
|
can be exercised with an in-process ASGI test client.
|
|
|
|
``session_timeout`` reaps stateful sessions idle for that many seconds so a
|
|
long-running shared server does not leak memory when IDE clients disconnect
|
|
without sending a DELETE. ``None`` (or <= 0) disables reaping; it is forced
|
|
to ``None`` in stateless mode, which has no sessions to reap.
|
|
"""
|
|
try:
|
|
import contextlib
|
|
|
|
from starlette.applications import Starlette
|
|
from starlette.middleware import Middleware
|
|
from starlette.routing import Route
|
|
|
|
from mcp.server.streamable_http_manager import StreamableHTTPSessionManager
|
|
from mcp.server.transport_security import TransportSecuritySettings
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
'HTTP transport needs the mcp extra (mcp + starlette + uvicorn). '
|
|
'Run: pip install "graphifyy[mcp]"'
|
|
) from e
|
|
|
|
# A blank key (e.g. --api-key "" or an empty GRAPHIFY_API_KEY) must not be
|
|
# mistaken for "auth on" — normalize it to None so the gate is unambiguous.
|
|
api_key = (api_key or "").strip() or None
|
|
|
|
server = _build_server(graph_path)
|
|
|
|
# DNS-rebinding protection. When the operator binds a wildcard address they
|
|
# are intentionally exposing the server, so accept any Host header; for a
|
|
# loopback/specific bind, restrict Host to that address (with and without
|
|
# the port) plus the localhost aliases.
|
|
if host in ("0.0.0.0", "::", ""):
|
|
security = TransportSecuritySettings(enable_dns_rebinding_protection=False)
|
|
else:
|
|
allowed = {host, "localhost", "127.0.0.1"}
|
|
allowed |= {f"{h}:{port}" for h in list(allowed)}
|
|
security = TransportSecuritySettings(allowed_hosts=sorted(allowed))
|
|
|
|
# The SDK rejects a non-positive timeout and forbids one in stateless mode.
|
|
idle_timeout = None if (stateless or not session_timeout or session_timeout <= 0) else session_timeout
|
|
|
|
manager = StreamableHTTPSessionManager(
|
|
app=server,
|
|
json_response=json_response,
|
|
stateless=stateless,
|
|
security_settings=security,
|
|
session_idle_timeout=idle_timeout,
|
|
)
|
|
|
|
@contextlib.asynccontextmanager
|
|
async def lifespan(_app):
|
|
# The session manager owns an anyio task group that must wrap the whole
|
|
# server lifetime, so enter it here rather than per-request.
|
|
async with manager.run():
|
|
yield
|
|
|
|
middleware = []
|
|
if api_key:
|
|
middleware.append(Middleware(_ApiKeyMiddleware, api_key=api_key))
|
|
|
|
return Starlette(
|
|
routes=[Route(path, endpoint=_MCPASGIApp(manager))],
|
|
middleware=middleware,
|
|
lifespan=lifespan,
|
|
)
|
|
|
|
|
|
def serve_http(
|
|
graph_path: str | None = None,
|
|
*,
|
|
host: str = "127.0.0.1",
|
|
port: int = 8080,
|
|
api_key: str | None = None,
|
|
path: str = "/mcp",
|
|
json_response: bool = False,
|
|
stateless: bool = False,
|
|
session_timeout: float | None = 3600.0,
|
|
) -> None:
|
|
"""Start the MCP server over Streamable HTTP (MCP spec 2025-03-26).
|
|
|
|
Serves the same tools/resources as the stdio transport, so a single shared
|
|
process can host the graph for a whole team. Clients point their IDE MCP
|
|
config at ``http://<host>:<port><path>`` (default ``/mcp``).
|
|
|
|
``api_key`` (or the ``GRAPHIFY_API_KEY`` env var) enables a simple header
|
|
check (``Authorization: Bearer <key>`` or ``X-API-Key: <key>``). OAuth is a
|
|
deliberate follow-up. Binding ``0.0.0.0`` exposes the server beyond
|
|
localhost — set an api_key when you do.
|
|
"""
|
|
graph_path = graph_path or _default_graph_json()
|
|
try:
|
|
import uvicorn
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
'HTTP transport needs the mcp extra (mcp + starlette + uvicorn). '
|
|
'Run: pip install "graphifyy[mcp]"'
|
|
) from e
|
|
|
|
api_key = (api_key or "").strip() or None
|
|
|
|
app = _build_http_app(
|
|
graph_path,
|
|
host=host,
|
|
port=port,
|
|
api_key=api_key,
|
|
path=path,
|
|
json_response=json_response,
|
|
stateless=stateless,
|
|
session_timeout=session_timeout,
|
|
)
|
|
|
|
auth_note = "api-key required" if api_key else "no auth (set --api-key to require one)"
|
|
print(
|
|
f"graphify MCP server (streamable-http) on http://{host}:{port}{path} - {auth_note}",
|
|
file=sys.stderr,
|
|
)
|
|
if host in ("0.0.0.0", "::", "") and not api_key:
|
|
print(
|
|
f"WARNING: binding {host or '0.0.0.0'} with no api-key exposes the graph "
|
|
"unauthenticated on the network. Set --api-key (or GRAPHIFY_API_KEY).",
|
|
file=sys.stderr,
|
|
)
|
|
uvicorn.run(app, host=host, port=port)
|
|
|
|
|
|
def _main(argv: list[str] | None = None) -> None:
|
|
import argparse
|
|
import os
|
|
|
|
parser = argparse.ArgumentParser(
|
|
prog="python -m graphify.serve",
|
|
description="Serve a graphify knowledge graph over MCP (stdio or Streamable HTTP).",
|
|
)
|
|
parser.add_argument(
|
|
"graph_path",
|
|
nargs="?",
|
|
default=None,
|
|
help="Path to graph.json (default: graphify-out/graph.json)",
|
|
)
|
|
parser.add_argument(
|
|
"--graph",
|
|
dest="graph_flag",
|
|
default=None,
|
|
metavar="PATH",
|
|
help="Path to graph.json — alias for the positional argument",
|
|
)
|
|
parser.add_argument(
|
|
"--transport",
|
|
choices=["stdio", "http"],
|
|
default="stdio",
|
|
help="Transport to serve on (default: stdio)",
|
|
)
|
|
parser.add_argument("--host", default="127.0.0.1", help="HTTP bind host (default: 127.0.0.1)")
|
|
parser.add_argument("--port", type=int, default=8080, help="HTTP bind port (default: 8080)")
|
|
parser.add_argument(
|
|
"--api-key",
|
|
default=os.environ.get("GRAPHIFY_API_KEY"),
|
|
help="Require this key on the HTTP transport (env: GRAPHIFY_API_KEY)",
|
|
)
|
|
parser.add_argument("--path", default="/mcp", help="HTTP mount path (default: /mcp)")
|
|
parser.add_argument(
|
|
"--json-response",
|
|
action="store_true",
|
|
help="Return plain JSON responses instead of SSE streams",
|
|
)
|
|
parser.add_argument(
|
|
"--stateless",
|
|
action="store_true",
|
|
help="Run without per-session state (for load-balanced / CI deployments)",
|
|
)
|
|
parser.add_argument(
|
|
"--session-timeout",
|
|
type=float,
|
|
default=3600.0,
|
|
help="Reap stateful sessions idle this many seconds (default: 3600; 0 disables)",
|
|
)
|
|
args = parser.parse_args(argv)
|
|
graph_path = args.graph_flag or args.graph_path or _default_graph_json()
|
|
|
|
if args.transport == "http":
|
|
serve_http(
|
|
graph_path,
|
|
host=args.host,
|
|
port=args.port,
|
|
api_key=args.api_key,
|
|
path=args.path,
|
|
json_response=args.json_response,
|
|
stateless=args.stateless,
|
|
session_timeout=args.session_timeout,
|
|
)
|
|
else:
|
|
serve(graph_path)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_main()
|