ragmcp/mcp_codebase.py

import os
import signal
import json
import ast
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")
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 = []

    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', '<anon>')}\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"<script(?:\s+[^>]*)?>(.*?)</script>", 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 <script ...> attributes

            # Extract script context (module, context="module")
            is_module = 'context="module"' in script_attrs
            script_type = "script_module" if is_module else "script"

            ts_helper = Path("./tools/parse_ts.js")
            if ts_helper.exists():
                tmp = filepath.parent / f".tmp_{filepath.name}_{idx}.ts"
                try:
                    tmp.write_text(script_content, encoding="utf-8")
                    proc = subprocess.run(
                        ["node", str(ts_helper), str(tmp)],
                        capture_output=True, text=True, check=True, timeout=15
                    )
                    decls = json.loads(proc.stdout)
                    script_lines = script_content.splitlines(keepends=True)

                    for d in decls:
                        start_rel = max(0, d.get("start_line", 1) - 1)
                        end_rel = d.get("end_line", start_rel + 1)
                        chunk_code = "".join(script_lines[start_rel:end_rel])
                        doc = d.get("doc_comment", "").strip()
                        abs_start = script_start_line + start_rel

                        # Extract component relationships
                        decl_name = d.get("name")
                        decl_type = d.get("type")

                        # Track props for component interface
                        if decl_type == "variable" and decl_name and not is_module:
                            # Look for prop patterns: export let prop = ...
                            if re.search(r'export\s+let\s+' + re.escape(decl_name), chunk_code):
                                component_props.append(decl_name)
                                decl_type = "prop"
                            # Track reactive declarations: $: reactiveVar = ...
                            elif chunk_code.strip().startswith('$:'):
                                component_context.append(decl_name)
                                decl_type = "reactive"

                        chunk_text = f"File: {filepath}\nType: {decl_type}\nName: {decl_name}\nDoc: {doc}\nCode:\n{chunk_code}"

                        metadata = {
                            "file": str(filepath),
                            "name": decl_name,
                            "type": decl_type,
                            "line": abs_start,
                            "language": "typescript",
                            "block_type": script_type,
                            "component_name": component_name
                        }

                        # Add specific metadata for different declaration types
                        if decl_type == "prop":
                            metadata["is_exported"] = True
                            metadata["component_prop"] = True
                        elif decl_type == "reactive":
                            metadata["is_reactive"] = True
                        elif decl_type == "function":
                            metadata["is_function"] = True
                            # Try to detect event dispatchers
                            if "createEventDispatcher" in chunk_code:
                                metadata["is_event_dispatcher"] = True

                        chunks.append({
                            "text": chunk_text,
                            "metadata": metadata
                        })

                except Exception as e:
                    logger.warning(f"TS parser failed for {filepath}: {e}; falling back to heuristic")
                    chunks.append(_make_script_chunk(filepath, script_content, script_start_line, script_type, component_name))
                finally:
                    tmp.unlink(missing_ok=True)
            else:
                chunks.append(_make_script_chunk(filepath, script_content, script_start_line, script_type, component_name))
            idx += 1

        # Style blocks with CSS relationship extraction
        for m in re.finditer(r"<style(?:\s+[^>]*)?>(.*?)</style>", text, flags=re.DOTALL):
            style_content = m.group(1)
            style_start_line = text[:m.start(1)].count("\n") + 1

            # Extract CSS classes and relationships
            css_classes = re.findall(r'\.([a-zA-Z][\w-]*)\s*{', style_content)
            css_selectors = re.findall(r'([a-zA-Z][\w-]*)\s*{', style_content)

            chunk_text = f"File: {filepath}\nType: style\nComponent: {component_name}\n"
            if css_classes:
                chunk_text += f"CSS Classes: {', '.join(css_classes)}\n"
            chunk_text += f"Style Content:\n{style_content}"

            chunks.append({
                "text": chunk_text,
                "metadata": {
                    "file": str(filepath),
                    "name": f"style_block_{idx}",
                    "type": "style",
                    "line": style_start_line,
                    "language": "css",
                    "block_type": "style",
                    "component_name": component_name,
                    "css_classes": css_classes,
                    "css_selectors": css_selectors
                }
            })
            idx += 1

        # Markup with Svelte-specific element analysis
        markup = re.sub(r"<script[^>]*>.*?</script>", "", text, flags=re.DOTALL)
        markup = re.sub(r"<style[^>]*>.*?</style>", "", 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."""
    filepath = Path(filepath_str)
    chunks = []

    logger.info(f"🔧 Parsing Rust file: {filepath}")

    try:
        code = filepath.read_text(encoding="utf-8")
        lines = code.splitlines(keepends=True)

        # Try using rust-analyzer AST if available
        rust_helper = Path("./tools/parse_rust_ast")
        if rust_helper.exists():
            logger.info(f"  Using Rust AST helper: {rust_helper}")
            try:
                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)
                    chunk_code = "".join(lines[start_line:end_line])

                    # Build comprehensive metadata
                    metadata = {
                        "file": str(filepath),
                        "name": d.get("name", ""),
                        "type": d.get("type_", ""),
                        "line": start_line + 1,
                        "language": "rust",
                    }

                    # Add Rust-specific metadata
                    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")

                    # Log what we found
                    logger.info(f"    Declaration {i+1}: {metadata.get('type', 'unknown')} {metadata.get('name', 'unnamed')}")

                    chunk_text = f"File: {filepath}\nType: {d.get('type_')}\nName: {d.get('name')}\n"

                    # Add Rust-specific details to text
                    if d.get("visibility"):
                        chunk_text += f"Visibility: {d.get('visibility')}\n"
                    if d.get("is_async"):
                        chunk_text += "Async: yes\n"
                    if d.get("is_unsafe"):
                        chunk_text += "Unsafe: yes\n"
                    if d.get("generics"):
                        chunk_text += f"Generics: {d.get('generics')}\n"
                    if d.get("traits"):
                        chunk_text += f"Implements: {', '.join(d.get('traits', []))}\n"
                    if d.get("fields"):
                        chunk_text += f"Fields: {', '.join(d.get('fields', []))}\n"

                    chunk_text += f"Code:\n{chunk_code}"

                    chunks.append({
                        "text": chunk_text,
                        "metadata": metadata
                    })

                if chunks:
                    logger.info(f"✅ Successfully parsed {len(chunks)} chunks from {filepath}")
                    return tuple((c["text"], tuple(c["metadata"].items())) for c in chunks)
                else:
                    logger.warning(f"❌ AST helper returned declarations but no chunks were created")

            except Exception as e:
                logger.warning(f"Rust AST helper failed for {filepath}: {e}; falling back to regex")

        # Fallback: Regex-based Rust parsing
        logger.info(f"  Falling back to regex parsing for {filepath}")
        regex_chunks = _parse_rust_with_regex(filepath, code, lines)
        chunks.extend(regex_chunks)
        logger.info(f"  Regex found {len(regex_chunks)} chunks")

    except Exception as e:
        logger.warning(f"parse_rust_file failed {filepath}: {e}")

    final_result = tuple((c["text"], tuple(c["metadata"].items())) for c in chunks)
    logger.info(f"📦 Final result: {len(final_result)} chunks with metadata")
    return final_result

def _parse_rust_with_regex(filepath: Path, code: str, lines: list) -> list:
    """Fallback regex-based Rust parser."""
    chunks = []

    # Pattern for Rust functions (including async, unsafe, methods)
    func_pattern = re.compile(
        r'^(?:pub\s+)?(?:async\s+)?(?:unsafe\s+)?fn\s+(\w+)\s*[<(]',
        re.MULTILINE
    )

    # Pattern for structs
    struct_pattern = re.compile(
        r'^(?:pub\s+)?struct\s+(\w+)\s*(?:<[^>]*>)?\s*\{',
        re.MULTILINE
    )

    # Pattern for enums
    enum_pattern = re.compile(
        r'^(?:pub\s+)?enum\s+(\w+)\s*\{',
        re.MULTILINE
    )

    # Pattern for traits
    trait_pattern = re.compile(
        r'^(?:pub\s+)?trait\s+(\w+)\s*\{',
        re.MULTILINE
    )

    # Pattern for impl blocks
    impl_pattern = re.compile(
        r'^impl\s+(?:<[^>]*>)?\s*(\w+)\s*(?:<[^>]*>)?\s*\{',
        re.MULTILINE
    )

    # Pattern for modules
    mod_pattern = re.compile(
        r'^(?:pub\s+)?mod\s+(\w+)\s*\{',
        re.MULTILINE
    )

    # Parse functions
    for match in func_pattern.finditer(code):
        func_name = match.group(1)
        start_pos = match.start()
        start_line = code[:start_pos].count('\n')
        end_line = _find_rust_brace_block_end(lines, start_line)
        func_code = "".join(lines[start_line:end_line + 1])

        # Enhanced function analysis
        is_async = 'async' in match.group(0)
        is_unsafe = 'unsafe' in match.group(0)
        is_pub = 'pub' in match.group(0)

        chunk_text = f"File: {filepath}\nType: function\nName: {func_name}\n"
        if is_async:
            chunk_text += "Async: yes\n"
        if is_unsafe:
            chunk_text += "Unsafe: yes\n"
        if is_pub:
            chunk_text += "Visibility: pub\n"
        chunk_text += f"Code:\n{func_code}"

        metadata = {
            "file": str(filepath),
            "name": func_name,
            "type": "function",
            "line": start_line + 1,
            "language": "rust",
            "is_async": is_async,
            "is_unsafe": is_unsafe,
            "visibility": "pub" if is_pub else "private"
        }

        chunks.append({"text": chunk_text, "metadata": metadata})

    # Parse structs
    for match in struct_pattern.finditer(code):
        struct_name = match.group(1)
        start_pos = match.start()
        start_line = code[:start_pos].count('\n')
        end_line = _find_rust_brace_block_end(lines, start_line)
        struct_code = "".join(lines[start_line:end_line + 1])

        # Extract fields from struct
        fields = _extract_rust_struct_fields(struct_code)

        chunk_text = f"File: {filepath}\nType: struct\nName: {struct_name}\n"
        if fields:
            chunk_text += f"Fields: {', '.join(fields)}\n"
        chunk_text += f"Code:\n{struct_code}"

        metadata = {
            "file": str(filepath),
            "name": struct_name,
            "type": "struct",
            "line": start_line + 1,
            "language": "rust",
            "fields": fields
        }

        chunks.append({"text": chunk_text, "metadata": metadata})

    # Parse enums
    for match in enum_pattern.finditer(code):
        enum_name = match.group(1)
        start_pos = match.start()
        start_line = code[:start_pos].count('\n')
        end_line = _find_rust_brace_block_end(lines, start_line)
        enum_code = "".join(lines[start_line:end_line + 1])

        # Extract variants from enum
        variants = _extract_rust_enum_variants(enum_code)

        chunk_text = f"File: {filepath}\nType: enum\nName: {enum_name}\n"
        if variants:
            chunk_text += f"Variants: {', '.join(variants)}\n"
        chunk_text += f"Code:\n{enum_code}"

        metadata = {
            "file": str(filepath),
            "name": enum_name,
            "type": "enum",
            "line": start_line + 1,
            "language": "rust",
            "variants": variants
        }

        chunks.append({"text": chunk_text, "metadata": metadata})

    # Parse traits
    for match in trait_pattern.finditer(code):
        trait_name = match.group(1)
        start_pos = match.start()
        start_line = code[:start_pos].count('\n')
        end_line = _find_rust_brace_block_end(lines, start_line)
        trait_code = "".join(lines[start_line:end_line + 1])

        # Extract method signatures from trait
        methods = _extract_rust_trait_methods(trait_code)

        chunk_text = f"File: {filepath}\nType: trait\nName: {trait_name}\n"
        if methods:
            chunk_text += f"Methods: {', '.join(methods)}\n"
        chunk_text += f"Code:\n{trait_code}"

        metadata = {
            "file": str(filepath),
            "name": trait_name,
            "type": "trait",
            "line": start_line + 1,
            "language": "rust",
            "methods": methods
        }

        chunks.append({"text": chunk_text, "metadata": metadata})

    # Parse impl blocks
    for match in impl_pattern.finditer(code):
        impl_target = match.group(1)
        start_pos = match.start()
        start_line = code[:start_pos].count('\n')
        end_line = _find_rust_brace_block_end(lines, start_line)
        impl_code = "".join(lines[start_line:end_line + 1])

        # Extract methods from impl block
        impl_methods = _extract_rust_impl_methods(impl_code)

        chunk_text = f"File: {filepath}\nType: impl\nTarget: {impl_target}\n"
        if impl_methods:
            chunk_text += f"Implementation Methods: {', '.join(impl_methods)}\n"
        chunk_text += f"Code:\n{impl_code}"

        metadata = {
            "file": str(filepath),
            "name": f"impl_{impl_target}",
            "type": "impl",
            "line": start_line + 1,
            "language": "rust",
            "target": impl_target,
            "methods": impl_methods
        }

        chunks.append({"text": chunk_text, "metadata": metadata})

    return chunks

def _find_rust_brace_block_end(lines, start_line):
    """Find the closing brace for Rust code blocks."""
    brace_count = 0
    for i in range(start_line, len(lines)):
        line = lines[i]
        brace_count += line.count('{')
        brace_count -= line.count('}')
        if brace_count == 0:
            return i
    return len(lines) - 1

def _extract_rust_struct_fields(struct_code):
    """Extract field names from Rust struct definition."""
    fields = []
    # Look for field patterns: field_name: Type,
    field_matches = re.findall(r'(\w+)\s*:\s*[^,\n]+', struct_code)
    fields.extend(field_matches)
    return fields

def _extract_rust_enum_variants(enum_code):
    """Extract variant names from Rust enum definition."""
    variants = []
    # Look for variant patterns: VariantName,
    variant_matches = re.findall(r'(\w+)(?:\([^)]*\))?\s*,', enum_code)
    variants.extend(variant_matches)
    return variants

def _extract_rust_trait_methods(trait_code):
    """Extract method names from Rust trait definition."""
    methods = []
    # Look for method signatures in trait
    method_matches = re.findall(r'fn\s+(\w+)\s*\([^)]*\)', trait_code)
    methods.extend(method_matches)
    return methods

def _extract_rust_impl_methods(impl_code):
    """Extract method names from Rust impl block."""
    methods = []
    # Look for method implementations in impl block
    method_matches = re.findall(r'fn\s+(\w+)\s*\([^)]*\)', impl_code)
    methods.extend(method_matches)
    return methods

@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

# -----------------------------
# Build / load indexes with contextual weighting
# -----------------------------
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)

    graph = LocalGraph(root_path=CODEBASE_PATH)
    graph.clear()

    # 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":
            continue

        # Ensure file node exists
        file_node_id = f"file::{fpath}"
        graph.add_node(file_node_id, type="File", 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
        }

        # 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
                node_id = f"{lang}::{node_type}::{fpath}::{m['class']}.{name}"
                graph.add_node(node_id, **base_attrs, type=node_type,
                            class_name=m.get("class"),
                            return_type=m.get("return_type"),
                            parameters=m.get("parameters", []))

                # Connect method to its class
                class_node_id = f"{lang}::class::{fpath}::{m['class']}"
                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 != "void":
                    for type_id, type_data in graph.find_nodes(name=return_type):
                        graph.add_edge(node_id, type_id, "returns")
            else:
                # Regular function
                graph.add_node(node_id, **base_attrs, type=node_type,
                            return_type=m.get("return_type"),
                            parameters=m.get("parameters", []))
                graph.add_edge(file_node_id, node_id, "contains")

        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", []),  # Store fields as metadata, not separate nodes
                        variants=m.get("variants", []))  # For enums

            graph.add_edge(file_node_id, node_id, "contains")

            # Inheritance relationships
            extends_class = m.get("extends")
            if extends_class:
                for parent_id, parent_data in graph.find_nodes(name=extends_class):
                    graph.add_edge(node_id, parent_id, "extends")

            # Interface implementation
            implements_interfaces = m.get("implements", [])
            for interface_name in implements_interfaces:
                for interface_id, interface_data in graph.find_nodes(name=interface_name):
                    graph.add_edge(node_id, interface_id, "implements")

            # Bevy-specific: Plugin implementation detection
            if node_type == "struct" and name.endswith("Plugin"):
                graph.add_node(node_id, **base_attrs, type=node_type, is_bevy_plugin=True)

        elif node_type == "impl":
            # Implementation blocks
            target = m.get("target", "unknown")
            node_id = f"{lang}::{node_type}::{fpath}::{target}"

            graph.add_node(node_id, **base_attrs, type=node_type,
                        target=target,
                        traits=m.get("traits", []),
                        methods=m.get("methods", []))

            graph.add_edge(file_node_id, node_id, "contains")

            # Connect to target type
            for target_id, target_data in graph.find_nodes(name=target):
                graph.add_edge(node_id, target_id, "implements_for")

            # Connect to implemented traits
            for trait_name in m.get("traits", []):
                for trait_id, trait_data in graph.find_nodes(name=trait_name):
                    graph.add_edge(node_id, trait_id, "implements")

            # Bevy-specific: Plugin detection
            if target.endswith("Plugin"):
                graph.add_node(node_id, **base_attrs, type=node_type, is_bevy_plugin_impl=True)

        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")

        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")

        # Handle imports if present
        for imp in m.get("imports", []):
            graph.add_edge(file_node_id, imp, "imports")

        # Handle function calls if present
        for call in m.get("calls", []):
            graph.add_edge(node_id, call, "calls")

        # Rust-specific relationships
        if lang == "rust":
            if node_type == "struct":
                # Connect struct fields as metadata, not separate nodes
                fields = m.get("fields", [])
                for field in fields:
                    # Store field info as node attribute instead of creating separate nodes
                    if "field_types" not in graph.nodes[node_id]:
                        graph.nodes[node_id]["field_types"] = []
                    graph.nodes[node_id]["field_types"].append(field)

            elif node_type == "enum":
                # Connect enum variants as metadata
                variants = m.get("variants", [])
                for variant in variants:
                    if "variants" not in graph.nodes[node_id]:
                        graph.nodes[node_id]["variants"] = []
                    graph.nodes[node_id]["variants"].append(variant)

            elif node_type == "function":
                # Connect function parameters to their types
                parameters = m.get("parameters", [])
                for param in parameters:
                    # Extract type from "Type param_name" format
                    param_parts = param.split()
                    if len(param_parts) >= 2:
                        param_type = param_parts[0]
                        if param_type not in ['self', '&self', '&mut self', 'mut']:
                            for type_id, type_data in graph.find_nodes(name=param_type):
                                graph.add_edge(node_id, type_id, "uses_parameter")

        # Bevy-specific relationship detection
        if lang == "rust" and node_type == "function":
            # Detect Bevy system functions
            function_name = name.lower()
            if any(keyword in function_name for keyword in ['system', 'plugin', 'build', 'update']):
                graph.nodes[node_id]["is_bevy_system"] = True

            # Detect query parameters for system dependencies
            parameters = m.get("parameters", [])
            for param in parameters:
                if 'Query' in param:
                    graph.nodes[node_id]["has_bevy_query"] = True
                if 'Res<' in param or 'ResMut<' in param:
                    graph.nodes[node_id]["has_bevy_resource"] = True
                if 'Commands' in param:
                    graph.nodes[node_id]["has_bevy_commands"] = True

    # Second pass for Bevy-specific relationships
    bevy_connections = 0

    for node_id, node_data in graph.graph.nodes(data=True):
        if node_data.get('lang') == 'rust':
            # Connect Plugin impls to their build functions
            if node_data.get('type') == 'impl' and node_data.get('is_bevy_plugin_impl'):
                plugin_struct = node_data.get('target')
                if plugin_struct:
                    # Find the build method in this impl
                    for method_id, method_data in graph.find_nodes(type="method", class_name=plugin_struct):
                        if method_data.get('name') == 'build':
                            graph.add_edge(node_id, method_id, "defines_plugin")
                            bevy_connections += 1

            # Connect systems to their plugins
            elif node_data.get('is_bevy_system'):
                # Try to find which plugin this system belongs to
                file_path = node_data.get('file', '')
                if file_path:
                    # Look for plugins in the same file
                    for plugin_id, plugin_data in graph.find_nodes(type="struct", file=file_path):
                        if plugin_data.get('name', '').endswith('Plugin'):
                            graph.add_edge(plugin_id, node_id, "contains_system")
                            bevy_connections += 1

    if bevy_connections > 0:
        logger.info(f"Added {bevy_connections} Bevy-specific relationships")
    logger.info("Building method parameter type relationships...")
    param_connections = 0
    for node_id, node_data in graph.graph.nodes(data=True):
        if node_data.get('type') in ['method', 'function', 'constructor']:
            parameters = node_data.get('parameters', [])
            for param in parameters:
                # Extract type from parameter string "TypeName paramName"
                param_type = param.split()[0] if param and ' ' in param else None
                if param_type and param_type not in ['void', 'int', 'String', 'boolean', 'long', 'double', 'float']:
                    # Try to find this type in the graph
                    for type_id, type_data in graph.find_nodes(name=param_type):
                        graph.add_edge(node_id, type_id, "uses_parameter")
                        param_connections += 1

    logger.info(f"Added {param_connections} parameter type 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, weighted_embs):
            self.weighted_embs = weighted_embs
            self.idx = 0

        def embed_documents(self, texts):
            # Return the pre-computed weighted embeddings
            start = self.idx
            end = start + len(texts)
            result = self.weighted_embs[start:end]
            self.idx = end
            return result

        def batch_embed_documents(texts: List[str]) -> List[List[float]]:
            # Embed documents in batches
            all_embeddings = []
            for i in range(0, len(texts), EMBEDDING_BATCH_SIZE):
                batch = texts[i:i + EMBEDDING_BATCH_SIZE]
                embeddings = embeddings.embed_documents(batch)
                all_embeddings.extend(embeddings)
            return all_embeddings

        def embed_query(self, text):
            # For queries, use the base embedding model (no weighting)
            return embeddings.embed_query(text)

    weighted_emb_func = WeightedEmbeddingFunction(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")

# -----------------------------
# 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'<Instruct>: {instruction}\n'
                f'<Query>: {query}\n'
                f'<Document>: {chunk[:8000]}\n'  # Limit document length
                f'<|im_end|>\n'
                f'<|im_start|>assistant\n<think>\n\n</think>\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

# -----------------------------
# 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")

    # Limit expansion to reasonable bounds (max 100 lines of context)
    max_context = 100
    if context_end - context_start > max_context:
        context_end = context_start + max_context
        context_info.append("truncated to 100 lines")

    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() → { "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
            graph = LocalGraph(str(CODEBASE_PATH))
            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",
                    "search_codebase": "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
            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."""
    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):
        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):
        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):
        lang = data.get('language', 'unknown')
        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']

    # Relationship density
    avg_edges_per_node = edges / nodes if nodes > 0 else 0

    # Most connected nodes (hubs)
    degree_centrality = dict(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]
        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 search_codebase(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: search_codebase("user authentication", 5, True) → 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."

    graph = LocalGraph(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']:
            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") → 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 search_codebase.
When not to use - For browsing the entire file or unrelated sections; use search_codebase first to pinpoint relevant parts.
Example - read_file_lines_tool("src/auth/login.py", start=45, end=60) → 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"✅ 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() → '✅ 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"✅ 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("✓ Caches cleared")
    except Exception as e:
        logger.warning(f"Cache clearing error: {e}")

    # Note: Chroma auto-persists, no explicit close needed
    logger.info("✓ 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("🚀 MCP RAG Server is ready!")
        logger.info("Use 'python serve_http.py' for HTTP server")
        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)