langchain-iceberg 0.1.1

LangChain integration for Apache Iceberg with native PyIceberg API support

LangChain Iceberg Toolkit

A native LangChain integration for Apache Iceberg that enables AI-powered natural language queries over your data lakes. Built with PyIceberg for direct API access (not SQL strings) and featuring Iceberg-specific capabilities like time-travel, snapshots, and partition-aware queries.

Features

• 🚀 Native PyIceberg Integration - Direct API access, not SQL strings
• 🔍 Iceberg-Specific Tools - Snapshots, time-travel, partition-aware queries
• 📊 Optional Semantic Layer - YAML-driven metrics and dimensions
• 💬 Zero SQL Required - Natural language to Iceberg queries
• 🏢 Enterprise-Ready - Query limits and timeout protection

Installation

Using pip (standard)

pip install langchain-iceberg

For semantic layer support:

pip install langchain-iceberg[semantic]

Using uv (recommended by LangChain)

uv is a fast Python package installer recommended by LangChain:

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install package
uv pip install langchain-iceberg

# With semantic layer
uv pip install "langchain-iceberg[semantic]"

See INSTALL_WITH_UV.md for more details.

Quick Start

Basic Usage

from langchain_iceberg import IcebergToolkit
from langchain_openai import ChatOpenAI
from langchain.agents import create_react_agent

# Initialize toolkit
toolkit = IcebergToolkit(
    catalog_name="prod",
    catalog_config={
        "type": "rest",
        "uri": "http://localhost:8181",
        "warehouse": "s3://my-warehouse"
    }
)

# Get tools
tools = toolkit.get_tools()

# Create agent
llm = ChatOpenAI(model="gpt-4")
agent = create_react_agent(llm, tools)

# Query with natural language
result = agent.invoke({
    "input": "Show me the top 10 orders by amount from the sales.orders table"
})

print(result)

Direct Tool Usage

from langchain_iceberg import IcebergToolkit

toolkit = IcebergToolkit(
    catalog_name="rest",
    catalog_config={
        "type": "rest",
        "uri": "http://localhost:8181",
        "warehouse": "s3://warehouse/wh/"
    }
)

tools = toolkit.get_tools()

# Use tools directly
list_ns = next(t for t in tools if t.name == "iceberg_list_namespaces")
namespaces = list_ns.run({})
print(namespaces)

query = next(t for t in tools if t.name == "iceberg_query")
results = query.run({
    "table_id": "test.orders",
    "filters": "status = 'completed'",
    "limit": 10
})
print(results)

With Semantic Layer

# Load semantic YAML for business-friendly metrics
toolkit = IcebergToolkit(
    catalog_name="prod",
    catalog_config={...},
    semantic_yaml="s3://bucket/semantic.yaml"
)

tools = toolkit.get_tools()
# Now includes auto-generated metric tools like get_total_revenue, get_order_count, etc.

agent = create_react_agent(llm, tools)

# Business question (no SQL needed!)
result = agent.invoke({
    "input": "What was Q4 2024 revenue by customer segment?"
})

Time-Travel Queries

# Query historical data
result = agent.invoke({
    "input": "Compare this month's revenue to the same period last year using time-travel"
})

Available Tools

The toolkit provides the following tools:

Catalog Exploration

• iceberg_list_namespaces - List all namespaces in the catalog
• iceberg_list_tables - List tables in a namespace
• iceberg_get_schema - Get table schema with sample data

Query Execution

• iceberg_query - Execute queries with filters and column selection
• iceberg_plan_query - LLM-assisted query planning

Time-Travel (Iceberg-Specific)

• iceberg_snapshots - List table snapshots
• iceberg_time_travel - Query data at a specific point in time

Semantic Layer (Auto-Generated)

• get_{metric_name} - Auto-generated tools from YAML metrics

Documentation

• Installation Guide
• Quick Start Guide
• Semantic Layer Guide
• API Reference

Requirements

• Python 3.10+
• Apache Iceberg catalog (REST, Hive, Glue, or Nessie)
• Cloud storage (S3, ADLS, or GCS)

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

License

Apache 2.0 License - see LICENSE file for details.

Support

• GitHub Issues: Report a bug or request a feature
• Documentation: Full documentation

Roadmap

• Core toolkit with catalog exploration
• Query execution tools
• Time-travel and snapshot tools
• Semantic layer with YAML support
• Governance features (access control, PII protection) - Planned for future release
• Query planner tool