pgraf 1.0.0a2

pgraf turns Postgres into a lightning fast property graph engine for use in AI Agents

pgraf

pgraf turns PostgreSQL into a lightning fast property graph engine with vector search capabilities, designed for use in AI agents and applications.

Features

• Typed Models: Strong typing with Pydantic models for nodes, edges, and content
• Vector Search: Built-in support for embeddings and semantic search
• Property Graph: Full property graph capabilities with typed nodes and labeled edges
• Asynchronous API: Modern async/await API for high-performance applications
• PostgreSQL Backend: Uses PostgreSQL's power for reliability and scalability

📚 Documentation | 🚀 Quick Start | 📖 API Reference

Installation

Prerequisites

• Python 3.12+
• PostgreSQL 14+ with pgvector extension installed

Installing pgraf

# From PyPI
pip install pgraf

# From source
git clone https://github.com/gmr/pgraf.git
cd pgraf
pip install -e .

Database Setup

1. Create a database:

   createdb pgraf
   

2. Apply the schema (includes pgvector extension creation):

   psql -d pgraf -f schema/pgraf.sql
   

The schema file creates the pgvector extension, necessary tables, indexes, and stored procedures for the graph functionality.

Usage

Basic Example

import asyncio
from pgraf import graph

async def main():
    # Initialize the graph with PostgreSQL connection
    pgraf = graph.PGraf(url="postgresql://postgres:postgres@localhost:5432/pgraf")

    try:
        # Add a simple node
        person = await pgraf.add_node(
            labels=["person"],
            properties={"name": "Alice", "age": 30}
        )

        # Add a node with content and vector embeddings
        document = await pgraf.add_node(
            labels=["document"],
            properties={
                "tags": ["example"],
                "title": "Sample Document",
                "url": "https://example.com"
            },
            mimetype="text/plain",
            content="This is a sample document that will be embedded in vector space."
        )

        # Create a relationship between nodes
        await pgraf.add_edge(
            source=person.id,
            target=document.id,
            labels=["CREATED"],
            properties={"timestamp": "2023-01-01"}
        )

        # Retrieve nodes
        all_people = []
        async for node in pgraf.get_nodes(
            labels=["person"],
            properties={"name": "Alice"}
        ):
            all_people.append(node)

        # Traverse the graph
        traversal_results = await pgraf.traverse(
            start_node=person.id,
            edge_labels=["CREATED"],
            direction="outgoing",
            max_depth=2
        )

        # Print traversal results
        for node, edge in traversal_results:
            print(f"Node: {node.labels[0] if node.labels else 'Unknown'} {node.id}")
            if edge:
                print(f"  via edge: {edge.labels[0] if edge.labels else 'Unknown'}")

    finally:
        await pgraf.aclose()


if __name__ == "__main__":
    asyncio.run(main())

Semantic Search Example

import asyncio
from pgraf import graph, models
from sentence_transformers import SentenceTransformer

async def main():
    # Initialize the graph
    pgraf = graph.PGraf(url="postgresql://postgres:postgres@localhost:5432/pgraf")

    try:
        # Add some documents with content for vector embedding
        await pgraf.add_node(
            labels=["document"],
            properties={"title": "Climate Change Overview"},
            content="Climate change is the long-term alteration of temperature and weather patterns.",
            mimetype="text/plain"
        )

        await pgraf.add_node(
            labels=["document"],
            properties={"title": "Machine Learning Basics"},
            content="Machine learning is a branch of AI focused on building models that learn from data.",
            mimetype="text/plain"
        )

        await pgraf.add_node(
            labels=["document"],
            properties={"title": "Graph Databases"},
            content="Graph databases store data in nodes and edges, representing entities and relationships.",
            mimetype="text/plain"
        )

        # No need to explicitly generate embeddings - they're created
        # automatically when nodes with content are added

        # Perform semantic search
        # This automatically generates an embedding for the query text
        results = await pgraf.search(
            query="How do databases represent connections between data points?",
            labels=["document"],
            limit=2
        )

        # Print results sorted by relevance
        for result in results:
            print(f"Match: {result.properties.get('title')} (Score: {result.similarity:.4f})")
            print(f"Content: {result.content[:100]}...")
            print()

        # For custom queries, the search method automatically converts query to embeddings
        # You just need to provide the query text, and it will use the internal embedding model
        query_text = "AI techniques for data analysis"

        # The search method handles embedding generation internally
        custom_results = await pgraf.search(
            query=query_text,
            labels=["document"],
            similarity_threshold=0.3,  # Adjust similarity threshold as needed
            limit=2
        )

        for result in custom_results:
            print(f"Custom search match: {result.properties.get('title')} (Score: {result.similarity:.4f})")

    finally:
        await pgraf.aclose()

if __name__ == "__main__":
    asyncio.run(main())

Requirements

• Python 3.12+
• PostgreSQL 14+

License

See LICENSE for details.