modaic 0.4.1

Modular Agent Infrastructure Collection, a python framework for managing and sharing DSPy agents

Modaic 🐙

Modular Agent Infrastructure Collection, a Python framework for building AI agents with structured context management, database integration, and retrieval-augmented generation (RAG) capabilities.

Overview

Modaic provides a comprehensive toolkit for creating intelligent agents that can work with diverse data sources including tables, documents, and databases. Built on top of DSPy, it offers a way to share and manage declarative agent architectures with integrated vector, SQL, and graph database support.

Key Features

• Hub Support: Load and share precompiled agents from Modaic Hub
• Context Management: Structured handling of molecular and atomic context types
• Database Integration: Support for Vector (Milvus, Pinecone, Qdrant), SQL (SQLite, MySQL, PostgreSQL), and Graph (Memgraph, Neo4j)
• Agent Framework: Precompiled and auto-loading agent architectures
• Table Processing: Advanced Excel/CSV processing with SQL querying capabilities

Installation

Using uv (recommended)

uv add modaic

Optional (for hub operations):

export MODAIC_TOKEN="<your-token>"

Using pip

Please note that you will not be able to push agents to the Modaic Hub with pip.

pip install modaic

Quick Start

Creating a Simple Agent

from modaic import PrecompiledAgent, PrecompiledConfig

class WeatherConfig(PrecompiledConfig):
    weather: str = "sunny"

class WeatherAgent(PrecompiledAgent):
    config: WeatherConfig

    def __init__(self, config: WeatherConfig, **kwargs):
        super().__init__(config, **kwargs)

    def forward(self, query: str) -> str:
        return f"The weather in {query} is {self.config.weather}."

agent = WeatherAgent(WeatherConfig())
print(agent(query="Tokyo"))

Save and load locally:

agent.save_precompiled("./my-weather")

from modaic import AutoAgent, AutoConfig

cfg = AutoConfig.from_precompiled("./my-weather", local=True)
loaded = AutoAgent.from_precompiled("./my-weather", local=True)
print(loaded(query="Kyoto"))

Working with Tables

from pathlib import Path
from modaic.context import Table, TableFile
import pandas as pd

# Load from Excel/CSV
excel = TableFile.from_file(
    file_ref="employees.xlsx",
    file=Path("employees.xlsx"),
    file_type="xlsx",
)
csv = TableFile.from_file(
    file_ref="data.csv",
    file=Path("data.csv"),
    file_type="csv",
)

# Create from DataFrame
df = pd.DataFrame({"col1": [1, 2, 3], "col2": [4, 5, 6]})
table = Table(df=df, name="my_table")

# Query with SQL (refer to in-memory table as `this`)
result = table.query("SELECT * FROM this WHERE col1 > 1")

# Convert to markdown
markdown = table.markdown()

Database Integration

SQL Database

from modaic.databases import SQLDatabase, SQLiteBackend

# Configure and connect
backend = SQLiteBackend(db_path="my_database.db")
db = SQLDatabase(backend)

# Add table
db.add_table(table)

# Query
rows = db.fetchall("SELECT * FROM my_table")

Vector Database

Graph Database

from modaic.context import Context, Relation
from modaic.databases import GraphDatabase, MemgraphConfig, Neo4jConfig

# Configure backend (choose one)
mg = GraphDatabase(MemgraphConfig())
# or
neo = GraphDatabase(Neo4jConfig())

# Define nodes
class Person(Context):
    name: str
    age: int

class KNOWS(Relation):
    since: int

alice = Person(name="Alice", age=30)
bob = Person(name="Bob", age=28)

# Save nodes
alice.save(mg)
bob.save(mg)

# Create relationship (Alice)-[KNOWS]->(Bob)
rel = (alice >> KNOWS(since=2020) >> bob)
rel.save(mg)

# Query
rows = mg.execute_and_fetch("MATCH (a:Person)-[r:KNOWS]->(b:Person) RETURN a, r, b LIMIT 5")

from modaic import Embedder
from modaic.context import Text
from modaic.databases import VectorDatabase, MilvusBackend

# Setup embedder and backend
embedder = Embedder("openai/text-embedding-3-small")
backend = MilvusBackend.from_local("vector.db")  # milvus lite

# Initialize database
vdb = VectorDatabase(backend=backend, embedder=embedder, payload_class=Text)

# Create collection and add records
vdb.create_collection("my_collection", payload_class=Text)
vdb.add_records("my_collection", [Text(text="hello world"), Text(text="modaic makes sharing agents easy")])

# Search
results = vdb.search("my_collection", query="hello", k=3)
top_hit_text = results[0][0].context.text

Architecture

Agent Types

1. PrecompiledAgent: Statically defined agents with explicit configuration
2. AutoAgent: Dynamically loaded agents from Modaic Hub or local repositories

Database Support

Database Type	Providers	Use Case
Vector	Milvus	Semantic search, RAG
SQL	SQLite, MySQL, PostgreSQL	Structured queries, table storage

Examples

TableRAG Example

The TableRAG example demonstrates a complete RAG pipeline for table-based question answering:

from modaic.precompiled_agent import PrecompiledConfig, PrecompiledAgent
from modaic.context import Table
from modaic.databases import VectorDatabase, SQLDatabase
from modaic.types import Indexer

class TableRAGConfig(PrecompiledConfig):
    k_recall: int = 50
    k_rerank: int = 5

class TableRAGAgent(PrecompiledAgent):
    config: TableRAGConfig # ! Important: config must be annotated with the config class
    
    def __init__(self, config: TableRAGConfig, indexer: Indexer, **kwargs):
        super().__init__(config, **kwargs)
        self.indexer = indexer
        # Initialize DSPy modules for reasoning
    
    def forward(self, user_query: str) -> str:
        # Retrieve relevant tables
        # Generate SQL queries
        # Combine results and provide answer
        pass

Support

For issues and questions:

• GitHub Issues: https://github.com/modaic-ai/modaic/issues
• Docs: https://docs.modaic.dev