pgml-chat 0.3.0

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 and OpenSourceAI 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: Sign up for a free GPU-powered database
• Python version >=3.8
• (Optional) OpenAI API key

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://raw.githubusercontent.com/postgresml/postgresml/master/pgml-apps/pgml-chat/.env.template

3. Copy the template file to .env

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

DATABASE_URL=<POSTGRES_DATABASE_URL starts with postgres://>
OPENAI_API_KEY=<OPENAI_API_KEY> # Optional

Usage

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

(pgml-bot-builder-py3.9) pgml-chat % pgml-chat % pgml-chat --help
usage: pgml-chat [-h] --collection_name COLLECTION_NAME [--root_dir ROOT_DIR] [--stage {ingest,chat}] [--chat_interface {cli,slack,discord}] [--chat_history CHAT_HISTORY] [--bot_name BOT_NAME]
                 [--bot_language BOT_LANGUAGE] [--bot_topic BOT_TOPIC] [--bot_topic_primary_language BOT_TOPIC_PRIMARY_LANGUAGE] [--bot_persona BOT_PERSONA]
                 [--chat_completion_model CHAT_COMPLETION_MODEL] [--max_tokens MAX_TOKENS] [--vector_recall_limit VECTOR_RECALL_LIMIT]

PostgresML Chatbot Builder

options:
  -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)
  --chat_history CHAT_HISTORY
                        Number of messages from history used for generating response (default: 0)
  --bot_name BOT_NAME   Name of the bot (default: PgBot)
  --bot_language BOT_LANGUAGE
                        Language of the bot (default: English)
  --bot_topic BOT_TOPIC
                        Topic of the bot (default: PostgresML)
  --bot_topic_primary_language BOT_TOPIC_PRIMARY_LANGUAGE
                        Primary programming language of the topic (default: SQL)
  --bot_persona BOT_PERSONA
                        Persona of the bot (default: Engineer)
  --chat_completion_model CHAT_COMPLETION_MODEL
  --max_tokens MAX_TOKENS
                        Maximum number of tokens to generate (default: 256)
  --vector_recall_limit VECTOR_RECALL_LIMIT
                        Maximum number of documents to retrieve from vector recall (default: 1)

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 output logging the pipelines progress.

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.

Prompt Engineering

In addition to relevant context retrieved from vector search, system prompt to generate accurate responses with minimum hallucinations requires prompt engineering. Different chat completion models require different system prompts. Since the prompts including the context are long, they suffer from lost in the middle problem described in this paper. Below are some of the prompts that we have used for different chat completion models.

Default prompt (GPT-3.5 and open source models)

Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.

GPT-4 System prompt

You are an assistant to answer questions about {topic}.\ 
Your name is {name}. You speak like {persona} in {language}. Use the given list of documents to answer user's question.\
Use the conversation history if it is applicable to answer the question. \n Use the following steps:\n \
1. Identify if the user input is really a question. \n \
2. If the user input is not related to the {topic} then respond that it is not related to the {topic}.\n \
3. If the user input is related to the {topic} then first identify relevant documents from the list of documents. \n \
4. If the documents that you found relevant have information to completely and accurately answers the question then respond with the answer.\n \
5. If the documents that you found relevant have code snippets then respond with the code snippets. \n \
6. Most importantly, don't make up code snippets that are not present in the documents.\n \
7. If the user input is generic like Cool, Thanks, Hello, etc. then respond with a generic answer. \n"

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.

Roadmap

• Use a collection for chat history that can be retrieved and used to generate responses.
• 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.