wehub-resource-sync
693 lines
25 KiB
Python
693 lines
25 KiB
Python
"""Tools 2, 3, 5, 6, 9: query / search / stats helpers."""
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from __future__ import annotations
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import logging
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from pathlib import Path
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from typing import Any
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from ..context_savings import attach_context_savings, estimate_file_tokens
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from ..embeddings import EmbeddingStore
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from ..graph import _sanitize_name, edge_to_dict, node_to_dict
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from ..hints import generate_hints, get_session
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from ..incremental import get_changed_files, get_db_path, get_staged_and_unstaged
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from ..search import hybrid_search
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from ._common import _BUILTIN_CALL_NAMES, _get_store, _resolve_graph_file_paths
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Tool 2: get_impact_radius
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# ---------------------------------------------------------------------------
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_QUERY_PATTERNS = {
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"callers_of": "Find all functions that call a given function",
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"callees_of": "Find all functions called by a given function",
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"imports_of": "Find all imports of a given file or module",
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"importers_of": "Find all files that import a given file or module",
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"children_of": "Find all nodes contained in a file or class",
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"tests_for": "Find all tests for a given function or class",
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"inheritors_of": "Find all classes that inherit from a given class",
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"file_summary": "Get a summary of all nodes in a file",
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}
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def get_impact_radius(
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changed_files: list[str] | None = None,
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max_depth: int = 2,
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max_results: int = 500,
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repo_root: str | None = None,
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base: str = "HEAD~1",
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detail_level: str = "standard",
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) -> dict[str, Any]:
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"""Analyze the blast radius of changed files.
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Args:
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changed_files: Explicit list of changed file paths (relative to repo root).
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If omitted, auto-detects from git diff.
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max_depth: How many hops to traverse in the graph (default: 2).
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max_results: Maximum impacted nodes to return (default: 500).
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repo_root: Repository root path. Auto-detected if omitted.
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base: Git ref for auto-detecting changes (default: HEAD~1).
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detail_level: "standard" (full output) or "minimal" (summary only).
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Returns:
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Changed nodes, impacted nodes, impacted files, connecting edges,
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plus ``truncated`` flag and ``total_impacted`` count.
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"""
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store, root = _get_store(repo_root)
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try:
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if changed_files is None:
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changed_files = get_changed_files(root, base)
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if not changed_files:
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changed_files = get_staged_and_unstaged(root)
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if not changed_files:
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return {
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"status": "ok",
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"summary": "No changed files detected.",
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"changed_nodes": [],
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"impacted_nodes": [],
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"impacted_files": [],
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"truncated": False,
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"total_impacted": 0,
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}
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# Resolve user-facing paths to the file paths stored in the graph.
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original_tokens = estimate_file_tokens(root, changed_files)
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abs_files = _resolve_graph_file_paths(store, root, changed_files)
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result = store.get_impact_radius(
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abs_files, max_depth=max_depth, max_nodes=max_results
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)
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changed_dicts = [node_to_dict(n) for n in result["changed_nodes"]]
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impacted_dicts = [node_to_dict(n) for n in result["impacted_nodes"]]
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edge_dicts = [edge_to_dict(e) for e in result["edges"]]
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truncated = result["truncated"]
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total_impacted = result["total_impacted"]
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summary_parts = [
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f"Blast radius for {len(changed_files)} changed file(s):",
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f" - {len(changed_dicts)} nodes directly changed",
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f" - {len(impacted_dicts)} nodes impacted (within {max_depth} hops)",
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f" - {len(result['impacted_files'])} additional files affected",
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]
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if truncated:
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summary_parts.append(
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f" - Results truncated: showing {len(impacted_dicts)}"
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f" of {total_impacted} impacted nodes"
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)
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if detail_level == "minimal":
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impacted_count = len(impacted_dicts)
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if impacted_count > 20:
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risk = "high"
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elif impacted_count > 5:
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risk = "medium"
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else:
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risk = "low"
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key_entities = [
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n["name"] for n in impacted_dicts[:5]
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]
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minimal_response = {
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"status": "ok",
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"summary": "\n".join(summary_parts),
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"risk": risk,
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"impacted_file_count": len(result["impacted_files"]),
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"key_entities": key_entities,
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"truncated": truncated,
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}
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attach_context_savings(minimal_response, original_tokens=original_tokens)
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return minimal_response
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response = {
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"status": "ok",
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"summary": "\n".join(summary_parts),
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"changed_files": changed_files,
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"changed_nodes": changed_dicts,
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"impacted_nodes": impacted_dicts,
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"impacted_files": result["impacted_files"],
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"edges": edge_dicts,
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"truncated": truncated,
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"total_impacted": total_impacted,
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}
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attach_context_savings(response, original_tokens=original_tokens)
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return response
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finally:
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store.close()
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# ---------------------------------------------------------------------------
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# Tool 3: query_graph
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# ---------------------------------------------------------------------------
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def query_graph(
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pattern: str,
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target: str,
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repo_root: str | None = None,
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detail_level: str = "standard",
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) -> dict[str, Any]:
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"""Run a predefined graph query.
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Args:
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pattern: Query pattern. One of: callers_of, callees_of, imports_of,
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importers_of, children_of, tests_for, inheritors_of, file_summary.
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target: The node name, qualified name, or file path to query about.
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repo_root: Repository root path. Auto-detected if omitted.
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detail_level: "standard" (full output) or "minimal" (summary only).
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Returns:
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Matching nodes and edges for the query.
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"""
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store, root = _get_store(repo_root)
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try:
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if pattern not in _QUERY_PATTERNS:
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return {
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"status": "error",
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"error": (
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f"Unknown pattern '{pattern}'. "
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f"Available: {list(_QUERY_PATTERNS.keys())}"
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),
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}
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results: list[dict] = []
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edges_out: list[dict] = []
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# For callers_of, skip common builtins early (bare names only)
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# "Who calls .map()?" returns hundreds of useless hits.
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# Qualified names (e.g. "utils.py::map") bypass this filter.
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if (
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pattern == "callers_of"
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and target in _BUILTIN_CALL_NAMES
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and "::" not in target
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):
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return {
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"status": "ok", "pattern": pattern, "target": target,
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"description": _QUERY_PATTERNS[pattern],
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"summary": (
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f"'{target}' is a common builtin "
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"— callers_of skipped to avoid noise."
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),
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"results": [], "edges": [],
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}
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# Resolve target - try as-is, then as absolute path, then search.
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# file_summary targets are paths, so skip broad node search.
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node = None
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if pattern != "file_summary":
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node = store.get_node(target)
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if not node:
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abs_target = str(root / target)
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node = store.get_node(abs_target)
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if not node:
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# Search by name
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candidates = store.search_nodes(target, limit=5)
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if len(candidates) == 1:
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node = candidates[0]
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target = node.qualified_name
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elif len(candidates) > 1:
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return {
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"status": "ambiguous",
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"summary": (
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f"Multiple matches for '{target}'. "
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"Please use a qualified name."
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),
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"candidates": [node_to_dict(c) for c in candidates],
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}
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if not node and pattern != "file_summary":
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return {
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"status": "not_found",
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"summary": f"No node found matching '{target}'.",
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}
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qn = node.qualified_name if node else target
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if pattern == "callers_of":
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seen_sources: set[str] = set()
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for e in store.get_edges_by_target(qn):
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if e.kind == "CALLS":
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if e.source_qualified not in seen_sources:
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seen_sources.add(e.source_qualified)
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caller = store.get_node(e.source_qualified)
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if caller:
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results.append(node_to_dict(caller))
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edges_out.append(edge_to_dict(e))
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# Fallback: CALLS edges store unqualified target names
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# (e.g. "generateTestCode") while qn is fully qualified
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# (e.g. "file.ts::generateTestCode"). Search by plain name too.
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if node:
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for e in store.search_edges_by_target_name(node.name):
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if e.source_qualified not in seen_sources:
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seen_sources.add(e.source_qualified)
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caller = store.get_node(e.source_qualified)
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if caller:
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results.append(node_to_dict(caller))
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edges_out.append(edge_to_dict(e))
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elif pattern == "callees_of":
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seen_targets: set[str] = set()
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for e in store.get_edges_by_source(qn):
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if e.kind == "CALLS":
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if e.target_qualified not in seen_targets:
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seen_targets.add(e.target_qualified)
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callee = store.get_node(e.target_qualified)
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if callee:
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results.append(node_to_dict(callee))
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elif "::" not in e.target_qualified:
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results.append({
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"kind": "Function",
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"name": e.target_qualified,
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"qualified_name": e.target_qualified,
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})
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edges_out.append(edge_to_dict(e))
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elif pattern == "imports_of":
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for e in store.get_edges_by_source(qn):
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if e.kind == "IMPORTS_FROM":
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results.append({"import_target": e.target_qualified})
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edges_out.append(edge_to_dict(e))
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elif pattern == "importers_of":
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# Find edges where target matches this file.
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# Use resolve() to canonicalize the path, matching how
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# _resolve_module_to_file stores edge targets.
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abs_target = (
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str((root / target).resolve()) if node is None
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else node.file_path
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)
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for e in store.get_edges_by_target(abs_target):
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if e.kind == "IMPORTS_FROM":
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results.append({
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"importer": e.source_qualified,
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"file": e.file_path,
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})
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edges_out.append(edge_to_dict(e))
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elif pattern == "children_of":
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for e in store.get_edges_by_source(qn):
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if e.kind == "CONTAINS":
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child = store.get_node(e.target_qualified)
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if child:
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results.append(node_to_dict(child))
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elif pattern == "tests_for":
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for e in store.get_edges_by_target(qn):
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if e.kind == "TESTED_BY":
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test = store.get_node(e.source_qualified)
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if test:
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results.append(node_to_dict(test))
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# Also search by naming convention
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name = node.name if node else target
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test_nodes = store.search_nodes(f"test_{name}", limit=10)
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test_nodes += store.search_nodes(f"Test{name}", limit=10)
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seen = {r.get("qualified_name") for r in results}
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for t in test_nodes:
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if t.qualified_name not in seen and t.is_test:
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results.append(node_to_dict(t))
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elif pattern == "inheritors_of":
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for e in store.get_edges_by_target(qn):
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if e.kind in ("INHERITS", "IMPLEMENTS"):
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child = store.get_node(e.source_qualified)
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if child:
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results.append(node_to_dict(child))
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edges_out.append(edge_to_dict(e))
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# Fallback: INHERITS/IMPLEMENTS edges store unqualified base names
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# (e.g. "Animal") while qn is fully qualified
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# (e.g. "sample.dart::Animal"). Search by plain name too. See: #87
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if not results and node:
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for kind in ("INHERITS", "IMPLEMENTS"):
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for e in store.search_edges_by_target_name(node.name, kind=kind):
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child = store.get_node(e.source_qualified)
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if child:
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results.append(node_to_dict(child))
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edges_out.append(edge_to_dict(e))
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elif pattern == "file_summary":
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graph_paths = _resolve_graph_file_paths(store, root, [target])
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for graph_path in graph_paths:
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for n in store.get_nodes_by_file(graph_path):
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results.append(node_to_dict(n))
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summary = (
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f"Found {len(results)} result(s) "
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f"for {pattern}('{target}')"
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)
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if detail_level == "minimal":
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minimal_results = [
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{
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k: r[k]
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for k in ("name", "kind", "file_path")
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if k in r
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}
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for r in results[:5]
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]
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return {
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"status": "ok",
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"pattern": pattern,
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"target": target,
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"description": _QUERY_PATTERNS[pattern],
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"summary": summary,
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"result_count": len(results),
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"results": minimal_results,
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}
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return {
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"status": "ok",
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"pattern": pattern,
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"target": target,
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"description": _QUERY_PATTERNS[pattern],
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"summary": summary,
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"results": results,
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"edges": edges_out,
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}
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finally:
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store.close()
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# ---------------------------------------------------------------------------
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# Tool 5: semantic_search_nodes
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# ---------------------------------------------------------------------------
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def semantic_search_nodes(
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query: str,
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kind: str | None = None,
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limit: int = 20,
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repo_root: str | None = None,
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context_files: list[str] | None = None,
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model: str | None = None,
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provider: str | None = None,
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detail_level: str = "standard",
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) -> dict[str, Any]:
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"""Search for nodes by name, keyword, or semantic similarity.
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Uses hybrid search (FTS5 BM25 + vector embeddings merged via Reciprocal
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Rank Fusion) as the primary search path, with graceful fallback to
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keyword matching.
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Args:
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query: Search string to match against node names and qualified names.
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kind: Optional filter by node kind (File, Class, Function, Type, Test).
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limit: Maximum results to return (default: 20).
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repo_root: Repository root path. Auto-detected if omitted.
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context_files: Optional list of file paths. Nodes in these files
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receive a relevance boost.
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detail_level: "standard" (full output) or "minimal" (summary only).
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Returns:
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Ranked list of matching nodes.
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"""
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store, root = _get_store(repo_root)
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try:
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results = hybrid_search(
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store, query, kind=kind, limit=limit, context_files=context_files,
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model=model, provider=provider,
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)
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search_mode = "hybrid"
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if not results:
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search_mode = "keyword"
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summary = f"Found {len(results)} node(s) matching '{query}'" + (
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f" (kind={kind})" if kind else ""
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)
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if detail_level == "minimal":
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minimal_results = [
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{
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k: r[k]
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for k in ("name", "kind", "file_path", "score")
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if k in r
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}
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for r in results[:5]
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]
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return {
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"status": "ok",
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"query": query,
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"search_mode": search_mode,
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"summary": summary,
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"results": minimal_results,
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}
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result: dict[str, object] = {
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"status": "ok",
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"query": query,
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"search_mode": search_mode,
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"summary": summary,
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"results": results,
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}
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result["_hints"] = generate_hints(
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"semantic_search_nodes", result, get_session()
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)
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return result
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finally:
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store.close()
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# ---------------------------------------------------------------------------
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# Tool 6: list_graph_stats
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# ---------------------------------------------------------------------------
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def list_graph_stats(repo_root: str | None = None) -> dict[str, Any]:
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"""Get aggregate statistics about the knowledge graph.
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Args:
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repo_root: Repository root path. Auto-detected if omitted.
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Returns:
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Total nodes, edges, breakdown by kind, languages, and last update time.
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"""
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store, root = _get_store(repo_root)
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try:
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stats = store.get_stats()
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summary_parts = [
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f"Graph statistics for {root.name}:",
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f" Files: {stats.files_count}",
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f" Total nodes: {stats.total_nodes}",
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f" Total edges: {stats.total_edges}",
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f" Languages: {', '.join(stats.languages) if stats.languages else 'none'}",
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f" Last updated: {stats.last_updated or 'never'}",
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"",
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"Nodes by kind:",
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]
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for kind, count in sorted(stats.nodes_by_kind.items()):
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summary_parts.append(f" {kind}: {count}")
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summary_parts.append("")
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summary_parts.append("Edges by kind:")
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for kind, count in sorted(stats.edges_by_kind.items()):
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summary_parts.append(f" {kind}: {count}")
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# Add embedding info if available
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emb_store = EmbeddingStore(get_db_path(root))
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try:
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emb_count = emb_store.count()
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summary_parts.append("")
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summary_parts.append(f"Embeddings: {emb_count} nodes embedded")
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if not emb_store.available:
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summary_parts.append(
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" (install sentence-transformers for semantic search)"
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)
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finally:
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emb_store.close()
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return {
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"status": "ok",
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"summary": "\n".join(summary_parts),
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"total_nodes": stats.total_nodes,
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"total_edges": stats.total_edges,
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"nodes_by_kind": stats.nodes_by_kind,
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"edges_by_kind": stats.edges_by_kind,
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"languages": stats.languages,
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"files_count": stats.files_count,
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"last_updated": stats.last_updated,
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"embeddings_count": emb_count,
|
|
}
|
|
finally:
|
|
store.close()
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Tool 9: find_large_functions
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def find_large_functions(
|
|
min_lines: int = 50,
|
|
kind: str | None = None,
|
|
file_path_pattern: str | None = None,
|
|
limit: int = 50,
|
|
repo_root: str | None = None,
|
|
) -> dict[str, Any]:
|
|
"""Find functions, classes, or files exceeding a line-count threshold.
|
|
|
|
Useful for identifying decomposition targets, code-quality audits,
|
|
and enforcing size limits during code review.
|
|
|
|
Args:
|
|
min_lines: Minimum line count to flag (default: 50).
|
|
kind: Filter by node kind: Function, Class, File, or Test.
|
|
file_path_pattern: Filter by file path substring (e.g. "components/").
|
|
limit: Maximum results (default: 50).
|
|
repo_root: Repository root path. Auto-detected if omitted.
|
|
|
|
Returns:
|
|
Oversized nodes with line counts, ordered largest first.
|
|
"""
|
|
store, root = _get_store(repo_root)
|
|
try:
|
|
nodes = store.get_nodes_by_size(
|
|
min_lines=min_lines,
|
|
kind=kind,
|
|
file_path_pattern=file_path_pattern,
|
|
limit=limit,
|
|
)
|
|
|
|
results = []
|
|
for n in nodes:
|
|
d = node_to_dict(n)
|
|
d["line_count"] = (
|
|
(n.line_end - n.line_start + 1)
|
|
if n.line_start and n.line_end
|
|
else 0
|
|
)
|
|
# Make file_path relative for readability
|
|
try:
|
|
d["relative_path"] = str(Path(n.file_path).relative_to(root))
|
|
except ValueError:
|
|
d["relative_path"] = n.file_path
|
|
results.append(d)
|
|
|
|
summary_parts = [
|
|
f"Found {len(results)} node(s) with >= {min_lines} lines"
|
|
+ (f" (kind={kind})" if kind else "")
|
|
+ (f" matching '{file_path_pattern}'" if file_path_pattern else "")
|
|
+ ":",
|
|
]
|
|
for r in results[:10]:
|
|
summary_parts.append(
|
|
f" {r['line_count']:>4} lines | {r['kind']:>8} | "
|
|
f"{r['name']} ({r['relative_path']}:{r['line_start']})"
|
|
)
|
|
if len(results) > 10:
|
|
summary_parts.append(f" ... and {len(results) - 10} more")
|
|
|
|
return {
|
|
"status": "ok",
|
|
"summary": "\n".join(summary_parts),
|
|
"total_found": len(results),
|
|
"min_lines": min_lines,
|
|
"results": results,
|
|
}
|
|
finally:
|
|
store.close()
|
|
|
|
|
|
# -------------------------------------------------------------------
|
|
# traverse_graph: free-form BFS / DFS traversal
|
|
# -------------------------------------------------------------------
|
|
|
|
|
|
def traverse_graph_func(
|
|
query: str,
|
|
mode: str = "bfs",
|
|
depth: int = 3,
|
|
token_budget: int = 2000,
|
|
repo_root: str | None = None,
|
|
) -> dict[str, Any]:
|
|
"""BFS/DFS traversal from best-matching node.
|
|
|
|
Args:
|
|
query: Search string to find the starting node.
|
|
mode: "bfs" (breadth-first) or "dfs" (depth-first).
|
|
depth: Max traversal depth (1-6). Default: 3.
|
|
token_budget: Approximate token limit for results.
|
|
repo_root: Repository root path.
|
|
"""
|
|
store, root = _get_store(repo_root)
|
|
try:
|
|
results = hybrid_search(store, query, limit=1)
|
|
if not results:
|
|
return {
|
|
"error": f"No node matching '{query}'",
|
|
"nodes": [],
|
|
}
|
|
|
|
start_qn = results[0]["qualified_name"]
|
|
depth = max(1, min(depth, 6))
|
|
|
|
# BFS / DFS traversal
|
|
visited: dict[str, int] = {} # qn -> depth
|
|
queue: list[tuple[str, int]] = [
|
|
(start_qn, 0),
|
|
]
|
|
traversal: list[dict] = []
|
|
approx_tokens = 0
|
|
|
|
while queue:
|
|
if mode == "bfs":
|
|
current_qn, cur_depth = queue.pop(0)
|
|
else:
|
|
current_qn, cur_depth = queue.pop()
|
|
|
|
if current_qn in visited:
|
|
continue
|
|
if cur_depth > depth:
|
|
continue
|
|
|
|
visited[current_qn] = cur_depth
|
|
node = store.get_node(current_qn)
|
|
if not node:
|
|
continue
|
|
|
|
entry = {
|
|
"name": _sanitize_name(node.name),
|
|
"qualified_name": node.qualified_name,
|
|
"kind": node.kind,
|
|
"file": node.file_path,
|
|
"depth": cur_depth,
|
|
}
|
|
approx_tokens += len(str(entry)) // 4
|
|
if approx_tokens > token_budget:
|
|
break
|
|
|
|
traversal.append(entry)
|
|
|
|
# Get neighbours
|
|
out_edges = store.get_edges_by_source(
|
|
current_qn
|
|
)
|
|
in_edges = store.get_edges_by_target(
|
|
current_qn
|
|
)
|
|
for e in out_edges:
|
|
tgt = e.target_qualified
|
|
if tgt not in visited:
|
|
queue.append((tgt, cur_depth + 1))
|
|
for e in in_edges:
|
|
src = e.source_qualified
|
|
if src not in visited:
|
|
queue.append((src, cur_depth + 1))
|
|
|
|
return {
|
|
"start_node": start_qn,
|
|
"mode": mode,
|
|
"max_depth": depth,
|
|
"nodes_visited": len(traversal),
|
|
"traversal": traversal,
|
|
"truncated": approx_tokens > token_budget,
|
|
"next_tool_suggestions": [
|
|
"query_graph callers_of"
|
|
" -- focused relationship query",
|
|
"get_impact_radius"
|
|
" -- blast radius analysis",
|
|
],
|
|
}
|
|
finally:
|
|
store.close()
|