Module embeddings 

Semantic search via embeddings.

This module provides embedding generation and vector similarity search for semantic code search. It supports multiple embedding providers:

• OpenAI: Uses text-embedding-3-small for high-quality embeddings
• Local: Uses fastembed for local, offline embeddings

Architecture

Embeddings are stored in SQLite as JSON-encoded float arrays. Similarity search is performed in Rust using cosine similarity for accuracy and portability (no native vector extensions required).

Usage

let provider = OpenAIProvider::new("sk-...")?;
let embedding = provider.embed("fn authenticate(user: &str)")?;

let results = search_similar(&db, "authentication functions", 10)?;

Re-exports

pub use local::LocalProvider;

pub use openai::OpenAIProvider;

Modules

local

Local embedding provider using fastembed.

openai

OpenAI embedding provider.

Structs

Embedding

A vector embedding.

SearchResult

Search result from similarity search.

Constants

LOCAL_EMBEDDING_DIM

OPENAI_EMBEDDING_DIM

Embedding dimension for different models

Traits

EmbeddingProvider

Trait for embedding providers.

Functions

cosine_similarity

Compute cosine similarity between two vectors.

dot_product

Compute dot product similarity between two vectors.

embed_missing_symbols

Embed all symbols that don’t have embeddings yet.

normalize

Normalize a vector to unit length.

semantic_search

Perform semantic similarity search using embeddings.