pgml-chat 0.1.1

PostgresML bot builder for all your documentation

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, make sure you have the following:

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

Getting started

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

pip install pgml-chat

pgml-chat will be installed in your PATH.

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.\
                \nReturn any relevant text verbatim.\n{context}\nQuestion: {question}\n \
                If the context is empty then ask for clarification and suggest user to send an email to [e-mail] or join PostgresML [Discord](https://discord.gg/DmyJP3qJ7U)."

Usage

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

(pgml-bot-builder-py3.9) pgml-chat % pgml-chat --help
usage: pgml-chat [-h] --collection_name COLLECTION_NAME [--root_dir ROOT_DIR] [--stage {ingest,chat}] [--chat_interface {cli,slack}]

PostgresML Chatbot Builder

optional arguments:
  -h, --help            show this help message and exit
  --collection_name COLLECTION_NAME
                        Name of the collection (schema) to store the data in PostgresML database (default: None)
  --root_dir ROOT_DIR   Input folder to scan for markdown files. Required for ingest stage. Not required for chat stage (default: None)
  --stage {ingest,chat}
                        Stage to run (default: chat)
  --chat_interface {cli, slack, discord}
                        Chat interface to use (default: cli)

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

You will see the following output:

[15:39:12] DEBUG    [15:39:12] - Using selector: KqueueSelector 
           INFO     [15:39:12] - Starting pgml_chatbot           
           INFO     [15:39:12] - Scanning <root directory> for markdown files
[15:39:13] INFO     [15:39:13] - Found 85 markdown files 
Extracting text from markdown ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
           INFO     [15:39:13] - Upserting documents into database                                      
[15:39:32] INFO     [15:39:32] - Generating chunks       
[15:39:33] INFO     [15:39:33] - Starting chunk count: 0            
[15:39:35] INFO     [15:39:35] - Ending chunk count: 576                                                  
           INFO     [15:39:35] - Total documents: 85 Total chunks: 576                                                                                            
           INFO     [15:39:35] - Generating embeddings           
[15:39:36] INFO     [15:39:36] - Splitter ID: 2                                                                
[15:40:47] INFO     [15:40:47] - Embeddings generated in 71.073 seconds                

Chat

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

Command Line Interface

In this step, we start chatting with the chatbot at the command line. You can increase the log level to ERROR to suppress the logs. CLI is the default chat interface.

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

You should be able to interact with the bot as shown below. Control-C to exit.

User (Ctrl-C to exit): Who are you?
PgBot: I am PgBot, an AI assistant here to answer your questions about PostgresML, an open source software. How can I assist you today?
User (Ctrl-C to exit): What is PostgresML?
Found relevant documentation.... 
PgBot: PostgresML is an open source software that allows you to unlock the full potential of your data and drive more sophisticated insights and decision-making processes. It provides a dashboard with analytical views of the training data and 
model performance, as well as integrated notebooks for rapid iteration. PostgresML is primarily written in Rust using Rocket as a lightweight web framework and SQLx to interact with the database.

If you have any further questions or need more information, please feel free to send an email to [e-mail] or join the PostgresML Discord community at https://discord.gg/DmyJP3qJ7U.

Slack

Setup You need SLACK_BOT_TOKEN and SLACK_APP_TOKEN to run the chatbot on Slack. You can get these tokens by creating a Slack app. Follow the instructions here to create a Slack app.Include the following environment variables in your .env file:

SLACK_BOT_TOKEN=<SLACK_BOT_TOKEN>
SLACK_APP_TOKEN=<SLACK_APP_TOKEN>

In this step, we start chatting with the chatbot on Slack. You can increase the log level to ERROR to suppress the logs.

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

If you have set up the Slack app correctly, you should see the following output:

⚡️ Bolt app is running!

Once the slack app is running, you can interact with the chatbot on Slack as shown below. In the example here, name of the bot is PgBot. This app responds only to direct messages to the bot.

Discord

Setup You need DISCORD_BOT_TOKEN to run the chatbot on Discord. You can get this token by creating a Discord app. Follow the instructions here to create a Discord app. Include the following environment variables in your .env file:

DISCORD_BOT_TOKEN=<DISCORD_BOT_TOKEN>

In this step, we start chatting with the chatbot on Discord. You can increase the log level to ERROR to suppress the logs.

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

If you have set up the Discord app correctly, you should see the following output:

2023-08-02 16:09:57 INFO     discord.client logging in using static token

Once the discord app is running, you can interact with the chatbot on Discord as shown below. In the example here, name of the bot is pgchat. This app responds only to direct messages to the bot.

Developer Guide

1. 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 .

2. Create a .env file in the root directory of the project and add all the environment variables discussed in Getting Started section.
3. All the logic is in pgml_chat/main.py
4. Check the roadmap for features that you would like to work on.
5. If you are looking for features that are not included here, please open an issue and we will add it to the roadmap.

## Options
You can control the behavior of the chatbot by setting the following environment variables:
- `SYSTEM_PROMPT`: This is the prompt that is used to initialize the chatbot. You can customize this prompt to change the behavior of the chatbot. For example, you can change the name of the chatbot or the location of the chatbot.
- `BASE_PROMPT`: This is the prompt that is used to generate responses to user queries. You can customize this prompt to change the behavior of the chatbot. 
- `MODEL`: This is the open source embedding model used to generate embeddings for the documents. You can change this to use a different model.

## Roadmap
- ~~`hyerbot --chat_interface {cli, slack, discord}` that supports Slack, and Discord.~~
- 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 converstaions using converstaion buffer. Use a collection for chat history that can be retrieved and used to generate responses.