PostgresML Chatbot Builder

A command line tool to build and deploy a knowledge-based chatbot using PostgresML and OpenAI API.

There are two stages in building a knowledge-based chatbot:

• Build a knowledge base by ingesting documents, chunking documents, generating embeddings, and indexing these embeddings for fast query.
• Generate responses to user queries by retrieving relevant documents and generating responses using OpenAI API.

This tool automates the above two stages and provides a command line interface to build and deploy a knowledge-based chatbot.

Prerequisites

Before you begin, ensure you have the following:

• PostgresML Database: Sign up for a free GPU-powered database.
• Python version >=3.8.
• OpenAI API key.

Getting Started

1. Setup:

   1. Create a virtual environment and install pgml-chat using pip:

      pip install pgml-chat

   2. Download .env.template file from PostgresML Github repository:

      wget https://github.com/postgresml/postgresml/blob/master/pgml-apps/pgml-chat/.env.template

   3. Copy the template file to .env.

   4. Update environment variables with your OpenAI API key and PostgresML database credentials.

      OPENAI_API_KEY=<OPENAI_API_KEY>
      DATABASE_URL=<POSTGRES_DATABASE_URL starts with postgres://>
      MODEL=hkunlp/instructor-xl
      MODEL_PARAMS={"instruction": "Represent the Wikipedia document for retrieval: "}
      QUERY_PARAMS={"instruction": "Represent the Wikipedia question for retrieving supporting documents: "}
      SYSTEM_PROMPT="You are an assistant to answer questions about an open-source software named PostgresML. Your name is PgBot. You are based out of San Francisco, California."
      BASE_PROMPT="Given relevant parts of a document and a question, create a final answer.\ 
                  Include a SQL query in the answer wherever possible.                     Use the following portion of a long document to see if any of the text is relevant to answer the question.                    

Return any relevant text verbatim. {context} Question: {question} If the context is empty, then ask for clarification and suggest the user send an email to team@postgresml.org or join PostgresML Discord." ```

2. Usage:

   • You can get help on the command line interface by running:

     (pgml-bot-builder-py3.9) pgml-chat % pgml-chat --help

   • Ingest:

     • In this step, we ingest documents, chunk documents, generate embeddings, and index these embeddings for fast query.

       LOG_LEVEL=DEBUG pgml-chat --root_dir <directory> --collection_name <collection_name> --stage ingest

   • Chat:

     • You can interact with the bot using the command line interface, Slack, or Discord.

     • Command Line Interface:

       • In this step, we start chatting with the chatbot at the command line. CLI is the default chat interface.

         LOG_LEVEL=ERROR pgml-chat --collection_name <collection_name> --stage chat --chat_interface cli

     • Slack:

       • Setup:

         • You need SLACK_BOT_TOKEN and SLACK_APP_TOKEN to run the chatbot on Slack. Include the following environment variables in your .env file:

           SLACK_BOT_TOKEN=<SLACK_BOT_TOKEN>
           SLACK_APP_TOKEN=<SLACK_APP_TOKEN>

         • Start the chatbot on Slack:

           LOG_LEVEL=ERROR pgml-chat --collection_name <collection_name> --stage chat --chat_interface slack

     • Discord:

       • Setup:

         • You need DISCORD_BOT_TOKEN to run the chatbot on Discord. Include the following environment variable in your .env file:

           DISCORD_BOT_TOKEN=<DISCORD_BOT_TOKEN>

         • Start the chatbot on Discord:

           pgml-chat --collection_name <collection_name> --stage chat --chat_interface discord

3. Developer Guide:

   • Clone this repository, start a poetry shell, and install dependencies:

     git clone https://github.com/postgresml/postgresml
     cd postgresml/pgml-apps/pgml-chat
     poetry shell
     poetry install
     pip install .

   • Create a .env file in the root directory of the project and add all the environment variables discussed in Getting Started section.

   • All the logic is in pgml_chat/main.py.

   • Check the roadmap for features that you'd like to work on.

   • For additional features not listed, open an issue, and they will be added to the roadmap.

4. Options:

   • Control the chatbot's behavior by setting the following environment variables:
     • SYSTEM_PROMPT
     • BASE_PROMPT
     • MODEL

5. Roadmap:

   • Support for file formats like rst, html, pdf, docx, etc.
   • Support for open-source models in addition to OpenAI for chat completion.
   • Support for multi-turn conversations using a conversation buffer.