chimeradb 0.1.1

Knowledge graph + vector search + SQL analytics in SQLite

ChimeraDB

Semantic search + graph queries + SQL analytics. All in one SQLite file.

The only database that combines vector embeddings, Cypher graph patterns, and full SQL for LLM apps. No separate vector DB, no separate graph DB, no infrastructure.

Examples • Docs

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Quick Start

pip install chimeradb

from chimeradb import KnowledgeGraph

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

# 1. Vector embeddings - semantic search
kg.cypher("CREATE (p:Person {name: 'Alice', bio: 'ML engineer building LLM agents'})")
kg.cypher("CREATE (p:Person {name: 'Bob', bio: 'AI researcher focused on NLP'})")

results = kg.search("who works on language models?", top_k=2)
# Finds both Alice and Bob even though query doesn't match exactly

# 2. Cypher - graph relationships
kg.cypher("CREATE (alice:Person {name: 'Alice'})")
kg.cypher("CREATE (bob:Person {name: 'Bob'})")
kg.cypher("CREATE (acme:Company {name: 'Acme AI'})")
kg.cypher("MATCH (alice:Person {name: 'Alice'}), (acme:Company) CREATE (alice)-[:WORKS_AT]->(acme)")

# Simple pattern matching - no messy SQL joins
colleagues = kg.cypher("MATCH (p:Person)-[:WORKS_AT]->(:Company)<-[:WORKS_AT]-(colleague) RETURN colleague")

# 3. SQL - analytics and aggregation
stats = kg.query("""
    SELECT
        json_extract(properties, '$.name') as company,
        COUNT(*) as employee_count
    FROM graph_nodes
    WHERE labels LIKE '%Company%'
    GROUP BY company
""")

# The power: combine all three in one query!

Why ChimeraDB?

Three powerful tools, one simple database:

1. Vector embeddings - Search by meaning, not keywords. Find "machine learning expert" when the text says "AI researcher"
2. Cypher graph queries - Express relationships naturally: MATCH (person)-[:WORKS_AT]->(company) beats complex SQL joins
3. Full SQL analytics - Aggregate, filter, join with the full power of SQLite when you need it

The combination is the killer feature:

• RAG systems: Semantic search + relationship context
• AI agents: Graph traversal + analytical reasoning
• Recommendations: Similarity search + collaborative filtering

Zero infrastructure:

• One SQLite file
• Runs anywhere (laptop, server, edge device)
• Works with any language (Python, Node.js, Go, Rust...)

Installation

Python Package

pip install chimeradb

Platform Support:

• ✅ macOS (Intel & ARM64)
• ✅ Linux (x86_64)
• ⚠️ Windows: Not tested. For Windows users, build extensions manually from sqlite-graph and sqlite-vector source, or use WSL.

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

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