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semantic-codebase-search

Index codebases into LanceDB vector stores and retrieve relevant snippets via natural language queries.

2|Updated Apr 30, 2026
Search Tags:#codebase#embedding#vector-search#natural-language#code-discovery
One-click install
npx skills add https://github.com/ialameh/sift-coder --skill semantic-codebase-search
Or copy as Structured Prompt for Agentโ–ผ
Please help me install this Agent Skill.
Skill: semantic-codebase-search
Source: https://github.com/ialameh/sift-coder/tree/main/skills/semantic-codebase-search
Command: npx skills add https://github.com/ialameh/sift-coder --skill semantic-codebase-search

SYSTEM DOCUMENTATION & REQUIREMENTS

๐Ÿ’ก This Skill requires lancedb, ollama, jq, nomic-embed-text.

What problem does it solve?

This skill enables fast, semantic discovery of code across large codebases by indexing files into a vector store and retrieving relevant snippets via natural language queries.

Core Features & Use Cases

  • Vector-based semantic search over code files using LanceDB embeddings
  • Natural language query understanding and relevance scoring
  • Index management and incremental updates for evolving codebases
  • Use Case: Quickly locate function implementations or usage examples across a large repo

Quick Start

Index your codebase and start querying it with natural language to surface relevant snippets.

Frequently Asked Questions about semantic-codebase-search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search a large codebase using natural language queries?โ–ผ

You can index your codebase into a vector store using LanceDB and retrieve relevant code snippets by querying with natural language, enabling fast semantic code discovery across large repositories.

Can I use LanceDB and Ollama for vector-based code discovery?โ–ผ

Yes, this approach integrates LanceDB for vector storage and Ollama with the nomic-embed-text model to generate embeddings, enabling semantic code retrieval and reuse.

What is the best way to find function implementations or usage examples across a large repo?โ–ผ

Vector-based semantic search indexes files into embeddings and retrieves relevant snippets via natural language queries, allowing you to quickly locate function implementations or usage examples across large repos.

Do I need nomic-embed-text to index code files for semantic search?โ–ผ

Yes, nomic-embed-text is required to generate vector embeddings for your code files, which are then stored in LanceDB to enable natural language query retrieval and incremental index updates.

Does semantic code search support incremental updates for evolving codebases?โ–ผ

Yes, this workflow supports index management and incremental updates for evolving codebases, allowing you to update the vector store as files change without performing a full reindex every time.

What are the limitations of vector search for code discovery across different programming languages?โ–ผ

Vector search for code discovery is applicable across programming languages, but its effectiveness depends on embedding quality. It is designed for locating code snippets and contextual understanding rather than precise syntactic matching.

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