Vectorcode

VectorCode - Semantic Code Search

VectorCode provides semantic code search using RAG (Retrieval-Augmented Generation) and vector embeddings. It indexes code files into ChromaDB and enables finding relevant code by meaning rather than exact text matching.

When to Use This Skill

• Finding code with similar functionality but different naming conventions
• Discovering implementation examples across large codebases
• Understanding where specific concepts/patterns are used
• Retrieving contextual code for AI-assisted development
• Searching when you don't know exact variable/function names

Available MCP Tools

List Projects

mcp__vectorcode__ls - Get list of indexed projects (no parameters)

Semantic Search

mcp__vectorcode__query - Search for code by concept

• query_messages: Array of keywords (separate phrases, include related terms)
• n_query: Number of files to retrieve (increase if context insufficient)
• project_root: Project path (get from ls first)

Index Files

mcp__vectorcode__vectorise - Add files to index

• paths: Array of file paths (accurate, case-sensitive)
• project_root: Project identifier

Manage Index

mcp__vectorcode__files_ls - List indexed files in project mcp__vectorcode__files_rm - Remove files from index

Query Best Practices

1. Always run ls first to get valid project_root values
2. Break queries into keywords - "auth login password" not "authentication login with password"
3. Include related terms - For "function" add: "return value", "parameter", "arguments"
4. Add imported names - If searching for imported class/function, include its name
5. Try orthogonal keywords on retry - If first query fails, use different but related terms
6. Don't repeat exact keywords - Avoid using same query terms from previous attempts
7. Increase file count - If results lack context, increase n_query parameter
8. Don't escape special characters - Use raw strings

CLI Commands

Initialize project:

cd /path/to/project
vectorcode init                    # Setup project
vectorcode vectorise src/          # Index source directory

Update after changes:

vectorcode update                  # Re-index changed files

Search manually:

vectorcode query "authentication logic" -n 10

Configuration

Location: home/.config/vectorcode/config.json5

{
    "db_url": "http://127.0.0.1:8000"  // ChromaDB server URL
}

MCP Setup (in home/.claude/settings.json):

"vectorcode": {
    "command": "vectorcode-mcp-server"
}

Result Handling

• Provide references - Include file paths and line ranges when answering
• Don't paste full source - Reference code locations, not entire files
• Paths are relative - All paths relative to project root
• No external edits - Don't suggest edits outside working directory

Workflow Example

1. Run mcp__vectorcode__ls → get project list
2. Run mcp__vectorcode__query with:
   - query_messages: ["authentication", "user", "login", "password", "validation"]
   - n_query: 10
   - project_root: "/Users/yuri/Workdir/WIP/myapp"
3. Analyze returned files and line ranges
4. Provide answer with file references: "Authentication is handled in src/auth/login.py:45-78"

Limitations

• Requires ChromaDB server running at configured URL
• Retrieval may not be accurate for single file requests (retrieve multiple)
• Results depend on quality of embeddings and chunking
• Best for semantic search, not exact pattern matching (use grep for that)

Troubleshooting

No results: Check project indexed (files_ls), try broader keywords, increase n_query Files not updating: Run vectorcode update or re-vectorize specific files Project not found: Run ls to verify project_root value, check if initialized

Key Difference from grep/ripgrep

• VectorCode: Finds conceptually similar code (semantic understanding)
• grep: Finds exact text patterns (literal matching)

Use VectorCode when you know WHAT you're looking for but not WHERE or HOW it's named.