Databao Context Engine

Semantic context for your LLMs — generated automatically.
No more copying schemas. No manual documentation. Just accurate answers.

Website • Quickstart • Data Sources • Contributing

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What is Databao Context Engine?

Databao Context Engine is a Python library that automatically generates governed semantic context from your databases, BI tools, documents, and spreadsheets.

Use it with any LLM to deliver accurate, context-aware answers — without copying schemas or writing documentation by hand.

You can add Databao Context Engine as a standard Python dependency in your code or via Databao CLI (coming soon).

Your data sources → Context Engine → Unified semantic graph → Any LLM

Why choose Databao Context Engine?

Feature	What it means for you
Auto-generated context	Extracts schemas, relationships, and semantics automatically
Runs locally	Your data never leaves your environment
MCP integration	Works with Claude Desktop, Cursor, and any MCP-compatible tool
Multiple sources	Databases, dbt projects, spreadsheets, documents
Built-in benchmarks	Measure and improve context quality over time
LLM agnostic	OpenAI, Anthropic, Ollama, Gemini — use any model
Governed & versioned	Track, version, and share context across your team
Dynamic or static	Serve context via MCP server or export as artifact

Installation

Databao Context Engine is available on PyPI and can be installed with uv, pip, or another package manager.

Using uv

uv add databao-context-engine

Using pip

pip install databao-context-engine

Supported data sources

• Athena
• BigQuery
• ClickHouse
• DuckDB
• MSSQL
• MySQL
• PostgreSQL
• Snowflake
• SQLite
• dbt projects
• PDF files
• Markdown and text files

Supported LLMs

Provider	Configuration
Ollama	languageModel: OLLAMA: runs locally, free

Quickstart

1. Create a domain

# Initialize the domain in an existing directory
from databao_context_engine import init_dce_domain

domain_manager = init_dce_domain(Path(tempfile.mkdtemp()))

# Or use an existing project
from databao_context_engine import DatabaoContextDomainManager

domain_manager = DatabaoContextDomainManager(domain_dir=Path("path/to/project"))

2. Configure data sources

from databao_context_engine import (
    DatasourceConnectionStatus,
    DatasourceType,
)

# Create a new datasource
postgres_datasource_id = domain_manager.create_datasource_config(
    DatasourceType(full_type="postgres"),
    datasource_name="my_postgres_datasource",
    config_content={
        "connection": {"host": "localhost", "user": "dev", "password": "pass"}
    },
).datasource.id

# Check the connection to the datasource is valid
check_result = domain_manager.check_datasource_connection()

assert len(check_result) == 1
assert check_result[0].datasource_id == postgres_datasource_id
assert check_result[0].connection_status == DatasourceConnectionStatus.VALID

3. Build context

build_result = domain_manager.build_context()

assert len(build_result) == 1
assert build_result[0].datasource_id == postgres_datasource_id
assert build_result[0].datasource_type == DatasourceType(full_type="postgres")
assert build_result[0].context_file_path.is_file()

4. Use the built contexts

Create a context engine

# Switch to the engine if you're already using a domain_manager
context_engine = domain_manager.get_engine_for_domain()

# Or directly create a context engine from the path to your DCE domaint
from databao_context_engine import DatabaoContextEngine

context_engine = DatabaoContextEngine(domain_dir=Path("path/to/project"))

Get all built contexts

# Switch to the engine to use the context built
all_built_contexts = context_engine.get_all_contexts()
assert len(all_built_contexts) == 1
assert all_built_contexts[0].datasource_id == postgres_datasource_id

print(all_built_contexts[0].context)

Search in built contexts

# Run a vector similarity search
results = context_engine.search_context("my search query")

print(f"Found {len(results)} results for query")
print(
    "\n\n".join(
        [f"{str(result.datasource_id)}\n{result.context_result}" for result in results]
    )
)

Contributing

We’d love your help! Here’s how to get involved:

• ⭐ Star this repo — it helps others find us!
• 🐛 Found a bug? Open an issue
• 💡 Have an idea? We’re all ears — create a feature request
• 👍 Upvote issues you care about — helps us prioritize
• 🔧 Submit a PR
• 📝 Improve docs — typos, examples, tutorials — everything helps!

New to open source? No worries! We're friendly and happy to help you get started. 🌱

For more details, see CONTRIBUTING.

📄 License

Apache 2.0 — use it however you want. See the LICENSE file for details.

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