Pacha

Connect realtime data to your AI

What is it?

Pacha is an AI tool that does retrieval of context for a natural language query using a SQL interface and a Python programming environment. Pacha is specially built to work with Hasura DDN for authorized multi-source querying.

You would use Pacha with your favourite LLMs to generate grounded responses in your AI apps/agents/chatbots.

Getting Started

Prerequisites

• Python version 3.12 or later
• Docker Compose version 2.22.0 or later
• Access to OpenAI or Anthropic.
• A datasource available in Hasura Connector Hub

Clone the repo

git clone https://github.com/hasura/pacha.git
cd pacha

Install Python Dependencies

• Install Poetry
• Run poetry install to install Python dependencies.

Setup Hasura DDN

• Create a Hasura account at hasura.io/ddn
• Scaffold a local Hasura project on a hub connector datasource like this:

poetry run ddn_setup -hc <hub-connector-name> -c <connection string> --dir ddn_project

Example (with Postgres):

poetry run ddn_setup -hc 'hasura/postgres' -c 'postgresql://postgres:postgres@localhost:5432/postgres' --dir ddn_project

Example (with SQL Server):

poetry run ddn_setup -hc 'hasura/sqlserver' -c 'Server=localhost,21433;Uid=SA;Database=sakila;Pwd=Password!;TrustServerCertificate=true' --dir ddn_project

• The above generated metadata is where you would configure row / column access control rules for your data.
• Start a local Hasura engine with:

docker compose -f ddn_project/docker-compose.hasura.yaml up -d

Running Pacha

examples/chat_with_tool.py is a CLI chat interface that uses Pacha as a tool provided to a supported LLM.

ANTHROPIC_API_KEY=<api-key> poetry run chat_with_tool -d ddn -u <DDN SQL URL> -H <header to pass to DDN> --llm anthropic

Example:

ANTHROPIC_API_KEY=<api-key> poetry run chat_with_tool -d ddn -u http://localhost:3000/v1/sql -H 'x-hasura-role: admin' --llm anthropic

You can also run Pacha with OpenAI:

OPENAI_API_KEY=<api-key> poetry run chat_with_tool -d ddn -u <DDN SQL URL> -H <header to pass to DDN> --llm openai

Customizing

Running against a custom SQL backend

If you want to run against a custom SQL backend that's not Hasura DDN, you can implement the DataEngine class in pacha/data_engine, and pass that to the Pacha SDK. See usage here.

You can see example Postgres implementation of DataEngine in pacha/data_engine/postgres.py.

Running against a custom LLM

You can run Pacha against any LLM that supports function/tool calling by using the Pacha tool directly from pacha/sdk/tools.