pranavfirebase-rag 0.1.4

A Firebase Firestore RAG system that bridges natural language prompts with structured database queries. It converts user input into intelligent retrieval operations, allowing developers to interact with Firestore using plain English instead of writing queries manually.

Author

Pranav Verma

Firebase RAG CLI (Firestore Natural Language Query Engine)

A Retrieval-Augmented Generation (RAG) system that allows you to query Firebase Firestore using natural language prompts, powered by Llama 3 via Ollama.

This tool converts plain English questions into structured Firestore queries and returns results directly from your database.

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Features

• Natural language querying for Firestore
• Schema-aware query generation (required)
• Powered by Llama 3 via Ollama
• CLI-based initialization
• Firebase Admin SDK integration
• Fully local execution (no cloud dependency for query processing)
• Private key remains local to your machine during execution
• Simple setup and execution

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Security & Privacy

This tool is designed with a local-first architecture:

• All query processing happens locally on your machine
• Llama 3 runs locally via Ollama
• Firebase service account key (firebase-key.json) is used only locally by Firebase Admin SDK
• The private key is never sent to any external server by this library
• Users provide their own credentials, and all database access happens from their local environment
• No user queries, schema data, or Firestore results are transmitted to third-party services by this tool

Note: The Firebase private key is used locally to authenticate requests with Firebase Admin SDK. It is not exposed to the internet by this library.

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Prerequisites

1. Install Ollama and Llama 3

pip install ollama
ollama pull llama3

Make sure Ollama is installed and running on your system.

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2. Firebase Setup

You must have a Firebase project and a service account key file.

Download your firebase-key.json from Firebase Console.

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Installation

pip install pranavfirebase-rag

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Initialization

After installing the package, run:

my-library init

This command will generate the following files in your project directory:

schema.json
firebase-key.json
rag.py

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Configuration

1. schema.json (Required)

You must define your Firestore schema in this file.

Example:

{
  "users": {
    "Age": "int",
    "Name": "string",
    "Department": "string",
    "Salary": "int"
  }
}

This schema is mandatory and used for query parsing and structured retrieval.

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2. firebase-key.json

Paste your Firebase service account credentials into this file.

Important:

• The private key is stored locally on your machine
• It is only used by Firebase Admin SDK for authentication
• This library does not transmit it anywhere

Example structure:

{
  "type": "service_account",
  "project_id": "your-project-id",
  "private_key": "-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n",
  "client_email": "your-client-email"
}

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3. rag.py

This file is the main chatbot entry point.

Important:

• Replace the default collection name (e.g. employees) with your Firestore collection name
• Do not modify internal logic unless required

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Usage

Run the chatbot:

python rag.py

You will enter an interactive terminal where you can query your database.

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Example Queries

• Show all users above 25
• List employees in AI department
• Get users with salary greater than 100000
• Find all names in the users collection
• Show users younger than 30

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How It Works

User Input → Llama 3 (Ollama) → Schema Parser → Query Builder → Firestore (Firebase Admin SDK) → Response Output

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Architecture

User Input → Llama 3 (Ollama) → Schema Parser → Query Builder → Firestore → Response Output

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Notes

• Schema definition is required (not optional)
• Firebase credentials must be valid
• Ollama + Llama 3 must be installed and running before execution
• Collection name must be correctly set in rag.py

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Requirements

• Python 3.8+
• Firebase Admin SDK
• Ollama
• Llama 3 model

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