Code Intelligence

Synapse gives your AI agent elite code understanding—not just keyword matching, but structural comprehension of your entire codebase. By fusing two retrieval strategies with surgical AST parsing, Synapse surfaces the most relevant context with extreme precision.

💡Precision Retrieval

Most code search tools force a choice between exact text matching or semantic similarity. Synapse does both, fusing the results via Reciprocal Rank Fusion (RRF) to ensure your AI gets the right code every time.

The Hybrid Search Engine

Synapse runs a Hybrid Rank Fusion algorithm that combines the strengths of lexical and semantic retrieval in a single query via search_hybrid.

Strategy	Engine	Best For...
Lexical	BM25	Exact function names, identifiers, unique error messages.
Semantic	Vector	Conceptual search, "plain English" descriptions, finding similar logic.

// Example: "Find logic that handles user permission validation"
search_hybrid({ query: "validate user permissions" })
// Returns exact symbols like `validatePermissions()` AND semantically 
// relevant code like `checkUserRole()` or `hasAccess()`.

Surgical AST-Aware Chunking

Traditional tools split code at arbitrary line limits, often severing functions in half. Synapse uses Abstract Syntax Tree (AST) parsing (via tree-sitter) to index code at its natural boundaries.

Every chunk is:

Atomic: A complete function, class, or method. Never a fragment.
Self-Contained: Retains its parent class, imports, and type signatures.
Language-Aware: Optimized for TypeScript, JavaScript, Python, Go, Rust, and Bash.

Symbol Resolution Suite

Synapse builds a comprehensive, cross-linked symbol index for your workspace, allowing agents to navigate code like a senior developer.

Tool	Capability	Use Case
`find_definition`	Declaration Jump	Jump directly to the source of a specific function.
`find_usages`	Reference Finding	See everywhere a symbol is used before refactoring.
`find_callers`	Impact Analysis	Identify every function that invokes a critical API.
`get_symbol`	Meta-Discovery	Get full metadata including exports and visibility.
`summarize_project`	Architectural Map	Get a high-level overview of the project structure.

Why it Matters for AI Agents

Standard RAG (Retrieval-Augmented Generation) often fails because it misses the structural relationships in code. Synapse's Code Intelligence layer ensures that when an agent asks for context, it receives:

The correct logic (via Hybrid Search).
The complete structure (via AST-Aware Chunking).
The related symbols (via Symbol Resolution).

This "Surgical Context" approach reduces token waste and eliminates "hallucinations" caused by incomplete code snippets.

Next: Learn how code-level facts are connected to architectural history in the Knowledge Graph.