❄️ Snowflake GPT Agent: GPT for Running Snowflake Query - Demo

Powered by langchain, GPT4All and inspired by privategpt

💾 Installation

1. Navigate to the directory where the repository was downloaded

   cd snowflakegpt
   

2. Install the required dependencies

   pip install -r requirements.txt
   

3. Configure OpenAI Key

   • If Using OpenAI key, simply export OPENAI_API_KEY=*****
   • If want to use config file, rename config_template.ini -> config.ini file inside the snowdflakegpt dir & update either Azure or OpenAI config

   By completing these steps, you have properly configured the API Keys for your project.

Pick the Model

Add the model details in config.ini

[model]
MODEL_TYPE=azure/openai/LlamaCpp/GPT4All #supports LlamaCpp or GPT4All as well
LLAMA_EMBEDDINGS_MODEL=/path/to/ggml-model-q4_0.bin #Path to your GPT4All or LlamaCpp supported LLM
MODEL_PATH=/path/to/ggml-gpt4all-j-v1.3-groovy.bin #Path to your LlamaCpp supported embeddings model

Uploading Data in Vector Database

1. Run docker compose up
2. Run python -m snowgpt upload

🔧 Usage

1. Run snowdflakegpt Python module in your terminal with default GPT model

python -m snowgpt "What is the population of India?"

2. Run the agent by passing specific model.

python -m snowgpt "How many women between the ages 39 and 45 in India" gpt-4_8k_ascent

Current supported list of deployment names are

• gpt-35-turbo
• gpt-4_8k_ascent
• gpt-4_32k_ascent

Default is gpt-35-turbo

🚒 Improvements / TODO

LLM Chains

Maybe a sequential chain to have an intermediate function to validate the query and make corrections

Custom Agent

Creating a Custom Agent to control the query generation. This may need a custom Agent & custom Tools .