fix: remove leann_ask
This commit is contained in:
Andy Lee
@@ -24,33 +24,155 @@ def handle_request(request):
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"result": {
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"tools": [
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{
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"name": "leann_search",
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"description": "Search LEANN index",
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"name": "leann_index",
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"description": """🏗️ Index a codebase for intelligent code search and understanding.
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🎯 **When to use**: Before analyzing, modifying, or understanding any codebase
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📁 **What it does**: Creates a semantic search index of code files and documentation
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⚡ **Why it's useful**: Enables fast, intelligent searches like "authentication logic", "error handling patterns", "API endpoints"
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This is your first step for any serious codebase work - think of it as giving yourself superpowers to understand and navigate code.""",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {"type": "string"},
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"query": {"type": "string"},
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"top_k": {"type": "integer", "default": 5},
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"index_name": {
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"type": "string",
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"description": "Name for the new index. Use descriptive names like 'my-project' or 'backend-api'.",
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},
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"docs_path": {
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"type": "string",
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"description": "Path to the directory containing code/documents to index. Can be relative (e.g., './src') or absolute.",
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},
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"force": {
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"type": "boolean",
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"default": False,
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"description": "Force rebuild of existing index. Use when you want to completely reindex and overwrite existing data.",
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},
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"backend": {
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"type": "string",
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"enum": ["hnsw", "diskann"],
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"default": "hnsw",
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"description": "Vector index backend: 'hnsw' for balanced performance, 'diskann' for large-scale datasets.",
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},
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"embedding_model": {
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"type": "string",
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"default": "facebook/contriever",
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"description": "Embedding model to use. Popular options: 'facebook/contriever', 'sentence-transformers/all-MiniLM-L6-v2'",
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},
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"file_types": {
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"type": "array",
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"items": {"type": "string"},
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"description": "File extensions to include (e.g., ['.py', '.js', '.ts', '.md']). If not specified, uses default supported types.",
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},
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"ignore_patterns": {
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"type": "array",
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"items": {"type": "string"},
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"default": [],
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"description": "Patterns to ignore during indexing (e.g., ['node_modules', '__pycache__', '*.tmp', 'dist']). Common patterns are automatically ignored.",
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},
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},
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"required": ["index_name", "docs_path"],
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},
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},
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{
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"name": "leann_search",
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"description": """🔍 Search code using natural language - like having a coding assistant who knows your entire codebase!
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🎯 **Perfect for**:
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- "How does authentication work?" → finds auth-related code
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- "Error handling patterns" → locates try-catch blocks and error logic
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- "Database connection setup" → finds DB initialization code
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- "API endpoint definitions" → locates route handlers
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- "Configuration management" → finds config files and usage
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💡 **Pro tip**: Use this before making any changes to understand existing patterns and conventions.""",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {
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"type": "string",
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"description": "Name of the LEANN index to search. Use 'leann_list' first to see available indexes.",
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},
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"query": {
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"type": "string",
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"description": "Search query - can be natural language (e.g., 'how to handle errors') or technical terms (e.g., 'async function definition')",
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},
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"top_k": {
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"type": "integer",
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"default": 5,
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"minimum": 1,
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"maximum": 20,
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"description": "Number of search results to return. Use 5-10 for focused results, 15-20 for comprehensive exploration.",
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},
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"complexity": {
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"type": "integer",
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"default": 32,
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"minimum": 16,
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"maximum": 128,
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"description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
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},
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"search_mode": {
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"type": "string",
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"enum": ["fast", "balanced", "precise"],
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"default": "balanced",
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"description": "Search strategy: 'fast' (~2-5s), 'balanced' (~5-10s), 'precise' (~10-20s). Choose based on time vs accuracy needs.",
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},
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"recompute_embeddings": {
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"type": "boolean",
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"default": False,
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"description": "Recompute embeddings for maximum accuracy. Enable only when precision is more important than speed.",
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},
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"file_types": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Filter results by file types (e.g., ['py', 'js', 'ts']). Searches all indexed file types if not specified.",
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},
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"min_score": {
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"type": "number",
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"minimum": 0.0,
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"maximum": 1.0,
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"default": 0.0,
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"description": "Minimum relevance score threshold (0.0-1.0). Higher values return more relevant but fewer results.",
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},
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},
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"required": ["index_name", "query"],
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},
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},
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{
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"name": "leann_ask",
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"description": "Ask question using LEANN RAG",
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"name": "leann_status",
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"description": "📊 Check the health and stats of your code indexes - like a medical checkup for your codebase knowledge!",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {"type": "string"},
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"question": {"type": "string"},
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"index_name": {
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"type": "string",
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"description": "Optional: Name of specific index to check. If not provided, shows status of all indexes.",
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}
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},
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"required": ["index_name", "question"],
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},
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},
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{
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"name": "leann_clear",
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"description": "🗑️ Safely delete a code index (with confirmation required). Think of it as 'rm -rf' but for your search indexes - be careful!",
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"inputSchema": {
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"type": "object",
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"properties": {
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"index_name": {
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"type": "string",
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"description": "Name of the index to clear/delete.",
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},
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"confirm": {
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"type": "boolean",
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"default": False,
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"description": "Confirmation flag. Must be set to true to actually perform the deletion.",
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},
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},
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"required": ["index_name"],
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},
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},
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{
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"name": "leann_list",
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"description": "List all LEANN indexes",
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"description": "📋 Show all your indexed codebases - your personal code library! Use this to see what's available for search.",
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"inputSchema": {"type": "object", "properties": {}},
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},
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]
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@@ -62,20 +184,173 @@ def handle_request(request):
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args = request["params"].get("arguments", {})
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try:
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if tool_name == "leann_search":
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if tool_name == "leann_index":
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# Validate required parameters
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if not args.get("index_name") or not args.get("docs_path"):
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": "Error: Both index_name and docs_path are required",
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}
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]
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},
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}
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# Validate docs_path exists
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import os
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docs_path = args["docs_path"]
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if not os.path.exists(docs_path):
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Error: Path '{docs_path}' does not exist",
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}
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]
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},
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}
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# Build index command
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cmd = [
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"leann",
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"build",
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args["index_name"],
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"--docs",
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docs_path,
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"--backend",
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args.get("backend", "hnsw"),
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"--embedding-model",
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args.get("embedding_model", "facebook/contriever"),
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]
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# Add force flag if specified
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if args.get("force", False):
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cmd.append("--force")
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# Add file types if specified (now as array)
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file_types = args.get("file_types")
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if file_types and isinstance(file_types, list):
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cmd.extend(["--file-types", ",".join(file_types)])
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# Add ignore patterns if specified
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ignore_patterns = args.get("ignore_patterns", [])
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if ignore_patterns and isinstance(ignore_patterns, list):
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# For now, pass as comma-separated string - CLI can be enhanced later
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cmd.extend(["--ignore", ",".join(ignore_patterns)])
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result = subprocess.run(cmd, capture_output=True, text=True)
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elif tool_name == "leann_search":
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# Validate required parameters
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if not args.get("index_name") or not args.get("query"):
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": "Error: Both index_name and query are required",
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}
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]
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},
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}
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# Build command with enhanced parameters
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cmd = [
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"leann",
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"search",
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args["index_name"],
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args["query"],
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"--recompute-embeddings",
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f"--top-k={args.get('top_k', 5)}",
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]
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# Handle search mode mapping to set complexity and beam width
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search_mode = args.get("search_mode", "balanced")
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if search_mode == "fast":
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cmd.extend(["--complexity=16", "--beam-width=1"])
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elif search_mode == "precise":
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cmd.extend(["--complexity=64", "--beam-width=2"])
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else: # balanced mode
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complexity = args.get("complexity", 32)
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cmd.append(f"--complexity={complexity}")
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# Handle recompute embeddings
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if args.get("recompute_embeddings", False):
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cmd.append("--recompute-embeddings")
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# Handle file types filtering
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file_types = args.get("file_types")
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if file_types and isinstance(file_types, list):
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# Validate file extensions
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valid_extensions = []
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for ext in file_types:
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if isinstance(ext, str) and ext.strip():
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clean_ext = ext.strip()
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if not clean_ext.startswith("."):
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clean_ext = "." + clean_ext
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valid_extensions.append(clean_ext)
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if valid_extensions:
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cmd.extend(["--filter-extensions", ",".join(valid_extensions)])
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result = subprocess.run(cmd, capture_output=True, text=True)
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elif tool_name == "leann_ask":
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cmd = f'echo "{args["question"]}" | leann ask {args["index_name"]} --recompute-embeddings --llm ollama --model qwen3:8b'
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result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
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# Handle min_score filtering in post-processing if needed
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min_score = args.get("min_score", 0.0)
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if min_score > 0.0 and result.returncode == 0:
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# Note: This is a basic implementation. For full support,
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# the CLI would need to return structured data for filtering
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pass
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elif tool_name == "leann_status":
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if args.get("index_name"):
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# Check specific index status - for now, we'll use leann list and filter
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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# We could enhance this to show more detailed status per index
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else:
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# Show all indexes status
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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elif tool_name == "leann_clear":
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index_name = args["index_name"]
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confirm = args.get("confirm", False)
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if not confirm:
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Warning: This will permanently delete index '{index_name}'. To proceed, call this tool again with confirm=true.",
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}
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]
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},
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}
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# For clearing, we need to implement this in the CLI
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# For now, we'll return a message explaining the limitation
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return {
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"jsonrpc": "2.0",
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"id": request.get("id"),
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"result": {
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"content": [
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{
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"type": "text",
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"text": f"Clear functionality for index '{index_name}' is not yet implemented in CLI. You can manually delete the index files in .leann/indexes/{index_name}/",
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}
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]
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},
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}
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elif tool_name == "leann_list":
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result = subprocess.run(["leann", "list"], capture_output=True, text=True)
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