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SHARC uses state-of-the-art embeddings to understand the semantic meaning of code. This page explains the embedding model, generation process, and optimization techniques.

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Embedding Model

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Sharc-Embed

SHARC’s embedding model is optimized for code:
PropertyValue
ModelSharc-Embed
Dimensions4096
Max Tokens8192

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Key Features

  1. Code-optimized: Trained on large code corpora
  2. High dimensionality: 4096 dims capture nuanced semantics
  3. Long context: 8K tokens handles large functions

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Embedding Generation

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Process Overview

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Batching Strategy

Embeddings are generated in optimized batches for efficiency. Multiple batches are processed concurrently, enabling fast indexing even for large codebases.

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Vector Storage

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Full Dimensions

Unlike some implementations that truncate embeddings, SHARC stores the full 4096 dimensions: 
MRL Truncation (other tools):
  4096 → 1024 dims (75% information loss)

SHARC:
  4096 → 4096 dims (no loss)

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Normalization

Vectors are L2-normalized for cosine similarity: 
// Before normalization
[0.5, 0.3, 0.8, ...]

// After normalization (unit length)
[0.47, 0.28, 0.75, ...] // ||v|| = 1
This enables efficient cosine similarity via dot product.

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Context Injection

Embeddings capture more than raw code. SHARC injects context:

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For Code (AST-parsed)

// Original function:
async authenticate(user: string): Promise<boolean> { ... }

// With injected context:
// Context: class AuthService > module auth (services/auth.ts)
async authenticate(user: string): Promise<boolean> { ... }
The embedding now “knows”:
  • This is a method in AuthService
  • It’s in the auth module
  • File is services/auth.ts

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With Decorators

// TypeScript/JavaScript with decorators:
// Context: class UserController @Controller("/users") (controllers/user.ts)
// @Get("/:id") @Auth
async getUser(id: string): Promise<User> { ... }

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For Documentation

// Original:
## Authentication
Users must provide valid credentials...

// With file context:
// File: docs/security/authentication.md
## Authentication
Users must provide valid credentials...

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Semantic Properties

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What Embeddings Capture

PropertyExample
Function purpose”authentication” vs “validation”
Code patternsasync/await, error handling
Data structuresarrays, objects, classes
Domain concepts”user”, “payment”, “order”
Relationshipscaller-callee, inheritance

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Similarity Examples

Query: "user authentication"

High similarity (0.9+):
- async function authenticateUser(credentials) { ... }
- class AuthenticationService { verify() { ... } }

Medium similarity (0.7-0.9):
- function validateUserInput(input) { ... }
- const userSession = { authenticated: true }

Low similarity (< 0.5):
- const styles = { color: 'red' }
- function calculateTax(amount) { ... }

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Indexing Performance

Indexing time depends on codebase size:
CodebaseApproximate Time
Small (~500 files)~15-20 seconds
Medium (e.g., Hono)~30-45 seconds
Large (e.g., Next.js)~3-4 minutes
Subsequent incremental syncs use Merkle diffs and complete near-instantly for unchanged codebases.

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Troubleshooting

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Slow Embedding Generation

  1. Re-index the codebase if search results look stale.
  2. Ensure your codebase indexing completed successfully.
  3. Retry with a more specific semantic query.