@elyracode/semantic-index

Local semantic code search for Elyra. Embeds your codebase so the agent can find relevant code by meaning, not just keywords.

Install

elyra install npm:@elyracode/semantic-index

Configuration

Configure the embeddings endpoint via environment variables. The defaults use the OpenAI API, but you can point at any OpenAI-compatible endpoint -- including fully local ones.

| Env var | Description | Default | |---------|-------------|---------| | ELYRA_EMBED_BASE_URL | OpenAI-compatible embeddings base URL. For fully local/private indexing, point at Ollama (http://localhost:11434/v1) or LM Studio. | https://api.openai.com/v1 | | ELYRA_EMBED_MODEL | Embedding model. For Ollama use nomic-embed-text. | text-embedding-3-small | | ELYRA_EMBED_API_KEY | API key for the embeddings endpoint. Falls back to OPENAI_API_KEY. Not needed for local Ollama. | -- |

Tools

| Tool | Description | |------|-------------| | semantic_index_build | Index the project's tracked source files. Run this once, and re-run after big changes. | | semantic_search | Find code relevant to a natural-language query. |

How it works

The index is stored locally at .elyra/semantic-index.json and never leaves your machine -- the only network traffic is the embedding requests to your configured endpoint.

For full data sovereignty, use a local embeddings endpoint (such as Ollama) so nothing leaves your machine at all.

Usage

Once installed, ask Elyra to find code by meaning:

> Where is rate limiting handled?
> How does the app verify webhook signatures?
> Find the code that retries failed jobs

The agent builds the index with semantic_index_build, then uses semantic_search to locate relevant code.

Skill

This package includes the elyra-semantic-index skill, which guides the agent on when and how to use semantic search effectively.