chimeradb 0.1.0

Knowledge graph + vector search + SQL analytics in SQLite

ChimeraDB

Knowledge graph + vector search + SQL analytics in SQLite.

For LLM apps that need structured memory: RAG, AI agents, question answering, recommendations.

Examples • Docs

---

Quick Start

pip install chimeradb

from chimeradb import KnowledgeGraph

# Auto-embeddings enabled by default
kg = KnowledgeGraph("my.db")

# Create nodes with Cypher
kg.cypher("CREATE (d:Document {text: 'LLMs are transforming software'})")
kg.cypher("CREATE (d:Document {text: 'RAG combines retrieval with generation'})")

# Or bulk insert with SQL
import json
kg.execute(
    "INSERT INTO graph_nodes (labels, properties) VALUES (?, ?)",
    (json.dumps(["Document"]), json.dumps({"text": "Vector databases enable semantic search"}))
)
kg.commit()

# Semantic search
results = kg.search("how do AI apps work?", top_k=3)
for r in results:
    print(f"{r['properties']['text'][:50]}: {r['similarity']:.1%}")

# Graph traversal
network = kg.traverse("node_id", direction="outgoing", max_depth=3)

# SQL analytics
stats = kg.query("SELECT COUNT(*) FROM graph_nodes")

What You Get

• Semantic search: Embeddings auto-generated on every insert
• Graph queries: Cypher for patterns, SQL for complex analytics
• Zero infrastructure: Single SQLite file, runs anywhere
• Any language: Pure SQL extensions work with Python, Node.js, Go, Rust, etc.

Installation

Python Package

pip install chimeradb

Or from source:

git clone https://github.com/codimusmaximus/chimeradb.git
cd chimeradb
./setup.sh && source .venv/bin/activate

SQL Only (Any Language)

# macOS ARM64
mkdir -p extensions
curl -L https://github.com/agentflare-ai/sqlite-graph/releases/latest/download/libgraph.dylib -o extensions/libgraph.dylib
curl -L https://github.com/sqliteai/sqlite-vector/releases/latest/download/vector-macos-arm64.dylib -o extensions/vector.dylib

Then load in any SQLite client:

.load extensions/libgraph
.load extensions/vector

Use from Python, Node.js, Go, Rust, Java, C++, or any language with SQLite support.

Python API

from chimeradb import KnowledgeGraph

# Create database
kg = KnowledgeGraph("my_graph.db")  # Or ":memory:"

# Optional: disable embeddings or use different model
# kg = KnowledgeGraph("my.db", embedding_model=None)
# kg = KnowledgeGraph("my.db", embedding_model="text-embedding-3-small")

# Add nodes
kg.add_entity(
    entity_id="person1",
    labels=["Person"],
    properties={"name": "Alice", "bio": "AI researcher"},
    embed_field="bio"
)

# Add relationships
kg.add_relationship(
    from_id="person1",
    to_id="company1",
    relation_type="WORKS_AT",
    properties={"since": 2020}
)

# Semantic search
results = kg.search("machine learning expert", top_k=10)

# Graph traversal
network = kg.traverse("person1", direction="outgoing", max_depth=3)

# SQL queries
data = kg.query("""
    SELECT json_extract(properties, '$.name') as name
    FROM graph_nodes
    WHERE json_extract(properties, '$.role') = 'Engineer'
""")

kg.close()

Examples

• 00_sql_only.sql: Pure SQL usage (no Python)
• 01_getting_started.py: Python API basics
• 02_basic.py: Semantic search + graph traversal + SQL analytics
• 03_advanced.py: Research paper recommendations with graph analysis

Performance

M1 MacBook Pro:

• Inserts: 10,000+ nodes/second
• Vector search: < 1ms for 100k embeddings
• Storage: ~500 bytes per node (with 384-dim embeddings)

Good for:

• Prototypes and MVPs
• Small-to-medium apps (millions of nodes)
• Edge AI and on-device intelligence

Not designed for:

• Billions of nodes
• Distributed systems
• High-write concurrent workloads

Requirements

• Python 3.8+
• macOS (ARM64 or Intel) - Linux and Windows coming soon
• sentence-transformers (auto-installed by setup.sh)

Documentation

• Getting Started Guide
• Cypher Guide
• Labels Guide
• Limitations

Tech Stack

Built on:

• SQLite - World's most deployed database
• sqlite-vector - Vector similarity search
• sqlite-graph - Cypher queries

License

MIT - see LICENSE