update output
This commit is contained in:
+87
-85
@@ -5007,7 +5007,7 @@ def _build_enhanced_result(chunk_text: str, meta: dict, score: float, graph: Opt
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def _get_result_graph_context(meta: dict, graph: Optional[LocalGraph] = None) -> dict:
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"""Get relevant graph context for a search result with cross-file awareness."""
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"""Get MOST relevant graph context - optimized for LLM tokens."""
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if graph is None or graph.graph.number_of_nodes() == 0:
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return {}
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@@ -5032,61 +5032,68 @@ def _get_result_graph_context(meta: dict, graph: Optional[LocalGraph] = None) ->
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node_id = f"svelte::component::{file_path}::{entity_name}"
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if node_id and node_id in graph.graph:
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# Get direct relationships WITH file info
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neighbors = list(graph.neighbors(node_id))
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if neighbors:
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context['relationships'] = []
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context['cross_file_deps'] = [] # NEW: Track cross-file dependencies
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# PRIORITY 1: Cross-file dependencies (highest value for LLM)
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cross_file_rels = []
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incoming_cross_file = []
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for neighbor_id, neighbor_data in neighbors[:10]: # Show more neighbors
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# Outgoing cross-file relationships
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for neighbor_id, neighbor_data in graph.neighbors(node_id):
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neighbor_file = neighbor_data.get('file', '')
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if neighbor_file and neighbor_file != file_path:
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edge_data = graph.graph.edges[node_id, neighbor_id]
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rel_type = edge_data.get('type', 'related')
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neighbor_name = neighbor_data.get('name', neighbor_id.split('::')[-1])
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neighbor_file = neighbor_data.get('file', '')
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neighbor_type = neighbor_data.get('type', '')
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# Build compact relationship string with file info
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if neighbor_file and neighbor_file != file_path:
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# Cross-file relationship - show file
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rel_str = f"{rel_type}:{neighbor_name}@{neighbor_file}"
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context['cross_file_deps'].append(rel_str)
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# Compress representation based on relationship importance
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if rel_type in ['extends', 'implements', 'calls']:
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# High-value relationships - keep full info
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cross_file_rels.append(f"{rel_type}:{neighbor_type}:{neighbor_name}@{neighbor_file}")
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else:
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# Same-file relationship - omit file for brevity
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rel_str = f"{rel_type}:{neighbor_name}"
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# Medium-value - compress type
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cross_file_rels.append(f"{rel_type}:{neighbor_name}@{neighbor_file}")
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context['relationships'].append(rel_str)
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# NEW: Find reverse dependencies (what depends on THIS entity)
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incoming = []
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# PRIORITY 2: Reverse dependencies (what uses this entity)
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for predecessor in graph.graph.predecessors(node_id):
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pred_data = graph.graph.nodes[predecessor]
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pred_file = pred_data.get('file', '')
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pred_name = pred_data.get('name', '')
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edge_data = graph.graph.edges[predecessor, node_id]
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rel_type = edge_data.get('type', 'related')
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# Only show cross-file incoming dependencies
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# Only show cross-file incoming (same-file is less valuable)
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if pred_file and pred_file != file_path:
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incoming.append(f"{rel_type}:{pred_name}@{pred_file}")
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edge_data = graph.graph.edges[predecessor, node_id]
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rel_type = edge_data.get('type', 'related')
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if incoming:
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context['used_by'] = incoming[:10]
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if rel_type in ['calls', 'extends', 'implements']:
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incoming_cross_file.append(f"{rel_type}:{pred_name}@{pred_file}")
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# Get file-level context (same as before)
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file_node_id = f"file::{file_path}"
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if file_node_id in graph.graph:
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file_neighbors = list(graph.neighbors(file_node_id))
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if file_neighbors:
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context['file_entities'] = []
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for neighbor_id, neighbor_data in file_neighbors[:5]:
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neighbor_type = neighbor_data.get('type', '')
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neighbor_name = neighbor_data.get('name', '')
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if neighbor_type and neighbor_name and neighbor_name != entity_name:
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context['file_entities'].append(f"{neighbor_type}:{neighbor_name}")
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# Apply aggressive limits for token conservation
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if cross_file_rels:
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# Sort by relationship importance
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priority_order = {'extends': 0, 'implements': 1, 'calls': 2}
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cross_file_rels.sort(key=lambda x: priority_order.get(x.split(':')[0], 99))
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context['xfile'] = cross_file_rels[:6] # Only top 6 cross-file deps
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if incoming_cross_file:
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context['used_by'] = incoming_cross_file[:4] # Only top 4 reverse deps
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# PRIORITY 3: Critical same-file context (only if cross-file is sparse)
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if not context.get('xfile') and not context.get('used_by'):
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file_node_id = f"file::{file_path}"
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if file_node_id in graph.graph:
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file_entities = []
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for neighbor_id, neighbor_data in graph.neighbors(file_node_id):
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neighbor_type = neighbor_data.get('type', '')
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neighbor_name = neighbor_data.get('name', '')
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if (neighbor_type in ['class', 'struct', 'interface', 'component'] and
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neighbor_name != entity_name):
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file_entities.append(f"{neighbor_type}:{neighbor_name}")
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if file_entities:
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context['file'] = file_entities[:3] # Only top 3 same-file entities
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return context
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def _extract_docstring(chunk_text: str) -> str:
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"""Extract docstring or comments from chunk text."""
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lines = chunk_text.split('\n')
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@@ -5125,74 +5132,69 @@ def _extract_docstring(chunk_text: str) -> str:
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def _format_enhanced_results(results: List[Dict], query: str, result_type: str) -> str:
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"""Format enhanced results with dense LLM-optimized context."""
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"""Ultra-compact formatting optimized for LLM token usage."""
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if not results:
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return f"# {result_type.title()}: {query}\nNo results found.\n"
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lines = [
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f"# {result_type.title()}: {query}",
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f"results[{len(results)}]{{file,line,type,name,score,content,context}}:"
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f"Found {len(results)} results:"
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]
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for result in results:
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context_parts = []
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for i, result in enumerate(results, 1):
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# Extract core info
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file_path = result.get('file', '')
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line_num = result.get('line', '')
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entity_type = result.get('type', 'code')
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entity_name = result.get('name', '')
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score = result.get('score', 0.0)
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content = result.get('content', '')[:350] # Strict content limit
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# Graph context as dense key:value pairs
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graph_context = result.get('graph_context', {})
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# Cross-file dependencies (highest value)
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if graph_context.get('cross_file_deps'):
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deps = '|'.join(graph_context['cross_file_deps'][:8])
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context_parts.append(f"xfile:{deps}")
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# Build ultra-compact context string
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context_parts = []
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# All relationships
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if graph_context.get('relationships'):
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rels = '|'.join(graph_context['relationships'][:12])
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context_parts.append(f"rels:{rels}")
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# Cross-file deps (highest priority)
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if graph_context.get('xfile'):
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# Compress further: remove file paths, keep only key info
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compressed_deps = []
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for dep in graph_context['xfile'][:4]: # Only top 4
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parts = dep.split('@')[0] # Remove file path
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compressed_deps.append(parts)
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context_parts.append(f"deps:{'|'.join(compressed_deps)}")
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# Reverse dependencies
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# Reverse deps
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if graph_context.get('used_by'):
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used = '|'.join(graph_context['used_by'][:6])
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context_parts.append(f"used:{used}")
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compressed_used = []
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for used in graph_context['used_by'][:3]: # Only top 3
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parts = used.split('@')[0] # Remove file path
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compressed_used.append(parts)
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context_parts.append(f"used:{'|'.join(compressed_used)}")
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# File context
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if graph_context.get('file_entities'):
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entities = '|'.join(graph_context['file_entities'][:10])
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context_parts.append(f"file:{entities}")
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# Same-file context (lowest priority)
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if graph_context.get('file') and not context_parts:
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context_parts.append(f"file:{'|'.join(graph_context['file'][:2])}")
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# Docstring (if valuable)
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context_str = " ".join(context_parts)
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# Ultra-compact row format
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row = f"{i}. {file_path}:{line_num} {entity_type}:{entity_name} (score:{score:.2f})"
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if context_str:
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row += f" [{context_str}]"
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lines.append(row)
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lines.append(f" Content: {content}")
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# Add docstring only if highly relevant and short
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docstring = result.get('docstring', '')
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if docstring and len(docstring) > 10: # Only include substantial docs
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context_parts.append(f"doc:{docstring[:200]}")
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if docstring and len(docstring) < 100 and any(keyword in query.lower() for keyword in ['how', 'what', 'why', 'documentation']):
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lines.append(f" Docs: {docstring}")
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context_str = ";".join(context_parts)
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# Minimal row with essential info
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row = [
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result.get('file', ''),
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result.get('line', ''),
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result.get('type', 'code'),
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result.get('name', ''),
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f"{result.get('score', 0.0):.2f}",
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result.get('content', '')[:500], # More content tokens
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context_str
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]
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# Efficient escaping
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escaped_row = []
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for field in row:
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field_str = str(field)
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if ',' in field_str or ';' in field_str:
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escaped = field_str.replace('"', '\\"')
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escaped_row.append(f'"{escaped}"')
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else:
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escaped_row.append(field_str)
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lines.append(" " + ",".join(escaped_row))
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lines.append("") # Empty line between results
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return "\n".join(lines)
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def _extract_clean_content(chunk_text: str) -> str:
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"""Extract clean content from chunk text by removing duplicate metadata."""
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lines = chunk_text.split('\n')
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