import os import signal import json from collections import defaultdict, deque import re import subprocess import logging from pathlib import Path from typing import List, Dict, Tuple, Any, Optional from threading import RLock from contextlib import contextmanager from functools import lru_cache import time import hashlib import pathspec import requests import numpy as np from mcp.server.fastmcp import FastMCP from langchain_ollama import OllamaEmbeddings from langchain_community.vectorstores import Chroma from rank_bm25 import BM25Okapi import javalang import sqlparse import sqlglot from enhanced_toon import EnhancedToon from graph.graph import LocalGraph os.environ["ANONYMIZED_TELEMETRY"] = "False" # ----------------------------- # Configuration # ----------------------------- logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", handlers=[ logging.FileHandler("mcp_codebase.log"), # Log to file logging.StreamHandler() # Also log to console ] ) logger = logging.getLogger("rag-mcp") OLLAMA_BASE_URL = "http://127.0.0.1:11434" os.environ["OLLAMA_HOST"] = OLLAMA_BASE_URL os.environ["OLLAMA_API_BASE"] = OLLAMA_BASE_URL CODEBASE_PATH = Path("./working_repo") CODEBASE_PATH.mkdir(exist_ok=True) VECTOR_DB_PATH = Path(os.environ.get("VECTOR_DB_PATH", "./chroma_db")) BM25_INDEX_PATH = Path(os.environ.get("BM25_INDEX_PATH", "./bm25_index.json")) RAGIGNORE_PATH = CODEBASE_PATH / ".ragignore" # Models / behavior EMBEDDING_MODEL = "bge-m3" # Ollama local embedding model RERANKER_MODEL_OLLAMA = "dengcao/Qwen3-Reranker-8B:Q4_K_M" # prompt-based reranker via Ollama ENABLE_RERANK = True # toggle reranking RERANK_TOP_N = 10 # Hybrid weights (vector first: 80%, BM25: 20%) VECTOR_WEIGHT = 0.8 BM25_WEIGHT = 0.2 # Embedding batch configuration EMBEDDING_BATCH_SIZE = 200 # Contextual weighting configuration CONTEXTUAL_WEIGHTS = { # SQL weights "sql_schema": 1.3, # CREATE TABLE, ALTER TABLE, schema definitions "sql_function": 1.2, # Function/procedure definitions "sql_view": 1.15, # VIEW definitions "sql_index": 1.1, # INDEX definitions # Python weights "python_class": 1.25, # Class definitions (enhanced) "python_function": 1.15, # Function definitions "python_method": 1.2, # Method definitions (enhanced) "python_async": 1.1, # Async functions # Go weights "go_type": 1.25, # Type definitions (struct, interface) "go_function": 1.15, # Function definitions "go_method": 1.2, # Method definitions (enhanced) "go_struct": 1.3, # Struct definitions (enhanced) # Java weights "java_class": 1.3, # Class definitions (enhanced) "java_interface": 1.25, # Interface definitions (enhanced) "java_method": 1.2, # Method definitions (enhanced) "java_constructor": 1.15, # Constructor definitions (enhanced) "java_enum": 1.2, # Enum definitions (enhanced) "java_field": 1.1, # Field definitions (enhanced) # Rust weights (new) "rust_struct": 1.3, # Struct definitions "rust_enum": 1.25, # Enum definitions "rust_trait": 1.35, # Trait definitions (very important in Rust) "rust_impl": 1.2, # Implementation blocks "rust_function": 1.15, # Function definitions "rust_method": 1.2, # Method definitions "rust_async": 1.1, # Async functions "rust_unsafe": 1.15, # Unsafe blocks/functions # TypeScript/JavaScript weights (for Svelte scripts) "typescript_interface": 1.25, # Interface definitions "typescript_class": 1.2, # Class definitions "typescript_function": 1.15, # Function definitions "typescript_type": 1.2, # Type definitions # Svelte weights (new) "svelte_component": 1.4, # Component definitions (very important) "svelte_script": 1.1, # Script blocks "svelte_style": 1.05, # Style blocks "svelte_markup": 1.15, # Markup/template "svelte_prop": 1.25, # Component props (enhanced) "svelte_reactive": 1.2, # Reactive declarations # Default weight for unspecified types "default": 1.0 } SUPPORTED_EXTENSIONS = { ".py", ".go", ".rs", ".ts", ".tsx", ".js", ".jsx", ".svelte", ".sql", ".pgsql", ".sh", ".bash", ".zsh", ".rb", ".java", ".cpp", ".c", ".h", ".hpp" } # MCP mcp = FastMCP("codebase-rag") # Global state vectorstore: Optional[Chroma] = None bm25: Optional[BM25Okapi] = None bm25_corpus: List[str] = [] chunks_metadata: List[Dict[str, Any]] = [] embeddings: Optional[OllamaEmbeddings] = None _startup_lock = RLock() index_build_time: float = 0.0 # ----------------------------- # Thread-Safe Context Manager # ----------------------------- @contextmanager def safe_search(): """Thread-safe context manager for search operations.""" with _startup_lock: if vectorstore is None or bm25 is None: raise RuntimeError("Indexes not ready. Please wait for initialization or rebuild.") yield # ----------------------------- # File Hashing for Cache Invalidation # ----------------------------- def get_file_hash(filepath: Path) -> str: """Generate hash of file content + mtime for cache key.""" try: stat = filepath.stat() content_sample = filepath.read_bytes()[:1024] # First 1KB for speed hash_input = f"{stat.st_mtime}:{stat.st_size}:{content_sample}".encode() return hashlib.md5(hash_input).hexdigest() except Exception: return "" # ----------------------------- # Contextual Weight Calculator # ----------------------------- def calculate_contextual_weight(metadata: Dict[str, Any]) -> float: """ Calculate contextual weight multiplier based on chunk metadata. Higher weights for schema-defining statements, function definitions, etc. """ language = metadata.get("language", "").lower() chunk_type = metadata.get("type", "").lower() block_type = metadata.get("block_type", "").lower() # SQL-specific weights if language == "sql": if chunk_type in ("create", "alter", "create table", "alter table"): return CONTEXTUAL_WEIGHTS.get("sql_schema", 1.0) elif chunk_type in ("function", "procedure", "create function", "create procedure"): return CONTEXTUAL_WEIGHTS.get("sql_function", 1.0) elif chunk_type in ("view", "create view"): return CONTEXTUAL_WEIGHTS.get("sql_view", 1.0) elif chunk_type in ("index", "create index"): return CONTEXTUAL_WEIGHTS.get("sql_index", 1.0) # Python-specific weights (enhanced) elif language == "python": if chunk_type == "classdef": return CONTEXTUAL_WEIGHTS.get("python_class", 1.0) elif chunk_type in ("functiondef", "asyncfunctiondef"): if metadata.get("is_method"): return CONTEXTUAL_WEIGHTS.get("python_method", 1.0) elif chunk_type == "asyncfunctiondef" or metadata.get("is_async"): return CONTEXTUAL_WEIGHTS.get("python_async", 1.0) else: return CONTEXTUAL_WEIGHTS.get("python_function", 1.0) # Go-specific weights (enhanced) elif language == "go": if chunk_type in ("struct", "interface"): return CONTEXTUAL_WEIGHTS.get("go_struct", 1.0) elif chunk_type == "type": return CONTEXTUAL_WEIGHTS.get("go_type", 1.0) elif chunk_type == "method": return CONTEXTUAL_WEIGHTS.get("go_method", 1.0) elif chunk_type == "func": return CONTEXTUAL_WEIGHTS.get("go_function", 1.0) # Java-specific weights (enhanced) elif language == "java": if chunk_type == "class": return CONTEXTUAL_WEIGHTS.get("java_class", 1.0) elif chunk_type == "interface": return CONTEXTUAL_WEIGHTS.get("java_interface", 1.0) elif chunk_type == "method": return CONTEXTUAL_WEIGHTS.get("java_method", 1.0) elif chunk_type == "constructor": return CONTEXTUAL_WEIGHTS.get("java_constructor", 1.0) elif chunk_type == "enum": return CONTEXTUAL_WEIGHTS.get("java_enum", 1.0) elif chunk_type == "field": return CONTEXTUAL_WEIGHTS.get("java_field", 1.0) # Rust-specific weights (new) elif language == "rust": if chunk_type == "struct": return CONTEXTUAL_WEIGHTS.get("rust_struct", 1.0) elif chunk_type == "enum": return CONTEXTUAL_WEIGHTS.get("rust_enum", 1.0) elif chunk_type == "trait": return CONTEXTUAL_WEIGHTS.get("rust_trait", 1.0) elif chunk_type == "impl": return CONTEXTUAL_WEIGHTS.get("rust_impl", 1.0) elif chunk_type == "function": if metadata.get("is_async"): return CONTEXTUAL_WEIGHTS.get("rust_async", 1.0) elif metadata.get("is_unsafe"): return CONTEXTUAL_WEIGHTS.get("rust_unsafe", 1.0) elif metadata.get("is_method", False): return CONTEXTUAL_WEIGHTS.get("rust_method", 1.0) else: return CONTEXTUAL_WEIGHTS.get("rust_function", 1.0) # TypeScript/JavaScript weights (for Svelte scripts) elif language == "typescript": if chunk_type == "interface": return CONTEXTUAL_WEIGHTS.get("typescript_interface", 1.0) elif chunk_type == "class": return CONTEXTUAL_WEIGHTS.get("typescript_class", 1.0) elif chunk_type == "function": return CONTEXTUAL_WEIGHTS.get("typescript_function", 1.0) elif chunk_type == "type": return CONTEXTUAL_WEIGHTS.get("typescript_type", 1.0) # Svelte-specific weights (new) elif language == "svelte": if chunk_type == "component": return CONTEXTUAL_WEIGHTS.get("svelte_component", 1.0) elif chunk_type == "prop": return CONTEXTUAL_WEIGHTS.get("svelte_prop", 1.0) elif chunk_type == "reactive": return CONTEXTUAL_WEIGHTS.get("svelte_reactive", 1.0) elif block_type == "markup": return CONTEXTUAL_WEIGHTS.get("svelte_markup", 1.0) elif block_type == "script": return CONTEXTUAL_WEIGHTS.get("svelte_script", 1.0) elif block_type == "style": return CONTEXTUAL_WEIGHTS.get("svelte_style", 1.0) # CSS weights (for Svelte styles) elif language == "css": if block_type == "style": return CONTEXTUAL_WEIGHTS.get("svelte_style", 1.0) return CONTEXTUAL_WEIGHTS.get("default", 1.0) def apply_contextual_weights_to_embeddings(embeddings_list: List[List[float]], metadata_list: List[Dict[str, Any]]) -> List[List[float]]: """ Apply contextual weights to embedding vectors by scaling them. This biases the vector space without changing the embedding model. """ weighted_embeddings = [] if len(embeddings_list) != len(metadata_list): raise ValueError( f"Embedding/metadata mismatch: {len(embeddings_list)} embeddings vs {len(metadata_list)} metadata. " "This will break downstream systems (Chroma)." ) for embedding, metadata in zip(embeddings_list, metadata_list): weight = calculate_contextual_weight(metadata) # Convert to numpy for easier manipulation emb_array = np.array(embedding) # Scale the embedding vector by the weight # This effectively increases the magnitude, making it more "important" weighted_emb = emb_array * weight # Optionally normalize to maintain consistent vector magnitudes # Comment out if you want the raw weighted vectors norm = np.linalg.norm(weighted_emb) if norm > 0: weighted_emb = weighted_emb / norm * np.linalg.norm(emb_array) weighted_embeddings.append(weighted_emb.tolist()) if weight != 1.0: logger.debug(f"Applied weight {weight:.2f} to {metadata.get('file')}:{metadata.get('type')}") return weighted_embeddings # ----------------------------- # Utility: .ragignore # ----------------------------- def load_ragignore(base_path: Path) -> pathspec.PathSpec: if RAGIGNORE_PATH.exists(): with open(RAGIGNORE_PATH, "r", encoding="utf-8") as f: return pathspec.PathSpec.from_lines("gitwildmatch", f) default_patterns = [ ".git/", "node_modules/", "__pycache__/", "*.pyc", "*.so", "*.dll", "*.exe", "venv/", ".venv/", "dist/", "build/", "*.log", "*.tmp", ".DS_Store" ] return pathspec.PathSpec.from_lines("gitwildmatch", default_patterns) # ----------------------------- # Python AST chunking with caching # ----------------------------- @lru_cache(maxsize=1000) def parse_python_file_cached(filepath_str: str, file_hash: str) -> tuple: """Cached Python AST parsing with enhanced metadata for graph relationships.""" filepath = Path(filepath_str) chunks = [] try: code = filepath.read_text(encoding="utf-8") tree = ast.parse(code) lines = code.splitlines(keepends=True) # Track imports for module relationships imports = [] for node in ast.walk(tree): # Extract imports for module dependencies if isinstance(node, (ast.Import, ast.ImportFrom)): import_info = _extract_import_info(node) if import_info: imports.append(import_info) if isinstance(node, (ast.FunctionDef, ast.ClassDef, ast.AsyncFunctionDef)): docstring = ast.get_docstring(node) or "" start_line = node.lineno - 1 end_line = getattr(node, "end_lineno", start_line + 1) chunk_code = "".join(lines[start_line:end_line]) # Extract enhanced metadata metadata = { "file": str(filepath), "name": getattr(node, "name", ""), "type": type(node).__name__, "line": node.lineno, "language": "python", } # Add specific metadata based on node type if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): metadata.update(_extract_function_metadata(node, code)) elif isinstance(node, ast.ClassDef): metadata.update(_extract_class_metadata(node, code)) # Include imports in the context if imports: metadata["imports"] = imports chunk_text = f"File: {filepath}\nType: {type(node).__name__}\nName: {getattr(node,'name', '')}\nDocstring: {docstring}\n" # Add enhanced information to text if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): args = _format_arguments(node.args) returns = _extract_return_annotation(node) chunk_text += f"Arguments: {args}\n" if returns: chunk_text += f"Returns: {returns}\n" elif isinstance(node, ast.ClassDef): bases = [ast.unparse(base) for base in node.bases] if hasattr(ast, 'unparse') else [ast.dump(base) for base in node.bases] if bases: chunk_text += f"Base Classes: {', '.join(bases)}\n" chunk_text += f"\nCode:\n{chunk_code}" chunks.append({ "text": chunk_text, "metadata": metadata }) except Exception as e: logger.warning(f"parse_python_file: failed {filepath}: {e}") return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) def _extract_import_info(node): """Extract import information for module dependencies.""" if isinstance(node, ast.Import): return { "type": "import", "modules": [alias.name for alias in node.names], "level": 0 } elif isinstance(node, ast.ImportFrom): return { "type": "import_from", "module": node.module, "names": [alias.name for alias in node.names], "level": node.level } return None def _extract_function_metadata(node, code): """Extract enhanced metadata for functions/methods.""" metadata = {} # Extract arguments args = node.args arg_names = [arg.arg for arg in args.args] if args.vararg: arg_names.append(f"*{args.vararg.arg}") if args.kwarg: arg_names.append(f"**{args.kwarg.arg}") metadata["parameters"] = arg_names # Extract decorators decorators = [] for decorator in node.decorator_list: if isinstance(decorator, ast.Name): decorators.append(decorator.id) elif isinstance(decorator, ast.Attribute): decorators.append(ast.unparse(decorator) if hasattr(ast, 'unparse') else ast.dump(decorator)) elif isinstance(decorator, ast.Call): decorators.append(ast.unparse(decorator.func) if hasattr(ast, 'unparse') else ast.dump(decorator.func)) if decorators: metadata["decorators"] = decorators # Try to extract return annotation return_annotation = _extract_return_annotation(node) if return_annotation: metadata["return_type"] = return_annotation # Detect if it's a method (has 'self' or 'cls' as first argument) if arg_names and arg_names[0] in ('self', 'cls'): metadata["is_method"] = True # Try to find the containing class metadata["method_type"] = "classmethod" if arg_names[0] == 'cls' else "instancemethod" else: metadata["is_method"] = False return metadata def _extract_class_metadata(node, code): """Extract enhanced metadata for classes.""" metadata = {} # Extract base classes bases = [] for base in node.bases: if isinstance(base, ast.Name): bases.append(base.id) elif isinstance(base, ast.Attribute): bases.append(ast.unparse(base) if hasattr(ast, 'unparse') else ast.dump(base)) if bases: metadata["base_classes"] = bases # Extract decorators decorators = [] for decorator in node.decorator_list: if isinstance(decorator, ast.Name): decorators.append(decorator.id) if decorators: metadata["decorators"] = decorators # Try to detect class type from common patterns class_code = ast.unparse(node) if hasattr(ast, 'unparse') else ast.dump(node) if "metaclass" in class_code: metadata["has_metaclass"] = True # Look for common base class patterns for base in bases: if base in ("Exception", "BaseException"): metadata["class_type"] = "exception" break elif base in ("Enum", "IntEnum", "StrEnum"): metadata["class_type"] = "enum" break elif base in ("Model", "BaseModel"): metadata["class_type"] = "model" break return metadata def _format_arguments(args): """Format function arguments in a readable way.""" parts = [] # Positional arguments for arg in args.args: parts.append(arg.arg) # *args if args.vararg: parts.append(f"*{args.vararg.arg}") # Keyword-only arguments for arg in args.kwonlyargs: parts.append(arg.arg) # **kwargs if args.kwarg: parts.append(f"**{args.kwarg.arg}") return ", ".join(parts) def _extract_return_annotation(node): """Extract return type annotation if available.""" if hasattr(node, 'returns') and node.returns: if isinstance(node.returns, ast.Name): return node.returns.id elif isinstance(node.returns, ast.Attribute): return ast.unparse(node.returns) if hasattr(ast, 'unparse') else ast.dump(node.returns) elif isinstance(node.returns, ast.Subscript): return ast.unparse(node.returns) if hasattr(ast, 'unparse') else ast.dump(node.returns) return None def parse_python_file(filepath: Path) -> List[Dict]: """Parse Python file with caching.""" file_hash = get_file_hash(filepath) cached_result = parse_python_file_cached(str(filepath), file_hash) return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] @lru_cache(maxsize=1000) def parse_sql_file_cached(filepath_str: str, file_hash: str) -> tuple: """Cached SQL parsing. Returns tuple for hashability.""" filepath = Path(filepath_str) try: sql_code = filepath.read_text(encoding="utf-8") except Exception as e: logger.warning(f"parse_sql_file: cannot read {filepath}: {e}") return tuple() chunks: List[Dict[str, Any]] = [] statements = [s for s in sqlparse.split(sql_code) if s and s.strip()] lines = sql_code.splitlines() search_pos = 0 for stmt in statements: start_idx = sql_code.find(stmt, search_pos) if start_idx == -1: start_idx = sql_code.find(stmt) if start_idx == -1: start_line = len(lines) else: start_line = sql_code[:start_idx].count("\n") + 1 search_pos = start_idx + len(stmt) leading_comments = include_leading_comments(lines, start_line) stmt_clean = stmt.strip() meta: Dict[str, Any] = { "file": str(filepath), "name": None, "type": None, "line": start_line, "language": "sql" } try: # Special handling for CREATE TYPE ENUM (PostgreSQL) enum_match = re.match( r'CREATE\s+TYPE\s+(\w+)\s+AS\s+ENUM\s*\((.*?)\)', stmt_clean, re.IGNORECASE | re.DOTALL ) if enum_match: enum_name = enum_match.group(1) enum_values = enum_match.group(2) meta["type"] = "create type enum" meta["name"] = enum_name meta["enum_values"] = ",".join([v.strip().strip("'\"") for v in enum_values.split(",") if v.strip()]) chunk_text = f"File: {filepath}\nType: {meta['type']}\nName: {meta['name']}\n" if leading_comments: chunk_text += f"\nComments:\n{leading_comments}\n\n" chunk_text += f"SQL:\n{stmt_clean}" chunks.append({"text": chunk_text, "metadata": meta}) continue # Special handling for CREATE EXTENSION extension_match = re.match( r'CREATE\s+EXTENSION\s+(?:IF\s+NOT\s+EXISTS\s+)?(\w+)', stmt_clean, re.IGNORECASE ) if extension_match: extension_name = extension_match.group(1) meta["type"] = "create extension" meta["name"] = extension_name chunk_text = f"File: {filepath}\nType: {meta['type']}\nName: {meta['name']}\n" if leading_comments: chunk_text += f"\nComments:\n{leading_comments}\n\n" chunk_text += f"SQL:\n{stmt_clean}" chunks.append({"text": chunk_text, "metadata": meta}) continue # Special handling for CREATE TRIGGER trigger_match = re.match( r'CREATE\s+(?:OR\s+REPLACE\s+)?TRIGGER\s+(\w+)', stmt_clean, re.IGNORECASE ) if trigger_match: trigger_name = trigger_match.group(1) meta["type"] = "create trigger" meta["name"] = trigger_name chunk_text = f"File: {filepath}\nType: {meta['type']}\nName: {meta['name']}\n" if leading_comments: chunk_text += f"\nComments:\n{leading_comments}\n\n" chunk_text += f"SQL:\n{stmt_clean}" chunks.append({"text": chunk_text, "metadata": meta}) continue # Try sqlglot for standard SQL parsed = sqlglot.parse_one(stmt_clean, read="postgres") stmt_type = getattr(parsed, "key", None) or parsed.token_type if hasattr(parsed, "token_type") else None meta["type"] = str(stmt_type).lower() if stmt_type else "statement" tables = [t.this for t in parsed.find_all(sqlglot.exp.Table)] meta["tables"] = ",".join([str(t) for t in tables if isinstance(t, str)]) ctes = [] for cte in parsed.find_all(sqlglot.exp.CTE): try: alias = cte.alias_or_name if alias: ctes.append(alias) except Exception: pass meta["ctes"] = ",".join(ctes) funcs = [] for f in parsed.find_all(sqlglot.exp.Func): try: name = f.name if name: funcs.append(name) except Exception: pass meta["functions"] = ",".join(funcs) if meta.get("tables") and meta["tables"]: meta["name"] = meta["tables"].split(",")[0] elif ctes: meta["name"] = ctes[0] elif funcs: meta["name"] = funcs[0] chunk_text = f"File: {filepath}\nType: {meta.get('type')}\nName: {meta.get('name')}\n" if leading_comments: chunk_text += f"\nComments:\n{leading_comments}\n\n" chunk_text += f"SQL:\n{stmt_clean}" chunks.append({"text": chunk_text, "metadata": meta}) continue except Exception: pass # Fallback: simple heuristics if sqlglot fails try: parsed_tok = sqlparse.parse(stmt_clean)[0] first_token = parsed_tok.token_first(skip_cm=True) stmt_type = first_token.value.upper() if first_token else "UNKNOWN" except Exception: stmt_type = "UNKNOWN" meta["type"] = stmt_type.lower() chunk_text = f"File: {filepath}\nType: {meta['type']}\nName: {meta.get('name')}\n" if leading_comments: chunk_text += f"\nComments:\n{leading_comments}\n\n" chunk_text += f"SQL:\n{stmt_clean}" chunks.append({"text": chunk_text, "metadata": meta}) return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) def parse_sql_file(filepath: Path) -> List[Dict[str, Any]]: """Parse SQL file with caching.""" file_hash = get_file_hash(filepath) cached_result = parse_sql_file_cached(str(filepath), file_hash) return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] def include_leading_comments(lines: List[str], stmt_start_line: int, max_context_lines: int = 8) -> str: """ Collect contiguous comment lines immediately above stmt_start_line (1-based). Supports single-line comments (--), and block comments (/* ... */). Returns a string containing the comment block (no leading comment markers). """ comments: List[str] = [] idx = stmt_start_line - 2 # convert to 0-based index of the line above the statement collected = 0 # Collect single-line comments (-- ...) and blank lines (include blanks between comments) while idx >= 0 and collected < max_context_lines: raw = lines[idx] stripped = raw.strip() if stripped.startswith("--"): # remove leading -- and optional space comments.insert(0, stripped[2:].lstrip()) idx -= 1 collected += 1 continue # block comment end if stripped.endswith("*/"): # gather entire block block_lines = [] while idx >= 0: block_line = lines[idx].rstrip() block_lines.insert(0, block_line) if block_line.strip().startswith("/*"): break idx -= 1 # remove /* and */ markers and join cleaned = [] for bl in block_lines: s = bl.strip() if s.startswith("/*"): s = s[2:].lstrip() if s.endswith("*/"): s = s[:-2].rstrip() cleaned.append(s) comments = cleaned + comments break # blank line - allow if we already have comments (so comments may be separated by one blank) if stripped == "": if comments: comments.insert(0, "") idx -= 1 collected += 1 continue else: break # otherwise stop when non-comment found break # join into paragraph if comments: # remove possible leading/trailing empty lines while comments and comments[0] == "": comments.pop(0) while comments and comments[-1] == "": comments.pop() return "\n".join(comments).strip() return "" # ----------------------------- # Go AST via helper binary with caching # ----------------------------- # Replace the parse_go_file_cached function in mcp_codebase.py (around line 685) # The issue is that the Go binary outputs lowercase JSON keys, but Python expects uppercase @lru_cache(maxsize=1000) def parse_go_file_cached(filepath_str: str, file_hash: str) -> tuple: """Cached Go AST parsing with enhanced metadata for graph relationships.""" filepath = Path(filepath_str) chunks = [] helper = Path("./tools/parse_go_ast") if helper.exists(): try: proc = subprocess.run( [str(helper), str(filepath)], capture_output=True, text=True, check=True, timeout=20 ) decls = json.loads(proc.stdout) lines = filepath.read_text(encoding="utf-8").splitlines(keepends=True) for d in decls: # Use lowercase keys (matching the Go JSON output) start = max(0, d.get("start_line", 1) - 1) end = d.get("end_line", start + 1) name = d.get("name", "") typ = d.get("type", "") doc_comment = d.get("doc_comment", "") receiver = d.get("receiver", "") fields = d.get("fields", []) methods = d.get("methods", []) # Skip package declarations if typ == "package": continue # Build display type display_type = typ if receiver: display_type = f"method ({receiver})" chunk_code = "".join(lines[start:end]) chunk_text = f"File: {filepath}\nType: {display_type}\nName: {name}\n" if doc_comment: chunk_text += f"Doc:\n{doc_comment}\n\n" if fields: chunk_text += f"Fields: {', '.join(fields)}\n" if methods: chunk_text += f"Methods: {', '.join(methods)}\n" chunk_text += f"Code:\n{chunk_code}" # Build comprehensive metadata metadata = { "file": str(filepath), "name": name, "type": typ, "receiver": receiver, "line": start + 1, "language": "go", } # Enhanced metadata for graph relationships if fields: metadata["fields"] = fields if methods: metadata["interface_methods"] = methods if receiver: metadata["receiver_type"] = receiver # Extract receiver type without pointer for type matching receiver_base = receiver.replace('*', '') metadata["receiver_base_type"] = receiver_base chunks.append({ "text": chunk_text, "metadata": metadata }) if chunks: # If helper worked, return its results return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) except subprocess.CalledProcessError as e: logger.warning(f"parse_go_ast failed for {filepath}: {e.stderr}; falling back to regex") except json.JSONDecodeError as e: logger.warning(f"parse_go_ast output invalid JSON for {filepath}: {e}; falling back to regex") except Exception as e: logger.warning(f"parse_go_file helper failed for {filepath}: {e}; falling back to regex") # Fallback: Use regex-based parsing try: content = filepath.read_text(encoding="utf-8") lines = content.splitlines(keepends=True) # Pattern to match Go function declarations func_pattern = re.compile( r'^func\s+(?:\([^)]+\)\s+)?(\w+)\s*\([^)]*\)(?:\s*\([^)]*\)|\s+[\w\[\].*]+)?\s*\{', re.MULTILINE ) for match in func_pattern.finditer(content): func_name = match.group(1) start_pos = match.start() start_line = content[:start_pos].count('\n') end_line = find_brace_block_end_go(lines, start_line) func_code = "".join(lines[start_line:end_line + 1]) comments = extract_go_comments(lines, start_line) # Try to extract receiver type from function signature receiver_type = "" func_sig = content[start_pos:match.end()] receiver_match = re.search(r'func\s*\(([^)]+)\)', func_sig) if receiver_match: receiver_part = receiver_match.group(1) # Extract type from receiver (e.g., "u *User" -> "*User") receiver_type_match = re.search(r'\*?(\w+)', receiver_part.split()[-1] if ' ' in receiver_part else receiver_part) if receiver_type_match: receiver_type = receiver_type_match.group(0) chunk_text = f"File: {filepath}\nType: function\nName: {func_name}\n" if comments: chunk_text += f"Comments:\n{comments}\n\n" chunk_text += f"Code:\n{func_code}" metadata = { "file": str(filepath), "name": func_name, "type": "function", "line": start_line + 1, "language": "go", } # Receiver type for methods if receiver_type: metadata["receiver_type"] = receiver_type metadata["receiver_base_type"] = receiver_type.replace('*', '') metadata["type"] = "method" chunks.append({ "text": chunk_text, "metadata": metadata }) # Structs with field extraction struct_pattern = re.compile(r'^type\s+(\w+)\s+struct\s*\{', re.MULTILINE) for match in struct_pattern.finditer(content): struct_name = match.group(1) start_pos = match.start() start_line = content[:start_pos].count('\n') end_line = find_brace_block_end_go(lines, start_line) struct_code = "".join(lines[start_line:end_line + 1]) comments = extract_go_comments(lines, start_line) # Extract field names from struct field_names = [] struct_body_match = re.search(r'struct\s*\{([^}]+)\}', struct_code, re.DOTALL) if struct_body_match: field_lines = struct_body_match.group(1).split('\n') for line in field_lines: field_match = re.match(r'\s*(\w+)\s+', line.strip()) if field_match: field_names.append(field_match.group(1)) chunk_text = f"File: {filepath}\nType: struct\nName: {struct_name}\n" if comments: chunk_text += f"Comments:\n{comments}\n\n" if field_names: chunk_text += f"Fields: {', '.join(field_names)}\n" chunk_text += f"Code:\n{struct_code}" metadata = { "file": str(filepath), "name": struct_name, "type": "struct", "line": start_line + 1, "language": "go", } # Struct fields for graph relationships if field_names: metadata["fields"] = field_names chunks.append({ "text": chunk_text, "metadata": metadata }) # Interfaces with method extraction interface_pattern = re.compile(r'^type\s+(\w+)\s+interface\s*\{', re.MULTILINE) for match in interface_pattern.finditer(content): interface_name = match.group(1) start_pos = match.start() start_line = content[:start_pos].count('\n') end_line = find_brace_block_end_go(lines, start_line) interface_code = "".join(lines[start_line:end_line + 1]) comments = extract_go_comments(lines, start_line) # Extract method signatures from interface method_names = [] interface_body_match = re.search(r'interface\s*\{([^}]+)\}', interface_code, re.DOTALL) if interface_body_match: method_lines = interface_body_match.group(1).split('\n') for line in method_lines: method_match = re.match(r'\s*(\w+)\s*\([^)]*\)', line.strip()) if method_match: method_names.append(method_match.group(1)) chunk_text = f"File: {filepath}\nType: interface\nName: {interface_name}\n" if comments: chunk_text += f"Comments:\n{comments}\n\n" if method_names: chunk_text += f"Methods: {', '.join(method_names)}\n" chunk_text += f"Code:\n{interface_code}" metadata = { "file": str(filepath), "name": interface_name, "type": "interface", "line": start_line + 1, "language": "go", } # Interface methods for graph relationships if method_names: metadata["interface_methods"] = method_names chunks.append({ "text": chunk_text, "metadata": metadata }) except Exception as e: logger.warning(f"Regex-based Go parsing failed for {filepath}: {e}") return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) def find_brace_block_end_go(lines: List[str], start_line: int) -> int: """Find the end of a Go brace block starting at start_line.""" brace_count = 0 in_block = False for i in range(start_line, len(lines)): line = lines[i] for char in line: if char == '{': brace_count += 1 in_block = True elif char == '}': brace_count -= 1 if in_block and brace_count == 0: return i return len(lines) - 1 def extract_go_comments(lines: List[str], func_line_index: int, max_lines: int = 10) -> str: """Extract leading comments above a Go function/struct/interface.""" comments = [] idx = func_line_index - 1 while idx >= 0 and len(comments) < max_lines: line = lines[idx].strip() if line.startswith('//'): # Remove // and trim comments.insert(0, line[2:].strip()) idx -= 1 elif line.startswith('/*') or '*/' in line: # Block comment - collect entire block if '*/' in line and not line.startswith('/*'): # End of block, go backwards to find start block_lines = [line] idx -= 1 while idx >= 0: block_line = lines[idx].strip() block_lines.insert(0, block_line) if block_line.startswith('/*'): break idx -= 1 # Clean up block comment markers block_text = ' '.join(block_lines) block_text = block_text.replace('/*', '').replace('*/', '').replace('*', '').strip() comments.insert(0, block_text) idx -= 1 else: # Single line block comment clean = line.replace('/*', '').replace('*/', '').strip() comments.insert(0, clean) idx -= 1 elif line == '': # Allow one blank line if comments: idx -= 1 else: break else: break # Clean up while comments and comments[0] == '': comments.pop(0) while comments and comments[-1] == '': comments.pop(-1) return '\n'.join(comments) def parse_go_file(filepath: Path) -> List[Dict]: """Parse Go file with caching.""" file_hash = get_file_hash(filepath) cached_result = parse_go_file_cached(str(filepath), file_hash) if not cached_result: return chunk_text_file(filepath, "go") return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] # ----------------------------- # Java parsing (javalang + manual body extraction) with caching # ----------------------------- @lru_cache(maxsize=1000) def parse_java_file_cached(filepath_str: str) -> tuple: """Cached Java parsing. Returns tuple for hashability.""" filepath = Path(filepath_str) chunks = [] try: code = filepath.read_text(encoding="utf-8") lines = code.splitlines(keepends=True) tree = javalang.parse.parse(code) def find_brace_block_end(start_idx: int) -> int: brace_count = 0 for i in range(start_idx, len(lines)): for char in lines[i]: if char == '{': brace_count += 1 elif char == '}': brace_count -= 1 if brace_count == 0: return i return len(lines) - 1 for path, node in tree: if isinstance(node, javalang.tree.ClassDeclaration): start_line = node.position.line - 1 if node.position else 0 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) extends_class = None implements_interfaces = [] if node.extends: extends_class = node.extends.name if hasattr(node, 'implements') and node.implements: implements_interfaces = [impl.name for impl in node.implements] chunks.append({ "text": f"File: {filepath}\nType: Class\nName: {node.name}\nExtends: {extends_class or 'None'}\nImplements: {', '.join(implements_interfaces) or 'None'}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": node.name, "type": "class", "line": start_line + 1, "language": "java", "extends": extends_class, "implements": implements_interfaces } }) elif isinstance(node, javalang.tree.MethodDeclaration): if not hasattr(node, 'position') or not node.position: continue start_line = node.position.line - 1 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) # Find enclosing class name class_name = None for p in reversed(path): if isinstance(p, javalang.tree.ClassDeclaration): class_name = p.name break return_type = node.return_type.name if node.return_type else "void" parameters = [] if node.parameters: for param in node.parameters: param_type = param.type.name if param.type else "unknown" parameters.append(f"{param_type} {param.name}") chunks.append({ "text": f"File: {filepath}\nType: Method\nClass: {class_name or 'unknown'}\nName: {node.name}\nReturn: {return_type}\nParameters: {', '.join(parameters) or 'None'}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": node.name, "type": "method", "class": class_name, "line": start_line + 1, "language": "java", "return_type": return_type, "parameters": parameters } }) elif isinstance(node, javalang.tree.FieldDeclaration): # Fields may have multiple variable declarators if not hasattr(node, 'position') or not node.position: continue start_line = node.position.line - 1 # Field is usually one line, but include next if annotation end_line = start_line chunk_text = lines[start_line] doc = get_context_with_comments(filepath, start_line + 1) field_type = node.type.name if node.type else "unknown" for declarator in node.declarators: chunks.append({ "text": f"File: {filepath}\nType: Field\nName: {declarator.name}\nField Type: {field_type}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": declarator.name, "type": "field", "line": start_line + 1, "language": "java", "field_type": field_type } }) elif isinstance(node, javalang.tree.ConstructorDeclaration): if not hasattr(node, 'position') or not node.position: continue start_line = node.position.line - 1 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) class_name = None for p in reversed(path): if isinstance(p, javalang.tree.ClassDeclaration): class_name = p.name break chunks.append({ "text": f"File: {filepath}\nType: Constructor\nClass: {class_name or 'unknown'}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": f"{class_name} constructor", "type": "constructor", "class": class_name, "line": start_line + 1, "language": "java" } }) elif isinstance(node, javalang.tree.EnumDeclaration): start_line = node.position.line - 1 if node.position else 0 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) chunks.append({ "text": f"File: {filepath}\nType: Enum\nName: {node.name}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": node.name, "type": "enum", "line": start_line + 1, "language": "java" } }) elif isinstance(node, javalang.tree.InterfaceDeclaration): start_line = node.position.line - 1 if node.position else 0 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) chunks.append({ "text": f"File: {filepath}\nType: Interface\nName: {node.name}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": node.name, "type": "interface", "line": start_line + 1, "language": "java" } }) elif isinstance(node, javalang.tree.AnnotationDeclaration): start_line = node.position.line - 1 if node.position else 0 end_line = find_brace_block_end(start_line) chunk_text = "".join(lines[start_line:end_line + 1]) doc = get_context_with_comments(filepath, start_line + 1) chunks.append({ "text": f"File: {filepath}\nType: Annotation\nName: {node.name}\nDoc:\n{doc}\nCode:\n{chunk_text}", "metadata": { "file": str(filepath), "name": node.name, "type": "annotation", "line": start_line + 1, "language": "java" } }) except javalang.parser.JavaSyntaxError as e: logger.warning(f"Failed to parse Java file {filepath}: {e}") except Exception as e: logger.warning(f"Unexpected error parsing Java file {filepath}: {e}") return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) def parse_java_file(filepath: Path) -> List[Dict]: """Parse Java source file with caching.""" cached_result = parse_java_file_cached(str(filepath)) return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] # ----------------------------- # Svelte support with caching # ----------------------------- @lru_cache(maxsize=500) def parse_svelte_file_cached(filepath_str: str, file_hash: str) -> tuple: """Cached Svelte parsing with enhanced multi-language graph relationships.""" filepath = Path(filepath_str) chunks = [] try: text = filepath.read_text(encoding="utf-8") lines = text.splitlines(keepends=True) # Track component props and context for graph relationships component_props = [] component_context = [] # Extract component name from filename for graph relationships component_name = filepath.stem if component_name[0].islower(): component_name = component_name[0].upper() + component_name[1:] # PascalCase script_matches = list(re.finditer(r"]*)?>(.*?)", text, flags=re.DOTALL)) idx = 0 for m in script_matches: script_content = m.group(1) script_start_line = text[:m.start(1)].count("\n") + 1 script_attrs = m.group(0).split('>')[0] # Get ", "", text, flags=re.DOTALL) markup = re.sub(r"]*>.*?", "", markup, flags=re.DOTALL).strip() if markup: # Extract component usage and bindings from markup used_components = re.findall(r'<([A-Z][a-zA-Z]*)', markup) prop_bindings = re.findall(r'(\w+)={([^}]+)}', markup) event_handlers = re.findall(r'on:(\w+)=', markup) chunk_text = f"File: {filepath}\nType: markup\nComponent: {component_name}\n" if used_components: chunk_text += f"Uses Components: {', '.join(set(used_components))}\n" if prop_bindings: chunk_text += f"Prop Bindings: {', '.join([f'{prop}={val}' for prop, val in prop_bindings[:5]])}\n" if event_handlers: chunk_text += f"Event Handlers: {', '.join(set(event_handlers))}\n" chunk_text += f"Markup:\n{markup}" chunks.append({ "text": chunk_text, "metadata": { "file": str(filepath), "name": "markup", "type": "markup", "line": 1, "language": "svelte", "block_type": "markup", "component_name": component_name, "used_components": list(set(used_components)), "prop_bindings": [prop for prop, _ in prop_bindings], "event_handlers": list(set(event_handlers)), "component_props": component_props, # Props discovered in scripts "reactive_vars": component_context # Reactive context discovered } }) except Exception as e: logger.warning(f"parse_svelte_file failed {filepath}: {e}") return tuple() return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) def _make_script_chunk(filepath, script_content, start_line, block_type, component_name=None): """Enhanced script chunk creation with basic analysis.""" # Basic heuristic analysis for when TypeScript parser fails exports = re.findall(r'export\s+(?:let|const|function|class)\s+(\w+)', script_content) functions = re.findall(r'(?:export\s+)?function\s+(\w+)', script_content) imports = re.findall(r'import.*from\s+[\'"]([^\'"]+)[\'"]', script_content) chunk_text = f"File: {filepath}\nType: {block_type}\n" if component_name: chunk_text += f"Component: {component_name}\n" if exports: chunk_text += f"Exports: {', '.join(exports)}\n" if functions: chunk_text += f"Functions: {', '.join(functions)}\n" chunk_text += f"Code:\n{script_content}" metadata = { "file": str(filepath), "name": f"{block_type}_chunk", "type": block_type, "line": start_line, "language": "typescript" if block_type == "script" else "css", "block_type": block_type } if component_name: metadata["component_name"] = component_name if exports: metadata["exports"] = exports if imports: metadata["imports"] = imports return {"text": chunk_text, "metadata": metadata} def parse_svelte_file(filepath: Path) -> List[Dict]: """Parse Svelte file with caching.""" file_hash = get_file_hash(filepath) cached_result = parse_svelte_file_cached(str(filepath), file_hash) if not cached_result: return chunk_text_file(filepath, "svelte") return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] # ----------------------------- # Heuristic chunking for other languages # ----------------------------- def chunk_text_file(filepath: Path, language: str) -> List[Dict]: try: lines = filepath.read_text(encoding="utf-8").splitlines() except Exception as e: logger.warning(f"chunk_text_file read failed {filepath}: {e}") return [] chunks = [] current_chunk = [] start_line = 0 for i, ln in enumerate(lines): stripped = ln.strip() if stripped.startswith(("def ", "func ", "fn ", "class ", "struct ", "impl ", "export ", "const ", "let ")) or stripped.startswith(("//", "#", "--")) or stripped.endswith("{"): if current_chunk: text = "\n".join(current_chunk) chunks.append({ "text": f"File: {filepath}\nLanguage: {language}\n\n{text}", "metadata": {"file": str(filepath), "name": f"chunk_{len(chunks)}", "type": "code_block", "line": start_line + 1, "language": language} }) current_chunk = [] start_line = i current_chunk.append(ln) if current_chunk: text = "\n".join(current_chunk) chunks.append({ "text": f"File: {filepath}\nLanguage: {language}\n\n{text}", "metadata": {"file": str(filepath), "name": f"chunk_{len(chunks)}", "type": "code_block", "line": start_line + 1, "language": language} }) return chunks def parse_rust_file(filepath: Path) -> List[Dict]: """Parse Rust file with caching.""" file_hash = get_file_hash(filepath) cached_result = parse_rust_file_cached(str(filepath), file_hash) return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] @lru_cache(maxsize=1000) def parse_rust_file_cached(filepath_str: str, file_hash: str) -> tuple: """Cached Rust parsing with enhanced metadata for graph relationships. ALWAYS returns a tuple of (text, metadata_tuple) entries. If the Rust helper fails or produces no chunks, return a single fallback chunk containing the whole file text and minimal metadata. """ filepath = Path(filepath_str) chunks = [] logger.info(f"πŸ”§ Parsing Rust file: {filepath}") rust_helper = Path("./tools/src/target/release/rust_parser") if not rust_helper.is_file(): logger.warning(f"Rust parser binary not found: {rust_helper}. Using fallback whole-file chunk for {filepath}") code = filepath.read_text(encoding="utf-8") fallback_meta = { "file": str(filepath), "name": filepath.name, "type": "file", "line": 1, "language": "rust", } return ((code, tuple(fallback_meta.items())),) try: code = filepath.read_text(encoding="utf-8") lines = code.splitlines(keepends=True) proc = subprocess.run( [str(rust_helper), str(filepath)], capture_output=True, text=True, check=True, timeout=20 ) decls = json.loads(proc.stdout) logger.info(f" AST helper found {len(decls)} declarations") for i, d in enumerate(decls): start_line = max(0, d.get("start_line", 1) - 1) end_line = d.get("end_line", start_line + 1) # guard the end_line to be at least start_line+1 if end_line <= start_line: end_line = start_line + 1 # Make slice safe even if end_line > len(lines) chunk_code = "".join(lines[start_line:end_line]) metadata = { "file": str(filepath), "name": d.get("name", "") or filepath.name, "type": d.get("type_", "") or "unknown", "line": start_line + 1, "language": "rust", } # copy other fields defensively if d.get("visibility"): metadata["visibility"] = d.get("visibility") if d.get("is_async"): metadata["is_async"] = True if d.get("is_unsafe"): metadata["is_unsafe"] = True if d.get("generics"): metadata["generics"] = d.get("generics") if d.get("traits"): metadata["implements_traits"] = d.get("traits") if d.get("fields"): metadata["fields"] = d.get("fields") if d.get("methods"): metadata["methods"] = d.get("methods") if d.get("return_type"): metadata["return_type"] = d.get("return_type") if d.get("parameters"): metadata["parameters"] = d.get("parameters") logger.info(f" Declaration {i+1}: {metadata.get('type', 'unknown')} {metadata.get('name', 'unnamed')}") chunk_text = f"File: {filepath}\nType: {metadata.get('type')}\nName: {metadata.get('name')}\n" if metadata.get("visibility"): chunk_text += f"Visibility: {metadata.get('visibility')}\n" if metadata.get("is_async"): chunk_text += "Async: yes\n" if metadata.get("is_unsafe"): chunk_text += "Unsafe: yes\n" if metadata.get("generics"): chunk_text += f"Generics: {', '.join(metadata.get('generics', []))}\n" if metadata.get("implements_traits"): chunk_text += f"Implements: {', '.join(metadata.get('implements_traits', []))}\n" if metadata.get("fields"): chunk_text += f"Fields: {', '.join(metadata.get('fields', []))}\n" chunk_text += f"Code:\n{chunk_code}" chunks.append({ "text": chunk_text, "metadata": metadata }) except Exception as e: logger.warning(f"Rust AST helper failed for {filepath}: {e}") # If parser produced chunks, return them in the (text, metadata_tuple) format expected by parse_rust_file() if chunks: logger.info(f"βœ… Successfully parsed {len(chunks)} chunks from {filepath}") return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) # Fallback: emit a whole-file chunk to keep counts consistent logger.warning(f"❌ No chunks created from Rust AST for {filepath}. Emitting fallback whole-file chunk.") try: full_code = filepath.read_text(encoding="utf-8") except Exception: full_code = "" fallback_meta = { "file": str(filepath), "name": filepath.name, "type": "file", "line": 1, "language": "rust", } return ((full_code, tuple(fallback_meta.items())),) @lru_cache(maxsize=1000) def parse_shell_file_cached(filepath_str: str) -> tuple: """Cached shell script parsing. Returns tuple for hashability.""" filepath = Path(filepath_str) try: content = filepath.read_text(encoding="utf-8") chunks = [] lines = content.splitlines(keepends=True) patterns = [ r'^(\w+)\s*\(\s*\)\s*\{', r'^function\s+(\w+)\s*\{', r'^function\s+(\w+)\s*\(\s*\)\s*\{', r'^def\s+(\w+)\s*\(\s*\)\s*\{', ] for i, line in enumerate(lines): line_text = line.strip() for pattern in patterns: match = re.match(pattern, line_text) if match: func_name = match.group(1) start_line = i end_line = find_shell_function_end(lines, i) func_code = "".join(lines[start_line:end_line + 1]) # Extract leading comments leading_comments = extract_shell_comments(lines, start_line) chunk_text = f"File: {filepath}\nType: function\nName: {func_name}\n" if leading_comments: chunk_text += f"Comments:\n{leading_comments}\n\n" chunk_text += f"Code:\n{func_code}" chunks.append({ "text": chunk_text, "metadata": { "file": str(filepath), "name": func_name, "type": "function", "line": start_line + 1, "language": "shell", "has_braces": "{" in line_text } }) break # Move to next line after finding a match # If no functions found, fall back to basic chunking return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks) if chunks else tuple() except Exception as e: logger.warning(f"parse_shell_file failed {filepath}: {e}") return tuple() def parse_shell_file(filepath: Path) -> List[Dict]: """Enhanced shell script parsing with caching.""" cached_result = parse_shell_file_cached(str(filepath)) if not cached_result: return chunk_text_file(filepath, "shell") return [{"text": text, "metadata": dict(meta)} for text, meta in cached_result] def extract_shell_comments(lines: List[str], function_line_index: int, max_lines: int = 10) -> str: """Extract leading comments above a shell function.""" comments = [] idx = function_line_index - 1 while idx >= 0 and len(comments) < max_lines: line = lines[idx].strip() if line.startswith('#'): comments.insert(0, line[1:].strip()) # Remove the # and trim idx -= 1 elif line == '': # Allow one blank line between comments if comments: comments.insert(0, "") idx -= 1 else: break else: break # Clean up: remove leading/trailing empty lines while comments and comments[0] == "": comments.pop(0) while comments and comments[-1] == "": comments.pop(-1) return "\n".join(comments) def find_shell_function_end(lines: List[str], start_line_index: int) -> int: """ Find the end of a shell function by tracking brace nesting. Args: lines: List of file lines start_line_index: Starting line index (0-based) of the function Returns: Line index (0-based) where the function ends """ brace_count = 0 in_function = False for i in range(start_line_index, len(lines)): line = lines[i] # Count opening and closing braces for char in line: if char == '{': brace_count += 1 in_function = True elif char == '}': brace_count -= 1 # If we've returned to brace_count 0 and we were in a function, this is the end if in_function and brace_count == 0: return i # Special case: shell functions without braces (single line) if not in_function and i > start_line_index: # Look for function end patterns stripped = line.strip() if (stripped.startswith(('function ', 'def ')) or re.match(r'^\w+\(\s*\)', stripped) or stripped.endswith(')') and '()' not in line): # New function starting, so previous one ended return i - 1 # If we never found the end, return the last line return len(lines) - 1 # ----------------------------- # Detect language helper # ----------------------------- def detect_language(filepath: Path) -> str: ext = filepath.suffix.lower() lang_map = { ".py": "python", ".go": "go", ".rs": "rust", ".ts": "typescript", ".tsx": "typescript", ".js": "javascript", ".jsx": "javascript", ".svelte": "svelte", ".java": "java", ".sql": "sql", ".pgsql": "sql", ".sh": "shell" } return lang_map.get(ext, "unknown") # ----------------------------- # Indexing codebase # ----------------------------- def index_codebase(codebase_path: Path) -> Tuple[List[str], List[Dict[str, Any]]]: ragignore = load_ragignore(codebase_path) all_texts = [] all_metadata = [] logger.info(f"Indexing codebase at {codebase_path}") for filepath in codebase_path.rglob("*"): if not filepath.is_file(): continue rel = filepath.relative_to(codebase_path) if ragignore.match_file(str(rel)): continue if filepath.suffix.lower() not in SUPPORTED_EXTENSIONS: continue language = detect_language(filepath) if language == "python": chunks = parse_python_file(filepath) elif language == "go": chunks = parse_go_file(filepath) elif language == "java": chunks = parse_java_file(filepath) elif language == "svelte": chunks = parse_svelte_file(filepath) elif language == "sql": chunks = parse_sql_file(filepath) elif language == "shell": chunks = parse_shell_file(filepath)# elif language == "rust": chunks = parse_rust_file(filepath) else: chunks = chunk_text_file(filepath, language) for c in chunks: all_texts.append(c["text"]) all_metadata.append(clean_metadata_for_chroma(c["metadata"])) logger.info(f"Indexed {len(all_texts)} chunks") return all_texts, all_metadata def clean_metadata_for_chroma(metadata: Dict[str, Any]) -> Dict[str, Any]: """Clean metadata to only include str, int, float, bool values (no None).""" cleaned = {} for key, value in metadata.items(): if value is None: cleaned[key] = "" # Convert None to empty string elif isinstance(value, (str, int, float, bool)): cleaned[key] = value elif isinstance(value, list): # Convert lists to comma-separated strings cleaned[key] = ",".join(str(v) for v in value if v is not None) else: # Convert other types to strings cleaned[key] = str(value) return cleaned def build_indexes(): global vectorstore, bm25, bm25_corpus, chunks_metadata, embeddings, index_build_time logger.info("Building indexes with contextual weighting and batch embedding...") start_time = time.time() # ------------------------------------------------- # Step 1: Parse codebase and build enhanced graph # ------------------------------------------------- texts, metadatas = index_codebase(CODEBASE_PATH) if len(texts) != len(metadatas): # Helpful debug output to find offending files quickly logger.error( "Indexing mismatch: %d texts vs %d metadata entries. Aborting index build.", len(texts), len(metadatas), ) # raise so CI/dev runs fail fast and you can inspect logs raise ValueError(f"Indexing produced {len(texts)} texts but {len(metadatas)} metadata entries") logger.info("=== Sample metadata inspection ===") for i, m in enumerate(metadatas[:5]): # Check first 5 logger.info(f"Chunk {i}: type={m.get('type')}, name={m.get('name')}") logger.info(f" imports={m.get('imports')}") logger.info(f" calls={m.get('calls')}") logger.info(f" parameters={m.get('parameters')}") # FIX: Convert Path to string for LocalGraph graph = LocalGraph(root_path=str(CODEBASE_PATH)) graph.clear() # Track all created nodes for second-pass relationships created_nodes = {} # Create graph nodes/edges from metadata with enhanced relationships for m in metadatas: fpath = m.get("file") lang = m.get("language", "unknown") node_type = m.get("type", "unknown") name = m.get("name", "") block_type = m.get("block_type", "") component_name = m.get("component_name") # Skip empty/invalid nodes if not name or node_type == "unknown" or not fpath: continue # convert rust defs if "implements_traits" in m and m["implements_traits"]: m["implements"] = m["implements_traits"] # ---- File node --------------------------------------------- # Keep the same ID (`file::`) but store the full set of # attributes so that downstream code can rely on `name`, `lang`, # and `file` (even though `name` will just be the file path). file_node_id = f"file::{fpath}" if file_node_id not in graph.graph.nodes: graph.add_node( file_node_id, type="File", name=fpath, path=fpath, lang=lang, ) created_nodes[file_node_id] = { "type": "File", "name": fpath, "path": fpath, "lang": lang, } # Create node ID with language node_id = f"{lang}::{node_type}::{fpath}::{name}" # Base attributes for all nodes base_attrs = { "name": name, "file": fpath, "line": m.get("line"), "lang": lang # Always include language } # Store for second-pass processing created_nodes[node_id] = base_attrs.copy() created_nodes[node_id]["type"] = node_type # Create appropriate node based on type if node_type in ["function", "method", "constructor"]: # Function-like entities if node_type == "method" and "class" in m: # Method belongs to a class - use compound ID class_name = m['class'] node_id = f"{lang}::{node_type}::{fpath}::{class_name}.{name}" # Update created_nodes with new ID created_nodes[node_id] = base_attrs.copy() created_nodes[node_id]["type"] = node_type created_nodes[node_id]["class_name"] = class_name graph.add_node(node_id, **base_attrs, type=node_type, class_name=class_name, return_type=m.get("return_type"), parameters=m.get("parameters", [])) # Connect method to its class class_node_id = f"{lang}::class::{fpath}::{class_name}" graph.add_edge(class_node_id, node_id, "contains") # Connect return type if available return_type = m.get("return_type") if return_type and return_type not in ['void', '()', 'Result', '']: for type_id, type_data in graph.find_nodes(name=return_type): graph.add_edge(node_id, type_id, "returns") # Connect parameter types parameters = m.get("parameters", []) for param in parameters: if ':' in param: param_parts = param.split(':') if len(param_parts) >= 2: param_type = param_parts[-1].strip() param_type = ( param_type .replace('&', '') .replace('mut ', '') .replace('<', '') .replace('>', '') .replace(',', '') .strip() ) param_type = param_type.replace('&', '').replace('mut ', '').strip() if (param_type and param_type not in ['self', '&self', '&mut self', 'mut self'] and not param_type.startswith('&') and param_type not in ['i32', 'i64', 'f32', 'f64', 'bool', 'String', 'str']): for type_id, type_data in graph.find_nodes(name=param_type): graph.add_edge(node_id, type_id, "uses_parameter") elif node_type == "impl": # ---------- IMPL NODES ---------- node_id = f"{lang}::{node_type}::{fpath}::{name}" created_nodes[node_id] = base_attrs.copy() created_nodes[node_id]["type"] = node_type graph.add_node(node_id, **base_attrs, type=node_type) # Guard against empty lists that can be returned by the Rust parser target_name = None if "implements_traits" in m and isinstance(m["implements_traits"], list) and m["implements_traits"]: # assume first trait target_name = m["implements_traits"][0] elif "implements" in m and isinstance(m["implements"], list) and m["implements"]: target_name = m["implements"][0] if target_name: target_node_id = f"{lang}::struct::{fpath}::{target_name}" if target_node_id not in graph.graph: graph.add_node(target_node_id, name=target_name, file=fpath, lang=lang, type="struct") graph.add_edge(node_id, target_node_id, "implements") else: # Regular function graph.add_node(node_id, **base_attrs, type=node_type, return_type=m.get("return_type"), parameters=m.get("parameters", [])) # Connect function to file graph.add_edge(file_node_id, node_id, "contains") # Connect return type if available return_type = m.get("return_type") if return_type and return_type not in ['void', '()', 'Result', '']: for type_id, type_data in graph.find_nodes(name=return_type): graph.add_edge(node_id, type_id, "returns") # Connect parameter types parameters = m.get("parameters", []) for param in parameters: if ':' in param: param_parts = param.split(':') if len(param_parts) >= 2: param_type = param_parts[-1].strip() param_type = ( param_type .replace('&', '') .replace('mut ', '') .replace('<', '') .replace('>', '') .replace(',', '') .strip() ) param_type = param_type.replace('&', '').replace('mut ', '').strip() if (param_type and param_type not in ['self', '&self', '&mut self', 'mut self'] and not param_type.startswith('&') and param_type not in ['i32', 'i64', 'f32', 'f64', 'bool', 'String', 'str']): for type_id, type_data in graph.find_nodes(name=param_type): graph.add_edge(node_id, type_id, "uses_parameter") elif node_type in ["class", "struct", "interface", "enum"]: # Type definitions graph.add_node(node_id, **base_attrs, type=node_type, extends=m.get("extends"), implements=m.get("implements", []), fields=m.get("fields", []), variants=m.get("variants", [])) # Connect type to file graph.add_edge(file_node_id, node_id, "contains") # Store additional attributes for second-pass created_nodes[node_id]["extends"] = m.get("extends") created_nodes[node_id]["implements"] = m.get("implements", []) created_nodes[node_id]["fields"] = m.get("fields", []) created_nodes[node_id]["variants"] = m.get("variants", []) elif node_type == "trait": # Traits graph.add_node(node_id, **base_attrs, type=node_type, methods=m.get("methods", [])) graph.add_edge(file_node_id, node_id, "contains") created_nodes[node_id]["methods"] = m.get("methods", []) elif node_type == "module": # Modules graph.add_node(node_id, **base_attrs, type=node_type) graph.add_edge(file_node_id, node_id, "contains") else: # Generic code element fallback graph.add_node(node_id, **base_attrs, type=node_type) graph.add_edge(file_node_id, node_id, "contains") # ---------- IMPORTS ---------- if node_type == "import" and "parameters" in m: imports_raw = m["parameters"] # override metadata pathway else: imports_raw = m.get("imports", []) if isinstance(imports_raw, str): # Split the string back into a list imports = [imp.strip() for imp in imports_raw.split(",") if imp.strip()] else: imports = imports_raw graph._add_import_edges(file_node_id, imports) # ---------- FUNCTION CALLS ---------- calls_raw = m.get("calls", []) if isinstance(calls_raw, str): # Split the string back into a list calls = [c.strip() for c in calls_raw.split(",") if c.strip()] else: calls = calls_raw graph._add_call_edges(node_id, calls, fpath) # ------------------------------------------------- # Step 2: Second-pass relationship building # ------------------------------------------------- logger.info("Building second-pass relationships...") # Build type relationships type_connections = build_type_relationships(graph, created_nodes) logger.info(f"Added {type_connections} type relationships") # Build Bevy-specific relationships bevy_connections = build_bevy_relationships(graph, created_nodes) logger.info(f"Added {bevy_connections} Bevy-specific relationships") graph.save() graph.to_json() graph.to_toon() logger.info(f"Graph built with {graph.graph.number_of_nodes()} nodes and {graph.graph.number_of_edges()} edges.") # ------------------------------------------------- # Step 2: embedding/index pipeline # ------------------------------------------------- os.environ["OLLAMA_HOST"] = OLLAMA_BASE_URL embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL, base_url=OLLAMA_BASE_URL) # Generate base embeddings in batches logger.info(f"Generating embeddings for {len(texts)} documents in batches of {EMBEDDING_BATCH_SIZE}...") base_embeddings = batch_embed_documents(texts) # Apply contextual weights logger.info("Applying contextual weights...") weighted_embeddings = apply_contextual_weights_to_embeddings(base_embeddings, metadatas) # Create vector store with weighted embeddings logger.info("Creating vector store (Chroma) with weighted embeddings...") # Create a wrapper that will return our pre-computed weighted embeddings class WeightedEmbeddingFunction: def __init__(self, texts, weighted_embs): """ texts: list[str] weighted_embs: list[list[float]] """ # Map each text to a queue of its embeddings (duplicate-safe) self.lookup = defaultdict(deque) for t, emb in zip(texts, weighted_embs): self.lookup[t].append(emb) def embed_documents(self, docs): """ Chroma may pass docs in any size or order. Always return correct per-document embeddings. """ result = [] for d in docs: if not self.lookup[d]: raise ValueError( f"No embedding left for text {repr(d[:80])} β€” " f"likely mismatch between texts and embeddings." ) result.append(self.lookup[d].popleft()) return result def embed_query(self, q): """ Queries should use base model, not weighted context embeddings. """ return embeddings.embed_query(q) weighted_emb_func = WeightedEmbeddingFunction( texts=texts, weighted_embs=weighted_embeddings ) vectorstore = Chroma.from_texts( texts=texts, embedding=weighted_emb_func, persist_directory=str(VECTOR_DB_PATH), metadatas=metadatas ) # Build BM25 index logger.info("Building BM25 index...") tokenized = [t.lower().split() for t in texts] bm25 = BM25Okapi(tokenized) bm25_corpus = texts chunks_metadata = metadatas # Save BM25 index and metadata BM25_INDEX_PATH.write_text( json.dumps({"corpus": texts, "metadata": metadatas}, indent=2), encoding="utf-8" ) index_build_time = time.time() elapsed = index_build_time - start_time logger.info(f"Index build complete with contextual weighting applied. Total time: {elapsed:.1f}s") def build_type_relationships(graph, created_nodes): """Build type relationships between nodes (parameter types, return types, etc.)""" connections = 0 for node_id, node_data in created_nodes.items(): node_type = node_data.get("type") # Handle function/method parameter and return types if node_type in ["function", "method", "constructor"]: connections += _connect_function_types(graph, node_id, node_data) # Handle class/struct inheritance and implementation elif node_type in ["class", "struct", "interface"]: connections += _connect_class_relationships(graph, node_id, node_data) # Handle impl blocks elif node_type == "impl": connections += _connect_impl_relationships(graph, node_id, node_data) return connections def _connect_function_types(graph, node_id, node_data): """Connect function nodes to their parameter and return types""" connections = 0 # Connect return type return_type = node_data.get("return_type") if return_type and _is_meaningful_type(return_type): return_type_clean = _clean_type_name(return_type) for type_id, type_data in graph.find_nodes(name=return_type_clean): graph.add_edge(node_id, type_id, "returns") connections += 1 # Connect parameter types parameters = node_data.get("parameters", []) for param in parameters: param_type = _extract_parameter_type(param) if param_type and _is_meaningful_type(param_type): param_type_clean = _clean_type_name(param_type) for type_id, type_data in graph.find_nodes(name=param_type_clean): graph.add_edge(node_id, type_id, "uses_parameter") connections += 1 return connections def _connect_class_relationships(graph, node_id, node_data): """Connect class/struct nodes to parent classes and interfaces""" connections = 0 # Inheritance extends = node_data.get("extends") if extends and _is_meaningful_type(extends): for parent_id, parent_data in graph.find_nodes(name=extends): graph.add_edge(node_id, parent_id, "extends") connections += 1 # Interface implementation implements = node_data.get("implements", []) for interface in implements: if _is_meaningful_type(interface): for interface_id, interface_data in graph.find_nodes(name=interface): graph.add_edge(node_id, interface_id, "implements") connections += 1 return connections def _connect_impl_relationships(graph, node_id, node_data): """Connect impl blocks to their targets and traits""" connections = 0 target = node_data.get("target") if target and _is_meaningful_type(target): # Connect to target type for target_id, target_data in graph.find_nodes(name=target): graph.add_edge(node_id, target_id, "implements_for") connections += 1 # Connect to implemented traits traits = node_data.get("traits", []) for trait in traits: if _is_meaningful_type(trait): for trait_id, trait_data in graph.find_nodes(name=trait): graph.add_edge(node_id, trait_id, "implements") connections += 1 return connections def build_bevy_relationships(graph, created_nodes): """Build Bevy-specific relationships""" connections = 0 # Find all plugin structs and impls plugin_structs = {} plugin_impls = {} for node_id, node_data in created_nodes.items(): node_type = node_data.get("type") name = node_data.get("name", "") # Collect plugin structs if node_type in ["struct", "class"] and name.endswith("Plugin"): plugin_structs[name] = node_id # Collect plugin impls elif node_type == "impl" and node_data.get("target", "").endswith("Plugin"): plugin_impls[node_data["target"]] = node_id # Connect plugin impls to their structs for plugin_name, impl_id in plugin_impls.items(): if plugin_name in plugin_structs: struct_id = plugin_structs[plugin_name] graph.add_edge(impl_id, struct_id, "implements_plugin") connections += 1 # Find build method in this impl impl_methods = created_nodes[impl_id].get("methods", []) for method_name in impl_methods: if method_name == "build": # Find the method node method_id = f"{created_nodes[impl_id]['lang']}::method::{created_nodes[impl_id]['file']}::{plugin_name}.{method_name}" if method_id in created_nodes: graph.add_edge(impl_id, method_id, "defines_plugin") connections += 1 # Connect systems to plugins (simplified - same file assumption) for node_id, node_data in created_nodes.items(): if node_data.get("type") in ["function", "method"]: # Simple heuristic for Bevy systems function_name = node_data.get("name", "").lower() if any(keyword in function_name for keyword in ['system', 'update', 'setup']): file_path = node_data.get("file") # Look for plugins in the same file for plugin_id, plugin_data in created_nodes.items(): if (plugin_data.get("file") == file_path and plugin_data.get("type") in ["struct", "class"] and plugin_data.get("name", "").endswith("Plugin")): graph.add_edge(plugin_id, node_id, "contains_system") connections += 1 return connections def _extract_parameter_type(param): """Extract type from parameter string, handling multiple formats""" if not param: return None # Handle "type: name" format (TypeScript/Python) if ':' in param: parts = param.split(':') if len(parts) >= 2: return parts[1].strip() # Handle "Type name" format (Rust/Java/C++) if ' ' in param: parts = param.split() if len(parts) >= 2: return parts[0].strip() # Handle complex types with generics if '<' in param and '>' in param: # Extract the base type before generics base_type = param.split('<')[0].strip() return base_type return param.strip() def _clean_type_name(type_name): """Clean type names by removing common modifiers""" if not type_name: return "" # Remove common modifiers modifiers = ['&', 'mut ', 'const ', 'static ', 'pub ', 'private ', 'protected '] cleaned = type_name for mod in modifiers: cleaned = cleaned.replace(mod, '') # Remove trailing references/pointers cleaned = cleaned.rstrip('*&') return cleaned.strip() def _is_meaningful_type(type_name): """Check if a type name is meaningful (not primitive, void, etc.)""" if not type_name: return False meaningless_types = [ 'void', 'int', 'i32', 'i64', 'f32', 'f64', 'bool', 'str', 'String', 'char', 'u8', 'u16', 'u32', 'u64', 'usize', 'isize', 'self', '&self', '&mut self', 'mut self', 'Self', 'Result', 'Option', 'Vec', 'String' ] cleaned = _clean_type_name(type_name) return (cleaned and cleaned not in meaningless_types and not cleaned.startswith('&') and len(cleaned) > 1) # ----------------------------- # Build / load indexes with contextual weighting and batch embedding # ----------------------------- def batch_embed_documents(texts: List[str]) -> List[List[float]]: """Embed documents in batches for better performance.""" all_embeddings = [] total_batches = (len(texts) + EMBEDDING_BATCH_SIZE - 1) // EMBEDDING_BATCH_SIZE for i in range(0, len(texts), EMBEDDING_BATCH_SIZE): batch = texts[i:i + EMBEDDING_BATCH_SIZE] batch_num = i // EMBEDDING_BATCH_SIZE + 1 logger.info(f"Embedding batch {batch_num}/{total_batches} ({len(batch)} docs)...") batch_embeddings = embeddings.embed_documents(batch) all_embeddings.extend(batch_embeddings) return all_embeddings def load_indexes(): global vectorstore, bm25, bm25_corpus, chunks_metadata, embeddings, index_build_time logger.info("Loading indexes from disk...") embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL, base_url=OLLAMA_BASE_URL) vectorstore = Chroma(persist_directory=str(VECTOR_DB_PATH), embedding_function=embeddings) data = json.loads(BM25_INDEX_PATH.read_text(encoding="utf-8")) bm25_corpus = data["corpus"] chunks_metadata = data["metadata"] bm25 = BM25Okapi([t.lower().split() for t in bm25_corpus]) index_build_time = BM25_INDEX_PATH.stat().st_mtime logger.info("Indexes loaded.") # ----------------------------- # Analytics Helper Functions # ----------------------------- def count_by_language(metadata_list: List[Dict[str, Any]]) -> Dict[str, int]: """Count chunks by programming language.""" counts = {} for meta in metadata_list: lang = meta.get("language", "unknown") counts[lang] = counts.get(lang, 0) + 1 return counts def count_by_type(metadata_list: List[Dict[str, Any]]) -> Dict[str, int]: """Count chunks by type (function, class, etc.).""" counts = {} for meta in metadata_list: typ = meta.get("type", "unknown") counts[typ] = counts.get(typ, 0) + 1 return counts def calculate_avg_chunk_size(corpus: List[str]) -> float: """Calculate average chunk size in characters.""" if not corpus: return 0.0 return sum(len(text) for text in corpus) / len(corpus) # ----------------------------- # Hybrid search 80/20 + normalization # ----------------------------- def hybrid_search(query: str, k: int = 30) -> List[Tuple[str, Dict[str, Any], float]]: with safe_search(): # Vector results (Chroma returns (doc, score) where lower score might be better depending on backend) vector_results = vectorstore.similarity_search_with_score(query, k=50) # Normalize vector scores so that higher is better in [0,1] vec_values = [s for _, s in vector_results] if vector_results else [1.0] vmin, vmax = (min(vec_values), max(vec_values)) if vector_results else (0.0, 1.0) vector_scores = {} for doc, s in vector_results: # If Chroma returns distance-like (smaller better), invert; otherwise adapt # We'll normalize via (vmax - s) / (vmax - vmin + eps) eps = 1e-12 denom = (vmax - vmin) + eps norm = (vmax - s) / denom vector_scores[doc.page_content] = norm # BM25 tokenized_query = query.lower().split() bm25_scores = bm25.get_scores(tokenized_query) top_idxs = sorted(range(len(bm25_scores)), key=lambda i: bm25_scores[i], reverse=True)[:50] max_b = max((bm25_scores[i] for i in top_idxs), default=1.0) bm25_doc_scores = {bm25_corpus[i]: (bm25_scores[i] / max_b if max_b > 0 else 0.0) for i in top_idxs} # Fusion 80/20 all_docs = set(vector_scores.keys()) | set(bm25_doc_scores.keys()) fused = {} for d in all_docs: v = vector_scores.get(d, 0.0) b = bm25_doc_scores.get(d, 0.0) fused[d] = VECTOR_WEIGHT * v + BM25_WEIGHT * b # Sort by fused score descending sorted_items = sorted(fused.items(), key=lambda x: x[1], reverse=True) # Build accurate mapping from document text to its true metadata text_to_meta = {text: meta for text, meta in zip(bm25_corpus, chunks_metadata)} results = [] for doc_text, score in sorted_items[:k]: meta = text_to_meta.get(doc_text, {}) results.append((doc_text, meta, score)) return results # ----------------------------- # Enhanced Reranker with Batch Processing # ----------------------------- def rerank_with_ollama_enhanced(query: str, candidates: List[str], top_k: int = 5) -> List[Tuple[str, float]]: """Enhanced Qwen3 Reranker with batch processing and better scoring.""" if not candidates: return [] # Use domain-specific instruction for code search instruction = "Given a technical code search query, retrieve relevant code implementations, function definitions, or examples that directly address the query requirements" batch_scores = [] # Process in smaller batches to avoid timeouts batch_size = 5 for i in range(0, len(candidates), batch_size): batch = candidates[i:i + batch_size] batch_prompts = [] for chunk in batch: system_prompt = '<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n' user_prompt = ( f'<|im_start|>user\n' f': {instruction}\n' f': {query}\n' f': {chunk[:8000]}\n' # Limit document length f'<|im_end|>\n' f'<|im_start|>assistant\n\n\n\n\n' ) batch_prompts.append(system_prompt + user_prompt) # Score batch for prompt, chunk in zip(batch_prompts, batch): try: resp = requests.post( f"{OLLAMA_BASE_URL}/api/generate", json={ "model": RERANKER_MODEL_OLLAMA, "prompt": prompt, "max_tokens": 3, "temperature": 0.0, "stream": False }, timeout=20 ) j = resp.json() response_text = j.get("response", "").strip().lower() # Enhanced scoring with confidence levels score = parse_reranker_response(response_text) batch_scores.append((chunk, score)) except requests.exceptions.Timeout: logger.warning("Reranker request timeout, assigning default score") batch_scores.append((chunk, 0.0)) except Exception as e: logger.warning(f"Rerank call failed: {e}") batch_scores.append((chunk, 0.0)) # Sort by score descending and return top_k batch_scores.sort(key=lambda x: x[1], reverse=True) return batch_scores[:top_k] def parse_reranker_response(response_text: str) -> float: """Parse Qwen3 Reranker response and convert to confidence score.""" response_lower = response_text.strip().lower() # Exact matches from the model if response_lower == "yes": return 1.0 elif response_lower == "no": return 0.0 # Handle variations and partial matches yes_indicators = ["yes", "relevant", "correct", "matches", "appropriate", "suitable"] no_indicators = ["no", "irrelevant", "incorrect", "unrelated", "inappropriate"] yes_count = sum(1 for indicator in yes_indicators if indicator in response_lower) no_count = sum(1 for indicator in no_indicators if indicator in response_lower) if yes_count > no_count: return 0.8 # Likely relevant but not confident elif no_count > yes_count: return 0.2 # Likely irrelevant but not confident else: # Ambiguous response return 0.5 # ----------------------------- # Comment-aware reference search (ripgrep) # ----------------------------- def get_context_with_comments(file_path: Path, match_line: int, max_context_lines: int = 5) -> str: try: lines = file_path.read_text(encoding="utf-8").splitlines() except Exception: return "" idx = match_line - 1 context_lines = [] look = idx - 1 collected = 0 while look >= 0 and collected < max_context_lines: line = lines[look] stripped = line.strip() if stripped.startswith(("#", "//", "--")) or stripped == "": context_lines.insert(0, line) look -= 1 collected += 1 else: break context_lines.append(lines[idx] if 0 <= idx < len(lines) else "") return "\n".join(context_lines) def search_symbol(symbol: str, root_dir: Path, top_k: int = 20) -> List[Dict[str, Any]]: matches = [] rg_cmd = [ "rg", "-n", "-i", "-C", "3", "--no-ignore", "--no-heading", "--color", "never", "--glob", "!node_modules", "--glob", "!__pycache__", "--glob", "!.git", ] for ext in SUPPORTED_EXTENSIONS: rg_cmd.extend(["--glob", f"*{ext}"]) rg_cmd.extend(["--", symbol, "."]) try: proc = subprocess.run( rg_cmd, cwd=root_dir, capture_output=True, text=True, timeout=30 ) if proc.returncode not in (0, 1): logger.warning(f"ripgrep returned code {proc.returncode}: {proc.stderr}") return [] for line in proc.stdout.splitlines(): if not line.strip() or line.startswith("--"): continue parts = line.split(":", 2) if len(parts) < 3: continue file_rel, lineno_str, snippet = parts try: lineno = int(lineno_str) except ValueError: continue matches.append({ "file": file_rel, "line": lineno, "context": snippet.strip() }) except subprocess.TimeoutExpired: logger.warning("ripgrep timed out") except Exception as e: logger.warning(f"ripgrep failed: {e}") return matches def _extract_imports(source: str) -> list[str]: """Return a list of fully‑qualified imports found in `source`.""" return [m.group(1).strip() for m in re.finditer(r'^\s*use\s+([^;]+);', source, re.MULTILINE)] def _extract_calls(source: str) -> list[str]: """Return a list of called function names (bare name only).""" return [m.group(1).split("::")[-1] for m in re.finditer(r'([A-Za-z_][A-Za-z0-9_:]*)\s*\(', source, re.MULTILINE)] # ----------------------------- # Additional tool: read file lines with context # ----------------------------- def read_file_lines(path: str, start: int = 1, end: Optional[int] = None) -> str: """ Read file lines with intelligent context inclusion. - Includes requested lines - Expands to include full function/class definition if within one - Includes leading comments (docstrings, inline comments) - Shows function signature, return type, decorators """ # Handle both absolute and relative paths if path.startswith('/'): p = Path(path) else: p = CODEBASE_PATH / Path(path) # Security: Validate path is within codebase try: resolved = p.resolve() codebase_resolved = CODEBASE_PATH.resolve() if not resolved.is_relative_to(codebase_resolved): return f"Error: Path '{path}' is outside the codebase directory" except (ValueError, OSError) as e: return f"Error: Invalid path '{path}': {e}" try: content = p.read_text(encoding="utf-8") lines = content.splitlines() if end is None: end = start + 50 # reasonable default # Clamp to valid range start_i = max(1, start) end_i = min(len(lines), end) # Detect language language = detect_language(p) # Build context-aware line range context_start, context_end, context_info = find_context_boundaries( lines, start_i, end_i, language ) # Extract the context lines context_lines = lines[context_start - 1:context_end] # Build output with metadata output = [] output.append(f"File: {path}") output.append(f"Language: {language}") output.append(f"Requested: lines {start_i}-{end_i}") output.append(f"Showing: lines {context_start}-{context_end} (with context)") if context_info: output.append(f"Context: {context_info}") output.append("\n" + "="*60) # Add line numbers for i, line in enumerate(context_lines, start=context_start): # Highlight the originally requested range marker = ">>> " if start_i <= i <= end_i else " " output.append(f"{marker}{i:4d} | {line}") output.append("="*60) return "\n".join(output) except Exception as e: return f"Failed to read {path}: {e}" def find_context_boundaries(lines: List[str], start: int, end: int, language: str) -> Tuple[int, int, str]: """ Find intelligent context boundaries around the requested line range. Returns: (context_start_line, context_end_line, description) """ # Convert to 0-based indexing for processing start_idx = start - 1 end_idx = end - 1 context_info = [] # Expand upward to include leading comments and function/class headers context_start = start # Step 1: Include leading comments above start comment_start = find_leading_comments_start(lines, start_idx, language) if comment_start < start_idx: context_start = comment_start + 1 context_info.append("leading comments") # Step 2: Check if we're inside a function/class and include its signature func_start, func_end, func_name, func_type = find_enclosing_function_or_class( lines, start_idx, end_idx, language ) if func_start is not None: context_start = min(context_start, func_start + 1) if func_end is not None and func_end > end_idx: context_end = func_end + 1 else: context_end = end if func_name: context_info.append(f"{func_type} '{func_name}'") else: context_end = end # Step 3: Include decorators (Python) or annotations (Java) if language == "python": decorator_start = find_decorators_start(lines, context_start - 1) if decorator_start < context_start - 1: context_start = decorator_start + 1 context_info.append("decorators") info_str = " + ".join(context_info) if context_info else "no additional context" return context_start, context_end, info_str def find_leading_comments_start(lines: List[str], line_idx: int, language: str) -> int: """Find the start of leading comments above the given line.""" comment_patterns = { "python": ("#", '"""', "'''"), "javascript": ("//", "/*"), "typescript": ("//", "/*"), "go": ("//", "/*"), "java": ("//", "/*"), "sql": ("--", "/*"), "rust": ("//", "/*"), } patterns = comment_patterns.get(language, ("#", "//", "/*", "--")) start_idx = line_idx i = line_idx - 1 # Track if we're in a block comment in_block = False while i >= 0: line = lines[i].strip() # Empty lines are OK if we already have comments if not line: if i < line_idx - 1: # Allow one blank line between comments i -= 1 continue else: break # Check for block comment end (we're going backwards) if "*/" in line: in_block = True start_idx = i i -= 1 continue # Check for block comment start if in_block and ("/*" in line or "/**" in line): start_idx = i in_block = False i -= 1 continue # Check for single-line comments is_comment = any(line.startswith(p) for p in patterns) if is_comment or in_block: start_idx = i i -= 1 else: break return start_idx def find_decorators_start(lines: List[str], line_idx: int) -> int: """Find Python decorators above the given line.""" i = line_idx - 1 while i >= 0: line = lines[i].strip() if line.startswith("@"): i -= 1 elif not line: # Allow blank lines i -= 1 else: return i + 1 return 0 def find_enclosing_function_or_class(lines: List[str], start_idx: int, end_idx: int, language: str) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """ Find the function or class that encloses the given line range. Returns: (start_line_idx, end_line_idx, name, type) or (None, None, None, None) """ if language == "python": return find_python_function_or_class(lines, start_idx, end_idx) elif language in ("javascript", "typescript"): return find_js_function(lines, start_idx, end_idx) elif language == "go": return find_go_function(lines, start_idx, end_idx) elif language == "java": return find_java_method_or_class(lines, start_idx, end_idx) elif language == "sql": return find_sql_function_or_block(lines, start_idx, end_idx) return None, None, None, None def find_python_function_or_class(lines: List[str], start_idx: int, end_idx: int) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """Find enclosing Python function or class.""" # Look backwards for def/class with proper indentation target_indent = None for i in range(start_idx, -1, -1): line = lines[i] stripped = line.lstrip() if stripped.startswith(("def ", "class ", "async def ")): indent = len(line) - len(stripped) # Check if this could be our enclosing scope if target_indent is None or indent < target_indent: # Extract name match = re.match(r'(?:async\s+)?(?:def|class)\s+(\w+)', stripped) if match: name = match.group(1) func_type = "class" if stripped.startswith("class") else "function" # Find the end by looking for next same-or-lower indent non-empty line end_i = find_python_block_end(lines, i, indent) return i, end_i, name, func_type return None, None, None, None def find_python_block_end(lines: List[str], start_idx: int, base_indent: int) -> Optional[int]: """Find the end of a Python block based on indentation.""" for i in range(start_idx + 1, len(lines)): line = lines[i] stripped = line.lstrip() if not stripped: # Skip empty lines continue indent = len(line) - len(stripped) if indent <= base_indent and stripped and not stripped.startswith("#"): return i - 1 return len(lines) - 1 def find_js_function(lines: List[str], start_idx: int, end_idx: int) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """Find enclosing JavaScript/TypeScript function.""" for i in range(start_idx, -1, -1): line = lines[i].strip() # Match various JS function patterns patterns = [ r'function\s+(\w+)', r'(?:const|let|var)\s+(\w+)\s*=\s*(?:async\s+)?\(', r'(\w+)\s*\([^)]*\)\s*{', # Arrow functions r'async\s+function\s+(\w+)', ] for pattern in patterns: match = re.search(pattern, line) if match: name = match.group(1) end_i = find_brace_block_end(lines, i) return i, end_i, name, "function" return None, None, None, None def find_go_function(lines: List[str], start_idx: int, end_idx: int) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """Find enclosing Go function.""" for i in range(start_idx, -1, -1): line = lines[i].strip() # Match Go function: func (receiver) name(params) returnType { match = re.match(r'func\s+(?:\([^)]+\)\s+)?(\w+)', line) if match: name = match.group(1) end_i = find_brace_block_end(lines, i) return i, end_i, name, "function" return None, None, None, None def find_java_method_or_class(lines: List[str], start_idx: int, end_idx: int) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """Find enclosing Java method or class.""" for i in range(start_idx, -1, -1): line = lines[i].strip() # Match class class_match = re.match(r'(?:public|private|protected)?\s*(?:static)?\s*class\s+(\w+)', line) if class_match: name = class_match.group(1) end_i = find_brace_block_end(lines, i) return i, end_i, name, "class" # Match method method_match = re.match(r'(?:public|private|protected)?\s*(?:static)?\s*(?:\w+(?:<[^>]+>)?)\s+(\w+)\s*\(', line) if method_match: name = method_match.group(1) end_i = find_brace_block_end(lines, i) return i, end_i, name, "method" return None, None, None, None def find_sql_function_or_block(lines: List[str], start_idx: int, end_idx: int) -> Tuple[Optional[int], Optional[int], Optional[str], Optional[str]]: """Find enclosing SQL function or procedure.""" for i in range(start_idx, -1, -1): line = lines[i].strip().upper() if line.startswith(("CREATE FUNCTION", "CREATE OR REPLACE FUNCTION", "CREATE PROCEDURE")): # Extract name match = re.search(r'(?:FUNCTION|PROCEDURE)\s+(\w+)', line, re.IGNORECASE) if match: name = match.group(1) # SQL functions typically end with $$ or END end_i = find_sql_function_end(lines, i) func_type = "procedure" if "PROCEDURE" in line else "function" return i, end_i, name, func_type return None, None, None, None def find_sql_function_end(lines: List[str], start_idx: int) -> Optional[int]: """Find end of SQL function (looks for $$ or END;).""" for i in range(start_idx + 1, len(lines)): line = lines[i].strip().upper() if "$$" in line or line.startswith("END;") or line == "END": return i return len(lines) - 1 def find_brace_block_end(lines: List[str], start_idx: int) -> Optional[int]: """Find the end of a brace-delimited block {}.""" brace_count = 0 started = False for i in range(start_idx, len(lines)): line = lines[i] for char in line: if char == '{': brace_count += 1 started = True elif char == '}': brace_count -= 1 if started and brace_count == 0: return i return len(lines) - 1 # ----------------------------- # MCP Tools # ----------------------------- @mcp.tool() def health_check() -> str: """What it does - Quick JSON status of the RAG server (ready, indexed path, chunk count, age, graph stats). When to use - Before any other query, to confirm the index is current and see codebase structure. When not to use - After you already know the server is healthy; it adds no value. Example - health_check() β†’ { "status":"ready","total_chunks":11234,"graph_nodes":4567,"graph_edges":12345,"index_age_hours":4.7 }""" try: with _startup_lock: # Check if we have a git repo in working_repo repo_info = "No repository loaded" if (CODEBASE_PATH / ".git").exists(): try: branch = subprocess.check_output( ["git", "-C", str(CODEBASE_PATH), "rev-parse", "--abbrev-ref", "HEAD"], text=True ).strip() repo_info = f"Loaded: {branch}" except: repo_info = "Git repository (no branch info)" # Load graph to check if it exists - FIX: Convert Path to string graph = LocalGraph(root_path=str(CODEBASE_PATH)) # Convert Path to str graph_loaded = graph.load() status = { "status": "ready" if vectorstore is not None else "no_index", "working_repository": str(CODEBASE_PATH), "repo_status": repo_info, "ollama_url": OLLAMA_BASE_URL, "config": { "embedding_model": EMBEDDING_MODEL, "vector_weight": VECTOR_WEIGHT, "bm25_weight": BM25_WEIGHT, "rerank_enabled": ENABLE_RERANK, "batch_size": EMBEDDING_BATCH_SIZE, "graph_enabled": True }, "Tools": { "NOTE": "THESE TOOLS ARE RESTRICTED BY .gitignore AS WELL AS .ragignore", "semantic_rag_search": "Hybrid RAG search with graph context (cross-file deps, inheritance, calls)", "find_code_references": "Find exact symbol references across codebase", "read_file_lines_tool": "Read specific file lines with syntax highlighting", "find_path": "Find files by glob patterns", "grep": "Search file contents with regex", "list_directory": "List directory contents" } } if bm25_corpus: status["statistics"] = { "total_chunks": len(bm25_corpus), "by_language": count_by_language(chunks_metadata), "by_type": count_by_type(chunks_metadata), "avg_chunk_size_chars": round(calculate_avg_chunk_size(bm25_corpus), 1), "index_age_hours": round((time.time() - index_build_time) / 3600, 1) if index_build_time > 0 else None } # Add graph statistics - FIX: Use graph.graph.nodes instead of graph.nodes if graph_loaded: graph_stats = _calculate_graph_stats(graph) status["graph"] = graph_stats else: status["graph"] = { "status": "not_loaded", "nodes": 0, "edges": 0, "message": "Graph will be built on next index rebuild" } return json.dumps(status, indent=2) except Exception as e: return json.dumps({"status": "error", "message": str(e)}, indent=2) def _calculate_graph_stats(graph: LocalGraph) -> dict: """Calculate comprehensive graph statistics.""" # FIX: Use graph.graph.nodes and graph.graph.edges instead of graph.nodes/graph.edges if graph.graph.number_of_nodes() == 0: return {"status": "empty", "nodes": 0, "edges": 0} # Basic counts nodes = graph.graph.number_of_nodes() edges = graph.graph.number_of_edges() # Count by node type node_types = {} for _, data in graph.graph.nodes(data=True): # FIX: graph.graph.nodes node_type = data.get('type', 'unknown') node_types[node_type] = node_types.get(node_type, 0) + 1 # Count by edge type edge_types = {} for _, _, data in graph.graph.edges(data=True): # FIX: graph.graph.edges edge_type = data.get('type', 'unknown') edge_types[edge_type] = edge_types.get(edge_type, 0) + 1 # Language distribution languages = {} for _, data in graph.graph.nodes(data=True): # FIX: graph.graph.nodes lang = data.get('lang', 'unknown') # Changed from 'language' to 'lang' to match our new attribute languages[lang] = languages.get(lang, 0) + 1 # File statistics files = [n for n, d in graph.graph.nodes(data=True) if d.get('type') == 'File'] # FIX: graph.graph.nodes # Relationship density avg_edges_per_node = edges / nodes if nodes > 0 else 0 # Most connected nodes (hubs) degree_centrality = dict(graph.graph.degree()) # FIX: graph.graph.degree() top_hubs = sorted(degree_centrality.items(), key=lambda x: x[1], reverse=True)[:5] top_hubs_info = [] for node_id, degree in top_hubs: node_data = graph.graph.nodes[node_id] # FIX: graph.graph.nodes top_hubs_info.append({ "name": node_data.get('name', node_id), "type": node_data.get('type', 'unknown'), "file": node_data.get('file', ''), "connections": degree }) return { "status": "loaded", "nodes": nodes, "edges": edges, "node_types": node_types, "edge_types": edge_types, "languages": languages, "files": len(files), "avg_edges_per_node": round(avg_edges_per_node, 2), "top_connected_nodes": top_hubs_info, "relationship_density": "high" if avg_edges_per_node > 2.0 else "medium" if avg_edges_per_node > 1.0 else "low" } @mcp.tool() def semantic_rag_search(query: str, top_k: int = 5, rerank: bool = True) -> str: """Hybrid RAG search: returns ranked code snippets + file/line + docstrings + graph context (cross-file deps, inheritance, callers/callees, type refs). Use for: Open questions ("how does auth work?"), unknown patterns, architecture exploration. Natural language queries. Don't use for: Single symbol/exact phrase (use find_code_references or grep then read_file_lines_tool or read), exploring known entity relationships. rerank=False: fast, 65% accuracy | rerank=True: slower, 95% accuracy Output includes: code content, docs, cross_file:[calls/extends/uses@filepath], used_by:[entity@filepath], file:[same-file entities] Example: semantic_rag_search("user authentication", 5, True) β†’ 5 results with code + "cross_file:calls:ValidateToken@auth/jwt.go|used_by:setupAuth@server/routes.go" """ if vectorstore is None or bm25_corpus is None: return "❌ Index not built. Please call init_repo() or rebuild_index() first." # FIX: Convert Path to string graph = LocalGraph(root_path=str(CODEBASE_PATH)) graph_loaded = graph.load() if not graph_loaded: logger.warning("Graph not loaded - search will proceed without graph context") try: hy = hybrid_search(query, k=max(top_k, RERANK_TOP_N)) if ENABLE_RERANK and rerank: try: candidates = [t for t, m, s in hy] rr = rerank_with_ollama_enhanced(query, candidates[:RERANK_TOP_N], top_k=top_k) # Build enhanced results with graph context results = [] for i, (chunk_text, score) in enumerate(rr, 1): meta = next((m for t, m, s in hy if t == chunk_text), {}) results.append(_build_enhanced_result(chunk_text, meta, score, graph)) return _format_enhanced_results(results, query, "search") except Exception as e: logger.warning(f"Rerank step failed: {e}") # Fall through to non-reranked results # Non-reranked results with graph context results = [] for i, (text, meta, score) in enumerate(hy[:top_k], 1): results.append(_build_enhanced_result(text, meta, score, graph)) return _format_enhanced_results(results, query, "search") except RuntimeError as e: return f"Error: {e}" except Exception as e: logger.exception("Search failed") return f"Search failed: {e}" def _build_enhanced_result(chunk_text: str, meta: dict, score: float, graph: Optional[LocalGraph] = None) -> dict: """Build enhanced result with graph context and important metadata.""" # Extract key metadata file_path = meta.get('file', '') line_num = meta.get('line', '') entity_type = meta.get('type', 'code') language = meta.get('language', '') entity_name = meta.get('name', '') # Extract clean content and docstring clean_content = _extract_clean_content(chunk_text) docstring = _extract_docstring(chunk_text) # Get graph context for this result graph_context = _get_result_graph_context(meta, graph) return { 'file': file_path, 'line': line_num, 'type': entity_type, 'language': language, 'name': entity_name, 'score': score, 'content': clean_content, 'docstring': docstring, 'graph_context': graph_context } def _get_result_graph_context(meta: dict, graph: Optional[LocalGraph] = None) -> dict: """Get relevant graph context for a search result with cross-file awareness.""" if graph is None or graph.graph.number_of_nodes() == 0: return {} context = {} file_path = meta.get('file', '') entity_name = meta.get('name', '') entity_type = meta.get('type', '') language = meta.get('language', '') # Build node ID node_id = None if entity_name and file_path and language: if entity_type in ['class', 'struct', 'interface', 'enum', 'impl']: node_id = f"{language}::{entity_type}::{file_path}::{entity_name}" elif entity_type in ['method', 'function', 'constructor']: class_name = meta.get('class') if class_name: node_id = f"{language}::{entity_type}::{file_path}::{class_name}.{entity_name}" else: node_id = f"{language}::{entity_type}::{file_path}::{entity_name}" elif entity_type == 'component' and language == 'svelte': node_id = f"svelte::component::{file_path}::{entity_name}" if node_id and node_id in graph.graph: # Get direct relationships WITH file info neighbors = list(graph.neighbors(node_id)) if neighbors: context['relationships'] = [] context['cross_file_deps'] = [] # NEW: Track cross-file dependencies for neighbor_id, neighbor_data in neighbors[:10]: # Show more neighbors edge_data = graph.graph.edges[node_id, neighbor_id] rel_type = edge_data.get('type', 'related') neighbor_name = neighbor_data.get('name', neighbor_id.split('::')[-1]) neighbor_file = neighbor_data.get('file', '') neighbor_type = neighbor_data.get('type', '') # Build compact relationship string with file info if neighbor_file and neighbor_file != file_path: # Cross-file relationship - show file rel_str = f"{rel_type}:{neighbor_name}@{neighbor_file}" context['cross_file_deps'].append(rel_str) else: # Same-file relationship - omit file for brevity rel_str = f"{rel_type}:{neighbor_name}" context['relationships'].append(rel_str) # NEW: Find reverse dependencies (what depends on THIS entity) incoming = [] for predecessor in graph.graph.predecessors(node_id): pred_data = graph.graph.nodes[predecessor] pred_file = pred_data.get('file', '') pred_name = pred_data.get('name', '') edge_data = graph.graph.edges[predecessor, node_id] rel_type = edge_data.get('type', 'related') # Only show cross-file incoming dependencies if pred_file and pred_file != file_path: incoming.append(f"{rel_type}:{pred_name}@{pred_file}") if incoming: context['used_by'] = incoming[:10] # Get file-level context (same as before) file_node_id = f"file::{file_path}" if file_node_id in graph.graph: file_neighbors = list(graph.neighbors(file_node_id)) if file_neighbors: context['file_entities'] = [] for neighbor_id, neighbor_data in file_neighbors[:5]: neighbor_type = neighbor_data.get('type', '') neighbor_name = neighbor_data.get('name', '') if neighbor_type and neighbor_name and neighbor_name != entity_name: context['file_entities'].append(f"{neighbor_type}:{neighbor_name}") return context def _extract_docstring(chunk_text: str) -> str: """Extract docstring or comments from chunk text.""" lines = chunk_text.split('\n') doc_lines = [] # Look for docstring patterns in_docstring = False for line in lines: stripped = line.strip() # Java/Go style comments if stripped.startswith('//') or stripped.startswith('/*') or stripped.startswith('*'): doc_lines.append(stripped) # Python style docstrings elif '"""' in line or "'''" in line: if not in_docstring: in_docstring = True else: in_docstring = False break elif in_docstring: doc_lines.append(stripped) # Specific doc patterns in the metadata section elif stripped.startswith('Doc:') and len(stripped) > 4: doc_content = stripped[4:].strip() if doc_content and doc_content not in ['None', '""']: doc_lines.append(doc_content) # Clean up docstring if doc_lines: docstring = ' '.join(doc_lines) docstring = re.sub(r'\s+', ' ', docstring) return docstring.strip()[:300] # Reasonable limit return "" def _format_enhanced_results(results: List[Dict], query: str, result_type: str) -> str: """Format enhanced results with dense LLM-optimized context.""" if not results: return f"# {result_type.title()}: {query}\nNo results found.\n" lines = [ f"# {result_type.title()}: {query}", f"results[{len(results)}]{{file,line,type,name,score,content,context}}:" ] for result in results: context_parts = [] # Graph context as dense key:value pairs graph_context = result.get('graph_context', {}) # Cross-file dependencies (highest value) if graph_context.get('cross_file_deps'): deps = '|'.join(graph_context['cross_file_deps'][:8]) context_parts.append(f"xfile:{deps}") # All relationships if graph_context.get('relationships'): rels = '|'.join(graph_context['relationships'][:12]) context_parts.append(f"rels:{rels}") # Reverse dependencies if graph_context.get('used_by'): used = '|'.join(graph_context['used_by'][:6]) context_parts.append(f"used:{used}") # File context if graph_context.get('file_entities'): entities = '|'.join(graph_context['file_entities'][:10]) context_parts.append(f"file:{entities}") # Docstring (if valuable) docstring = result.get('docstring', '') if docstring and len(docstring) > 10: # Only include substantial docs context_parts.append(f"doc:{docstring[:200]}") context_str = ";".join(context_parts) # Minimal row with essential info row = [ result.get('file', ''), result.get('line', ''), result.get('type', 'code'), result.get('name', ''), f"{result.get('score', 0.0):.2f}", result.get('content', '')[:500], # More content tokens context_str ] # Efficient escaping escaped_row = [] for field in row: field_str = str(field) if ',' in field_str or ';' in field_str: escaped = field_str.replace('"', '\\"') escaped_row.append(f'"{escaped}"') else: escaped_row.append(field_str) lines.append(" " + ",".join(escaped_row)) return "\n".join(lines) def _extract_clean_content(chunk_text: str) -> str: """Extract clean content from chunk text by removing duplicate metadata.""" lines = chunk_text.split('\n') content_lines = [] # Skip the first few metadata lines (File:, Type:, Name:, etc.) skip_metadata = True for line in lines: stripped = line.strip() if skip_metadata: if any(stripped.startswith(prefix) for prefix in ['File:', 'Type:', 'Name:', 'Language:', 'Doc:']): continue # Once we hit actual content, stop skipping if stripped and not any(stripped.startswith(prefix) for prefix in ['File:', 'Type:', 'Name:', 'Language:', 'Doc:', 'SQL:', 'Code:']): skip_metadata = False content_lines.append(stripped) else: content_lines.append(stripped) # Join and clean up content = ' '.join(content_lines) content = re.sub(r'\s+', ' ', content) # Normalize whitespace return content.strip() @mcp.tool() def find_code_references(symbol: str, top_k: int = 20) -> str: """What it does - Fast, unranked lookup of all file/line occurrences of a symbol. When to use - After identifying a symbol in a search hit, or when you need every use of a class/method. When not to use - When you need context-rich code; use read_file_lines_tool after getting the locations. Example - find_code_references("AuthService") β†’ list of references.""" matches = search_symbol(symbol, CODEBASE_PATH) if not matches: return f"# References: {symbol}\nNo references found.\n" # Build clean TOON format directly results = [] for i, m in enumerate(matches[:top_k], 1): results.append({ 'file': m['file'], 'line': m['line'], 'context': m['context'] }) return _format_reference_results(results, symbol) def _format_reference_results(results: List[Dict], symbol: str) -> str: """Format reference results in clean TOON format.""" if not results: return f"# References: {symbol}\nNo references found.\n" lines = [f"# References: {symbol}", f"references[{len(results)}]{{file,line,context}}:"] for result in results: row = [ result.get('file', ''), result.get('line', ''), result.get('context', '')[:300] # Reasonable limit ] # Escape fields if needed escaped_row = [] for field in row: field_str = str(field) if any(char in field_str for char in [',', '"', '\n', '\r']): escaped = field_str.replace('"', '\\"') escaped_row.append(f'"{escaped}"') else: escaped_row.append(field_str) lines.append(" " + ",".join(escaped_row)) return "\n".join(lines) @mcp.tool() def read_file_lines_tool(path: str, start: int = 1, end: Optional[int] = None) -> str: """What it does - Reads a requested line range and auto-expands to include surrounding context (function body, docstring, comments). When to use - To inspect a specific hit from find_code_references or a snippet from semantic_rag_search. When not to use - For browsing the entire file or unrelated sections; use semantic_rag_search first to pinpoint relevant parts. Example - read_file_lines_tool("src/auth/login.py", start=45, end=60) β†’ full function with context. Note: This tool treats / as '/home/popertots/Crussell/' so adjust paths accordingly""" return EnhancedToon.file_content_results(read_file_lines(path, start, end), path) @mcp.tool() def init_repo(git_url: str) -> str: """What it does - Initialise the RAG stack with a brand-new Git repository.""" import shutil from pathlib import Path import subprocess # ----- 1. Always wipe and recreate working_repo --------------------- if CODEBASE_PATH.exists(): shutil.rmtree(CODEBASE_PATH) logger.info(f"Wiped existing working repo: {CODEBASE_PATH}") CODEBASE_PATH.mkdir(exist_ok=True) # ----- 2. Clone fresh ----------------------------------------------- try: subprocess.run( ["git", "clone", git_url, str(CODEBASE_PATH)], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, ) logger.info(f"Cloned {git_url} to {CODEBASE_PATH}") except subprocess.CalledProcessError as e: return f"❌ Failed to clone {git_url}: {e.stderr.strip()}" # ----- 3. Reset ALL global state ------------------------------------ global vectorstore, bm25, bm25_corpus, chunks_metadata, index_build_time vectorstore = None bm25 = None bm25_corpus = None chunks_metadata = None index_build_time = 0 # ----- 4. Remove any stale indices ---------------------------------- for artefact in ("chroma_db", "bm25_index.json", "embeddings", ".mcp_cache"): artefact_path = CODEBASE_PATH / artefact if artefact_path.exists(): try: if artefact_path.is_dir(): shutil.rmtree(artefact_path) else: artefact_path.unlink() logger.info(f"Removed stale artefact: {artefact_path}") except Exception as e: logger.warning(f"Could not delete {artefact_path}: {e}") # ----- 5. Re-build the index ---------------------------------------- try: build_indexes() logger.info("Index rebuilt successfully") except Exception as e: logger.exception("Index rebuild failed") return f"❌ Index rebuild failed: {e}" # ----- 6. Report success -------------------------------------------- try: branch_name = subprocess.check_output( ["git", "-C", str(CODEBASE_PATH), "rev-parse", "--abbrev-ref", "HEAD"], text=True, ).strip() git_hash = subprocess.check_output( ["git", "-C", str(CODEBASE_PATH), "rev-parse", "HEAD"], text=True, ).strip() # Verify the index was actually built if vectorstore is None or bm25_corpus is None: return f"⚠️ Repository cloned but index failed to build properly" return f"βœ… Initialized repository at {CODEBASE_PATH}\n Branch: {branch_name}\n Commit: {git_hash[:8]}\n Indexed chunks: {len(bm25_corpus)}" except Exception as e: return f"⚠️ Repository cloned but status check failed: {e}" @mcp.tool() def rebuild_index() -> str: """What it does - Re-creates all embeddings, BM25, and metadata indexes after major code changes. When to use - When the codebase has been pulled or refactored and you suspect stale search results, and only after explicit instruction to do so. When not to use - On every query; it's expensive and unnecessary if the index is already up-to-date. Example - rebuild_index() β†’ 'βœ… Index rebuilt (13.2 s); 11,234 chunks'""" with _startup_lock: try: # Clear LRU caches parse_python_file_cached.cache_clear() parse_sql_file_cached.cache_clear() parse_go_file_cached.cache_clear() parse_java_file_cached.cache_clear() parse_svelte_file_cached.cache_clear() parse_shell_file_cached.cache_clear() LocalGraph(root_path=CODEBASE_PATH).clear() t0 = time.time() build_indexes() dt = time.time() - t0 return f"βœ… Index rebuilt successfully! (took {dt:.1f}s)\n\nStatistics:\n" + json.dumps({ "total_chunks": len(bm25_corpus), "by_language": count_by_language(chunks_metadata), "by_type": count_by_type(chunks_metadata) }, indent=2) except Exception as e: logger.exception("Index rebuild failed") return f"❌ Rebuild failed: {e}" # ----------------------------- # Startup # ----------------------------- def signal_handler(sig, frame): """Handle graceful shutdown on Ctrl+C.""" logger.info("\n=== Shutdown signal received ===") # Clear any LRU caches to release memory try: parse_python_file_cached.cache_clear() parse_sql_file_cached.cache_clear() parse_go_file_cached.cache_clear() parse_java_file_cached.cache_clear() parse_svelte_file_cached.cache_clear() parse_shell_file_cached.cache_clear() logger.info("βœ“ Caches cleared") except Exception as e: logger.warning(f"Cache clearing error: {e}") # Note: Chroma auto-persists, no explicit close needed logger.info("βœ“ Indexes are already persisted to disk") logger.info("Goodbye!\n") # Force exit immediately to avoid thread hang os._exit(0) if __name__ == "__main__": # Register signal handlers signal.signal(signal.SIGINT, signal_handler) signal.signal(signal.SIGTERM, signal_handler) try: logger.info("="*60) logger.info("πŸš€ MCP RAG Server is ready!") logger.info("Press Ctrl+C to stop") # Just run in stdio mode by default mcp.run(transport='stdio') except KeyboardInterrupt: signal_handler(signal.SIGINT, None) except Exception as e: logger.exception("Fatal error during startup") os._exit(1)