cicada-vector 0.1.1

A poor man's vector database for Python. Zero dependencies, JSONL storage.

Cicada Vector 🦗

A lightweight semantic search engine for developers.

Cicada Vector is a simple, zero-dependency semantic search engine and RAG database. It explores a different approach to code intelligence: maximizing semantic awareness while minimizing complexity and dependencies.

Why this exists?

The original Cicada is powerful because it deeply understands code structure (SCIP, ASTs). However, that power often requires heavy dependencies and longer setup times.

Cicada Vector takes a complementary path: It focuses on Semantic Awareness and ease of use. By combining local LLM embeddings (via Ollama) with a hybrid database, it provides robust search capabilities with a minimal footprint.

Features

• Lightweight: Minimal Python codebase. Zero dependencies (Standard Library only) for the core engine.
• Instant Install: No waiting for heavy ML libraries to compile.
• Semantic Intelligence: Understands intent. Searching for "auth" finds login logic, even if the word "auth" isn't present.
• Hybrid Search: Combines Vector semantic search with Keyword exact matching. It won't miss specific identifiers like UserAuth_v2.
• Simple RAG: A "Search Broad -> Scan Specific" pipeline that pinpoints relevant code snippets.
• MCP Ready: Built-in Model Context Protocol server for immediate use with AI assistants.

Tools

1. cigrep (Zero-config Semantic Search)

The fastest way to search your code semantically. No setup needed.

cigrep "how do I handle authentication"

It automatically indexes changed files in the background and searches instantly.

MCP Server

AI assistants can use your local knowledge base directly:

Using uvx (Recommended):

# Start the server (stdio mode)
export CICADA_HYBRID_DIR=./my_db
uvx --from cicada-vector cicada-vec-server

Using pip:

pip install 'cicada-vector[server]'
export CICADA_HYBRID_HYBRID_DIR=./my_db
cicada-vec-server

Tools: search_vectors, search_hybrid, search_code_context, index_directory.

Quick Start

from cicada_vector import HybridDB

# Initialize
db = HybridDB("./my_knowledge_base")

# Add data (get vector from Ollama/OpenAI)
db.add(id="auth.py", vector=[...], text="def login()...", meta={"path": "..."})

# Search with confidence scores
results = db.search(query_text="login", query_vector=[...], k=5)
for id, score, meta in results:
    print(f"[{score:.4f}] Found {id}")

The Stack

• Brains: Ollama (Recommended: nomic-embed-text)
• Storage: JSONL (Human-readable, append-only)
• Engine: Pure Python (with optional Numpy acceleration)

---

Part of the Cicada suite. Simple, effective code intelligence.