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

Quick Start with Docker

Prerequisites

• Docker installed on your system
• Hasura CLI (optional, for local development)

Setup

1. Clone the Pacha repository:

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

2. Create an empty SQLite database file:

   touch pacha.db

   This creates an empty pacha.db file in the current directory, which will be used as the SQLite database for Pacha.

3. Copy the example environment file and configure it:

   cp .pacha.env.example .pacha.env

   Edit the .pacha.env file and fill in the required details:

   PORT=5001
   ANTHROPIC_API_KEY=your_anthropic_api_key
   SECRET_KEY=your_secret_key
   CORS_ORIGINS="https://console.hasura.io"
   

4. Build and start the Pacha server:

   docker compose up --build

Accessing Pacha UI

1. Open https://console.hasura.io/local/chat to open Pacha UI on Hasura Console.

2. Use the settings icon (gear) next to the "Pacha Chat" title to configure:

   • Pacha server URL (e.g., http://localhost:5001 if running locally)
   • Secret token (if you've set one in your .pacha.env file)

Development Notes

• For local development or custom builds, you can modify the Dockerfile in the engine directory.
• Ensure all required environment variables are set in your .pacha.env file or passed to the Docker container.

Troubleshooting

• If you encounter CORS issues, verify the CORS_ORIGINS setting in your .pacha.env file.
• For API key related errors, double-check your ANTHROPIC_API_KEY in the .pacha.env file.

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.